bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantitative Inversion of Lunar Surface Chemistry Based on Hyperspectral Feature Bands and Extremely Randomized Trees Algorithm
Authors: Wu Shuangshuang , Chen Jianping , Li Li , Zhang Cheng , Huang Rujin , Zhang Quanping
Published: October 2022

In situ resource utilization (ISRU) is required for the operation of both medium and long-term exploration missions to provide metallic materials for the construction of lunar base infrastructure and H2O and O2 for life support. The study of the distribution of the lunar surface elements (Fe, Ti, Al, and Si) is the basis for the in situ utilization of mineral resources. With the arrival of the era of big data, the application of big data concepts and technical methods to lunar surface chemistry inversion has become an inevitable trend. This paper is guided by big data theory, and the Apollo 17 region and the area near the Copernicus crater are selected for analysis. The dimensionality of the first-order differential spectral features of lunar soil samples is reduced based on Pearson correlation analysis and the successive projections algorithm (SPA), and the extremely randomized trees (Extra-Trees) algorithm is applied to Chang’E-1 Interference Imaging Spectrometer (IIM) data to establish a prediction model for the lunar surface chemistry and generate FeO, TiO2, Al2O3, and SiO2 distribution maps. The results show that the optimum number of variables for FeO, TiO2, Al2O3, and SiO2 is 17, 5, 8, and 30, respectively. The accuracy of the Extra-Trees model using the best variables was improved over that of the original band model, with determination coefficients (R2) of 0.962, 0.944, 0.964, and 0.860 for FeO, TiO2, Al2O3, and SiO2, and root mean square errors (RMSEs) of 1.028, 0.672, 0.942, and 0.897, respectively. The modeling feature variables and model preference methods in this study can improve the inversion accuracy of chemical abundance to some extent, demonstrating the potential of IIM data in predicting chemical abundance and providing a good data basis for lunar geological evolution studies and ISRU.
Hyper-LGNet: Coupling Local and Global Features for Hyperspectral Image Classification
Authors: Zhang Tianxiang , Wang Wenxuan , Wang Jing , Cai Yuanxiu , Yang Zhifang , Li Jiangyun
Published: October 2022

Hyperspectral sensors provide an opportunity to capture the intensity of high spatial/spectral information and enable applications for high-level earth observation missions, such as accurate land cover mapping and target/object detection. Currently, convolutional neural networks (CNNs) are good at coping with hyperspectral image processing tasks because of the strong spatial and spectral feature extraction ability brought by hierarchical structures, but the convolution operation in CNNs is limited to local feature extraction in both dimensions. In the meanwhile, the introduction of the Transformer structure has provided an opportunity to capture long-distance dependencies between tokens from a global perspective; however, Transformer-based methods have a restricted ability to extract local information because they have no inductive bias, as CNNs do. To make full use of these two methods’ advantages in hyperspectral image processing, a dual-flow architecture named Hyper-LGNet to couple local and global features is firstly proposed by integrating CNN and Transformer branches to deal with HSI spatial-spectral information. In particular, a spatial-spectral feature fusion module (SSFFM) is designed to maximally integrate spectral and spatial information. Three mainstream hyperspectral datasets (Indian Pines, Pavia University and Houston 2013) are utilized to evaluate the proposed method’s performance. Comparative results show that the proposed Hyper-LGNet achieves state-of-the-art performance in comparison with the other nine approaches concerning overall accuracy (OA), average accuracy (AA) and kappa index. Consequently, it is anticipated that, by coupling CNN and Transformer structures, this study can provide novel insights into hyperspectral image analysis.
End-to-End Radar HRRP Target Recognition Based on Integrated Denoising and Recognition Network
Authors: Liu Xiaodan , Wang Li , Bai Xueru
Published: October 2022

For high-resolution range profile (HRRP) radar target recognition in a low signal-to-noise ratio (SNR) scenario, traditional methods frequently perform denoising and recognition separately. In addition, they assume equivalent contributions of the target and the noise regions during feature extraction and fail to capture the global dependency. To tackle these issues, an integrated denoising and recognition network, namely, IDR-Net, is proposed. The IDR-Net achieves denoising through the denoising module after adversarial training, and learns the global relationship of the generated HRRP sequence using the attention-augmented temporal encoder. Furthermore, a hybrid loss is proposed to integrate the denoising module and the recognition module, which enables end-to-end training, reduces the information loss during denoising, and boosts the recognition performance. The experimental results on the measured HRRPs of three types of aircraft demonstrate that IDR-Net obtains higher recognition accuracy and more robustness to noise than traditional methods.
Filtered Convolution for Synthetic Aperture Radar Images Ship Detection
Authors: Zhang Luyang , Wang Haitao , Wang Lingfeng , Pan Chunhong , Huo Chunlei , Liu Qiang , Wang Xinyao
Published: October 2022

Synthetic aperture radar (SAR) image ship detection is currently a research hotspot in the field of national defense science and technology. However, SAR images contain a large amount of coherent speckle noise, which poses significant challenges in the task of ship detection. To address this issue, we propose filter convolution, a novel design that replaces the traditional convolution layer and suppresses coherent speckle noise while extracting features. Specifically, the convolution kernel of the filter convolution comes from the input and is generated by two modules: the kernel-generation module and local weight generation module. The kernel-generation module is a dynamic structure that generates dynamic convolution kernels using input image or feature information. The local weight generation module is based on the statistical characteristics of the input images or features and is used to generate local weights. The introduction of local weights allows the extracted features to contain more local characteristic information, which is conducive to ship detection in SAR images. In addition, we proved that the fusion of the proposed kernel-generation module and the local weight module can suppress coherent speckle noise in the SAR image. The experimental results show the excellent performance of our method on a large-scale SAR ship detection dataset-v1.0 (LS-SSDD-v1.0). It also achieved state-of-the-art performance on a high-resolution SAR image dataset (HRSID), which confirmed its applicability.
Feature-Ensemble-Based Crop Mapping for Multi-Temporal Sentinel-2 Data Using Oversampling Algorithms and Gray Wolf Optimizer Support Vector Machine
Authors: Zhang Haitian , Gao Maofang , Ren Chao
Published: October 2022

Accurate spatial distribution and area of crops are important basic data for assessing agricultural productivity and ensuring food security. Traditional classification methods tend to fit most categories, which will cause the classification accuracy of major crops and minor crops to be too low. Therefore, we proposed an improved Gray Wolf Optimizer support vector machine (GWO-SVM) method with oversampling algorithm to solve the imbalance-class problem in the classification process and improve the classification accuracy of complex crops. Fifteen feature bands were selected based on feature importance evaluation and correlation analysis. Five different smote methods were used to detect samples imbalanced with respect to major and minor crops. In addition, the classification results were compared with support vector machine (SVM) and random forest (RF) classifier. In order to improve the classification accuracy, we proposed a combined improved GWO-SVM algorithm, using an oversampling algorithm(smote) to extract major crops and minor crops and use SVM and RF as classification comparison methods. The experimental results showed that band 2 (B2), band 4 (B4), band 6 (B6), band 11 (B11), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) had higher feature importance. The classification results oversampling- based of smote, smote-enn, borderline-smote1, borderline-smote2, and distance-smote were significantly improved, with accuracy 2.84%, 2.66%, 3.94%, 4.18%, 6.96% higher than that those without 26 oversampling, respectively. At the same time, compared with SVM and RF, the overall accuracy of improved GWO-SVM was improved by 0.8% and 1.1%, respectively. Therefore, the GWO-SVM model in this study not only effectively solves the problem of equilibrium of complex crop samples in the classification process, but also effectively improves the overall classification accuracy of crops in complex farming areas, thus providing a feasible alternative for large-scale and complex crop mapping.
Soil Moisture, Organic Carbon, and Nitrogen Content Prediction with Hyperspectral Data Using Regression Models
Authors: Datta Dristi , Paul Manoranjan , Murshed Manzur , Teng Shyh Wei , Schmidtke Leigh
Published: October 2022

Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.
Colorful Image Colorization with Classification and Asymmetric Feature Fusion
Authors: Wang Zhiyuan , Yu Yi , Li Daqun , Wan Yuanyuan , Li Mingyang
Published: October 2022

An automatic colorization algorithm can convert a grayscale image to a colorful image using regression loss functions or classification loss functions. However, the regression loss function leads to brown results, while the classification loss function leads to the problem of color overflow and the computation of the color categories and balance weights of the ground truth required for the weighted classification loss is too large. In this paper, we propose a new method to compute color categories and balance the weights of color images. In this paper, we propose a new method to compute color categories and balance weights of color images. Furthermore, we propose a U-Net-based colorization network. First, we propose a category conversion module and a category balance module to obtain the color categories and to balance weights, which dramatically reduces the training time. Second, we construct a classification subnetwork to constrain the colorization network with category loss, which improves the colorization accuracy and saturation. Finally, we introduce an asymmetric feature fusion (AFF) module to fuse the multiscale features, which effectively prevents color overflow and improves the colorization effect. The experiments show that our colorization network has peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) metrics of 25.8803 and 0.9368, respectively, for the ImageNet dataset. As compared with existing algorithms, our algorithm produces colorful images with vivid colors, no significant color overflow, and higher saturation.
CPACK: An Intelligent Cyber-Physical Access Control Kit for Protecting Network
Authors: Yu Haisheng , Liu Zhixian , Zou Sai , Wang Wenyong
Published: October 2022

Access Control Lists (ACL) are critical to protecting network and cyber-physical systems. Traditional firewalls mostly use reactive methods to enforce ACLs, so that new ACL updates cannot take effect immediately. In this paper, based on our previous work, we propose CPACK, an intelligent cyber-physical access control kit, which uses a smart algorithm to upgrade the ACL list. CPACK adopts a proactive way to enforce ACL and reacts to a new ACL update and network view update in real time. We implement CPACK on both Floodlight and ONOS controller. We then conduct a large number of experiments to compare CPACK with the Floodlight firewall application. The experimental results show that CPACK has a better performance than the existing Floodlight firewall application. CPACK is also integrated into the new version of Floodlight and ONOS controller.
A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications
Authors: Wang Will Ke , Chen Ina , Hershkovich Leeor , Yang Jiamu , Shetty Ayush , Singh Geetika , Jiang Yihang , Kotla Aditya , Shang Jason Zisheng , Yerrabelli Rushil , Roghanizad Ali R. , Shandhi Md Mobashir Hasan , Dunn Jessilyn
Published: October 2022

Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used for modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents the non-deep learning models that are commonly used for time series classification in biomedical applications that can achieve high performance. Objective: We performed a systematic review to characterize the techniques that are used in time series classification of digital clinical measures throughout all the stages of data processing and model building. Methods: We conducted a literature search on PubMed, as well as the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles that report on the academic research about digital clinical measures from a five-year period between June 2016 and June 2021. We identified and categorized the research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely used machine learning classifiers were the most commonly used technique, and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also the second-best classification technique. Conclusions: In this review paper, summaries of the time series classification models and interpretation methods for biomedical applications are summarized and categorized. While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation, however many different methods have proven successful.
The Effect of a Statewide Policy on High School Emergency Action Plans
Authors: Johnson Samuel T. , Koester Michael C. , Bovbjerg Viktor E. , Norcross Marc F.
Published: October 2022

Institutions sponsoring athletics must be prepared for emergencies. Due to this, more governing bodies are requiring a sports-related emergency action plan (EAP). Yet, the effects of these policies are unknown. We compared adoption of EAPs and associated best practices in Oregon high schools before and after a policy requiring an EAP. Athletic directors were invited to complete a survey during the year before the policy went into effect and again the following year. We assessed whether the school had a written EAP and if they did, was the EAP venue specific, available at the venue, distributed to personnel, and annually reviewed and rehearsed. Pre/post-policy proportions were analyzed using Fisher exact tests for all schools and then schools that completed both surveys. There was a significant increase of schools that reported having an EAP after the policy went into effect (all schools: 55% to 99% [p < 0.001] and schools responding both years: 60% to 98% [p < 0.001]). Venue specific EAPs also significantly increased but only when analyzing all responses (59% to 71% [p = 0.03]). No best practice recommendations related to EAP availability, distribution, review, or rehearsal changed after the policy. Schools met the minimum requirements of the policy, but other related best practices did not significantly improve.
Does Financial Leverage Mediates Corporate Governance and Firm Performance?
Authors: Huynh Quang Linh , Hoque Mohammad Enamul , Susanto Perengki , Watto Waqas Ahmad , Ashraf Maryam
Published: October 2022

This research aims to explore the impact of corporate governance on firm performance while considering financial leverage as a mediating variable. This study was conducted in the non-financial sector of Pakistan, and data was collected from financial statements. A sample of 150 firms was selected from those registered on the Pakistan Stock Exchange during the period of 2011–2021. Results show that corporate governance is associated with firm performance. Board size has a positive relationship with firm performance; as board size increases, the performance of the firm also increases. Board independence is positively and significantly associated with firm performance. Audit committee size is also positively associated with firm performance. Female directors on the board are also associated with positive firm performance. Board independence, board size, audit committee, and female directorship were positively associated with financial leverage. Corporate governance protects the interest of shareholders and transfers risk from shareholders to debt holders. Results show that corporate governance enhances the financial distress cost by enhancing the debt ratio in the financial leverage. Financial leverage partially mediates the board size and board independence with firm performance, while audit committee size and female directorship relationship with firm performance are fully mediated.
Characteristics of Traditional Food Products as a Segment of Sustainable Consumption in Vojvodina’s Hospitality Industry
Authors: Kalenjuk Pivarski Bojana , Šmugović Stefan , Tekić Dragana , Ivanović Velibor , Novaković Aleksandra , Tešanović Dragan , Banjac Maja , Đerčan Bojan , Peulić Tatjana , Mutavdžić Beba , Lazarević Jasmina , Vukelić Nataša
Published: October 2022

One can observe the sustainability of traditional food products (TFPs) consumption in terms of their market, production, and technical characteristics. These characteristics must be acknowledged by the management section of a catering facility responsible for purchasing and consuming these products. Consequently, this research was conducted among the management of catering facilities (chefs, managers, and owners) in Vojvodina (northern region of Serbia) from 300 different facilities. The research was conducted using a questionnaire. The data acquired were statistically processed using the non-parametric Mann–Whitney and Kruskal–Wallis tests and presented in this paper. Special attention was given to differences in perceiving the characteristics of TFP of Vojvodina (TASQ) as seen from the socio-demographic aspects of the respondents and the characteristics of catering facilities. The research deduced that the sensory quality of a product, menu diversity, freshness, local production, price, and seasonality are the prominent characteristics that affect the TFP’s choice of purchase and consumption. The least essential characteristics are organic production and brand. The differences in perception of the characteristics were based on the respondent’s age, education level, and working position, as well as catering offer (domestic, national, international, and combined) and the location of a catering facility (urban/rural).
Sustainable Society: Wellbeing and Technology—3 Case Studies in Decision Making
Authors: Simpson Edward , Bradley David , Palfreyman John , White Roger
Published: October 2022

Throughout history, technology has provided many and significant improvements to the way we live, but the current pace of development now often exceeds the ability for the full potential of any technological innovation to be explored and implemented before further innovations are introduced. This pace of change results both in missed opportunities for a technology in its ability to contribute to effective solutions in addressing issues such as reducing adverse environmental impact or improving the health of society. In considering the nature of technological innovation and development, the associated engineering design processes can themselves be characterized as being associated with a highly complex, iterative problem-solving exercises, involving the integration and synthesis of a wide range of technologies. This in turn requires the design team to manage trade-offs across a range of primary constraints, as for instance embodied energy in manufacturing, energy consumption in use, capital costs and operating and resource recovery costs. Further investigation into the complexity of societal issues and means for achieving a more effective and fuller utilization of both existing resources and technologies is necessary to place sustainability as a priority of the decision making process. To support discussion and provide context, three case studies are presented. The first case study examines a strategic framework adopting metrics aligned with environmental issues used as proxies for evaluating wellbeing and common good. The second case study examines the specific contribution of eHealth to wellbeing and the balance of technological, societal and political issues in determining outcomes. The third case study considers how technology might be embedded as part of the process of obtaining meta-data from within a small rural community to demonstrate the impact of mitigation strategies associated with the reduction of its carbon footprint, and hence on climate change. In doing so, the paper seeks to bring together issues surrounding environmental problems in relation to a technology driven engineering design process while positioning them in the context of social benefits arising from sustainable decision making.
Technological Developments and Remediation Mechanisms for Phytoremediation of PCB-Contaminated Soils
Authors: Li Minghao , Sun Shimei
Published: October 2022

Polychlorinated biphenyls (PCBs) have been detected in a variety of environmental media, and they have been continuously introduced into the environment for industrial reasons, despite their harmful impact upon ecological health. Considering the adverse effects of PCBs, many researchers have begun to analyze remediation technologies for PCB-contaminated soil. In this study, the rise and fall of PCBs, as well as their physical and chemical properties and environmental impact, were reviewed. The pollution status of PCBs in soil was summarized. Based on the analysis and comparison of various remediation technologies, the limitations of several phytoremediation technologies in PCB-contaminated soils were analyzed. The influencing factors and transformation mechanisms of the phytoremediation of PCB-contaminated soil were investigated, and the research direction of enhancing the degradation of PCBs through the use of plants was also discussed.
Modification of Soil Physical Properties by Maize Straw Biochar and Earthworm Manure to Enhance Hydraulic Characteristics under Greenhouse Condition
Authors: Liu Xinna , Zhang Jie , Wang Qian , Shaghaleh Hiba , Chang Tingting , Hamoud Yousef Alhaj
Published: October 2022

The deterioration of soil physical properties had led to a decrease in soil–water availability in facility agriculture. Thus, an experiment was set up with five soil treatments of 0% (CK, No additives), 3% biochar (BA3, Mass ratio), 3% earthworm manure (QA3), 5% biochar (BA5), and 5% earthworm manure (QA5) to investigate the effects on soil physical properties and hydraulic characteristics under greenhouse conditions. The physical properties of soil including the soil bulk density (BD) and total porosity (TP) were measured; the results showed that BA5 provided the lowest soil BD (1.24 g·cm−3) and the highest TP (53.09%) and was 13.8% higher than CK. More importantly, the saturated hydraulic conductivity (KS), field capacity (FC), permanent wilting point (PWP), and available water content (AWC) of the soils treated with biochar and earthworm manure were significantly higher than those of CK. At the same application rate, the effect of biochar on soil–water permeability and water-retention capacity was significantly higher than that of earthworm manure, in which the soil–water-characteristic curve (SWCC) showed that as BA5 > BA3 > QA5 > QA3 > CK, the FC and AWC increased from 28.90% and 14.13% under CK, respectively, to 40.73% and 21.91% under BA5, respectively; and the KS, FC, PWP and AWC of BA5 increased by 45.93%, 40.91%, 27.46% and 54.96% compared with CK, respectively. The results revealed that the improvement of the soil TP was conducive to the enhancement of the soil KS and FC, enhanced the soil–water permeability and the water-retention capacity, and ultimately increased the AWC. From the perspective of improving the facility soil and economic benefits, the application of 5% biochar is considered to be the most beneficial.
Towards Sustainable Urban Food Systems: Potentials, Impacts and Challenges of Grassroots Initiatives in the Foodshed of Muenster, Germany
Authors: Wittenberg Janina , Gernert Maria , El Bilali Hamid , Strassner Carola
Published: October 2022

Solving fundamental sustainability challenges in our food systems requires political, institutional and socio-technical transformations. Indeed, sustainability transitions are needed. In this paper, we explore the role of civil society in the form of bottom–up grassroots initiatives in the transition towards a sustainable urban food system and examine their potentials, impacts and challenges in the foodshed of Münster in Germany. To this end, relevant initiatives in Münster have been researched and mapped according to explicit criteria, and case studies have been compiled for two of them using questionnaires, interviews and desk research: a community-supported agriculture (CSA) farm Entrup 119 and an urban gardening initiative GrüneBeete e.V. The results indicate that many initiatives in Münster focus on education and information, i.e., raising awareness, rather than offering material alternatives. Six initiatives were studied in more depth using desk research and a questionnaire. Key leverage points identified by the initiatives are in policy, education, networks and communication. Two of these were studied as cases. We see these transition pioneers as paradigmatic role models, providing room for experimentation, social learning and empowerment.
Study of Steel Slag Eroded by Oxalic Acid and Recovery of Leachate
Authors: Huang Xiaoming , Yan Feng , Guo Rongxin , He Huan , Li Hao
Published: October 2022

The effective resource utilization of steel slag from bulk solid waste can achieve good social, environmental and economic benefits. In order to restrain the volume expansion of steel slag and apply it as a building material, in this work, oxalic acid was used for the treatment of steel slag, and the mechanism of oxalic acid restraining the expansion of steel slag was explored. This study recovered the main metal ions in steel slag while restraining its volume expansion. The volume stability of the eroded steel slag and the phase composition, microstructure, roughness and pore size distribution of the steel slag after oxalic acid erosion were investigated. The hydration process of the steel slag was characterized using the pH value, the ion distribution of the leachate was measured, and the leachate was recovered via ultraviolet catalysis and evaporation crystallization. The results showed that a large number of calcium and iron minerals in the steel slag reacted with adipic acid to form calcium oxalate, which precipitated on the surface and opened pores in the steel slag during the process of adipic acid erosion, which improved the compactness of the steel slag’s surface and improved the volume stability of the steel slag. After erosion, the surface roughness, specific surface area and porosity of the steel slag decreased, and the average pore diameter (μm) decreased from 1.717 to 0.208. In addition, the pH value was stable at approximately 7.3 over long-term interaction with water. The leachate was mainly composed of iron, calcium and silicon ions, accounting for 35.43%, 17.17% and 17.05%, respectively, which were recovered by ethanol and ammonia to obtain calcium oxalate, ferrous oxalate, ammonium oxalate and a metal hydroxide. The hydration activity of the steel slag treated with oxalic acid decreased and the volume expansibility of the steel slag was effectively restrained. The expansion rate of the steel slag decreased from 3.59% to 1.69% and the volume stability of the steel slag improved. The leachate recovery efficiency was high and was close to 100%. The utilization of steel slag as a resource was realized and the environmental problems caused by the steel slag were effectively solved.
Population Aging, Industrial Intelligence and Export Technology Complexity
Authors: Wu Kexu , Tang Zhiwei , Zhang Longpeng
Published: October 2022

The ageing of the population has become a serious test for all countries and regions, and industrial intelligence, as a new development model that integrates traditional industries with modern technology, will contribute to the deep integration of the industrial and innovation chains and thus to the enhancement of national core competitiveness. Based on the dual influence of population ageing and industrial intelligence, this paper uses the 2016 version of the World Input-Output Database (WIOD) data for 16 manufacturing industries in 43 countries from 2000 to 2014 to construct an econometric regression model to empirically test the relationship between population ageing, industrial intelligence and technological complexity of exports. The results of the study show, firstly, that population ageing plays a positive role in the technical complexity of exports. Secondly, the introduction of industrial intelligence mitigates the adverse effects of an ageing population through a complementary substitution mechanism on the one hand, and promotes industrial upgrading and transformation through the infiltration and expansion effects of industrial intelligence on the other, which in turn has a positive impact on the increase in technological sophistication of exports. In addition, the paper further divides the level of industry technology, the level of national development and the age structure of the ageing population, and explores the impact of industry intelligence in different dimensions. The results show that industrial intelligence can have a positive impact on export technological sophistication at the industry level, at the national level and in terms of ageing demographics. The research results provide a new way of thinking, through which countries around the world can formulate population policies and industrial policies and improve the complexity of export technology under the background of aging.
A System Thinking Normative Approach towards Integrating the Environment into Value-Added Accounting—Paving the Way from Carbon to Environmental Neutrality
Authors: Miehe Robert , Finkbeiner Matthias , Sauer Alexander , Bauernhansl Thomas
Published: October 2022

Life Cycle Assessment (LCA) is increasingly being applied in corporate accounting. Recently, especially carbon footprinting (CF) has been adopted as ‘LCA light’ in accordance with the Greenhouse Gas Protocol. According to the strategy ‘balance, reduce, substitute, compensate’, the approach is intended to provide the basis for optimization towards climate neutrality. However, two major problems arise: (1) due to the predominant focus on climate neutrality, other decisive life-cycle impact categories are often ignored, resulting in a misrecognition of potential trade-offs, and (2) LCA is not perceived as an equal method alongside cost and value-added accounting in everyday business, as it relies on a fundamentally different system understanding. In this paper, we present basic considerations for merging the business and life-cycle perspectives and introduce a novel accounting system that combines elements of traditional operational value-added accounting, process and material flow analysis as well as LCA. The method is based on an extended system thinking, a set of principles, a calculation system, and external cost factors for the impact categories climate change, stratospheric ozone depletion, air pollution, eutrophication and acidification. As a scientifically robust assessment method, the presented approach is intended to be applied in everyday operations in manufacturing companies, providing a foundation for a fundamental change in industrial thought patterns on the way to the total avoidance of negative environmental impacts (i.e., environmental neutrality). Therefore, this is validated in two application examples in the German special tools industry, proving its practicability and reproducibility as well as the suitability of specifically derived indicators for the selective optimization of production systems.
Exploring the Efficiency of Future Multimodal Networks: A Door-to-Door Case in Europe
Authors: Bagamanova Margarita , Mujica Mota Miguel , Di Vito Vittorio
Published: October 2022

It is expected that future transportation technologies will positively impact how passengers travel to their destinations. Europe aims to integrate air transport into the overall multimodal transport network to provide better service to passengers, while reducing travel time and making the network more resilient to disruptions. This study presents an approach that investigates these aspects by developing a simulation platform consisting of different models, allowing us to simulate the complete door-to-door trajectory of passengers. To address the future potential, we devised scenarios considering three time horizons: 2025, 2035, and 2050. The experimental design allowed us to identify potential obstacles for future travel, the impact on the system’s resilience, and how the integration of novel technology affects proxy indicators of the level of service, such as travel time or speed. In this paper, we present for the first time an innovative methodology that enables the modelling and simulation of door-to-door travel to investigate the future performance of the transport network. We apply this methodology to the case of a travel trajectory from Germany to Amsterdam considering a regional and a hub airport; it was built considering current information and informed assumptions for future horizons. Results indicate that, with the new technology, the system becomes more resilient and generally performs better, as the mean speed and travel time are improved. Furthermore, they also indicate that the performance could be further improved considering other elements such as algorithmic governance.
Molecular Symmetry of Permethylated β-Cyclodextrins upon Complexation
Authors: Bethanis Kostas , Christoforides Elias , Andreou Athena , Eliopoulos Elias
Published: October 2022

The Cn molecular symmetry implicated by the schemes with which cyclodextrins (CDs), the well-known cyclic oligosaccharides, are introduced in the literature, is not valid. Numerous studies have shown that CDs are rather flexible with their macrocycle adopting various conformations that enable the inclusion complexation of guest molecules of various shapes. In this work, the loss and gain of the C7 symmetry of the heptakis (2, 3, 6-tri-O-methyl)-β-CD (TM-β-CD) is investigated by means of its conformation geometrical features in its hydrated form and upon complexation with molecules of different shapes. For this, the crystal structure of the inclusion complex of a bulky guest molecule (giberellic acid) in TM-β-CD is presented for the first time and compared with the previously determined crystal structures of monohydrated TM-β-CD and the inclusion complex of a linear monoterpenoid (geraniol) in TM-β-CD. The structural investigation was complemented by molecular dynamics simulations in an explicit solvent, based on the crystallographically determined models. The crucial role of the guest, in the symmetry gain of the host, reveals a pronounced induced-fit complexation mechanism for permethylated CDs.
Research on the Pricing Decisions of a Video Platform Based on Interaction
Authors: Li Weifeng , Jiang Minghui , Zhan Wentao
Published: October 2022

Video platforms allow users to interact with others. They enhance the user experience by providing interaction functions, such as “like”, “comment”, and “share”. In order to explore the value of the users’ behavior to the video platforms, we constructed a video platform operation model that considered interaction and then identified the logical relationships implied by the parameters. We adopted the mathematical model method and analyzed the entire video platform system using numerical optimization techniques. From the pricing decision, we obtained the equilibrium result for the video platform profits and analyzed the favorable market demands. We complemented this strategy by proposing a model that enables platforms to consider the promotion behavior of advertisers. Finally, we expanded the basic model by analyzing the competitive strategies of two video platforms in the market. Our research shows that interactivity, advertisement nuisance, and advertiser profitability are important factors that influence video platform pricing strategies. When interactivity is weak, the platforms need to adjust their pricing to obtain a share of the users in the market. However, they need to obtain all the users in the market to achieve optimal profit. In addition, it is profitable for platforms to adopt promotion strategies when the users are highly sensitive to promotions.
Visceral Leishmaniasis after Anti-Interleukin 17A (IL-17A) Therapy in a Patient Affected by Psoriatic Arthritis
Authors: Lupia Tommaso , Corcione Silvia , Fornari Valentina , Rizzello Barbara , Bosio Roberta , Brusa Maria Teresa , De Rosa Francesco Giuseppe
Published: October 2022

The reactivation of latent Leishmania infection in chronic diseases and immunocompromised hosts is a broad and heterogeneous field in medicine and infectious diseases. We reported one of the first cases of Visceral Leishmaniasis occurring in a Caucasian middle-aged man living in an endemic country (Italy) for Leishmania infantum infection following secukinumab treatment for psoriatic arthritis. The patient was cured with a Liposomal Amphotericin B (L-AmB, 3 mg/Kg on days 1–5, followed by a dose on days 10, 17, 24, 31 and 38) regimen, after which his anti-interleukin 17 treatment was restarted—without recurrence in the follow-up.
Surveillance of Tahyna Orthobunyavirus in Urban Areas in Croatia—The “One Health” Approach
Authors: Stevanovic Vladimir , Vilibic-Cavlek Tatjana , Savic Vladimir , Klobucar Ana , Kovac Snjezana , Curman Posavec Marcela , Petrinic Suncica , Bogdanic Maja , Santini Marija , Tesic Vanja , de Albuquerque Soares Nathalia , Barbic Ljubo
Published: October 2022

Background: Tahyna orthobunyavirus (TAHV) is a neglected mosquito-borne bunyavirus. Although the virus is widespread in continental Europe, TAHV infections are rarely reported. We analyzed the prevalence of TAHV in humans and different animal species as well as mosquitoes collected in urban areas of Zagreb and its surroundings in the period from 2020 to 2022. Methods: The study included 32 patients with neuroinvasive disease (NID), 218 asymptomatic individuals, 98 horses, 94 pet animals (dogs and cats), and 4456 Aedes vexans mosquitoes. Cerebrospinal fluid (CSF) and urine samples of patients with NID were tested for the TAHV RNA using a real-time reverse transcription-polymerase chain reaction (RT-qPCR). Human and animal serum samples were tested for TAHV-neutralizing (NT) antibodies using a virus-neutralization test (VNT). Mosquito pools were tested for TAHV RNA using an RT-qPCR. Results: TAHV NT antibodies were detected in 3/9.4% of patients with NID, 8/3.7% of asymptomatic individuals, 29/29.6% of horses, and 11/11.7% of pet animals. There was no difference in the seroprevalence according to age, sex, and area of residence in asymptomatic individuals. In addition, TAHV seropositivity did not differ according to age and sex in pet animals. None of the tested mosquito pools was TAHV RNA-positive. Conclusions: The presented results highlight the importance of interdisciplinary surveillance (“One Health”) of this neglected viral zoonosis.
Cloning, Exogenous Expression and Function Analysis of Interferon–γ from Gadus macrocephalus
Authors: Jiang Jielan , Gu Jie , Zhan Aijun , Mao Mingguang , Liu Yumeng , Wang Haishan , Mao Yunxiang
Published: October 2022

Interferon γ (IFN–γ) is now considered to be one of the key molecules in the regulation of innate and adaptive immunity. The function of IFN–γ is best described in humans, but less of IFN–γ in fish species has been described at protein level. In the present study, IFN–γ from Gadus macrocephalus (GmIFN–γ) has been examined in terms of bioinformatics, prokaryotic expression, yeast expression, antiviral activity and immune regulatory function. The cDNA of GmIFN–γ contains an open reading frame of 570 nucleotides, coding 189 amino acids. The mature protein contains a nuclear localization signal motif and an obvious IFN–γ signature sequence at the C-terminal. GmIFN–γ is very similar to that of Atlantic cod, with homology up to 89.89%, but less than 32% to other species. GmIFN–γ can be detected in the gills, spleen, intestine, brain and kidney. Interestingly, during early development, a strong signal of GmIFN–γ was not detected until 40 days post hatching. Prokaryotic expression plasmid pET–32a–GmIFN–γ was constructed, and the expression products in BL21 were confirmed by Mass Spectrometry. Meanwhile, the plasmid pGAPZA–GmIFN–γ with Myc tag was constructed and transmitted into Pichia pastoris yeast GS115, and the products were tested using Western blot. The purified GmIFN–γ from either BL21 or yeast has a strong antivirus (Spring viremia of carp virus) effect. The vector of pcDNA3.1–GmIFN–γ was expressed in EPC cell lines; high transcript levels of MHC class I chain-related protein A (MICA) gene were detected; and the exogenous GmIFN–γ protein could also induce MICA expression, indicating that GmIFN–γ could stimulate immune response. The yeast GS115 with GmIFN–γ protein, which is an inclusion body, was given to zebrafish orally, and the transcript of zebrafish IFN–γ was upregulated significantly; however, genes of the interferon type–I signal pathway were not well stimulated.
The Multidimensional Vaccine Hesitancy Scale: A Validation Study
Authors: Balgiu Beatrice Adriana , Sfeatcu Ruxandra , Țâncu Ana Maria Cristina , Imre Marina , Petre Ana , Tribus Laura
Published: October 2022

Vaccination hesitancy (VH) is a phenomenon which increases the occurrence of vaccine-preventable diseases. The study tests the validity of the Multidimensional Vaccine Hesitancy Scale (MVHS) in the case of a sample of Romanian adults (n = 528; Meanage = 30.57). The latter filled in an online cross-sectional survey. The construct validity of MVHS was assessed by using confirmatory factor analysis (CFA), the reliability was calculated by using the internal consistency, and the convergent and discriminant validity was assessed by using the composite reliability (CR), and average variance extracted (AVE). The obtained model was invariant across gender. The structural equation model was designed for predictive validity by using the partial least square method (PLS-SEM) which analyses the relation between the MVHS dimensions and the vaccination willingness. The results show support for the 8-factor structure of the scale (χ2/df = 2.48; CFI = 0.95; RMSEA = 0.053). The Cronbach’s coefficients α > 0.70; McDonald’s ω > 0.70 and CR > 0.80 have very good values. The structural equation model shows that there are more dimensions of the scale which predict vaccination hesitancy in various types of vaccines—the main predictors remain the dimensions of health risk and healthy condition. The study’s conclusion led to the idea that the MVHS is suitable for medical practice and for research on the analysis of vaccination behaviours and intentions.