bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing Urban LiDAR Flight Path Planning Using a Genetic Algorithm and a Dual Parallel Computing Framework
Authors: Vo Anh Vu , Laefer Debra F. , Byrne Jonathan
Published: November 2021

This paper introduces a genetic algorithm (GA) and a beam tracing algorithm incorporated within a dual parallel computing framework to optimize urban aerial laser scanning (ALS) missions to maximize vertical façade data capture, as needed for many three-dimensional reconstruction and modeling workflows. The optimization employs a low-density point cloud from the site of interest as a spatial representation of the urban scene. The GA is suitable for LiDAR flight path optimization due to its capability of handling open-ended problems that have many solutions. However, GAs require evaluating a very large number of candidates. The use of an initial point cloud allows realistic modeling of the urban environment in the optimization at the cost of high data input volumes. To cope with the computational and data demands, a dual parallel computing framework was devised. The parallel computing framework consists of two layers of parallelization. In the upper layer, multiple evaluators work in parallel and in conjunction with a main multi-threading GA optimizer to perform GA operations and evaluate the flight paths. In the lower layer, to evaluate assigned flight paths, each evaluator distributes its data and computation to multiple executors, which can reside on multiple physical nodes of a distributed-memory computing cluster. In addition to parallelism, the data partitioning on the lower layer allows out-of-core computation. Namely, data partitions are efficiently transferred between disks and memory so that only relevant subsets of data are kept in the main memory. The objective of the proposed method is threefold: (1) search for flight paths that yield the highest numbers of vertical points, (2) create a means to explicitly consider the detailed spatial configuration of urban environments, and (3) assure that the proposed optimization strategy is fast and can scale to large problem sizes. Multiple experiments were conducted and demonstrated the success of the proposed method. Converged results were achieved after dozens of generations within two hours. Two flight paths identified by the GA as the most and the least optimal candidates were deployed in real flight missions. The optimal flight path captured 16% more vertical points than the least optimal one, slightly higher than the 13% predicted. Both layers of parallelization were efficient: 13.1/16 for the lower layer and 3.2/4 for the upper layer. The two complementary layers of parallelization allowed flexible and efficient use of distributed computing resources to reduce the runtime. The scalability of the proposed approach was successfully demonstrated up to a data size of 460 million points. The optimization results were realistic and aligned well with the test flight results.
Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain
Authors: Aird Jeanie A. , Quon Eliot W. , Barthelmie Rebecca J. , Debnath Mithu , Doubrawa Paula , Pryor Sara C.
Published: November 2021

We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully identifies and characterizes wakes in scans with varying resolutions and geometries, and can capture wake characteristics in spatially heterogeneous fields resulting from data quality control procedures and complex background flow fields. The geometry, spatial extent and locations of wakes and wake fragments exhibit close accord with results from visual inspection. The model exhibits a 95% success rate in identifying wakes when they are present in scans and characterizing their shape. To test model robustness to varying image quality, we reduced the scan density to half the original resolution through down-sampling range gates. This causes a reduction in skill, yet 92% of wakes are still successfully identified. When grouping scans by meteorological conditions and utilizing the CNN for wake characterization under full and half resolution, wake characteristics are consistent with a priori expectations for wake behavior in different inflow and stability conditions.
Assessment of Urban Ecological Quality and Spatial Heterogeneity Based on Remote Sensing: A Case Study of the Rapid Urbanization of Wuhan City
Authors: Li Jingye , Gong Jian , Guldmann Jean-Michel , Yang Jianxin
Published: November 2021

Rapid urbanization significantly affects the productivity of the terrestrial ecosystem and the foundation of regional ecosystem services, thereby detrimentally influencing the ecological environment and urban ecological security. The United Nations’ Sustainable Development Goals (SDGs) also require accurate and timely assessments of where people live in order to develop, implement and monitor sustainable development policies. Sustainable development also emphasizes the process of protecting the ecological environment for future generations while maintaining the current needs of mankind. We propose a comprehensive evaluation method for urban ecological quality (UEQ) using Landsat TM/ETM+/OLI/TIRS images to extract remote sensing information representing four ecological elements, namely humidity, greenness, heat and dryness. An improved comprehensive remote sensing ecological index (IRSEI) evaluation model is constructed by combining the entropy weight method and principal component analysis. This modeling is applied to the city of Wuhan, China, from 1995 to 2020. Spatial autocorrelation analysis was conducted on the geographic clusters of the IRSEI. The results show that (1) from 1995 to 2015, the mean IRSEI of Wuhan city decreased from 0.60 to 0.47, indicating that environmental deterioration overwhelmed improvements; (2) the global Moran’s I for IRSEI ranged from 0.535 to 0.592 from 1995 to 2020, indicating significant heterogeneity in its spatial distribution, highlighting that high and low clusters gradually developed at the edge of the city and at the city center, respectively; (3) the high clusters are mainly distributed in the Huangpi and Jiangxia districts, and the low clusters at the city center, which exhibits a dense population and intense human activity. This paper uses remote sensing index methods to evaluate UEQ as a scientific theoretical basis for the improvement of UEQ, the control of UEQ and the formulation of urban sustainable development strategies in the future. Our results show that the UEQ method is a low-cost, feasible and simple technique that can be used for territorial spatial control and spatiotemporal urban sustainable development.
Building Extraction from Remote Sensing Images with Sparse Token Transformers
Authors: Chen Keyan , Zou Zhengxia , Shi Zhenwei
Published: November 2021

Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have provided a better perspective for modeling long-range context in images, but usually suffer from high computational complexity and memory usage. In this paper, we explored the potential of using transformers for efficient building extraction. We design an efficient dual-pathway transformer structure that learns the long-term dependency of tokens in both their spatial and channel dimensions and achieves state-of-the-art accuracy on benchmark building extraction datasets. Since single buildings in remote sensing images usually only occupy a very small part of the image pixels, we represent buildings as a set of “sparse” feature vectors in their feature space by introducing a new module called “sparse token sampler”. With such a design, the computational complexity in transformers can be greatly reduced over an order of magnitude. We refer to our method as Sparse Token Transformers (STT). Experiments conducted on the Wuhan University Aerial Building Dataset (WHU) and the Inria Aerial Image Labeling Dataset (INRIA) suggest the effectiveness and efficiency of our method. Compared with some widely used segmentation methods and some state-of-the-art building extraction methods, STT has achieved the best performance with low time cost.
Small-Sized Vehicle Detection in Remote Sensing Image Based on Keypoint Detection
Authors: Yu Lijian , Zhi Xiyang , Hu Jianming , Jiang Shikai , Zhang Wei , Chen Wenbin
Published: November 2021

The vehicle detection in remote sensing images is a challenging task due to the small size of the objects and interference of a complex background. Traditional methods require a large number of anchor boxes, and the intersection rate between these anchor boxes and an object’s real position boxes needs to be high enough. Moreover, the size and aspect ratio of each anchor box need to be designed manually. For small objects, more anchor boxes need to be set. To solve these problems, we regard the small object as a keypoint in the relevant background and propose an anchor-free vehicle detection network (AVD-kpNet) to robustly detect small-sized vehicles in remote sensing images. The AVD-kpNet framework fuses features across layers with a deep layer aggregation architecture, preserving the fine features of small objects. First, considering the correlation between the object and the surrounding background, a 2D Gaussian distribution strategy is adopted to describe the ground truth, instead of a hard label approach. Moreover, we redesign the corresponding focus loss function. Experimental results demonstrate that our method has a higher accuracy for the small-sized vehicle detection task in remote sensing images compared with several advanced methods.
Synergetic Classification of Coastal Wetlands over the Yellow River Delta with GF-3 Full-Polarization SAR and Zhuhai-1 OHS Hyperspectral Remote Sensing
Authors: Tu Canran , Li Peng , Li Zhenhong , Wang Houjie , Yin Shuowen , Li Dahui , Zhu Quantao , Chang Maoxiang , Liu Jie , Wang Guoyang
Published: November 2021

The spatial distribution of coastal wetlands affects their ecological functions. Wetland classification is a challenging task for remote sensing research due to the similarity of different wetlands. In this study, a synergetic classification method developed by fusing the 10 m Zhuhai-1 Constellation Orbita Hyperspectral Satellite (OHS) imagery with 8 m C-band Gaofen-3 (GF-3) full-polarization Synthetic Aperture Radar (SAR) imagery was proposed to offer an updated and reliable quantitative description of the spatial distribution for the entire Yellow River Delta coastal wetlands. Three classical machine learning algorithms, namely, the maximum likelihood (ML), Mahalanobis distance (MD), and support vector machine (SVM), were used for the synergetic classification of 18 spectral, index, polarization, and texture features. The results showed that the overall synergetic classification accuracy of 97% is significantly higher than that of single GF-3 or OHS classification, proving the performance of the fusion of full-polarization SAR data and hyperspectral data in wetland mapping. The synergy of polarimetric SAR (PolSAR) and hyperspectral imagery enables high-resolution classification of wetlands by capturing images throughout the year, regardless of cloud cover. The proposed method has the potential to provide wetland classification results with high accuracy and better temporal resolution in different regions. Detailed and reliable wetland classification results would provide important wetlands information for better understanding the habitat area of species, migration corridors, and the habitat change caused by natural and anthropogenic disturbances.
Detection of Outliers in LiDAR Data Acquired by Multiple Platforms over Sorghum and Maize
Authors: Nazeri Behrokh , Crawford Melba
Published: November 2021

High-resolution point cloud data acquired with a laser scanner from any platform contain random noise and outliers. Therefore, outlier detection in LiDAR data is often necessary prior to analysis. Applications in agriculture are particularly challenging, as there is typically no prior knowledge of the statistical distribution of points, plant complexity, and local point densities, which are crop-dependent. The goals of this study were first to investigate approaches to minimize the impact of outliers on LiDAR acquired over agricultural row crops, and specifically for sorghum and maize breeding experiments, by an unmanned aerial vehicle (UAV) and a wheel-based ground platform; second, to evaluate the impact of existing outliers in the datasets on leaf area index (LAI) prediction using LiDAR data. Two methods were investigated to detect and remove the outliers from the plant datasets. The first was based on surface fitting to noisy point cloud data via normal and curvature estimation in a local neighborhood. The second utilized the PointCleanNet deep learning framework. Both methods were applied to individual plants and field-based datasets. To evaluate the method, an F-score was calculated for synthetic data in the controlled conditions, and LAI, the variable being predicted, was computed both before and after outlier removal for both scenarios. Results indicate that the deep learning method for outlier detection is more robust than the geometric approach to changes in point densities, level of noise, and shapes. The prediction of LAI was also improved for the wheel-based vehicle data based on the coefficient of determination (R2) and the root mean squared error (RMSE) of the residuals before and after the removal of outliers.
Predictive Intelligent Transportation: Alleviating Traffic Congestion in the Internet of Vehicles
Authors: Zhang Le , Khalgui Mohamed , Li Zhiwu
Published: November 2021

Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension’s decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality.
Detection of Ground Contact Times with Inertial Sensors in Elite 100-m Sprints under Competitive Field Conditions
Authors: Blauberger Patrick , Horsch Alexander , Lames Martin
Published: November 2021

This study describes a method for extracting the stride parameter ground contact time (GCT) from inertial sensor signals in sprinting. Five elite athletes were equipped with inertial measurement units (IMU) on their ankles and performed 34 maximum 50 and 100-m sprints. The GCT of each step was estimated based on features of the recorded IMU signals. Additionally, a photo-electric measurement system covered a 50-m corridor of the track to generate ground truth data. This corridor was placed interchangeably at the first and the last 50-ms of the track. In total, 863 of 889 steps (97.08%) were detected correctly. On average, ground truth data were underestimated by 3.55 ms. The root mean square error of GCT was 7.97 ms. Error analyses showed that GCT at the beginning and the end of the sprint was classified with smaller errors. For single runs the visualization of step-by-step GCT was demonstrated as a new diagnostic instrument for sprint running. The results show the high potential of IMUs to provide the temporal parameter GCT for elite-level athletes.
UV Hyperspectral Imaging as Process Analytical Tool for the Characterization of Oxide Layers and Copper States on Direct Bonded Copper
Authors: Al Ktash Mohammad , Stefanakis Mona , Englert Tim , Drechsel Maryam S. L. , Stiedl Jan , Green Simon , Jacob Timo , Boldrini Barbara , Ostertag Edwin , Rebner Karsten , Brecht Marc
Published: November 2021

Hyperspectral imaging and reflectance spectroscopy in the range from 200–380 nm were used to rapidly detect and characterize copper oxidation states and their layer thicknesses on direct bonded copper in a non-destructive way. Single-point UV reflectance spectroscopy, as a well-established method, was utilized to compare the quality of the hyperspectral imaging results. For the laterally resolved measurements of the copper surfaces an UV hyperspectral imaging setup based on a pushbroom imager was used. Six different types of direct bonded copper were studied. Each type had a different oxide layer thickness and was analyzed by depth profiling using X-ray photoelectron spectroscopy. In total, 28 samples were measured to develop multivariate models to characterize and predict the oxide layer thicknesses. The principal component analysis models (PCA) enabled a general differentiation between the sample types on the first two PCs with 100.0% and 96% explained variance for UV spectroscopy and hyperspectral imaging, respectively. Partial least squares regression (PLS-R) models showed reliable performance with R2c = 0.94 and 0.94 and RMSEC = 1.64 nm and 1.76 nm, respectively. The developed in-line prototype system combined with multivariate data modeling shows high potential for further development of this technique towards real large-scale processes.
Blood Pressure Continuous Measurement through a Wearable Device: Development and Validation of a Cuffless Method
Authors: De Marchi Beatrice , Frigerio Mattia , De Nadai Silvia , Longinotti-Buitoni Gianluigi , Aliverti Andrea
Published: November 2021

The present study aims to develop and validate a cuffless method for blood pressure continuous measurement through a wearable device. The goal is achieved according to the time-delay method, with the guiding principle of the time relation it takes for a blood volume to travel from the heart to a peripheral site. Inversely proportional to the blood pressure, this time relation is obtained as the time occurring between the R peak of the electrocardiographic signal and a marker point on the photoplethysmographic wave. Such physiological signals are recorded by using L.I.F.E. Italia’s wearable device, made of a sensorized shirt and wristband. A linear regression model is implemented to estimate the corresponding blood pressure variations from the obtained time-delay and other features of the photoplethysmographic wave. Then, according to the international standards, the model performance is assessed, comparing the estimates with the measurements provided by a certified digital sphygmomanometer. According to the standards, the results obtained during this study are notable, with 85% of the errors lower than 10 mmHg and a mean absolute error lower than 7 mmHg. In conclusion, this study suggests a time-delay method for continuous blood pressure estimates with good performance, compared with a reference device based on the oscillometric technique.
Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing
Authors: Ura Sharifu , Ghosh Angkush Kumar
Published: November 2021

Smart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain’s) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems.
A Measurement of ‘Walking-the-Wall’ Dynamics: An Observational Study Using Accelerometry and Sensors to Quantify Risk Associated with Vertical Wall Impact Attenuation in Trampoline Parks
Authors: Hossain Imam , Zhou Shilei , Ishac Karlos , Lind Edward , Sharwood Lisa , Eager David
Published: November 2021

This study illustrates the application of a tri-axial accelerometer and gyroscope sensor device on a trampolinist performing the walking-the-wall manoeuvre on a high-performance trampoline to determine the performer dynamic conditions. This research found that rigid vertical walls would allow the trampolinist to obtain greater control and retain spatial awareness at greater levels than what is achievable on non-rigid vertical walls. With a non-rigid padded wall, the reaction force from the wall can be considered a variable force that is not constrained, and would not always provide the feedback that the trampolinist needs to maintain the balance with each climb up the wall and fall from height. This research postulates that unattenuated vertical walls are safer than attenuated vertical walls for walking-the-wall manoeuvres within trampoline park facilities. This is because non-rigid walls would provide higher g-force reaction feedback from the wall, which would reduce the trampolinist’s control and stability. This was verified by measuring g-force on a horizontal rigid surface versus a non-rigid surface, where the g-force feedback was 27% higher for the non-rigid surface. Control and stability are both critical while performing the complex walking-the-wall manoeuvre. The trampolinist experienced a very high peak g-force, with a maximum g-force of approximately 11.5 g at the bottom of the jump cycle. It was concluded that applying impact attenuation padding to vertical walls used for walking-the-wall and similar activities would increase the likelihood of injury; therefore, padding of these vertical surfaces is not recommended.
Live Spoofing Detection for Automatic Human Activity Recognition Applications
Authors: Huszár Viktor Dénes , Adhikarla Vamsi Kiran
Published: November 2021

Human Activity Recognition (HAR) has become increasingly crucial in several applications, ranging from motion-driven virtual games to automated video surveillance systems. In these applications, sensors such as smart phone cameras, web cameras or CCTV cameras are used for detecting and tracking physical activities of users. Inevitably, spoof detection in HAR is essential to prevent anomalies and false alarms. To this end, we propose a deep learning based approach that can be used to detect spoofing in various fields such as border control, institutional security and public safety by surveillance cameras. Specifically, in this work, we address the problem of detecting spoofing occurring from video replay attacks, which is more common in such applications. We present a new database containing several videos of users juggling a football, captured under different lighting conditions and using different display and capture devices. We train our models using this database and the proposed system is capable of running in parallel with the HAR algorithms in real-time. Our experimental results show that our approach precisely detects video replay spoofing attacks and generalizes well, even to other applications such as spoof detection in face biometric authentication. Results show that our approach is effective even under resizing and compression artifacts that are common in HAR applications using remote server connections.
Autonomous Thermal Vision Robotic System for Victims Recognition in Search and Rescue Missions
Authors: Cruz Ulloa Christyan , Prieto Sánchez Guillermo , Barrientos Antonio , Del Cerro Jaime
Published: November 2021

Technological breakthroughs in recent years have led to a revolution in fields such as Machine Vision and Search and Rescue Robotics (SAR), thanks to the application and development of new and improved neural networks to vision models together with modern optical sensors that incorporate thermal cameras, capable of capturing data in post-disaster environments (PDE) with rustic conditions (low luminosity, suspended particles, obstructive materials). Due to the high risk posed by PDE because of the potential collapse of structures, electrical hazards, gas leakage, etc., primary intervention tasks such as victim identification are carried out by robotic teams, provided with specific sensors such as thermal, RGB cameras, and laser. The application of Convolutional Neural Networks (CNN) to computer vision is a breakthrough for detection algorithms. Conventional methods for victim identification in these environments use RGB image processing or trained dogs, but detection with RGB images is inefficient in the absence of light or presence of debris; on the other hand, developments with thermal images are limited to the field of surveillance. This paper’s main contribution focuses on implementing a novel automatic method based on thermal image processing and CNN for victim identification in PDE, using a Robotic System that uses a quadruped robot for data capture and transmission to the central station. The robot’s automatic data processing and control have been carried out through Robot Operating System (ROS). Several tests have been carried out in different environments to validate the proposed method, recreating PDE with varying conditions of light, from which the datasets have been generated for the training of three neural network models (Fast R-CNN, SSD, and YOLO). The method’s efficiency has been tested against another method based on CNN and RGB images for the same task showing greater effectiveness in PDE main results show that the proposed method has an efficiency greater than 90%.
Flexible IoT Gas Sensor Node for Automated Life Science Environments Using Stationary and Mobile Robots
Authors: Neubert Sebastian , Roddelkopf Thomas , Al-Okby Mohammed Faeik Ruzaij , Junginger Steffen , Thurow Kerstin
Published: November 2021

In recent years the degree of automation in life science laboratories increased considerably by introducing stationary and mobile robots. This trend requires intensified considerations of the occupational safety for cooperating humans, since the robots operate with low volatile compounds that partially emit hazardous vapors, which especially do arise if accidents or leakages occur. For the fast detection of such or similar situations a modular IoT-sensor node was developed. The sensor node consists of four hardware layers, which can be configured individually regarding basic functionality and measured parameters for varying application focuses. In this paper the sensor node is equipped with two gas sensors (BME688, SGP30) for a continuous TVOC measurement. In investigations under controlled laboratory conditions the general sensors’ behavior regarding different VOCs and varying installation conditions are performed. In practical investigations the sensor node’s integration into simple laboratory applications using stationary and mobile robots is shown and examined. The investigation results show that the selected sensors are suitable for the early detection of solvent vapors in life science laboratories. The sensor response and thus the system’s applicability depends on the used compounds, the distance between sensor node and vapor source as well as the speed of the automation systems.
Bordered Trajectories: The Impact of Institutional Bordering Practices on Young Refugees’ (Re-)Engagement with Post-15 Education in Greece
Authors: Hunt Lucy
Published: November 2021

Greece has been a site of various crises in recent years: firstly, the financial crash of 2008; secondly, the ongoing ‘refugee crisis’, which peaked in 2015; and thirdly, the current COVID-19 pandemic. This paper addresses the first of these crises, and particularly how state responses to increased migration flows shape young refugees’ (aged 15–25) (re-)engagement with post-15 learning opportunities upon arrival in the country. It is based on semi-structured interviews with young refugees living in Thessaloniki, conducted as part of an ethnographic doctoral project on educational decision-making. The findings reveal that three key institutional bordering practices in Greece—namely the bordering of space (via encampment), time (via enforced waiting), and public services (via administrative barriers)—played central roles in young refugees’ (re-)engagement with post-15 education; often causing their dreams to be diverted or downgraded. However, with determination and the support of willing gatekeepers, refugee youth found ways to (re)construct adapted learning trajectories despite, and in response to, these arrival challenges.
The Impact of CSR on Sustainable Innovation Ambidexterity: The Mediating Role of Sustainable Supply Chain Management and Second-Order Social Capital
Authors: Khan Asif , Chen Chih-Cheng , Suanpong Kwanrat , Ruangkanjanases Athapol , Kittikowit Santhaya , Chen Shih-Chih
Published: November 2021

As the concept of corporate social responsibility advances, it is crucial to recognize the broad roles of sustainability and the Sustainable Development Goals (SDGs) influencing the implementation of corporate social responsibility (CSR) initiatives. This research contributes to the expanding field of CSR, sustainable innovation ambidexterity (SIA), sustainable supply chain management (SSCM), and second-order social capital (SOSC). This research produced a theoretical framework based on social exchange theory, social capital theory, and Carroll’s CSP model to investigate the impact of CSR on SIA, SSCM, and SOSC. Furthermore, this study examined the mediating effects of SSCM and SOSC on the correlation between CSR and SIA. Additionally, this study presents a model to explain the impact of SSCM and SOSC on SIA. This study concentrated on top-level managers of several manufacturing businesses situated in Pakistan. A total of 42 manufacturing businesses were chosen utilizing a convenience cluster sampling method. As per the results of this research, CSR was discovered to have a positive impact on SSCM, SIA, and, SOSC. Moreover, SOSC and SSCM were found to be in a significant relationship with SIA. Finally, SOSC and SSCM fully mediated the relationship between CSR and sustainable innovation ambidexterity. This research can guide companies by effectively delivering their finances in CSR initiatives. The findings also suggest that companies should concentrate on improving their CSR initiatives because CSR has a significant effect on SIA. The SDGs provide a road map for companies that can assist them to tactically manage their CSR initiatives according to the international and national sustainable development guidelines. Hence, the CSR–SDG tie is essential for the improvement in the role of CSR in sustainable development. Moreover, to improve and measure SSCM, SOSC, and SIA, policymakers and general managers should devote efforts to CSR.
Rights, Commons, and Social Capital: The Role of Cooperation in an Italian Agri-Food Supply Chain
Authors: Giovannetti Enrico , Bertolini Paola , Russo Margherita
Published: November 2021

The paper discusses the relationship between Commons, Social Capital, and sustainability in terms of resources used, tools available, and goals to be achieved. The conceptual framework differs from the traditional one, which considers Commons and Social Capital as different resources. The paper considers Commons and Social Capital as homogeneous assets defined by the rights related to the access, use, and reproduction of collective resources, material or immaterial, which are essential to reduce the difference between private and social costs in the economic processes. This approach derives from a definition of sustainability as a private and social responsibility in reproducing all the resources used in the life processes, minimizing the waste caused by their exhaustion and loss of fertility. The paper refers to the model of Commons by the school of Elinor Ostrom to explain the nature and role of Social Capital and to observe it in different units of analysis, with particular attention to the forms of cooperative enterprise. The last part of the work outlines field research on the Parmigiano Reggiano supply chain as a natural laboratory to test the theoretical hypotheses.
Social Impact Analysis of Products under a Holistic Approach: A Case Study in the Meat Product Supply Chain
Authors: Aranda Juan , Zambrana-Vásquez David , Del-Busto Felipe , Círez Fernando
Published: November 2021

Social impact assessment of products can be approached through different methodologies that need to be adapted to the particularities and features of the studied subject. Thus, the Social Life Cycle Assessment methodology can be used to assess different innovative practices of product manufacturing, under a circular economy approach, by identifying potential positive as well as negative impacts along products’ life cycle. This paper presents the results of the Social Life Cycle Impact Assessment of a reference product from the Spanish meat industry using existing and new innovative methods of social impact analysis. Worker discrimination, health and safety for workers, consumers and local community were identified as the social aspects with relevant significance into the business or for the influence on customer’s perception of the products studied. Therefore, results can represent a reference scenario for the future assessment of innovative solutions in the Spanish meet sector. Despite the scarce use of Social Life Cycle Impact Assessment, this case study is a good example of how this innovative kind of assessment can be helpful for companies to identify their weak and strong social performance areas and design strategies to improve in Social Responsibility Management.
Molecular Characterization and Population Genetic Structure of Fagopyrum Species Cultivated in Himalayan Regions
Authors: Dar Fayaz Ahmad , Tahir Inayatullah , Qari Sameer H , Abulfaraj Aala A , Aljabri Maha , Alharby Hesham F. , Hakeem Khalid Rehman , Rehman Reiaz Ul
Published: November 2021

Fagopyrum spp. (buckwheat) is a dicotyledonous pseudocereal crop mainly cultivated in the north-western Himalayan regions for its highly nutritional, antioxidant and therapeutic values. In the present investigation, molecular characterization was performed by using ISSR (inter simple sequence repeat) markers on 42 accessions of four buckwheat species (Fagopyrum esculentum, F. sagittatum, F. tataricum and F. kashmirianum). The 12 pre-screened ISSR primers amplified 102 bands, and amongst them 85 bands exhibited polymorphism with an average polymorphism of 82.73%. The results revealed that Shannon’s information indices (I) and Nei’s genetic diversity (H) were low for F. tataricum (I = 0.1028 ± 0.2307; H = 0.0707 ± 0.1617) and high for F. esculentum (I = 0.1715 ± 0.2622; H = 0.1164 ± 0.1796). It was estimated that within the accessions of Fagopyrum species, the species diversity (HT) and mean diversity (HS) were 0.3200 and 0.1041, respectively. Molecular variance partitioning by AMOVA also indicated a significant genetic differentiation accounting for 73% among and 27% within the accessions of Fagopyrum species. Overall, accessions of F. esculentum had the greatest distance from the other accessions of buckwheat species, which includes F. sagittatum, F. tataricum and F. kashmirianum as revealed by FST distance and Nei’s unbiased genetic distance. The dendograms based on UPGMA and PCoA segregated 42 accessions of four buckwheat species into three major groups. This study clearly reveals a considerable amount of genetic diversity at the intra-specific level in F. esculentum, F. sagittatum and F. kashmirianum accessions. The factors responsible for it are diverse geographical conditions, pollinating behavior and cultivation practices adapted in these regions. The study also indicated a close phylogenetic relationship between F. tataricum and F. kashmirianum.
Biochelator Assisted Phytoremediation for Cadmium (Cd) Pollution in Paddy Field
Authors: Wang Yangyang , Li Shaofeng , Wang Xiaoshu , Xu Jianfeng , Li Tongtong , Zhu Jia , Yang Ruyue , Wang Jinsheng , Chang Ming , Wang Lei
Published: November 2021

To evaluate the efficacy of biochelators for phytoremediation of cadmium (Cd), effects of biochelators (humic acid, HA; polyaspartic acid, PASP; rhamnolipid, RLs) and synthetic chelator (sodium tripolyphosphate, STPP) on the growth, accumulation, and antioxidant index of Cd were investigated in Iris sibirica L. The results indicated that the addition of HA increased the dry weight of leaves and stems, and the addition of PASP, RLs, and STPP increased the growth of leaves but inhibited the growth of stems. The determination of Cd accumulation indicated that HA decreased the content of Cd in all tissues, while the addition of PASP, RLs, and STPP increased the accumulation of Cd in stems and roots. In addition, the determination of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities indicated that the treatment of PASP and RLs increased the activities of antioxidant enzymes, which might be beneficial to the resistance of Cd for Iris sibirica L. Overall, these results indicated that biochelators may improve the phytoremediation efficacy of Iris sibirica L. on Cd polluted paddy field. Among the tested chelators, PASP exhibited the best comprehensive efficacy and the highest commercial potential.
Configuration-Based Promotion: A New Approach to Destination Image Sustainability
Authors: Li Yanan , Xiao Honggen , Bu Naipeng , Luo Jianji , Xia Hui , Kong Liyuan , Yu Haoyue
Published: November 2021

The assessment of tourist destination images should not only be the arrangement of multiple influencing factors. This study explores the complex causal relationship for tourist destination images based on a configuration perspective to enhance the overall tourism image using the fuzzy-set qualitative comparative analysis method. The configurational paths for promoting tourism image can be categorized into two types and nine configurations in Shandong Province. Therefore, this study recommends augmenting the tourism image of the entire region with the logical thinking of “segmentation–integration” and “enhancing strengths–supplementing weakness” and finally realizing a sustainable tourist destination image.
Consumer Motivation by Using Unified Theory of Acceptance and Use of Technology towards Electric Vehicles
Authors: Abbasi Haider , Johl Satirenjit , Shaari Zullina , Moughal Wajiha , Mazhar Muhammad , Musarat Muhammad , Rafiq Waqas , Farooqi Asaad , Borovkov Alexey
Published: November 2021

The transport sector is the leading source of growing greenhouse gas (GHG) emissions globally. To consider environmental degradation aspects due to transport, electric vehicles (EVs) have the prospect to lead road transport to electric mobility from conventional petroleum vehicles. Despite various eco-friendly benefits, the EV market penetration ratio is very low, especially in developing countries. The primary reason for low penetration is consumer limited motivation and knowledge about the EVs features. This paper uses a unified theory of acceptance and technology (UTAUT) model to assess consumer motivation and environmental knowledge towards EVs. This research used convenience random sampling to collect data and analyzed the results using the Partial Least Squares (PLS) method on the example of 199 respondents from Malaysia. The study results revealed that factors identified in the motivational context significantly influence consumer intentions to purchase EVs. Perceived environmental knowledge and technophilia have been included in UTAUT from a motivational perspective. Furthermore, a significant relationship between effort expectancy, social influence, technophilia, perceived environmental knowledge, and purchase intention towards electric vehicles has been observed, without performance expectancy. The study findings serve to inform policymakers and automakers to formulate effective marketing strategies to enhance consumer motivation, knowledge, and value creation for EVs in a sustainable era. Ultimately, the policies will help to encourage consumers to buy eco-friendly vehicles that will help reduce transport carbon emissions and attain sustainable development goals (SDGs).
Investors’ Delight? Climate Risk in Stock Valuation during COVID-19 and Beyond
Authors: Jacob Andrea , Nerlinger Martin
Published: November 2021

We use the COVID-19 pandemic period in 2020 as an exogenous shock event to assess in how far climate risks measured by carbon exposure have entered and established themselves in the valuation of global stocks. In addition to descriptive analyses, we conduct cross-sectional panel regressions to assess the influence of carbon intensity levels on return and risk characteristics during and after the shock period. Furthermore, a difference-in-differences model setup allows us to infer whether these influences were significantly different when comparing pre-shock, shock, and post-shock periods. We find that carbon intensity affected returns significantly and negatively during a time of high uncertainty. In fact, high-emitting stocks suffered significantly more compared to the pre-crisis period. However, they could make up for their additional losses in the recovery period. In line with their high-risk exposure towards stranded assets and climate policy uncertainty, carbon-intensive stocks face higher risk levels in more stable economic times, thus justifying a carbon premium.
Digital Entrepreneurship Services Evolution: Analysis of Quadruple and Quintuple Helix Innovation Models for Open Data Ecosystems
Authors: Kitsios Fotis , Kamariotou Maria , Grigoroudis Evangelos
Published: November 2021

Open data hackathons are events where the actors from an ecosystem collaborate to build platforms that will benefit the public, creating a win–win scenario for all of them. Sadly, many digital services produced in hackathons are discarded only by providing access to open data that cannot boost economic benefits. Therefore, it is necessary to create a model that fosters value and entrepreneurship for the open data ecosystem, aiming to develop an economically self-sustained ecosystem. The purpose of this paper is to identify the challenges participants of open data hackathons can face to present a model that will support the improvement of these contests. This paper uses the quadruple/quintuple helix innovation model to drive innovation and entrepreneurship in Thessaloniki’s open data ecosystem to develop applications using open data. The results indicate that, although actors are aware of open data use, a new type of open data ecosystem that creates a win–win scenario between the entities in the open data ecosystem is required. The proposed model implies a full effect that promotes cooperation and networking among the entities in the city’s ecosystem towards achieving the aim of increasing citizens’ quality of life.
Climate Change-Greenhouse Gas Emissions Analysis and Forecast in Romania
Authors: Moiceanu Georgiana , Dinca Mirela Nicoleta
Published: November 2021

Greenhouse gases (GHG), such as carbon dioxide, methane, nitrous oxide, and other gases, are considered to be the main cause of global climate change, and this problem has received significant global attention. Carbon dioxide has been considered the most significant gas contributing to global climate change. Our paper presents an analysis of the greenhouse gas emissions in Romania along with a forecast for the years to come. For the study, data from the National Institute of Statistics and Eurostat were gathered and used for the analysis in order to present the results. To obtain the results, the data gathered were analyzed using forecasting methods that can be of help in solving some uncertainties that surround the future. The greenhouse gas (GHG) emissions trends in Romania were analyzed both for linear and exponential function methods. The obtained results showed that the linear function analysis of total GHG emissions in Romania had a forecast accuracy higher than the exponential function method. From the analytical methods used we can draw the conclusion that the emissions are on a descending scale and choosing a proper method is important in analyzing data.
Smart Integration of Electric Buses in Cities: A Technological Review
Authors: Verbrugge Boud , Hasan Mohammed Mahedi , Rasool Haaris , Geury Thomas , El Baghdadi Mohamed , Hegazy Omar
Published: November 2021

This paper provides a comprehensive overview of the state-of-the-art related to the implementation of battery electric buses (BEBs) in cities. In recent years, bus operators have started focusing on the electrification of their fleet to reduce the air pollutants in cities, which has led to a growing interest from the scientific community. This paper presents an analysis of the BEB powertrain topology and the charging technology of BEBs, with a particular emphasis on the power electronics systems. Moreover, the different key technical requirements to facilitate the operation of BEBs are addressed. Accordingly, an in-depth review on vehicle scheduling, charger location optimization and charging management strategies is carried out. The main findings concerning these research fields are summarized and discussed. Furthermore, potential challenges and required further developments are determined. Based on this analysis, it can be concluded that an accurate energy consumption assessment of their BEBs is a must for bus operators, that real-time, multi-objective smart charging management strategies with V2X features should be included when performing large bus fleet scheduling and that synchronized opportunity charging, smart green depot charging, and electric bus rapid transit can further reduce the impact on the grid. This review paper should help to enable a smarter and more efficient integration of BEBs in cities in the future.