bluuebunny/crossref_metadata_2025 huggingface dataset:
High Gain Compact UWB Antenna for Ground Penetrating Radar Detection and Soil Inspection
Authors: Saeidi Tale , Alhawari Adam R. H. , Almawgani Abdulkarem H. M. , Alsuwian Turki , Imran Muhammad Ali , Abbasi Qammer
Published: July 2022

An ultrawide bandwidth (UWB) antenna for ground-penetrating radar (GPR) applications is designed to check soil moisture and provide good-quality images of metallic targets hidden in the soil. GPR is a promising technology for detecting and identifying buried objects, such as landmines, and investigating soil in terms of moisture content and contamination. A paddle-shaped microstrip antenna is created by cutting a rectangular patch at one of its diametrical edges fed by the coplanar waveguide technique. The antenna is loaded by stubs, shorting pins, and a split-ring resonator (SRR) metamaterial structure to increase the antenna’s gain and enhance the bandwidth (BW) towards both the lower and higher end of the working BW. The antenna’s performance in soil inspection is studied in terms of the operating frequency range, different types of soil, different distances (e.g., 50 cm) between the antenna arrays and soil, S-parameters, and gain. Following this, the antenna’s ability to find a metallic target in the soil is tested, considering different array numbers, multi-targets, and locations. The antenna is designed on a thin layer of economic polytetrafluoroethylene (PTFE) substrate with dimensions 50 × 39 × 0.508 mm3 and works in the frequency range 1.9–9.2 GHz. In addition, two more resonances at 0.9 and 1.8 GHz are also achieved; hence, the antenna works for more than two application bands, such as the ISM- and L-bands. The measurement results validated excellent agreement with the simulated results. Furthermore, the recommended antenna offering a high gain of about 10.8 dBi and maximum efficiency above 97% proved able to discriminate between hidden objects and even recognize their shapes. Moreover, the reconstructed images show that the antenna can detect an object in the soil at any location.
Vision-Based Detection of Bolt Loosening Using YOLOv5
Authors: Sun Yuhang , Li Mengxuan , Dong Ruiwen , Chen Weiyu , Jiang Dong
Published: July 2022

Bolted connections have been widely applied in engineering structures, loosening will happen when bolted connections are subjected to continuous cyclic load, and a significant rotation between the nut and the bolt can be observed. Combining deep learning with machine vision, a bolt loosening detection method based on the fifth version of You Only Look Once (YOLOv5) is proposed, and the rotation of the nut is identified to detect the bolt loosening. Two different circular markers are added to the bolt and the nut separately, and then YOLOv5 is used to identify the circular markers, and the rotation angle of the nut against the bolt is calculated according to the center coordinate of each predicted box. A bolted connection structure is adopted to illustrate the effectiveness of the method. First, 200 images containing bolts and circular markers are collected to make the dataset, which is divided into a training set, verification set and test set. Second, YOLOv5 is used to train the model; the precision rate and recall rate are respectively 99.8% and 100%. Finally, the robustness of the proposed method in different shooting environments is verified by changing the shooting distance, shooting angle and light condition. When using this method to detect the bolt loosening angle, the minimum identifiable angle is 1°, and the maximum detection error is 5.91% when the camera is tilted 45°. The experimental results show that the proposed method can detect the loosening angle of the bolted connection with high accuracy; especially, the tiny angle of bolt loosening can be identified. Even under some difficult shooting conditions, the method still works. The early stage of bolt loosening can be detected by measuring the rotation angle of the nut against the bolt.
Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks
Authors: Volkmann Nina , Zelenka Claudius , Devaraju Archana Malavalli , Brünger Johannes , Stracke Jenny , Spindler Birgit , Kemper Nicole , Koch Reinhard
Published: July 2022

Injurious pecking against conspecifics is a serious problem in turkey husbandry. Bloody injuries act as a trigger mechanism to induce further pecking, and timely detection and intervention can prevent massive animal welfare impairments and costly losses. Thus, the overarching aim is to develop a camera-based system to monitor the flock and detect injuries using neural networks. In a preliminary study, images of turkeys were annotated by labelling potential injuries. These were used to train a network for injury detection. Here, we applied a keypoint detection model to provide more information on animal position and indicate injury location. Therefore, seven turkey keypoints were defined, and 244 images (showing 7660 birds) were manually annotated. Two state-of-the-art approaches for pose estimation were adjusted, and their results were compared. Subsequently, a better keypoint detection model (HRNet-W48) was combined with the segmentation model for injury detection. For example, individual injuries were classified using “near tail” or “near head” labels. Summarizing, the keypoint detection showed good results and could clearly differentiate between individual animals even in crowded situations.
Defect Detection Method of Carbon Fiber Sucker Rod Based on Multi-Sensor Information Fusion and DBN Model
Authors: Hua Chenquan , Chen Siwei , Xu Guoyan , Chen Yang
Published: July 2022

Because of its unique characteristics of small specific gravity, high strength, and corrosion resistance, the carbon fiber sucker rod has been widely used in petroleum production. However, there is still a lack of corresponding online testing methods to detect its integrity during the process of manufacturing. Ultrasonic nondestructive testing has become one of the most accepted methods for inspection of homogeneous and fixed-thickness composites, or layered and fixed-interface-shape composites, but a carbon fiber sucker rod with multi-layered structures and irregular interlayer interfaces increases the difficulty of testing. In this paper, a novel defect detection method based on multi-sensor information fusion and a deep belief network (DBN) model was proposed to identify online its defects. A water-immersed ultrasonic array with 32 ultrasonic probes was designed to realize the online and full-coverage scanning of carbon fiber rods in radial and axial positions. Then, a multi-sensor information fusion method was proposed to integrate amplitudes and times-of-flight of the received ultrasonic pulse-echo signals with the spatial angle information of each probe into defect images with obvious defects including small cracks, transverse cracks, holes, and chapped cracks. Three geometric features and two texture features from the defect images characterizing the four types of defects were extracted. Finally, a DBN-based defect identification model was constructed and trained to identify the four types of defects of the carbon fiber rods. The testing results showed that the defect identification accuracy of the proposed method was 95.11%.
An Image-Based Data-Driven Model for Texture Inspection of Ground Workpieces
Authors: Wang Yu-Hsun , Lai Jing-Yu , Lo Yuan-Chieh , Shih Chih-Hsuan , Lin Pei-Chun
Published: July 2022

Nowadays, the grinding process is mostly automatic, yet post-grinding quality inspection is mostly carried out manually. Although the conventional inspection technique may have cumbersome setup and tuning processes, the data-driven model, with its vision-based dataset, provides an opportunity to automate the inspection process. In this study, a convolutional neural network technique with transfer learning is proposed for three kinds of inspections based on 750–1000 surface raw images of the ground workpieces in each task: classifying the grit number of the abrasive belt that grinds the workpiece, estimating the surface roughness of the ground workpiece, and classifying the degree of wear of the abrasive belts. The results show that a deep convolutional neural network can recognize the texture on the abrasive surface images and that the classification model can achieve an accuracy of 0.9 or higher. In addition, the external coaxial white light was the most suitable light source among the three tested light sources: the external coaxial white light, the high-angle ring light, and the external coaxial red light. Finally, the model that classifies the degree of wear of the abrasive belts can also be utilized as the abrasive belt life estimator.
Indoor Visual Exploration with Multi-Rotor Aerial Robotic Vehicles
Authors: Rousseas Panagiotis , Karras George C. , Bechlioulis Charalampos P. , Kyriakopoulos Kostas J.
Published: July 2022

In this work, we develop a reactive algorithm for autonomous exploration of indoor, unknown environments for multiple autonomous multi-rotor robots. The novelty of our approach rests on a two-level control architecture comprised of an Artificial-Harmonic Potential Field (AHPF) for navigation and a low-level tracking controller. Owing to the AHPF properties, the field is provably safe while guaranteeing workspace exploration. At the same time, the low-level controller ensures safe tracking of the field through velocity commands to the drone’s attitude controller, which handles the challenging non-linear dynamics. This architecture leads to a robust framework for autonomous exploration, which is extended to a multi-agent approach for collaborative navigation. The integration of approximate techniques for AHPF acquisition further improves the computational complexity of the proposed solution. The control scheme and the technical results are validated through high-fidelity simulations, where all aspects, from sensing and dynamics to control, are incorporated, demonstrating the capacity of our method in successfully tackling the multi-agent exploration task.
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data
Authors: Ziegler Joceline , Pfitzner Bjarne , Schulz Heinrich , Saalbach Axel , Arnrich Bert
Published: July 2022

Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.94 for ε=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.
Fusion Methods for Face Presentation Attack Detection
Authors: Abdullakutty Faseela , Johnston Pamela , Elyan Eyad
Published: July 2022

Face presentation attacks (PA) are a serious threat to face recognition (FR) applications. These attacks are easy to execute and difficult to detect. An attack can be carried out simply by presenting a video, photo, or mask to the camera. The literature shows that both modern, pre-trained, deep learning-based methods, and traditional hand-crafted, feature-engineered methods have been effective in detecting PAs. However, the question remains as to whether features learned in existing, deep neural networks sufficiently encompass traditional, low-level features in order to achieve optimal performance on PA detection tasks. In this paper, we present a simple feature-fusion method that integrates features extracted by using pre-trained, deep learning models with more traditional colour and texture features. Extensive experiments clearly show the benefit of enriching the feature space to improve detection rates by using three common public datasets, namely CASIA, Replay Attack, and SiW. This work opens future research to improve face presentation attack detection by exploring new characterizing features and fusion strategies.
The Dynamic Effects of Oil Price Shocks on Exchange Rates—From a Time-Varying Perspective
Authors: Zhang Rufei , Zhang Haizhen , Gao Wang , Li Ting , Yang Shixiong
Published: July 2022

This study investigates the time-varying effects of three types of oil price shocks (oil demand, supply, and risk shock) on exchange rates by applying the time-varying parameter structural vector autoregression stochastic volatility (TVP-SVAR-SV) model. Through examining the impulse response of exchange rates to oil price shocks at different lag periods and time points, this paper contributes to the existing literature on the dynamic relationship between oil shocks and exchange rates. From the response at different lag periods, we find that oil price shocks have a significant time-varying impact on the exchange rate, among which oil demand shock has the most significant effect. The response of the exchange rate market to oil price shock shows an obvious short-term time-varying effect and is positive and negative alternately, with a certain periodicity. From the response at different time points, the time-varying effect of oil price shock on exchange rates is related to external shock, and is more intense during the global economic and political turmoil. This is the first empirical study using a novel method to examine the time-varying effects of oil price shock from different sources on exchange rates, providing investors and policy makers assistance to manage foreign exchange during global turmoil periods.
Hyperspectral Modeling of Soil Organic Matter Based on Characteristic Wavelength in East China
Authors: Zhao Mingsong , Gao Yingfeng , Lu Yuanyuan , Wang Shihang
Published: July 2022

Soil organic matter (SOM) is a key index of soil fertility. Visible and near-infrared (VNIR, 350–2500 nm) reflectance spectroscopy is an effective method for modeling SOM content. Characteristic wavelength screening and spectral transformation may improve the performance of SOM prediction. This study aimed to explore the optimal combination of characteristic wavelength selection and spectral transformation for hyperspectral modeling of SOM. A total of 219 topsoil (0–20 cm) samples were collected from two soil types in the East China. VNIR reflectance spectra were measured in the laboratory. Firstly, after spectral transformation (inverse-log reflectance (LR), continuum removal (CR) and first-order derivative reflectance (FDR)) of VNIR spectra, characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and uninformative variables elimination (UVE) algorithms. Secondly, the SOM prediction models were constructed based on the partial least squares regression (PLSR), random forest (RF) and support vector regression (SVR) methods using the full spectra and selected wavelengths, respectively. Finally, optimal SOM prediction models were selected for two soil types. The results were as follows: (1) The CARS algorithm screened 40–125 characteristic wavelengths from the full spectra. The UVE algorithm screened 105–884 characteristic wavelengths. (2) For two soil types and full spectra, CARS and UVE improved the SOM modeling precision based on the PLSR and SVR methods. The coefficient of determination (R2) value in the validation of the CARS-PLSR (PLSR model combined with CARS) and CARS-SVR (SVR model combined CARS) models ranged from 0.69 to 0.95, and the relative percent deviation (RPD) value ranged from 1.74 to 4.31. Lin’s concordance correlation coefficient (LCCC) values ranged from 0.83 to 0.97. The UVE-PLSR and UVE-SVR models showed moderate precision. (3) The PLSR and SVR modeling accuracies of Paddy soil were better than those for Shajiang black soil. RF models performed worse for both soil types, with the R2 values of validation ranging from 0.22 to 0.68 and RPD values ranging from 1.01 to 1.60. (4) For Paddy soil, the optimal SOM prediction models (highest R2 and RPD, lowest root mean square error (RMSE)) were CR-CARS-PLSR (R2 and RMSE: 0.97 and 1.21 g/kg in calibration sets, 0.95 and 1.72 g/kg in validation sets, RPD: 4.31) and CR-CARS-SVR (R2 and RMSE: 0.98 and 1.04 g/kg in calibration sets, 0.91 and 2.24 g/kg in validation sets, RPD: 3.37). For Shajiang black soil, the optimal SOM prediction models were LR-CARS-PLSR (R2 and RMSE: 0.95 and 0.93 g/kg in calibration sets, 0.86 and 1.44 g/kg in validation sets, RPD: 2.62) and FDR-CARS-SVR (R2 and RMSE: 0.99 and 0.45 g/kg in calibration sets, 0.83 and 1.58 g/kg in validation sets, RPD: 2.38). The results suggested that the CARS algorithm combined CR and FDR can significantly improve the modeling accuracy of SOM content.
Spatiotemporal Evolution Characteristics and the Climatic Response of Carbon Sources and Sinks in the Chinese Grassland Ecosystem from 2010 to 2020
Authors: Li Xiang , Lin Gang , Jiang Dong , Fu Jingying , Wang Yaxin
Published: July 2022

With the increase in global carbon dioxide emissions, China has put forward the goals of a carbon peak and carbon neutrality (double carbon) and formulated an action plan to consolidate and enhance the carbon sink capacity of the ecosystem. The Chinese grassland ecosystem (CGE) is widely distributed and is the key link for China to achieve the double carbon objectives. However, there is a relative lack of research on carbon sources and sinks in the CGE, so it is urgent to integrate and analyze the carbon sources and sinks in the grassland ecosystem on the national scale. Based on the refined grid data, the net ecosystem productivity (NEP) of the CGE was estimated by coupling the vegetation production model and soil respiration model. The results showed that the cumulative carbon sequestration of the CGE was 14.46 PgC from 2010 to 2020. In terms of spatial distribution, this shows that the differentiation characteristics are high in the northwest of China and low in the southeast of China, which strongly corresponds with the 400 mm isohyet and 0 °C isotherm of China. The results of the correlation analysis showed that the NEP of the CGE was positively correlated with precipitation and negatively correlated with temperature; that is, precipitation mainly promotes the accumulation of NEP, and temperature mainly inhibits it. The coupling effect of temperature and precipitation jointly affects the spatial change of carbon sources and sinks of the CGE. This study can provide a scientific basis for government departments to formulate targeted policies to deal with climate change, which is of great significance for China to improve ecosystem management, ensure ecological security and promote the realization of China’s double carbon goal.
The Mediating Role of Safety Climate in the Relationship between Transformational Safety Leadership and Safe Behavior—The Case of Two Companies in Turkey and Romania
Authors: Draghici Anca , Dursun Salih , Bașol Oğuz , Boatca Maria Elena , Gaureanu Alin
Published: July 2022

Safety leadership, safe behavior, and safety climate, which are important parts of occupational safety culture, are important in terms of preventing occupational accidents and making the working environment ergonomic. In this context, this study aims to examine the mediating effect of the safety climate on the relationship between transformational leadership and safe behavior. Research was carried out with 287 participants working in two manufacturing plants, one from Turkey and one from Romania. The two data sets were consolidated into a single database (both companies being providers of manufacturing products for the same client in the automotive industry) and were analyzed using the Preacher and Hayes plugin in the SPSS 21 package program because of the research interest in investigating safety behavior in the manufacturing field. The results of the analysis showed that the safety climate had a partial mediating effect on the relationship between transformational leadership and safe behavior. In addition, the study results demonstrated that transformational safety leadership has a significant influence on employees’ perceptions of safety climate and plays an important role in occupational safety-related behaviors. Research results were helpful for practitioners (managers in both companies) and researchers in understanding the importance of safety-climate and transformational safety leadership practices in increasing occupational safety-related behaviors.
Effect of Innovation Orientation of High-Tech SMEs “Small and Mid-Sized Enterprises in China” on Innovation Performance
Authors: Tong Tong , Rahman Azmawani Abd
Published: July 2022

Mass entrepreneurship and innovation refer to encouraging the broad masses of the people, including industry, agriculture, commerce, education, and soldiers, to participate in entrepreneurship, encouraging all Chinese people to participate in innovation, which Premier Li put forward at the 2014 Summer Davos Forum in Tianjin. After seven years of development, the innovation orientation of mass entrepreneurship and innovation has become an important engine leading China’s economic growth in the future. This research aims to examine the effect of innovation orientation on enterprise innovation performance. Based on a survey of 378 high-tech SMEs in Sichuan Province, China, structural equation modeling (SEM) was employed to examine the research model. The result shows that innovation orientation and organizational flexibility significantly affect enterprise innovation performance. Among the effects of innovation orientation on enterprise innovation performance, capability and resource flexibility play mediators. This research disclosed that important factors affecting the innovation performance of high-tech SMEs enterprises include resource flexibility and capability flexibility. To give better play to the positive impact of innovation orientation, we should strengthen the flexible strategy of organizations. Thus, committed to more flexible market development, promoting the vigorous development of new technologies, new industries, and new formats, and realizing the growth of enterprise innovation performance.
Barriers and Factors Affecting the E-Commerce Sustainability of Thai Micro-, Small- and Medium-Sized Enterprises (MSMEs)
Authors: Amornkitvikai Yot , Tham Siew Yean , Harvie Charles , Buachoom Wonlop Writthym
Published: July 2022

It is anticipated that e-commerce will contribute to achieving the 17th Sustainable Development Goal, which seeks to improve implementation mechanisms and revitalize global partnerships for sustainable development. However, MSMEs still face a digital gap compared to large enterprises, which affects their e-commerce sustainability. The study’s objective is to examine the factors and barriers affecting the e-commerce sustainability of Thai micro-, small- and medium-sized enterprises (MSMEs) based on a survey of retail and food and beverage (F&B) service MSMEs in metropolitan Bangkok. Estimations confirm the significance of the TOE framework for Thai MSMEs. Internal e-commerce tools (i.e., smartphones and websites) and external e-commerce platforms (i.e., social media, e-marketplaces, and food delivery platforms) can enhance e-commerce sustainability. However, the age of firms and owners (CEOs) affects e-commerce sustainability negatively. Exports, B2B e-commerce, and e-commerce experience can promote the e-commerce sustainability of Thai MSMEs. However, they perceive that many consumers are still not literate in using e-commerce. In addition, Thailand still has insufficient security to prevent hacking and malware. Therefore, Thai entrepreneurs’ e-commerce literacy is insufficient to enhance their e-commerce sustainability. On the other hand, sustainable e-commerce can increase customer satisfaction, loyalty, and trust through customer support, leading to more long-term online shopping. Hence, this study focuses on e-commerce sustainability-based economic dimensions, as measured by the percentage of e-commerce sales to total sales (e-commerce utilization/intensity).
Experimental Investigation of Wind Loads on Roof-Mounted Solar Arrays
Authors: Yao Jianfeng , Tu Zhibin , Wang Dong , Shen Guohui , Lou Wenjuan
Published: July 2022

An experimental study was conducted to investigate the aerodynamic loads on roof-mounted solar arrays. Four different parapet heights of 0 m, 0.9 m, 1.2 m, 1.5 m, and two tilt angles of 5° and 10°, are set to examine their effects on wind pressure coefficients. The statistics (means, standard deviations, skewness, kurtosis, maximum and minimum peaks) of wind pressure coefficients in different wind directions are exhibited and discussed in the current study. The results show that the oblique wind directions are critical for most measured locations, as the extreme of maximum and minimum peak pressure coefficients occur. The parapet can decrease wind loads on solar arrays, as expected. However, as the parapet is within a limited height in practice, it is not quite efficient to decrease wind loads despite the increasing of the parapet height. When the solar arrays are installed on the roof almost horizontally (tilt angle is less than 10°), the statistics of the aerodynamic load change a little with the decreasing of the tilt angle.
The Efficiency of National Innovation Policy Programs: The Case of Serbia
Authors: Đuričin Sonja , Beraha Isidora , Jovanović Olivera , Mosurović Ružičić Marija , Lazarević-Moravčević Marija , Paunović Mihailo
Published: July 2022

We aimed to assess the efficiency of the selected national innovation policy programs in the Republic of Serbia. We analyzed the impact of the Innovation Fund’s Mini-Grants and Matching Grants programs on the operating revenue growth of beneficiary micro, small, and medium enterprises. An econometric analysis of panel data was conducted. Because of the small number of periods observed, a model of individual effects was applied. Conclusions and recommendations were based on the results of random effects models. The findings indicate that program funding increased business revenues compared to the period before and that there was a direct link between indebtedness and revenue growth, which confirmed the positive impact of financing on the sustainable development prospects of beneficiaries through facilitating access to funding and innovation capacity improvement. These findings can have important policy implications as they provide guidelines for designing future actions and empirically confirm the need to increase public expenditures for innovation policy.
A Bibliometric Analysis of End-of-Life Vehicles Related Research: Exploring a Path to Environmental Sustainability
Authors: Yu Zhang , Khan Syed Abdul Rehman , Zia-ul-haq Hafiz Muhammad , Tanveer Muhammad , Sajid Muhammad Jawad , Ahmed Shehzad
Published: July 2022

Considering rapid economic development and continuously increasing environmental concerns, end-of-life vehicles (ELVs) have significant socioeconomic value as a crucial waste stream. The research relating to ELVs has rapidly evolved over the last few years. However, existing review studies focus on specific research themes, and thus, fail to present a complete picture. Hence, this research intends to explain the current research scenario relating to ELVs by reviewing the critical published studies of the last 22 years. A total of 1405 research publications were extracted from the Scopus database covering the period from 2000 to 2021. Mainly employing bibliometric analysis techniques, this research analyzes the quantity of literature, researchers, institutions, countries, and research themes to understand the current status and future trends in ELV recycling and management. The results revealed a considerable rise in the number of articles published in the last five years. The key producers of influential ELV research are listed as the United States, China, and the United Kingdom. Globally, Chinese universities have the most ELV-related articles published. Similarly, Serbian researcher Vladimir Simic authored the most ELV-related articles during the research period. This article also identifies various research themes: management and recycling, resource recovery and components, life cycle evaluation, and socioeconomic effects. The results also reveal a strong association between distinct ELV research clusters.
Assessment of National Innovation Ecosystems of the EU Countries and Ukraine in the Interests of Their Sustainable Development
Authors: Kuzior Aleksandra , Pidorycheva Iryna , Liashenko Viacheslav , Shevtsova Hanna , Shvets Nataliia
Published: July 2022

The purpose of the study is to reveal specific features of modern EU innovation policy in the context of its focus on sustainable European development and to conduct an assessment of the parameters of national innovation ecosystems of the EU member states with different innovation potential and Ukraine from the standpoint of their influence on the innovative development of countries worldwide. With the use of the correlation-regression analysis, the hypothesis of changing the parameters of national innovation ecosystems that affect the innovation of the EU member states and Ukraine in the global context depending on the level of their productivity and innovation potential was reiterated. The factors that have the greatest impact on the ranking of the countries in the Global Innovation Index, depending on which group the countries under study belong to according to the classification of the European Innovation Scoreboard, were identified. It was revealed that the set of such factors in each group of countries varies and has a different degree of influence on the level of their innovation development. Based on the results of the assessment, taking into account the need for a speedy post-war reconstruction of Ukraine, policy recommendations were made for Ukraine. Their implementation will ensure the systemic influence of the state on the national innovation ecosystem of the country.
The Market Disruption Path of Green-Oriented Trajectory-Transformed Technology Innovation: A Study of Consumer Lifestyles during the “Chasm” in China’s Electric Vehicle Market
Authors: Xiao Hailin , Zhang Xiaocai
Published: July 2022

Green-oriented trajectory-transformed technology (GTTT) innovation is a strategic path that leads simultaneously to a comprehensive green transformation of national economic and social development and a disruption of corporate competition. However, this type of innovation is nonmarket-oriented and naturally results in a deeper and wider “chasm” more than any market-oriented innovation between the early market and the mass market, which is difficult to bridge; this leads to theoretical and practical difficulties with respect to the formulation of market strategies. To bridge such a “chasm”, this paper explores the paths that facilitate a market launch strategy that is capable of bridging the market “chasm”. The paper identifies electric vehicles as an example of a GTTT product, based on the hierarchical characteristic model, investigates the impact of lifestyle on consumers’ purchase intention, examines the mediating effect of interpersonal influence susceptibilities, uses data collected via consumer questionnaires to test the research model, and thereby identifies the various consumer groups that are present during the “chasm” period and the characteristics they exhibit. The results show that fashion consciousness, leadership consciousness, environmental consciousness, and informational interpersonal influence are the market strategy paths that lead to market disruption, and fashion leaders and price-conscious environmentalists are the key consumer groups during the “chasm” period.
What about Responsible Consumption? A Survey Focused on Food Waste and Consumer Habits
Authors: Paužuolienė Jurgita , Šimanskienė Ligita , Fiore Mariantonietta
Published: July 2022

The article analyses the problems of food waste and responsible consumption that include taking into account environmental-social-health and economic impacts of products and services. The study raises the research question related to whether people consume food responsibly. Analysis of research literature sources, systematization, synthesis, generalization, quantitative research and data processing methods were used in the article. The questionnaire was arranged on the pollimill.com website, and the link was shared with selected possible respondents. The survey was carried out in Lithuania and in European countries. The survey sample is equal to 1080 respondents (566 respondents from Lithuania and 514 from Italy, Poland, Latvia, Germany and France). A simple random sample was used in this research. The survey highlighted that the majority of respondents in the survey state that food is not often wasted. In addition, findings show that the population of Lithuania emits slightly less food than the population of the European countries participating in the survey. These findings could be crucial for the future green directions from the side of policymakers.
Analysis of Carbon Emission Reduction in International Civil Aviation through the Lens of Shared Triple Bottom Line Value Creation
Authors: Guan Hong , Liu Hao , Saadé Raafat George
Published: July 2022

Overall, climate concerns have been on the global agenda for many years now. However, the aviation sector’s impact on climate change has been receiving increased attention recently. This is primarily due to the adoption of the 2016 carbon offsetting and reduction scheme for international civil aviation (CORSIA) which was introduced by the international civil aviation organization (ICAO). The aims of our study are to analyze ICAO’s carbon offsetting reduction scheme through the lens of the triple bottom line (TBL) value creation dimensions and to explore implementation issues relevant to its success and alignment between regulatory and commercial capabilities. Findings from our analysis were presented to a pilot focus group to further our understanding of the area. After cross-examination of the carbon emission reduction implementation issues against the TBL dimensions, we show the gap between regulatory schemes and the realities of the sustainable commercial aviation sector to meet climate goals. By highlighting the regulatory versus commercial social capabilities, our study illuminates the dimensions which need to be considered in regulatory practice, emphasizing the necessity for commercial sustainability. We finally provide recommendations to be considered for the successful implementation of CORSIA.
Total Variation-Based Metrics for Assessing Complementarity in Energy Resources Time Series
Authors: Cantor Diana , Ochoa Andrés , Mesa Oscar
Published: July 2022

The growing share of intermittent renewable energy sources raised complementarity to a central concept in the electricity supply industry. The straightforward case of two sources suggests that to guarantee supply, the time series of both sources should be negatively correlated. Extrapolation made Pearson’s correlation coefficient (ρ) the most widely used metric to quantify complementarity. This article shows several theoretical and practical drawbacks of correlation coefficients to measure complementarity. Consequently, it proposes three new alternative metrics robust to those drawbacks based on the natural interpretation of the concept: the Total Variation Complementarity Index (ϕ), the Variance Complementarity Index (ϕ′), and the Standard Deviation Complementarity Index (ϕs). We illustrate the use of the three indices by presenting one theoretical and three real case studies: (a) two first-order autoregressive processes, (b) one wind and one hydropower energy time series in Colombia at the daily time resolution, (c) monthly water inflows to two hydropower reservoirs of Colombia with different hydrologic regimes, and (d) monthly water inflows of the 15 largest hydropower reservoirs in Colombia. The conclusion is that ϕ outperforms the use of ρ to quantify complementarity because (i) ϕ takes into account scale, whereas ρ is insensitive to scale; (ii) ρ does not work for more than two sources; (iii) ρ overestimates complementarity; and (iv) ϕ takes into account other characteristics of the series. ϕ′ corrects the scale insensitivity of ρ. Moreover, it works with more than two sources. However, it corrects neither the overestimation nor the importance of other characteristics. ϕs improves ϕ′ concerning the overestimation, but it lets out other series characteristics. Therefore, we recommend total variation complementarity as an integral way of quantifying complementarity.
Fungal Species and Multi-Mycotoxin Associated with Post-Harvest Sorghum (Sorghum bicolor (L.) Moench) Grain in Eastern Ethiopia
Authors: Mohammed Abdi , Bekeko Zelalem , Yusufe Mawardi , Sulyok Michael , Krska Rudolf
Published: July 2022

Sorghum is the main staple food crop in developing countries, including Ethiopia. However, sorghum grain quantity and quality are affected by contaminating fungi both under field and post-harvest stage. The aim of the current study was to assessed fungal species and multi-mycotoxins associated with sorghum grain in post-harvest samples collected from eastern Ethiopia. Fungal genera of Aspergillus, Alternaria, Bipolaris, Fusarium, Mucor, Penicillium, and Rhizoctonia were recovered in the infected grain. A liquid chromatography-tandem mass spectrometric (LC-MS/MS) was used for quantification of multiple mycotoxins/fungal metabolites. Overall, 94 metabolites were detected and grouped into eight categories. All metabolites were detected either in one or more samples. Among major mycotoxins and derivatives, deoxynivalenol (137 μg/kg), zearalenone (121 μg/kg), ochratoxin A (115 μg/kg), and fumonisin B1 (112 μg/kg) were detected with maximum concentrations, while aflatoxin B1 had relatively lower concentrations (23.6 μg/kg). Different emerging mycotoxins were also detected, with tenuazonic acid (1515 μg/kg) occurring at the maximum concentration among Alternaria metabolites. Fusaric acid (2786 μg/kg) from Fusarium metabolites and kojic acid (4584 μg/kg) were detected with the maximum concentration among Fusarium and Aspergillus metabolites, respectively. Unspecific metabolites were recognized with neoechinulin A (1996 μg/kg) at the maximum concentration, followed by cyclo (L-Pro-L-Tyr) (574 μg/kg) and cyclo (L-Pro-L-Val) (410 μg/kg). Moreover, metabolites form other fungal genera and bacterial metabolites were also detected at varying levels. Apparently, the study revealed that sorghum grains collected across those districts were significantly contaminated with co-occurrences of several mycotoxins. Farmers should be the main target groups to be trained on the improved management of sorghum production.
Cyclopaldic Acid, the Main Phytotoxic Metabolite of Diplodia cupressi, Induces Programmed Cell Death and Autophagy in Arabidopsis thaliana
Authors: Samperna Simone , Masi Marco , Vurro Maurizio , Evidente Antonio , Marra Mauro
Published: July 2022

Cyclopaldic acid is one of the main phytotoxic metabolites produced by fungal pathogens of the genus Seiridium, causal agents, among others, of the canker disease of plants of the Cupressaceae family. Previous studies showed that the metabolite can partially reproduce the symptoms of the infection and that it is toxic to different plant species, thereby proving to be a non-specific phytotoxin. Despite the remarkable biological effects of the compound, which revealed also insecticidal, fungicidal and herbicidal properties, information about its mode of action is still lacking. In this study, we investigated the effects of cyclopaldic acid in Arabidopsis thaliana plants and protoplasts, in order to get information about subcellular targets and mechanism of action. Results of biochemical assays showed that cyclopaldic acid induced leaf chlorosis, ion leakage, membrane-lipid peroxidation, hydrogen peroxide production, inhibited root proton extrusion in vivo and plasma membrane H+-ATPase activity in vitro. qRT-PCR experiments demonstrated that the toxin elicited the transcription of key regulators of the immune response to necrotrophic fungi, of hormone biosynthesis, as well as of genes involved in senescence and programmed cell death. Confocal microscopy analysis of protoplasts allowed to address the question of subcellular targets of the toxin. Cyclopaldic acid targeted the plasma membrane H+-ATPase, inducing depolarization of the transmembrane potential, mitochondria, disrupting the mitochondrial network and eliciting overproduction of reactive oxygen species, and vacuole, determining tonoplast disgregation and induction of vacuole-mediated programmed cell death and autophagy.
The Updated Version of the A.Ne.Mo.S. GLE Alert System: The Case of the Ground-Level Enhancement GLE73 on 28 October 2021
Authors: Mavromichalaki Helen , Paschalis Pavlos , Gerontidou Maria , Papailiou Maria-Christina , Paouris Evangelos , Tezari Anastasia , Lingri Dimitra , Livada Maria , Stassinakis Argyris N. , Crosby Norma , Dierckxsens Mark
Published: July 2022

A ground-level enhancement (GLE) event is a sudden increase in cosmic ray intensity originated by solar sources and recorded by ground-based detectors. GLEs are invariably associated with large solar flares that can release and accelerate solar particles at high energies. The minimum kinetic energy of particles reaching the Earth’s surface is >433 MeV at sea level and about 300 MeV/n at high-mountain altitude of about 3000 m a.s.l. Even though these abrupt events linked to solar activity are quite rare, they can have a great impact on technological systems and human health when recorded. Therefore, the accurate and effective prognosis of such events is of great importance. In this paper, an overview of the most recently recorded GLE event and the first of solar cycle 25, i.e., GLE73, as well as a post-event analysis is presented. GLE73 was detected on 28 October 2021 and was associated with the active region AR12887 on the central part of the solar disk, which produced an X1.0 solar flare. The event was registered by several stations of the worldwide ground-based neutron monitor network. An accurate alert was issued successfully by the ESA R-ESC federated product GLE Alert Plus, as well as the updated GLE Alert++ System of the Athens Neutron Monitor Station (A.Ne.Mo.S.). It should be emphasized that the GLE Alert++ signal by NKUA/A.Ne.Mo.S. was issued 45 min earlier than the one issued by GOES. A short description and the advantages of this last system are provided.
Potential Immunologic and Integrative Methods to Enhance Vaccine Safety
Authors: Dattner Alan M.
Published: July 2022

Vaccine safety is measured by the disease protection it confers compared to the harm it may cause; both factors and their relative numbers have been the subject of disagreement. Cross-reactive attack of analogous self-antigens modified by dietary and microbiome factors is one of the poorly explored likely causes of harm. Screening for that and other risk factors might point out those most likely to develop severe vaccine reactions. Cooperation from those with opinions for and against vaccination in data gathering and vetting will lead to greater safety. Screening should include an integrative medical perspective regarding diet, microbiome, leaky gut, and other antigen sources. It might include emerging electronic technology or integrative energetic techniques vetted ultimately by cross-reactive lymphocyte testing or genetic evaluation. The knowledge gained from evaluating those with reactions could enhance the screening process and, since similar antigenic stimuli and reactions are involved, help long COVID sufferers. Centers for early identification and rescue from vaccine reactions could lower morbidity and mortality, and increase the percentage of people choosing to be vaccinated. Additional platforms for boosting; using lower dosage; other routes of administration, such as intranasal or intradermal needles; and possibly different antigens could make it easier to vaccinate globally to address the new variants of viruses rapidly arising.
Impact of Local Scour around a Bridge Pier on Migration of Waved-Shape Accumulation of Ice Particles under an Ice Cover
Authors: Hou Zhixing , Wang Jun , Sui Jueyi , Song Feihu , Li Zhicong
Published: July 2022

The migration of a waved-shape accumulation of ice particles under an ice cover (referred to as “ice wave” in this study) is a phenomenon of transport of ice particles during an ice accumulation process in rivers. The migration of an ice wave will affect the pier scour. On the other hand, the local scour at the pier will affect the migration of ice waves. The interaction between the migration of ice waves and local scour around a pier is a very complicated process since not only the channel bed deforms, but also the ice jam develops simultaneously. By conducting a series of flume experiments, the interaction between the local scour around bridge piers and the migration of ice waves was studied. By applying both continuity and momentum equations, an empirical equation has been derived for predicting the thickness of ice waves around the pier. The impacts of the scour hole on the thickness of ice waves around the pier have been studied. The thickness of the wave crest and the migration speed of ice waves have been investigated. Similar to a scour hole in a sand bed, an “ice scour hole” appeared at the bottom of the ice jam around the pier. The existence of the “ice scour hole” affects the development of ice waves. A formula for calculating ice transport capacity has been obtained. Results calculated using the derived formula are in good agreement with those of laboratory experiments.
THE USE OF A COMBINED SEAL TO REDUCE THE LOAD ACTING ON THE CYLINDER PISTON SEAL
Authors: Roman R.
Published: July 2022

In this scientific paper, the importance of the life of the cylinder piston seal for medium and high pres-sure reciprocating compressor units is considered. The scheme of forces acting on the seal (valid for both piston rings and cuff seals) is considered. The basic calculation formulas are given and a prom-ising design of a combined seal is proposed to increase the service life of the entire compressor unit operating mainly without lubrication of the flow part of the cylinder. To solve this problem, a hypothe-sis is put forward about the possibility of reducing the load on the seal by throttling the gas, thereby not reducing the performance of the piston unit. In addition, diagrams and photos of a promising de-sign of a combined seal (four combination options) are given. A detailed description of the experi-mental stand for determining the pressure drop (load) acting on the cuff seal is made. The procedure for conducting an experimental study is also described. As a result, it is found that the use of a com-bined seal allows to reduce the pressure drop on the first seal by about two times from 2 MPa to 1.1 MPa. Thus, the service life of the seal, theoretically, increases proportionally to the decrease in the pressure drop, also by two times.