bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Biodegradation and Eco-Toxic Properties of Novel Starch and Gelatine Blend Bioplastics
Authors: Mroczkowska Marta , Germaine Kieran , Culliton David , Kakouli Duarte Thomais , Neves Adriana Cunha
Published: December 2021

To combat the release of petroleum-derived plastics into the environment the European Commission has adopted the EU plastics strategy, which aims for a complete ban on single-use plastics by 2030. Environmentally friendly and sustainable packaging like bioplastic is being up taken at significant levels by companies and consumers. In this study, the environmental impact of novel gelatine–starch blend bioplastics is investigated. The assessments included ecotoxicology with different species that can be found in marine and soil environments to simulate natural conditions. Microalgae, plant, and nematode species were chosen as these are representative of their habitats and are known for their sensitivity to pollutants. Degradation rates of these novel bioplastics were assessed as well as microbiome analysis of the soil before and after bioplastic degradation. The main findings of this study are that (i) the bioplastic generated can be fully biodegraded in soil environments at moderate conditions (20 °C) leaving no physical traces; (ii) bioplastic did not exhibit significantly adverse effects on any organisms assessed in this study; (iii) microbiome analysis of the soil after biodegradation showed a decrease in alpha diversity and a significant increase of Actinobacteria and Firmicutes phyla, which were dominative in the soil.
Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation
Authors: Gellert Karol , Schlögl Erik
Published: December 2021

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features.
An Improved Assessment Method and Its Application to the Latest IMERG Rainfall Product in Mainland China
Authors: Xia Xinran , Fu Disong , Fei Ye , Shao Wei , Xia Xiangao
Published: December 2021

Quantification of uncertainties associated with satellite precipitation products is a prior requirement for their better applications in earth science studies. An improved scheme is developed in this study to decompose mean bias error (MBE) and mean square error (MSE) into three components, i.e., MBE and MSE associated hits, missed precipitation, and false alarms, respectively, which are weighted by their relative frequencies of occurrence (RFO). The trend of total MBE or MSE is then naturally decomposed into six components according to the chain rule for derivatives. Quantitative estimation of individual contributions to total MBE and MSE is finally derived. The method is applied to validation of Integrated MultisatellitE Retrievals for GPM (IMERG) in Mainland China. MBE associated with false alarms is an important driver for total MBE, while MSE associated with hits accounts for more than 85% of MSE, except in inland semi-arid area. The RFO of false alarms increases, whereas the RFO of missed precipitation decreases. Both factors lead in part to a growing trend for total MBE. Detection of precipitation should be improved in the IMERG algorithm. More specifically, the priority should be to reduce false alarms.
Progress Guidance Representation for Robust Interactive Extraction of Buildings from Remotely Sensed Images
Authors: Shu Zhen , Hu Xiangyun , Dai Hengming
Published: December 2021

Accurate building extraction from remotely sensed images is essential for topographic mapping, cadastral surveying and many other applications. Fully automatic segmentation methods still remain a great challenge due to the poor generalization ability and the inaccurate segmentation results. In this work, we are committed to robust click-based interactive building extraction in remote sensing imagery. We argue that stability is vital to an interactive segmentation system, and we observe that the distance of the newly added click to the boundaries of the previous segmentation mask contains progress guidance information of the interactive segmentation process. To promote the robustness of the interactive segmentation, we exploit this information with the previous segmentation mask, positive and negative clicks to form a progress guidance map, and feed it to a convolutional neural network (CNN) with the original RGB image, we name the network as PGR-Net. In addition, an adaptive zoom-in strategy and an iterative training scheme are proposed to further promote the stability of PGR-Net. Compared with the latest methods FCA and f-BRS, the proposed PGR-Net basically requires 1–2 fewer clicks to achieve the same segmentation results. Comprehensive experiments have demonstrated that the PGR-Net outperforms related state-of-the-art methods on five natural image datasets and three building datasets of remote sensing images.
The Influence of Green Space Patterns on Land Surface Temperature in Different Seasons: A Case Study of Fuzhou City, China
Authors: Yang Liuqing , Yu Kunyong , Ai Jingwen , Liu Yanfen , Lin Lili , Lin Lingchen , Liu Jian
Published: December 2021

Background: Urban green space (UGS) has been shown to play an important role in mitigating urban heat island (UHI) effects. In the context of accelerating urbanization, a better understanding of the landscape pattern mechanisms affecting the thermal environment is important for the improvement of the urban ecological environment. Methods: In this study, the relationship between land surface temperature (LST) and the spatial patterns of green space was analyzed using a bivariate spatial autocorrelation and spatial autoregression model in three seasons (summer, transition season (spring), and winter) with different grid scales in Fuzhou city. Results: Our results indicated that the LST in Fuzhou City has a significant spatial autocorrelation. The percentage of landscape and patch density area were negatively correlated with surface temperature. The results of our indicators differed according to the season, with population density and distance to the water indicators not being significant in the winter. The coefficient of determination was higher at the 510 m grid scale on this study’s scale. Conclusion: This study extends our understanding on the influence of UHI effects after accounting for different seasonal and spatial scale factors. It also provides a reference for urban planners to mitigate heat islands in the future.
Glacier Area and Snow Cover Changes in the Range System Surrounding Tarim from 2000 to 2020 Using Google Earth Engine
Authors: Zhang Jing , Jia Li , Menenti Massimo , Zhou Jie , Ren Shaoting
Published: December 2021

Glacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We studied the evolution of snow cover to understand whether it could explain the evolution of glacier area. In this study, we used the thresholding of the NDSI (Normalized Difference Snow Index) retrieved with MODIS data to extract annual glacier area and snow cover. We evaluated how the glacier trends related to snow cover area in five subregions in the Tarim Basin. The uncertainty in our retrievals was assessed by comparing MODIS results with the Landsat-5 TM in 2000 and Landsat-8 OLI in 2020 glacier delineation in five subregions. The glacier area in the Tarim Basin decreased by 1.32%/a during 2000–2020. The fastest reductions were in the East Tien Shan region, while the slowest relative reduction rate was observed in the West Tien Shan and Pamir, i.e., 0.69%/a and 1.08%/a, respectively, during 2000–2020. The relative glacier stability in Pamir may be related to the westerlies weather system, which dominates climate in this region. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. During the summer months, the sequence of multiple snowfall and snowmelt events leads to intermittent snow cover, which was the key feature applied to discriminate snow and glacier.
An Elevation Ambiguity Resolution Method Based on Segmentation and Reorganization of TomoSAR Point Cloud in 3D Mountain Reconstruction
Authors: Li Xiaowan , Zhang Fubo , Li Yanlei , Guo Qichang , Wan Yangliang , Bu Xiangxi , Liu Yunlong , Liang Xingdong
Published: December 2021

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method.
SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures
Authors: Meissner Thomas , Manaster Andrew
Published: December 2021

Sea-ice contamination in the antenna field of view constitutes a large error source in retrieving sea-surface salinity (SSS) with the spaceborne Soil Moisture Active Passive (SMAP) L-band radiometer. This is a major obstacle in the current NASA/Remote Sensing Systems (RSS) SMAP SSS retrieval algorithm in regards to obtaining accurate SSS measurements in the polar oceans. Our analysis finds a strong correlation between 8-day averaged SMAP L-band brightness temperature (TB) bias and TB measurements from the Advanced Microwave Scanning Radiometer (AMSR2) in the C-through Ka-band frequency range for sea-ice contaminated ocean scenes. We show how this correlation can be employed to develop: (1) a discriminant analysis that is able to reliably flag the SMAP observations for sea-ice contamination and (2) subsequently remove the sea-ice contamination from the SMAP observations, which results in significantly more accurate SMAP SSS retrievals near the sea-ice edge. We provide a case study that evaluates the performance of the proposed sea-ice flagging and correction algorithm. Our method is also able to detect drifting icebergs, which go often undetected in many available standard sea-ice products and thus result in spurious SMAP SSS retrievals.
A Convolutional Neural Network Combined with Attributed Scattering Centers for SAR ATR
Authors: Zhou Yu , Li Yi , Xie Weitong , Li Lu
Published: December 2021

It is very common to apply convolutional neural networks (CNNs) to synthetic aperture radar (SAR) automatic target recognition (ATR). However, most of the SAR ATR methods using CNN mainly use the image features of SAR images and make little use of the unique electromagnetic scattering characteristics of SAR images. For SAR images, attributed scattering centers (ASCs) reflect the electromagnetic scattering characteristics and the local structures of the target, which are useful for SAR ATR. Therefore, we propose a network to comprehensively use the image features and the features related to ASCs for improving the performance of SAR ATR. There are two branches in the proposed network, one extracts the more discriminative image features from the input SAR image; the other extracts physically meaningful features from the ASC schematic map that reflects the local structure of the target corresponding to each ASC. Finally, the high-level features obtained by the two branches are fused to recognize the target. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove the capability of the SAR ATR method proposed in this letter.
Wi-CAS: A Contactless Method for Continuous Indoor Human Activity Sensing Using Wi-Fi Devices
Authors: Hao Zhanjun , Zhang Daiyang , Dang Xiaochao , Liu Gaoyuan , Bai Yanhong
Published: December 2021

With the new coronavirus raging around the world, home isolation has become an effective way to interrupt the spread of the virus. Effective monitoring of people in home isolation has also become a pressing issue. However, the large number of isolated people and the privatized isolated spaces pose challenges for traditional sensing techniques. Ubiquitous Wi-Fi offers new ideas for sensing people indoors. Advantages such as low cost, wide deployment, and high privacy make indoor human activity sensing technology based on Wi-Fi signals increasingly used. Therefore, this paper proposes a contactless indoor person continuous activity sensing method based on Wi-Fi signal Wi-CAS. The method allows for the sensing of continuous movements of home isolated persons. Wi-CAS designs an ensemble classification method based on Hierarchical Clustering (HEC) for the classification of different actions, which effectively improves the action classification accuracy while reducing the processing time. We have conducted extensive experimental evaluations in real home environments. By recording the activities of different people throughout the day, Wi-CAS is very sensitive to unusual activities of people and also has a combined activity recognition rate of 94.3%. The experimental results show that our proposed method provides a low-cost and highly robust solution for supervising the activities of home isolates.
Comparative Analytical Study of SCMA Detection Methods for PA Nonlinearity Mitigation
Authors: Sfeir Elie , Mitra Rangeet , Kaddoum Georges , Bhatia Vimal
Published: December 2021

Non-orthogonal multiple access (NOMA) has emerged as a promising technology that allows for multiplexing several users over limited time-frequency resources. Among existing NOMA methods, sparse code multiple access (SCMA) is especially attractive; not only for its coding gain using suitable codebook design methodologies, but also for the guarantee of optimal detection using message passing algorithm (MPA). Despite SCMA’s benefits, the bit error rate (BER) performance of SCMA systems is known to degrade due to nonlinear power amplifiers at the transmitter. To mitigate this degradation, two types of detectors have recently emerged, namely, the Bussgang-based approaches and the reproducing kernel Hilbert space (RKHS)-based approaches. This paper presents analytical results on the error-floor of the Bussgang-based MPA, and compares it with a universally optimal RKHS-based MPA using random Fourier features (RFF). Although the Bussgang-based MPA is computationally simpler, it attains a higher BER floor compared to its RKHS-based counterpart. This error floor and the BER’s performance gap are quantified analytically and validated via computer simulations.
A Survey of Methods and Technologies Used for Diagnosis of Scoliosis
Authors: Karpiel Ilona , Ziębiński Adam , Kluszczyński Marek , Feige Daniel
Published: December 2021

The purpose of this article is to present diagnostic methods used in the diagnosis of scoliosis in the form of a brief review. This article aims to point out the advantages of select methods. This article focuses on general issues without elaborating on problems strictly related to physiotherapy and treatment methods, which may be the subject of further discussions. By outlining and categorizing each method, we summarize relevant publications that may not only help introduce other researchers to the field but also be a valuable source for studying existing methods, developing new ones or choosing evaluation strategies.
Research on Coupling Coordination and the Development of Green Shipping and Economic Growth in China
Authors: Deng Gaodan , Li Xinchun , Chen Jingxiao
Published: December 2021

The shipping industry is an important indicator of economic development, and it is closely related to economic growth. At present, China’s economy has moved from a high-speed growth stage to a high-quality development stage. The popularization of the green shipping concept also creates higher requirements for sustainable economic development. The core innovation point of this paper is to construct an index system of green shipping and economic growth in China, and to measure the coupling coordination degree and relative development degree of the two through the comprehensive development index in the form of overall and regional division. This is rarely covered in the existing literature. By constructing an index system for green shipping and economic growth, this paper calculates a comprehensive development index for the two systems and studies the degree of coupling coordination and relative development of the comprehensive system. The results show that, first, during the observation period, the comprehensive system of green shipping and economic growth in China has maintained a growth trend, rising from a near imbalance to a good coordination level, and the two have developed into a synchronous state. Second, the development of the Yangtze River Delta has been relatively rapid, closely followed by that of the Pearl River Delta, while the Bohai Rim area has seen the slowest development. Since 2010, China’s comprehensive system of 11 coastal provinces has mostly left the imbalanced state and it entered the coordination state by 2019. The results of the study provide some suggestions for the coordinated development of green shipping and economic growth.
Physicochemical Characterization of Cardoon “Cynara cardunculus” Wastes (Leaves and Stems): A Comparative Study
Authors: Hajji Nabih Meryem , El Hajam Maryam , Boulika Hamza , Hassan Montaser M. , Idrissi Kandri Noureddine , Hedfi Amor , Zerouale Abdelaziz , Boufahja Fehmi
Published: December 2021

The disposal of vegetable wastes in nature is harmful for marine habitats and biota. These types of waste are frequently used as fuel, generating polluting products, with undesired side effects on the environment. Therefore, it is essential to find better alternatives for the capitalisation of these waste products. Their diversified chemical composition can become a potential resource of high added value raw materials. The knowledge of the physicochemical properties of these wastes is therefore essential. The present work aimed for characterising the physicochemical properties of a plant residue belonging to the Asteraceae Family, collected from a vegetable market in Fez city, Morocco. The vegetal tissues were analysed by Scanning Electron Microscopy coupled with EDX, X-ray Diffraction, Fourier Transform Infrared Spectroscopy, Inductively Coupled Plasma Atomic Emission Spectroscopy, and by Thermogravimetric/Differential thermal analyses. Other additional parameters were also measured, such as moisture, volatile matter, ash, and fixed carbon contents. Acidic and basic surface functions were evaluated by Boehm’s method, and pH points at zero charge were equally calculated. The results revealed a strong congruence between the morphological and structural properties of this plant. These vegetal wastes comprise a homogeneous fibrous and porous aspect both in surface and in profile, with a crystalline structure characteristic of cellulose I. A mass loss of 86.49% for leaves and 87.91% for stems in the temperature range of 100 °C to 700 °C, and pHpzc of 8.39 for leaves and 7.35 for stems were found. This study clarifies the similarities and differences between the chemical composition and morphological structure of these vegetal wastes, paving the way for future value-added applications in appropriate fields.
Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control
Authors: Wang Xin , Atkin Jason , Bazmohammadi Najmeh , Bozhko Serhiy , Guerrero Josep M.
Published: December 2021

Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the feasibility of the model. In addition, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.
High-Pressure Processing for Sustainable Food Supply
Authors: Nabi Brera Ghulam , Mukhtar Kinza , Arshad Rai Naveed , Radicetti Emanuele , Tedeschi Paola , Shahbaz Muhammad Umar , Walayat Noman , Nawaz Asad , Inam-Ur-Raheem Muhammad , Aadil Rana Muhammad
Published: December 2021

Sustainable food supply has gained considerable consumer concern due to the high percentage of spoilage microorganisms. Food industries need to expand advanced technologies that can maintain the nutritive content of foods, enhance the bio-availability of bioactive compounds, provide environmental and economic sustainability, and fulfill consumers’ requirements of sensory characteristics. Heat treatment negatively affects food samples’ nutritional and sensory properties as bioactives are sensitive to high-temperature processing. The need arises for non-thermal processes to reduce food losses, and sustainable developments in preservation, nutritional security, and food safety are crucial parameters for the upcoming era. Non-thermal processes have been successfully approved because they increase food quality, reduce water utilization, decrease emissions, improve energy efficiency, assure clean labeling, and utilize by-products from waste food. These processes include pulsed electric field (PEF), sonication, high-pressure processing (HPP), cold plasma, and pulsed light. This review describes the use of HPP in various processes for sustainable food processing. The influence of this technique on microbial, physicochemical, and nutritional properties of foods for sustainable food supply is discussed. This approach also emphasizes the limitations of this emerging technique. HPP has been successfully analyzed to meet the global requirements. A limited global food source must have a balanced approach to the raw content, water, energy, and nutrient content. HPP showed positive results in reducing microbial spoilage and, at the same time, retains the nutritional value. HPP technology meets the essential requirements for sustainable and clean labeled food production. It requires limited resources to produce nutritionally suitable foods for consumers’ health.
A Distinctive Method of Online Interactive Learning in STEM Education
Authors: Abouhashem Azza , Abdou Rana Magdy , Bhadra Jolly , Santhosh Malavika , Ahmad Zubair , Al-Thani Noora Jabor
Published: December 2021

A breakthrough that has occurred in recent years is the emergence of the COVID-19 pandemic. It has affected various sectors of society, including the educational sector. It has prevented students from performing group-oriented hands-on activities and has eventually transformed their active learning environment in schools into virtual passive lectures at home. Therefore, to solve this impedance, we exercised several online STEM programs (five online STEM programs with repetitive cycles) for school students, including 140 students (middle and high school), 16 undergraduate (UG) secondary mentors, and 8 primary STEM professionals. Thus, the study revealed the results of a distinctive interactive online STEM teaching model that has been designed to overcome the virtual classroom’s impediments. The employed teaching model demonstrates an interactive learning environment that ensures students’ engagement, retention, and participation, driving them to STEM innovations. Various digital tools, including PowerPoint presentations, videos, online simulations, interactive quizzes, and innovative games were used as teaching aids. Both the synchronous and asynchronous means in a student-centered approach, along with the feedback mechanism, were implemented. Finally, the employed method’s effectiveness was revealed by the maximum student retention and STEM innovation rates, along with the model’s potentiality towards its replicability and sustainability. Thus, the outlook of such initiatives could further be broadened by its sustainability and replicability aspect towards vulnerable student communities such as academically introverted and specially challenged students.
Evaluation of the Consumer Perception of Sharing Economy: Cases of Latvia, Russia, Ukraine and Belarus
Authors: Tambovceva Tatjana , Titko Jelena , Svirina Anna , Atstaja Dzintra , Tereshina Maria
Published: December 2021

The overwhelming goal of large-scale cross-country research is to evaluate consumers’ perception of a sharing economy. The research was limited by the number of respondents, as well as by the countries represented in the survey. Latvia, Russia, Ukraine, and Belarus were mostly represented, and only these responses (757) were analyzed. The study used multilevel modelling of sharing economy elements (dependent variable) in relation to personal characteristics (age, gender, income, industry) nested by the self-assessed level of eco-friendliness (a key predictor for the attitude towards sharing economy). Findings: The key personal characteristics, which influence a person’s intention to be involved in the sharing economy practices, are level of income, education, and also self-perceived ecological friendliness. The sharing economy is not only a topic for investigation among academicians, but also an issue on the agenda of the European Commission, because it is considered as a driver for growth and job creation in the European Union. Despite an increasing interest and many studies, there is a limited number of studies focused on difference in perception of sharing economy depending on personal characteristics of respondents. This indicates the necessity of conducting such surveys, involving participants from different European countries. The given paper could be used as a methodological framework for other European researchers who are interested in the exploration of the topic regarding perception of the sharing economy.
Embodied Energy and Cost Assessments of a Concentrating Photovoltaic Module
Authors: Freier Raine Daria , Muhammad-Sukki Firdaus , Ramirez-Iniguez Roberto , Ardila-Rey Jorge Alfredo , Jafry Tahseen , Gamio Carlos
Published: December 2021

This paper focuses on the embodied energy and cost assessments of a static concentrating photovoltaic (CPV) module in comparison to the flat photovoltaic (PV) module. The CPV module employs a specific concentrator design from the Genetically Optimised Circular Rotational Square Hyperboloid (GOCRSH) concentrators, labelled as GOCRSH_A. Firstly, it discussed previous research on life cycle analyses for PV and CPV modules. Next, it compared the energy embodied in the materials of the GOCRSH_A module to the energy embodied in the materials of a flat PV module of the same electrical output. Lastly, a comparison in terms of cost is presented between the analysed GOCRSH_A module and the flat PV module. It was found that the GOCRSH_A module showed a reduction in embodied energy of 17% which indicates a reduction in embodied carbon. In terms of cost, the costs for the GOCRSH_A module were calculated to be 1.71 times higher than the flat PV module of the same electrical output. It is concluded that a trade-off is required between the embodied energy and cost impacts in order to bring this CPV technology into the market.
A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network
Authors: Cao Jianhua , Xia Xuhui , Wang Lei , Zhang Zelin , Liu Xiang
Published: December 2021

Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.
Exploring Social Media Data to Understand How Stakeholders Value Local Food: A Canadian Study Using Twitter
Authors: Chicoine Marilyne , Rodier Francine , Durif Fabien , Schillo Sandra , Dubé Laurette
Published: December 2021

The consumption of local food, a major trend in industrialized countries around the world has experienced an unprecedented craze in the pandemic context that we are experiencing. Since the beginning of the crisis and in various media, communication about local food seems inconsistent. However, companies would have every interest in better communicating the multifaceted areas of the locality that customers value or adopting the same language if they wish to collaborate with each other. This research aims to identify and evaluate the “fit” or the “gap” of the different local food’ meanings of Canadian agri-food stakeholders through data mining of one of their communication media: Twitter. Using tweets by over 1300 Twitter accounts from Canadian agri-food companies and a popular hashtag, we analyze a sample of their tweets in 2019 and 2020 by creating and using a local food’ keyword dictionary based on the concept of proximity. Term frequency and multivariate analysis of variance of 16,585 tweets about local food show significant differences in dimensions of proximity used in communications. This study shows the interest of using the concept of proximity to better define and understand the valuation of local food products. In addition, it offers a methodology capable of distinguishing the nuances of meaning of the locality of products using natural data that is accessible via social media.
Exploring the Role of Norms and Habit in Explaining Pro-Environmental Behavior Intentions in Situations of Use Robots and AI Agents as Providers in Tourism Sector
Authors: Sarmento Eduardo Moraes , Loureiro Sandra Maria Correia
Published: December 2021

In a pandemic situation, with climate change around the world, studies analyzing changes in travel patterns are welcome. This study combines three theories to propose a model on pro-environmental behavior intentions, namely, the theory of planned behavior, value–belief–norm theory and habit theory. This study aims to examine the role of social norms, personal norms and habit strength to explain pro-environmental behavior intentions. The authors collected 316 usable questionnaires from tourists in the well-known touristic Belem location in Lisbon. Personal norms were revealed to have the strongest association with pro-environmental behavior intentions, followed by habit strength. The study also identified different broad challenges to encouraging sustainable behaviors and use these to develop novel theoretical propositions and directions for future research. Finally, the authors outlined how practitioners aiming to encourage sustainable consumer behaviors can use this framework to achieve better results.
Influence of Different Allocation Methods for Recycling and Dynamic Inventory on CO2 Savings and Payback Times of Light-Weighted Vehicles Computed under Product- and Fleet-Based Analyses: A Case of Internal Combustion Engine Vehicles
Authors: Dunuwila Pasan , Hamada Ko , Takeyama Kentaro , Panasiuk Daryna , Hoshino Takeo , Morimoto Shinichiro , Tahara Kiyotaka , Daigo Ichiro
Published: December 2021

Light weighting by material substitution is a key to reducing GHG emissions during vehicle operation. The GHG benefits are a salient factor in selecting lightweight materials for vehicles. Although the literature has performed lightweight material selections using GHG benefits under product- and fleet-based life-cycle inventory (LCI) analyses, recycling effects have therein been accounted for by arbitrarily selecting allocation methods for recycling, as the consensus on their selection is absent. Furthermore, studies have mistreated the temporal variations of the LCI parameters (the dynamic inventory (DI)), though that could be an important factor affecting the overall LCI results when allocation methods for recycling are in place. Therefore, to investigate their influence on greenhouse gas (GHG) benefit evaluations, an LCI case study was conducted, centered on aluminum- and magnesium-substituted internal combustion engine vehicles (ICEVs) at the product- and fleet- levels. “CO2 savings” and the “CO2 payback time”, as well as four allocation methods for recycling, were considered to represent the GHG benefits and address the recycling effects, respectively. The dynamic inventory was based on the world average electricity grid mix change. The results indicate that changing the conditions of the DI and the allocation methods for recycling could alter the better performing material under fleet-based analyses. Therefore, we ascertained that the choice of the allocation method for recycling and conducting fleet-scale dynamic LCI analyses in the presence of the DI is pivotal for material selections.
Does Fish Conditioning in Aquaculture Increase Survival Success in the Wild? A Case Study on a Cyprinid Fish
Authors: Šmejkal Marek , Bartoň Daniel , Blabolil Petr , Podhorec Peter , Souza Allan T. , Stejskal Vlastimil , Stepanyshyna Yevdokiia , Tapkir Sandip
Published: December 2021

Many endangered fish species in the wild are artificially bred, and their populations are strengthened by the stocking of aquaculture-raised juveniles. Because fish from aquaculture are generally not well prepared for the challenging life in the wild, we tested whether training for selected challenges could improve fish survival after stocking. We chose conditioning on predation pressure (by learning predator image of northern pike Esox lucius using predator chemical cues and visual stimuli), increased rearing water velocity 20 cm × s−1, and direct exposure to predation. The juvenile cyprinid fish asp (Leuciscus aspius) was used as a model prey species. A total of 7949 asp were reared in four groups using a combination of high flow, predation, and control treatments (low flow, no predation; 2018, 2149, 1929, and 1856 individuals, respectively). Of these, 1800 individuals were released into three ponds with pike, and their mortality rates were monitored in relation to predation for two months after stocking using passive telemetry arrays. The remaining 6149 aquaculture-reared individuals were released directly into a large reservoir, while 1426 individuals that survived pike predation for two months were released after the pond experiment ended. Fish survival in a reservoir was monitored in 2020 and 2021 by boat electrofishing and passive telemetry. The effect of training in aquaculture was not detected in pond conditions, but the fish that survived direct predation from pike for two months in the semi-natural treatment were more likely to survive in the wild than their aquaculture-reared counterparts. In the laboratory environment, asp responded to predator chemical cues with an increase in shoal cohesion and swimming activity, which demonstrate their ability to detect chemical cues. However, exposure to more fluvial conditions did not result in increased critical swimming speed. The study suggests that conditioning tested in aquaculture may not be sufficient to prepare fish for the wild, while exposing fish to direct predation could increase fish survival.
The Three-Talk Model: Getting Both Evidence and Preferences into a Pre-Service Teacher Health Workshop
Authors: Zeyer Albert , Arnold Julia
Published: December 2021

We describe a pre-service teacher workshop about sustainable health decisions in school. This one-week workshop had two goals: to improve the ability of students to cope with health and illness as teachers in daily school life, and to improve scientific literacy in health contexts. In this way, the workshop aimed at creating a situation of mutual benefit between science education and health education, as it is suggested in the new science pedagogy called Science|Environment|Health. To reach this aim, the workshop was structured by the evidence-preference approach and the three-talk model, both originally developed for shared-decision making in medicine. In the evidence-preference approach, the experts (the physician, here the teacher) provide the best evidence available, while the laypersons (the patient, here the teacher students) bring in their preferences and, together with the experts, find their personal standpoint. This process is structured by the three-talk model, which is conceived as a characteristic succession of choice talk, option talk, and decision talk. We describe how the pre-service teacher workshop embraced this new approach, compare it to a scientific literacy point of view, and suggest how it could be applied in many other educational contexts, particularly in many issues of education for sustainability.
Generation of Hydrogen and Oxygen from Water by Solar Energy Conversion
Authors: Shapovalov Yuriy , Tokpayev Rustam , Khavaza Tamina , Nauryzbayev Mikhail
Published: December 2021

Photosynthesis is considered to be one of the promising areas of cheap and environmentally friendly energy. Photosynthesis involves the process of water oxidation with the formation of molecular oxygen and hydrogen as byproducts. The aim of the present article is to review the energy (light) phase of photosynthesis based on the published X-ray studies of photosystems I and II (PS-I and PS-II). Using modern ideas about semiconductors and biological semiconductor structures, the mechanisms of H+, O2↑, e− generation from water are described. At the initial stage, PS II produces hydrogen peroxide from water as a result of the photoenzymatic reaction, which is oxidized in the active center of PS-II on the Mn4CaO5 cluster to form O2↑, H+, e−. Mn4+ is reduced to Mn2+ and then oxidized to Mn4+ with the transfer of reducing the equivalents of PS-I. The electrons formed are transported to PS-I (P 700), where the electrochemical reaction of water decomposition takes place in a two-electrode electrolysis system with the formation of gaseous oxygen and hydrogen. The proposed functioning mechanisms of PS-I and PS-II can be used in the development of environmentally friendly technologies for the production of molecular hydrogen.
The FUor Star V2493 Cyg (HBC 722)—Eleven Years at Maximum Brightness
Authors: Semkov Evgeni , Ibryamov Sunay , Peneva Stoyanka
Published: December 2021

At the time of stellar evolution, young stellar objects go through processes of increased activity and instability. Star formation takes place in several stages during which the star accumulates enough mass to initiate thermonuclear reactions in the nucleus. A significant percentage of the mass of Sun-like stars accumulates during periods of increased accretion known as FUor outbursts. Since we know only about two dozen stars of this type, the study of each new object is very important for our knowledge. In this paper, we present data from photometric monitoring on a FUor object V2493 Cyg discovered in 2010. Our data were obtained in the optical region with BVRI Johnson–Cousins set of filters during the period from November 2016 to February 2021. The results of our observations show that during this period no significant changes in the brightness of the star were registered. We only detect variations with a small amplitude around the maximum brightness value. Thus, since 2013 V2493 Cyg remains at its maximum brightness, without a decrease in brightness. Such photometric behavior is not typical of other stars from FUor type. Usually, the light curves of FUors are asymmetrical, with a very rapid rise and gradual decline of the brightness. V2493 Cyg remains unique in this respect with a very rapid rise in brightness and prolonged retention in maximum light. Our period analysis made for the interval February 2013–February 2021 reveals a well-defined period of 914 ± 10 days. Such periodicity can be explained by dust structures remaining from star formation in orbit around the star.
SimBetaReg Web-Tool: The Easiest Way to Implement the Beta and Simplex Regression Models
Authors: Altun Emrah , El-Morshedy Mahmoud
Published: December 2021

When the response variable is defined on the (0,1) interval, the beta and simplex regression models are commonly used by researchers. However, there is no software support for these models to make their implementation easy for researchers. In this study, we developed a web-tool, named SimBetaReg, to help researchers who are not familiar with programming to implement the beta and simplex regression models. The developed application is free and works independently from the operating systems. Additionally, we model the incidence ratios of COVID-19 with educational and civic engagement indicators of the OECD countries using the SimBetaReg web-tool. Empirical findings show that when the educational attainment, years in education, and voter turnout increase, the incidence ratios of the countries decrease.
Unknown Extracellular and Bioactive Metabolites of the Genus Alexandrium: A Review of Overlooked Toxins
Authors: Long Marc , Krock Bernd , Castrec Justine , Tillmann Urban
Published: December 2021

Various species of Alexandrium can produce a number of bioactive compounds, e.g., paralytic shellfish toxins (PSTs), spirolides, gymnodimines, goniodomins, and also uncharacterised bioactive extracellular compounds (BECs). The latter metabolites are released into the environment and affect a large range of organisms (from protists to fishes and mammalian cell lines). These compounds mediate allelochemical interactions, have anti-grazing and anti-parasitic activities, and have a potentially strong structuring role for the dynamic of Alexandrium blooms. In many studies evaluating the effects of Alexandrium on marine organisms, only the classical toxins were reported and the involvement of BECs was not considered. A lack of information on the presence/absence of BECs in experimental strains is likely the cause of contrasting results in the literature that render impossible a distinction between PSTs and BECs effects. We review the knowledge on Alexandrium BEC, (i.e., producing species, target cells, physiological effects, detection methods and molecular candidates). Overall, we highlight the need to identify the nature of Alexandrium BECs and urge further research on the chemical interactions according to their ecological importance in the planktonic chemical warfare and due to their potential collateral damage to a wide range of organisms.
First Detection of SARS-CoV-2 Delta (B.1.617.2) Variant of Concern in a Dog with Clinical Signs in Spain
Authors: Fernández-Bastit Leira , Rodon Jordi , Pradenas Edwards , Marfil Silvia , Trinité Benjamin , Parera Mariona , Roca Núria , Pou Anna , Cantero Guillermo , Lorca-Oró Cristina , Carrillo Jorge , Izquierdo-Useros Nuria , Clotet Bonaventura , Noguera-Julián Marc , Blanco Julià , Vergara-Alert Júlia , Segalés Joaquim
Published: December 2021

Several cases of naturally infected dogs with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported despite the apparently low susceptibility of this species. Here, we document the first reported case of infection caused by the Delta (B.1.617.2) variant of concern (VOC) in a dog in Spain that lived with several household members suffering from Coronavirus Infectious Disease 2019 (COVID-19). The animal displayed mild digestive and respiratory clinical signs and had a low viral load in the oropharyngeal swab collected at the first sampling. Whole-genome sequencing indicated infection with the Delta variant, coinciding with the predominant variant during the fifth pandemic wave in Spain. The dog seroconverted, as detected 21 days after the first sampling, and developed neutralizing antibodies that cross-neutralized different SARS-CoV-2 variants. This study further emphasizes the importance of studying the susceptibility of animal species to different VOCs and their potential role as reservoirs in the context of COVID-19.
Immune Thrombocytopenia Induced by the Chimpanzee Adenovirus-Vectored Vaccine against SARS-CoV-2 Infection
Authors: Liao Po-Wei , Teng Chieh-Lin Jerry , Chou Cheng-Wei
Published: December 2021

We present a case of immune thrombocytopenia (ITP) induced by the chimpanzee adenovirus-vectored vaccine, without evidence of thrombosis, eight days after vaccine administration. The thrombocytopenia condition improved after administering steroid treatment. This adenovirus vaccine had been reported to induce rare side effects, such as immune thrombotic thrombocytopenia. This case report showed that it could also induce immune thrombocytopenia without the presence of thrombosis. Therefore, we should be cautious of this rare side effect as global vaccine administrations against coronavirus disease increase.