bluuebunny/crossref_metadata_2025 huggingface dataset:
Accurate Prediction of Earthquake-Induced Landslides Based on Deep Learning Considering Landslide Source Area
Authors: Li Yao , Cui Peng , Ye Chengming , Junior José Marcato , Zhang Zhengtao , Guo Jian , Li Jonathan
Published: September 2021

An earthquake-induced landslide (EQIL) is a rapidly changing process occurring at the Earth’s surface that is strongly controlled by the earthquake in question and predisposing conditions. Predicting locations prone to EQILs on a large scale is significant for managing rescue operations and disaster mitigation. We propose a deep learning framework while considering the source area feature of EQIL to model the complex relationship and enhance spatial prediction accuracy. Initially, we used high-resolution remote sensing images and a digital elevation model (DEM) to extract the source area of an EQIL. Then, 14 controlling factors were input to a stacked autoencoder (SAE) to search for robust features by sparse optimization, and the classifier took advantage of high-level abstract features to identify the EQIL spatially. Finally, the EQIL inventory collected from the Wenchuan earthquake was used to validate the proposed model. The results show that the proposed method significantly outperformed conventional methods, achieving an overall accuracy (OA) of 91.88%, while logistic regression (LR), support vector machine (SVM), and random forest (RF) achieved 80.75%, 82.22%, and 84.16%, respectively. Meanwhile, this study reveals that shallow machine learning models only take advantage of significant factors for EQIL prediction, but deep learning models can extract more effective information related to EQIL distribution from low-value density data, which is why its prediction accuracy is growing with increasing input factors. There is hope that new knowledge of EQILs can be represented by high-level abstract features extracted by hidden layers of the deep learning model, which are typically acquired by statistical methods.
An Entropy-Weighting Method for Efficient Power-Line Feature Evaluation and Extraction from LiDAR Point Clouds
Authors: Tan Junxiang , Zhao Haojie , Yang Ronghao , Liu Hua , Li Shaoda , Liu Jianfei
Published: September 2021

Power-line inspection is an important means to maintain the safety of power networks. Light detection and ranging (LiDAR) technology can provide high-precision 3D information about power corridors for automated power-line inspection, so there are more and more utility companies relying on LiDAR systems instead of traditional manual operation. However, it is still a challenge to automatically detect power lines with high precision. To achieve efficient and accurate power-line extraction, this paper proposes an algorithm using entropy-weighting feature evaluation (EWFE), which is different from the existing hierarchical-multiple-rule evaluation of many geometric features. Six significant features are selected (Height above Ground Surface (HGS), Vertical Range Ratio (VRR), Horizontal Angle (HA), Surface Variation (SV), Linearity (LI) and Curvature Change (CC)), and then the features are combined to construct a vector for quantitative evaluation. The feature weights are determined by an entropy-weighting method (EWM) to achieve optimal distribution. The point clouds are filtered out by the HGS feature, which possesses the highest entropy value, and a portion of non-power-line points can be removed without loss of power-line points. The power lines are extracted by evaluation of the other five features. To decrease the interference from pylon points, this paper analyzes performance in different pylon situations and performs an adaptive weight transformation. We evaluate the EWFE method using four datasets with different transmission voltage scales captured by a light unmanned aerial vehicle (UAV) LiDAR system and a mobile LiDAR system. Experimental results show that our method demonstrates efficient performance, while algorithm parameters remain consistent for the four datasets. The precision F value ranges from 98.4% to 99.7%, and the efficiency ranges from 0.9 million points/s to 5.2 million points/s.
Assessing the Use of Optical Satellite Images to Detect Volcanic Impacts on Glacier Surface Morphology
Authors: Martin Michael Dieter , Barr Iestyn , Edwards Benjamin , Spagnolo Matteo , Vajedian Sanaz , Symeonakis Elias
Published: September 2021

Globally, about 250 Holocene volcanoes are either glacier-clad or have glaciers in close proximity. Interactions between volcanoes and glaciers are therefore common, and some of the most deadly (e.g., Nevado del Ruiz, 1985) and most costly (e.g., Eyjafjallajökull, 2010) eruptions of recent years were associated with glaciovolcanism. An improved understanding of volcano-glacier interactions is therefore of both global scientific and societal importance. This study investigates the potential of using optical satellite images to detect volcanic impacts on glaciers, with a view to utilise detected changes in glacier surface morphology to improve glacier-clad volcano monitoring and eruption forecasting. Roughly 1400 optical satellite images are investigated from key, well-documented eruptions around the globe during the satellite remote sensing era (i.e., 1972 to present). The most common observable volcanic impact on glacier morphology (for both thick and thin ice-masses) is the formation of ice cauldrons and openings, often associated with concentric crevassing. Other observable volcanic impacts include ice bulging and fracturing due to subglacial dome growth; localized crevassing adjacent to supraglacial lava flows; widespread glacier crevassing, presumably, due to meltwater-triggered glacier acceleration and advance. The main limitation of using optical satellite images to investigate changes in glacier morphology is the availability of cloud- and eruption-plume-free scenes of sufficient spatial- and temporal resolution. Therefore, for optimal monitoring and eruption prediction at glacier-clad volcanoes, optical satellite images are best used in combination with other sources, including SAR satellite data, aerial images, ground-based observations and satellite-derived products (e.g., DEMs).
A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
Authors: Jiang Chunhua , Xu Tianhe , Nie Wenfeng , Fang Zhenlong , Wang Shuaimin , Xu Aigong
Published: September 2021

Global Navigation Satellite System (GNSS) ultra-rapid orbit is critical for geoscience and real-time engineering applications. To improve the computational efficiency and the accuracy of predicted orbit, a parallel approach for multi-GNSS ultra-rapid orbit determination is proposed based on Message Passing Interface (MPI)/Open Multi Processing (OpenMP). This approach, compared with earlier efficient methods, can improve the efficiency of multi-GNSS ultra-rapid orbit solution without changing the original observation data and retaining the continuity and consistency of the original parameters to be estimated. To obtain high efficiency, three steps are involved in the approach. First and foremost, the normal equation construction is optimized in parallel based on MPI. Second, equivalent reduction of the estimated parameters is optimized using OpenMP parallel method. Third, multithreading is used for parallel orbit extrapolation. Thus, GNSS ultra-rapid orbit determination is comprehensively optimized in parallel, and the computation efficiency is greatly improved. Based on the data from MGEX and IGS stations, experiments are carried out to analyze the performance of the proposed approach in computational efficiency, accuracy and stability. The results show that the approach greatly improves the efficiency of satellite orbit determination. It can realize 1-h update frequency for the multi-GNSS ultra-rapid orbit determination using 88 stations with four-system observations. The accuracy of the GPS, GLONASS, Galileo and BDS ultra-rapid orbit with 1-h update frequency using the parallel approach is approximately 33.4%,31.4%,40.1% and 32.8% higher than that of the original orbit, respectively. The root mean squares (RMS) of GPS, GLONASS, Galileo and BDS predicted orbit are about 3.2 cm, 5.1 cm, 5.6 cm and 11.8 cm. Moreover, the orbit provided by the proposed method has a better stability. The precision loss of all parallel optimization can be negligible and the original correlation between the parameters is fully preserved.
Crater Detection and Recognition Method for Pose Estimation
Authors: Chen Zihao , Jiang Jie
Published: September 2021

A crater detection and recognition algorithm is the key to pose estimation based on craters. Due to the changing viewing angle and varying height, the crater is imaged as an ellipse and the scale changes in the landing camera. In this paper, a robust and efficient crater detection and recognition algorithm for fusing the information of sequence images for pose estimation is designed, which can be used in both flying in orbit around and landing phases. Our method consists of two stages: stage 1 for crater detection and stage 2 for crater recognition. In stage 1, a single-stage network with dense anchor points (dense point crater detection network, DPCDN) is conducive to dealing with multi-scale craters, especially small and dense crater scenes. The fast feature-extraction layer (FEL) of the network improves detection speed and reduces network parameters without losing accuracy. We comprehensively evaluate this method and present state-of-art detection performance on a Mars crater dataset. In stage 2, taking the encoded features and intersection over union (IOU) of craters as weights, we solve the weighted bipartite graph matching problem, which is matching craters in the image with the previously identified craters and the pre-established craters database. The former is called “frame-frame match”, or FFM, and the latter is called “frame-database match”, or FDM. Combining the FFM with FDM, the recognition speed is enabled to achieve real-time on the CPU (25 FPS) and the average recognition precision is 98.5%. Finally, the recognition result is used to estimate the pose using the perspective-n-point (PnP) algorithm and results show that the root mean square error (RMSE) of trajectories is less than 10 m and the angle error is less than 1.5 degrees.
Coniferous Plantations Growing Stock Volume Estimation Using Advanced Remote Sensing Algorithms and Various Fused Data
Authors: Li Xinyu , Long Jiangping , Zhang Meng , Liu Zhaohua , Lin Hui
Published: September 2021

Spatial distribution prediction of growing stock volume (GSV) for supporting the sustainable management of forest ecosystems, is one of the most widespread applications of remote sensing. For this purpose, remote sensing data were used as predictor variables in combination with ground data obtained from field sample plots. However, with the increase in forest GSV values, the spectral reflectance of remote sensing imagery is often saturated or less sensitive to the GSV changes, making accurate estimation difficult. To improve this, we examined the GSV estimation performance and data saturation of four optical remote sensing image datasets (Landsat 8, Sentinel-2, ZiYuan-3, and GaoFen-2) in the subtropical region of Central South China. First, various feature variables were extracted and three optimization methods were used to select optimal feature variable combinations. Subsequently, k-nearest-neighbor (kNN), random forest regression, and categorical boosting algorithms were employed to build the GSV estimation models, and evaluate the GSV estimation accuracy and saturation. Second, Gram Schmidt (GS) and NNDiffuse pan sharpening (NND) methods were employed to fuse the optimal multispectral images and explore various image fusion schemes suitable for GSV estimation. We proposed an adaptive stacking (AdaStacking) model ensemble algorithm to further improve GSV estimation performance. The results indicated that Sentinel-2 had the highest GSV estimation accuracy exhibiting a minimum relative root mean square error of 20.06% and saturation of 434 m3/ha, followed by GaoFen-2 with a minimum relative root mean square error of 22.16% and a saturation of 409 m3/ha. Among the four fusion images, the NND-B2 image—obtained by fusing the GaoFen-2 green band and Sentinel-2 multispectral image with the NND method—had the best estimation accuracy. The estimated optimal RMSEs of NND-B2 were 24.4% and 16.5% lower than those of GaoFen-2 and Sentinel-2, respectively. Therefore, the fused image data based on GF-2 and Sentinel-2 can effectively couple the advantages of the two images and significantly improve the GSV estimation performance. Moreover, the proposed adaptive stacking model is more effective in GSV estimation than a single model. The GSV estimation saturation value of the AdaStacking model based on NND-B2 was 5.4% higher than that of the KNN-Maha model. The GSV distribution map estimated by AdaStacking model used the NND-B2 dataset corresponded accurately with the field observations. This study provides some insights into the optical image fusion scheme, feature selection, and adaptive modeling algorithm in GSV estimation for coniferous forest.
A Simplified Model for Optical Systems with Random Phase Screens
Authors: Haskel Malchiel , Stern Adrian
Published: September 2021

A first-order optical system with arbitrary multiple masks placed at arbitrary positions is the basic scheme of various optical systems. Generally, masks in optical systems have a non-shift invariant (SI) effect; thus, the individual effect of each mask on the output cannot be entirely separated. The goal of this paper is to develop a technique where complete separation might be achieved in the common case of random phase screens (RPSs) as masks. RPSs are commonly used to model light propagation through the atmosphere or through biological tissues. We demonstrate the utility of the technique on an optical system with multiple RPSs that model random scattering media.
Automated Driving with Cooperative Perception Based on CVFH and Millimeter-Wave V2I Communications for Safe and Efficient Passing through Intersections
Authors: Fukatsu Ryuichi , Sakaguchi Kei
Published: September 2021

The development of automated driving is actively progressing, and connected cars are also under development. Connected cars are the technology of connecting vehicles to networks so that connected vehicles can enhance their services. Safety services are among the main services expected in connected car society. Cooperative perception belongs to safety services and improves safety by visualizing blind spots. This visualization is achieved by sharing sensor data via wireless communications. Therefore, the number of visualized blind spots highly depends upon the performance of wireless communications. In this paper, we analyzed the required sensor data rate to be shared for the cooperative perception in order to realize safe and reliable automated driving in an intersection scenario. The required sensor data rate was calculated by the combination of recognition and crossing decisions of an automated driving vehicle to adopt realistic assumptions. In this calculation, CVFH was used to derive tight requirements, and the minimum required braking aims to alleviate the traffic congestion around the intersection. At the end of the paper, we compare the required sensor data rate with the outage data rate realized by conventional and millimeter-wave communications, and show that millimeter-wave communications can support safe crossing at a realistic velocity.
Gas Chromatography-Mass Spectrometry Analysis of Compounds Emitted by Pepper Yellow Leaf Curl Virus-Infected Chili Plants: A Preliminary Study
Authors: Agustika Dyah K. , Mercuriani Ixora S. , Ariyanti Nur Aeni , Purnomo Chandra W. , Triyana Kuwat , Iliescu Daciana D. , Leeson Mark S.
Published: September 2021

Pepper yellow leaf curl virus (PYLCV) is a threat to chili plants and can significantly reduce yields. This study aimed as a pilot project to detect PYLCV by analyzing compounds emitted by chili plants using gas chromatography-mass spectrometry (GC-MS). The samples investigated in this research were PYLCV-infected and PYLCV-undetected chili plants taken from commercial chili fields. The infection status was validated by using a polymerase chain reaction (PCR) test. A headspace technique was used to extract the volatile organic compounds emitted by plants. The analysis of GC-MS results began with pre-processing, analyzing sample compound variability with a boxplot analysis, and sample classification by using a multivariate technique. Unsupervised multivariate technique principal component analysis (PCA) was performed to discover whether GC-MS could identify PYLCV-infected or not. The results showed that PYLCV-infected and PYLCV-undetected chili plants could be differentiated, with a total percent variance of the first three principal components reaching 91.32%, and successfully discriminated between PYLCV-infected and PYLCV-undetected chili plants. However, more comprehensive studies are needed to find the potential biomarkers of the infected plants.
Development of Green and Efficient Extraction Methods of Quercetin from Red Onion Scales Wastes Using Factorial Design for Method Optimization: A Comparative Study
Authors: Abd-ElSalam Heba-Alla , Gamal Mohammed , Naguib Ibrahim , Al-Ghobashy Medhat , Zaazaa Hala , Abdelkawy M.
Published: September 2021

Waste resulting from edible plants is considered one of the best sources of valuable phytochemicals. A promising approach for using these appreciated wastes is extracting precious medically important constituents, for example, free quercetin. Two new cost-effective and green extraction methods are introduced in the present study: ultrasound-assisted glycerol extraction (UAGE) and microwave-assisted extraction (MAE). These extraction protocols are optimized using factorial design to define the highest yield of extraction, and HPLC-UV at 370 nm was used as a method of yield analysis. Quercetin remained stable during the whole process in both extraction protocols. A standard addition technique was performed to quantify quercetin in different extracts and eliminate the matrix effect. In UAGE and MAE, extraction yields were 16.55 ± 0.81 and 27.20 ± 1.55 mg/1g from red onion scales on a dry base, respectively. The amount of quercetin extracted using MAE was superior to UAGE in terms of time and yield. A greenness assessment of the offered studies compared to previously published relevant extraction methods was performed using the analytical eco-scale assessment method (ESA) and national environmental methods index (NEMI). MAE showed to be a greener method with a higher ESA score and a greener NEMI pictogram.
Review of Phosphite as a Plant Nutrient and Fungicide
Authors: Havlin John L. , Schlegel Alan J.
Published: September 2021

Phosphite (Phi)-containing products are marketed for their antifungal and nutritional value. Substantial evidence of the anti-fungal properties of Phi on a wide variety of plants has been documented. Although Phi is readily absorbed by plant leaves and/or roots, the plant response to Phi used as a phosphorus (P) source is variable. Negative effects of Phi on plant growth are commonly observed under P deficiency compared to near adequate plant P levels. Positive responses to Phi may be attributed to some level of fungal disease control. While only a few studies have provided evidence of Phi oxidation through cellular enzymes genetically controlled in plant cells, increasing evidence exists for the potential to manipulate plant genes to enhance oxidation of Phi to phosphate (Pi) in plants. Advances in genetic engineering to sustain growth and yield with Phi + Pi potentially provides a dual fertilization and weed control system. Further advances in genetic manipulation of plants to utilize Phi are warranted. Since Phi oxidation occurs slowly in soils, additional information is needed to characterize Phi oxidation kinetics under variable soil and environmental conditions.
Initial Study on Phytoextraction for Recovery of Metals from Sorted and Aged Waste-to-Energy Bottom Ash
Authors: Karlfeldt Fedje Karin , Edvardsson Viktoria , Dalek David
Published: September 2021

Sorted and aged bottom ash from Waste-to-Energy plants, i.e., MIBA (the Mineral fraction of Incinerator Bottom Ash) are potential source of metals that could be utilized to meet the increased demand from society. In this work, sunflowers (Helianthus annuus) and rapeseed (Brassica napus) were cultivated in conventional MIBA to evaluate the possibility for phytoextraction, mainly of Zn, during the period of one cultivation season in the Nordic climate. The results show that metal extraction from MIBA using rapeseed and sunflowers is workable but that neither of the used plants is optimal, mainly due to the inhibited root development and low water- and nutrient-holding capacities of MIBA. The addition of fertilizer is also important for growth. There was a simultaneous accumulation of numerous metals in both plant types, and the highest metal content was generally found in the roots. Calculations indicated that the ash from rapeseed root incineration contained about 2% Zn, and the contents of Co, Cu, and Pb were comparable to those in workable ores. This initial study shows that cultivation in and phytoextraction on MIBA is possible, and that the potential for increased metal extraction is high.
Validation of a Smartwatch-Based Workout Analysis Application in Exercise Recognition, Repetition Count and Prediction of 1RM in the Strength Training-Specific Setting
Authors: Oberhofer Katja , Erni Raphael , Sayers Mark , Huber Dominik , Lüthy Fabian , Lorenzetti Silvio
Published: September 2021

The goal of this study was to assess the validity, reliability and accuracy of a smartwatch-based workout analysis application in exercise recognition, repetition count and One Repetition Maximum (1RM) prediction in the strength training-specific setting. Thirty recreationally trained athletes performed four consecutive sets of barbell deadlift, barbell bench press and barbell back squat exercises with increasing loads from 60% to 80% of their estimated 1RM with maximum lift velocity. Data was measured using an Apple Watch Sport and instantaneously analyzed using an iOS workout analysis application called StrengthControl. The accuracies in exercise recognition and repetition count, as well as the reliability in predicting 1RM, were statistically analyzed and compared. The correct strength exercise was recognised in 88.4% of all the performed sets (N = 363) with accurate repetition count for the barbell back squat (p = 0.68) and the barbell deadlift (p = 0.09); however, repetition count for the barbell bench press was poor (p = 0.01). Only 8.9% of attempts to predict 1RM using the StrengthControl app were successful, with failed attempts being due to technical difficulties and time lag in data transfer. Using data from a linear position transducer instead, significantly different 1RM estimates were obtained when analysing repetition to failure versus load-velocity relationships. The present results provide new perspectives on the applicability of smartwatch-based strength training monitoring to improve athlete performance.
Just Transition and Workers’ Rights in the Global South: The Recent Argentine and Chilean Nationally Determined Contributions
Authors: Pucheta Mauro , Álvarez Alonso César , Silva Sánchez Pedro
Published: September 2021

Just transition tackles the consequences of a transition to a zero-carbon economy while addressing the issues of equity and justice so that nobody is left behind. It is a roadmap that informs the action of states towards sustainable development. The paper focuses on how Argentina and Chile, with a high percentage of informality in their economies and their labour markets, have embedded just transition into their legal orders through the submission of Nationally Determined Contributions (NDCs). The analysis of both experiences to protect workers is based upon three aspects: the Global South perspective; the 2015 ILO guidelines; the historical step that Argentina and Chile have reached in the submission of their second NDCs since these are the first Latin American countries to have expressly included just transition and work in their NDCs. After a thorough analysis, the paper concludes that this seems to be a promising first step that requires, nonetheless, a global approach in order to protect the environment and ensure that no one is left behind.
Insights from a Survey of Texas Gulf Coast Residents on the Social Factors Contributing to Willingness to Consume and Purchase Lionfish
Authors: Blakeway Raven D. , Ross Ashley D. , Jones Glenn A.
Published: September 2021

Indo-Pacific lionfish (Pterois volitans and P. miles) are the first marine teleost to become established in the Western Atlantic Ocean, Caribbean Sea, and Gulf of Mexico. Lionfish have been labeled a global conservation issue and pose major threats to local economies. To test whether commercial harvest of lionfish is a socially accepted management approach in Texas, we measured the components of an environmental behavior intention model with survey responses of Texas Gulf Coast residents (n = 420). Regression analyses of survey responses indicate that individuals were significantly more willing to consume lionfish if they had a high level of concern for the environmental problems posed by the invasive species and were more knowledgeable about the fish. Participation in an educational program that addresses lionfish was also found to be associated with greater willingness to consume lionfish among those who are moderately to highly concerned about the issue. The originality of this study is related to its contribution in identifying social factors that contribute to an individual’s willingness to consume lionfish. Insights from this study demonstrate the attitudinal and behavioral mechanisms that can be addressed to increase acceptance of using consumption as a sustainable management strategy to combat marine fish invasions.
Smallholder Farmers’ Perceptions of Climate Change and Adaptation Practices for Maize Production in Eastern Ethiopia
Authors: Teshome Helen , Tesfaye Kindie , Dechassa Nigussie , Tana Tamado , Huber Matthew
Published: September 2021

Climate change and variability is affecting maize (Zea mays L.) production in eastern Ethiopia but how farmers perceive the challenge and respond to it is not well documented. A study was conducted to analyze smallholder maize farmers’ perception of climate change/variability and identify their adaptation approaches and barriers for adaptation in the eastern highlands of Ethiopia. Meteorological data were assessed to provide evidence of the perceived change. A survey was conducted in six major maize-producing kebeles with a total of 364 respondents. A multi-stage sampling method was employed for selecting the sample units for the study. The data were analyzed using descriptive statistics and a multinomial logit model. The results indicated that 78% of the sampled smallholder maize farmers perceived increasing temperatures while 83% perceived decreasing amounts of rainfall. About 75% of the farmers indicated that they became aware of climate change and variability from their own experience and perceived deforestation as the main cause. The farmers perceived that drought, diseases and pests, dwindling soil fertility, and declining crop yields were the major impacts of climate change that affected maize production. The farmers’ major adaptation practices include adjusting planting dates, using improved maize varieties, intercropping, recommended mineral fertilizers, supplementary irrigation, and soil and water conservation measures. Econometric analysis revealed that low educational level, shortage of land, large family sizes, age, lack of access to irrigation water, lack of access to credit, and lack of access to extension services were the most important barriers to climate change adaptation in the area. It is concluded that farmers cultivating maize in the study area have perceived climate change and use certain adaptation strategies to counter its negative impacts on maize production. This implies that policies should be geared towards strengthening farmers’ efforts to adapt to climate change and alleviate the existing barriers in promoting adaptation strategies for enhancing the productivity of maize.
Sustainable Food Processing
Authors: Ačkar Đurđica
Published: September 2021

In the modern world, food consumption is inevitably linked to processing [...]
The Interplay between Urban Densification and Place Change in Tehran; Implications for Place-Based Social Sustainability
Authors: Dianati Vafa
Published: September 2021

Recent scholarship on urban social sustainability has redirected its attention to the role of place-based theories and practices in achieving and sustaining social outcomes. The notion of place and its centrality in everyday life of urban citizens could be used as an anchor point to study urbanisation processes and rapid urban changes. This paper employs a place-based framework of urban social sustainability in parallel to a framework of ‘place transformation’ to examine the consequences of soft densification on place attachment at the neighbourhood level in Tehran, Iran. Through analysing sixteen semi-structured interviews with residents, this paper argues that the temporal element of soft densification makes it a place undermining process, eradicating individual and collective place memory through resetting the time of the place. Moreover, the findings highlighted parallel trajectories in the meanings associated to place by residents which underscore the contradiction between ‘lived space’ and ‘conceived space’. Furthermore, it was found that loss of place attachment due to urban densification commonly leads to passive modes of response such changing lifestyle and daily routines, and voluntary relocating to adapt to the new socio-spatial order.
Enterprise Reciprocity and Risk Preferences and the Sustainable Cooperation of Innovation Activities in Industrial Parks
Authors: Li Wenjian , Zhang Yang , Wu Yuanyuan , Han Xue , Guo Benhai , Xie Gang
Published: September 2021

The sustainable cooperation of innovation in industrial parks is of great significance to the sustainable development of enterprises and parks. Factors explaining enterprise innovation cooperation activities in industrial parks have attracted great attention in scholarly research. In this article, a preference-based snowdrift game model on complex networks is proposed, where different combinations of enterprise reciprocity and risk preferences are introduced into the game model. The impact of these preferences on the sustainability of cooperation in mature and less-mature parks, characterized by different network styles, is examined through simulations. The investigation reveals that reciprocity and risk preferences have an effect on the sustainable emergence of enterprise cooperation under the constraints of a loss-to-profit ratio of cooperation, network average degree, and network style. Reciprocity preferences of enterprises are shown to have a greater impact on the sustainable emergence of cooperation than risk preference in two types of parks. Additionally, this advantage is more significant in less-mature parks. The results show the positive relationships between combinations of risk aversion and reciprocity preferences and the emergence of cooperation from a long-term perspective. This study concludes with a discussion of management suggestions and policy implications. The findings shed light on the understanding of the sustainable emergence of innovation cooperation in industrial parks.
Assessing the Impacts of the COVID-19 Pandemic over the Azores Region’s Touristic Companies
Authors: Castanho Rui Alexandre , Couto Gualter , Sousa Áurea , Pimentel Pedro , Batista Maria da Graça
Published: September 2021

It is understood that some types of tourism, as is the case of rural tourism, have an even more relevant role in achieving regional sustainability. Considering the actual COVID-19 sanitary crisis and its horizontal impacts over all the society and economy spheres, along with the relevance of rural tourism over the regional development, the current paper explores the impacts of the COVID-19 pandemic over the Azores Region’s Touristic Companies. Thus, through direct testing tools, such as questionnaires and site analysis, the present article enabled us to provide more insights into the impacts of the COVID-19 pandemic over the Azores Region’s touristic companies. The sample comprises 506 business managers of establishments of the touristic sector of the Azores, an outermost region of Portugal. The study has a descriptive focus and uses some methods of descriptive statistics, including the multiple response analysis. Most respondents consider that COVID-19 has a strong negative impact on their companies and expect a drop in turnover in 2020/2021. Our results suggest that government support is essential to offset the effects of the pandemic on companies of the tourism sector and show that the seal “Clean & Safe Azores” is an asset for companies operating in this region. Therefore, the results of this study could be used as a practical application for the Regional Authorities for the measurement of the impacts caused by the recent health crisis on the economy of the Azores.
Exploring Returnee Migrant Women, COVID-19 and Sustainability in Spain
Authors: Di Martino Maria Luisa
Published: September 2021

The COVID-19 pandemic has signified an historical change in human mobility. By transforming the patterns of people on the move, it has highlighted gender-based inequalities and women’s vulnerabilities. The link between COVID-19 and return migration shapes returnees’ readaptation process in their home countries, as returnees are embedded in a limbo between the pandemic’s pressure on the policy and socio-economic setting, on one hand, and their efforts for reintegration, on the other. Due to the pandemic, the gender-based imbalance has increased existing gender gaps both in migration and return, exacerbating women’s vulnerability. Thus, personal aspirations and professional expectations of highly educated women are caught in a system of socio-economic and geographical (im)mobility, which represents the principal outcome in their relocation and readaptation process. Based on a qualitative methodology through the analysis of ten life histories of highly educated returnee migrant women, this paper sheds light on the impacts of the COVID-19 pandemic on their migratory trajectories, providing a typology of them. Findings stress the necessity for more sustainable measures and resources for life–work balance and gender-sensitive policies, to promote a better integration process into the local labour market; to mitigate the effects of COVID-19 on returnee women, and to prevent the proliferation of mental health problems among returnee women.
The Impact of COVID-19 on Air Transportation Network in the United States, Europe, and China
Authors: Sun Xiaoqian , Wandelt Sebastian , Fricke Hartmut , Rosenow Judith
Published: September 2021

The air transportation industry has undergone unprecedented changes throughout the COVID-19 pandemic, as measured in terms of flight cancellations, aircraft retirements, airline bailouts, and disconnection of worldwide communities. In this study, we performed a cross-comparison of the impact COVID-19 had on three aviation centers of the world—the United States, Europe, and China. Methodologically, we analyzed the air transportation system as complex networks and by using time series analysis. We discovered that the peak of COVID-19 impact was around April/May 2020, followed by a strong recovery mostly in domestic subsystems. We found a homogeneous impact on the United States, a strong heterogeneous impact on Europe, and a rather short-term impact on China. Domestic flight connectivity recovered much faster than international flight connectivity, particularly for the Chinese air transportation system. Our study provided a comprehensive, data-driven analysis of the COVID-19 impact on air transportation for these three major regions, augmented by references to the rich scientific literature on this subject. We hope that our work opens up pathways to a better understanding and a higher degree of preparedness for future pandemics.
Effect of Phytoremediated Port Sediment as an Agricultural Medium for Pomegranate Cultivation: Mobility of Contaminants in the Plant
Authors: Martínez-Nicolás Juan José , Legua Pilar , Hernández Francisca , Martínez-Font Rafael , Giordani Edgardo , Melgarejo Pablo
Published: September 2021

Although the dredging of ports is a necessary management activity, it generates immense quantities of sediments, that are defined by the European Union as residues. On the other hand, the relevant peat demand for plant cultivation compromises its availability worldwide. In this context, the present work wanted to find an alternative substrate in order to replace and/or reduce the use of peat in agriculture, through the study of the suitability, concerning the exchange of substrate–plant–water pollutants, of the dredged remediated sediments as a fruit-growing media. Forty-five pomegranate trees (Punica granatum L. cv “Purple Queen”) were cultivated in three types of substrates (100% peat as a control, 100% dredged remediated sediments and 50% both mixed). The metal ion content and pesticide residues were analysed in the different plant parts (root, stem, leaves and fruits) and in drainage water. The results showed a limited transfer of pollutants. All the pollutants were below the legal limits, confirming that the dredged sediments could be used as a growing media, alone or mixed with other substrates. Thus, the results point out the need to open a European debate on the reuse and reconsideration of this residue from a circular economy point of view.
A Review of Circular Economy Research for Electric Motors and the Role of Industry 4.0 Technologies
Authors: Tiwari Divya , Miscandlon Jill , Tiwari Ashutosh , Jewell Geraint W.
Published: September 2021

The market for electric motors is experiencing a step-growth due to their adoption across a range of industrial sectors. This increased demand also highlights the importance of end-of-life management of electric motors and a requirement for appropriate strategies for the high value materials embedded in them. This paper aims to offer a holistic view on the circular economy research for electric motors and the role of Industry 4.0 technologies by presenting the state-of-the-art available in literature and comparing it with the industrial perspective. The literature review revealed the absence of a methodology for selecting the best end-of-life scenario for industrial electric motors. Recycling, which is an end-of-product-life strategy, was found to be the key focus area of research. Reuse, which is a better strategy in terms of waste hierarchy, was the least researched area due to lack of information about the condition and availability of returned products. In order to capture the current landscape within the UK for the repair, remanufacture and recycling of electrical machines, a structured survey of UK based companies was conducted. The survey revealed that nearly half of the companies do not undertake any repair strategies for electrical machine components; however, there was an aspiration from the respondents to migrate their companies towards more sustainable activities. The industry survey and the review of existing literature led to the identification of research trends, challenges and recommendations for future research.
Setting the Social Monitoring Framework for Nature-Based Solutions Impact: Methodological Approach and Pre-Greening Measurements in the Case Study from CLEVER Cities Milan
Authors: Mahmoud Israa H. , Morello Eugenio , Vona Chiara , Benciolini Maria , Sejdullahu Iliriana , Trentin Marina , Pascual Karmele Herranz
Published: September 2021

Nature-based solutions (NBS) are currently being deployed in many European Commission Horizon 2020 projects in reaction to the increasing number of environmental threats, such as climate change, unsustainable urbanization, degradation and loss of natural capital and ecosystem services. In this research, we consider the application of NBS as a catalyst for social inclusivity in urban regeneration strategies, enabled through civic participation in the co-creation of green interventions with respect to social cohesion and wellbeing. This article is focused on a social monitoring framework elaborated within the H2020 CLEVER Cities project, with the city of Milan as a case study. Firstly, we overviewed the major regeneration challenges and expected co-benefits of the project, which are mainly human health and wellbeing, social cohesion and environmental justice, as well as citizen perception about safety and security related to the NBS implementation process. Secondly, we examined the relevance of using NBS in addressing social co-benefits by analyzing data from questionnaires against a set of five major indicators, submitted to citizens and participants of activities during pre-greening interventions: (1) Place, use of space and relationship with nature, (2) Perceived ownership and sense of belonging, (3) Psychosocial issues, social interactions and social cohesion, (4) Citizen perception about safety and security, and lastly, we analyzed (5) knowledge about CLEVER interventions and NBS benefits in relation to socio-demographics of the questionnaires’ respondents. Thirdly, we cross-referenced a wind-rose multi-model of co-benefits analysis for NBS across the regeneration challenges of the project. Because of the COVID-19 emergency, in this research we mainly focused on site observations and online questionnaires, as well as on monitoring pre-greening scenarios in three Urban Living Labs (ULLs) in Milan, namely CLEVER Action Labs. Lastly, this study emphasizes the expected social added values of NBS impact over long-term urban regeneration projects. Insights from the pre-greening surveys results accentuate the importance of the NBS interventions in citizens’ perceptions about their wellbeing, general health and strong sense of neighborhood belonging. A wider interest towards civic participation in co-management and getting informed about NBS interventions in the Milanese context is also noted.
Understanding the Antecedents and Consequences of Service-Sales Ambidexterity: A Motivation-Opportunity-Ability (MOA) Framework
Authors: Ahmad Bilal , Da Liu , Asif Mirza Huzaifa , Irfan Muhammad , Ali Shahid , Akbar Muhammad Imad Ud Din
Published: September 2021

Drawing on the motivation, opportunity and ability (MOA) framework, we investigate the influence of charismatic leadership on salespeople’s service and sales activities—termed service-sales ambidextrous (SSA) behavior, which subsequently turn into service recovery performance outcomes. The primary aim of this research is to strengthen salespeople’s service quality in parallel to their selling activities while recovering a service failure. We validate the model using a sample of 344 business-to-business salespeople using partial least square structural equation modeling (PLS-SEM) technique. Study results show that charismatic leadership is positively and significantly related to service-sales ambidexterity. Likewise, service-sales ambidexterity has a positive and significant relationship with service recovery performance and adaptive selling behavior. Moreover, we found a significant relationship between adaptive selling behavior and service recovery performance. The results further specify that salesperson motivation, opportunity and ability to engage in SSA significantly moderate the relationship between charismatic leadership and service-sales ambidexterity. The results suggest the need for training programs that provide the salesperson with opportunities to understand the simultaneous implementation of selling strategies while also providing customer services.
The Effectiveness of Selected Devices to Reduce the Speed of Vehicles on Pedestrian Crossings
Authors: Kruszyna Maciej , Matczuk-Pisarek Marta
Published: September 2021

Accidents involving pedestrians often result in serious injury or death. The main goal of this conducted research is to evaluate selected devices that will help reduce the speed of vehicles on pedestrian crossings. Many devices from a group of “speed control measures” and “mid block tools” (refugee islands, speed tables, and raised pedestrian crossings) are examined to find the most effective ones. In our research, the range of reduction of a vehicle’s speed is used as a main measure of effectiveness, but a wider statistical analysis was conducted as well. One of the results of the research is the identification of three categories of devices referred to as high effectives (good), medium effectives (intermediate), and low or lack of effectives (bad). The content of the paper starts by highlighting the reasons to reduce the vehicle’s speed on pedestrian crossings (as an introduction). Next, we present the description of devices used to reduce the vehicle’s speed with a presentation of the research of their effectiveness. The studies that have been conducted are described in the following chapters: first, the characteristic of method and location, second, with discussion, the results of research and identification of the three categories of devices. The paper is then summarized by conclusions and comments. The research only covered the issues of road traffic engineering. The research was made in Poland, but the conclusions could be useful worldwide due to similar traffic rules and technical solutions.
Beer Industry in the Czech Republic: Reasons for Founding a Craft Brewery
Authors: Březinová Monika
Published: September 2021

The goal of this article is to evaluate the evolution of the brewing industry in the Czech Republic with an emphasis on the phenomenon of craft-brewery development. It deals with the influence of globalization on the structure of the Czech beer market and the rise of craft-breweries between 2000 and 2019. The main outputs come from research where a representative sample of 48 craft breweries was questioned from the Czech Republic. The result is the identification of the main factors influencing the increase of craft-breweries (legislation changes enabling entrepreneurship, increase of purchasing power of consumers, increase in demand for different beer styles, craft beers and specials, change of consumer behavior) but also the challenges that prevent their further expansion (lack of qualified brewers, complicated administration). The main motive for founding a craft brewery is an effort to improve beer culture in the Czech Republic and the ever-increasing demand for diversified beer (as opposed to the demand for the so-called euro-beers) and a good business opportunity stemming from this, which has been attracting more and more investors into this field.