bluuebunny/crossref_metadata_2025 huggingface dataset:
Fire Resistance Evaluation of Concrete Beams and Slabs Incorporating Natural Fiber-Reinforced Polymers
Authors: Kodur Venkatesh , Venkatachari Svetha , Bhatt Pratik , Matsagar Vasant A. , Singh Shamsher Bahadur
Published: February 2023

This paper presents a numerical study to evaluate the fire resistance of concrete beams and slabs incorporating natural fiber-reinforced polymers (FRP). A validated finite element model was applied to carry out a series of numerical studies on fire-exposed reinforced concrete (RC) beams and slabs strengthened with conventional and bio-based FRP composites. The model calculates the temperature-dependent moment–curvature relationship for various segments of the member at each time step, which are then used to calculate the moment capacity and deflection of the member. The variables in the beams and slabs include different strengthening techniques (externally bonded FRP and near-surface mounted FRP), different fiber composites, and fire insulation schemes (uninsulated and insulated). The results from the study indicate that the bio-based FRP-strengthened RC members undergo a faster degradation in moment capacity and also experience higher deflections under fire exposure. This leads to a lower fire resistance in RC members with bio-based FRP composites compared to beams and slabs with conventional FRP-strengthened concrete members. The addition of fire insulation to the bio-based FRP-strengthened members can enhance their fire performance and help achieve the required fire resistance ratings for use in building applications. In this study, the NSM CFRP-strengthened RC beams were found to have a fire resistance of 3 h without any fire insulation; however, the bio-based FRP-strengthened beams required a layer of vermiculite–gypsum-based fire insulation material (of about 25 mm) to achieve similar fire resistance.
Effect of Aromatic Chain Extenders on Polyurea and Polyurethane Coatings Designed for Defense Applications
Authors: Toader Gabriela , Moldovan Andreea Elena , Diacon Aurel , Dirloman Florin Marian , Rusen Edina , Podaru Alice , Rotariu Traian , Ginghina Raluca Elena , Hoza Oana Elisabeta
Published: February 2023

The present work describes the synthesis of new versatile polyurea (PU) and polyurethane (PUR) matrices, including different chain extenders, which facilitate the design of distinct, tunable properties, and high-performance derivatives. These polymers can be used for various defense and security applications, such as coatings for ballistic protection, CBRN protection, binders for energetic formulations, etc. Combining aliphatic and aromatic molecules in PU or PUR structures enables the synthesis of polymers with improved and controllable thermo-mechanical properties. Thus, for polyurea synthesis, we utilized two types of polymeric aliphatic diamines and three types of aromatic chain extenders (1,1’-biphenyl-4,4’-diamine, benzene-1,2-diamine, and 1,2-diphenylhydrazine). An analogous method was used to synthesize polyurethane films by employing one polymeric aliphatic polyol and three types of aromatic chain extenders (benzene-1,3-diol, benzene-1,4-diol, and benzene-1,2,3-triol). Subsequently, various analytic techniques (Fourier transform infrared spectroscopy–attenuated total reflectance (FTIR-ATR), single cantilever dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), frequency-dependent shear modulus survey, tensile tests, water contact angle measurements, and scanning electron microscopy (SEM) with energy-dispersive X-ray analysis (EDX)) have been utilized to characterize the synthesized materials and to evaluate the influence of each chain extender on their final properties.
The Use of Corn Stover-Derived Nanocellulose as a Stabilizer of Oil-in-Water Emulsion
Authors: Liu Lingling , Gerard Gina , Peng Zimeng , Yu Zhile
Published: February 2023

Agricultural byproducts such as corn stover are widely available sources for preparation of nanocellulose, which is an emerging green chemical with versatile applications. In this study, corn stover-derived nanocellulose was prepared via bleaching, alkaline treatment, 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) oxidation, and ultrasonication. The as-prepared TEMPO-oxidized cellulose nanofibril (TEMPO-CNF) was characterized by transmission electron microscopy, UV-Vis spectrophotometry, rheometry, and zeta potential measurement. Droplet size, phase behavior, and thermodynamic stability of TEMPO-CNF stabilized oil-in-water emulsions were investigated. Results show that TEMPO-CNF with a width of 4 nm, length of 353 nm, and surface charge of 1.48 mmol/g COO- can be prepared from corn stover. In addition, TEMPO-CNF can be used as an emulsion stabilizer for lemongrass essential oil loaded oil-in-water emulsion. This study is among the first to report that TEMPO-CNF improved the freeze-thaw stability of oil-in-water emulsions stabilized by small molecular weight surfactants (e.g., Tween 80).
A New Year’s Message 2023
Authors: Yusa Shin-ichi
Published: February 2023

We wish you all happiness, health and progress in the new year [...]
Poly(butylene adipate-co-terephthalate)/Poly(lactic acid) Polymeric Blends Electrospun with TiO2-R/Fe3O4 for Pollutant Photodegradation
Authors: Medeiros Alessandra Ruyz , Lima Fabiana da Silva , Rosenberger Andressa Giombelli , Dragunski Douglas Cardoso , Muniz Edvani Curti , Radovanovic Eduardo , Caetano Josiane
Published: February 2023

This work aimed to use the electrospinning technique to obtain PBAT/PLA polymer fibers, with the semiconductors rutile titanium dioxide (TiO2-R) and magnetite iron oxide (Fe3O4), in order to promote the photocatalytic degradation of environmental contaminants. The parameters used in the electrospinning process to obtain the fibers were distance from the needle to the collecting target of 12 cm, flow of 1 mL h−1 and voltage of 14 kV. The best mass ratio of semiconductors in the polymeric fiber was defined from a 22 experimental design, and the values obtained were 10% TiO2-R, 1% Fe3O4 at pH 7.0. Polymer fibers were characterized by Scanning Electron Microscopy (SEM), Differential Scanning Calorimetry (DSC), X-ray Diffraction (XRD), Thermogravimetric Analysis (TGA) and Fourier Transform Infrared (FTIR) techniques. SEM measurements indicated a reduction in fiber diameter after the incorporation of semiconductors; for the PBAT/PLA fiber, the average diameter was 0.9466 ± 0.2490 µm, and for the fiber with TiO2-R and Fe3O4 was 0.6706 ± 0.1447 µm. In the DSC, DRX, TGA and FTIR analyses, it was possible to identify the presence of TiO2-R and Fe3O4 in the fibers, as well as their interactions with polymers, demonstrating changes in the crystallinity and degradation temperature of the material. These fibers were tested against Reactive Red 195 dye, showing an efficiency of 64.0% within 24 h, showing promise for photocatalytic degradation of environmental contaminants.
Progress in Microtopography Optimization of Polymers-Based Pressure/Strain Sensors
Authors: Sun Shouheng , Wang Zhenqin , Wang Yuting
Published: February 2023

Due to the wide application of wearable electronic devices in daily life, research into flexible electronics has become very attractive. Recently, various polymer-based sensors have emerged with great sensing performance and excellent extensibility. It is well known that different structural designs each confer their own unique, great impacts on the properties of materials. For polymer-based pressure/strain sensors, different structural designs determine different response-sensing mechanisms, thus showing their unique advantages and characteristics. This paper mainly focuses on polymer-based pressure-sensing materials applied in different microstructures and reviews their respective advantages. At the same time, polymer-based pressure sensors with different microstructures, including with respect to their working mechanisms, key parameters, and relevant operating ranges, are discussed in detail. According to the summary of its performance and mechanisms, different morphologies of microstructures can be designed for a sensor according to its performance characteristics and application scenario requirements, and the optimal structure can be adjusted by weighing and comparing sensor performances for the future. Finally, a conclusion and future perspectives are described.
Shortening the Standard Testing Time for Residual Biogas Potential (RBP) Tests Using Biogas Yield Models and Substrate Physicochemical Characteristics
Authors: Liu Yanxin , Guo Weisi , Longhurst Philip , Jiang Ying
Published: February 2023

The residual biogas potential (RBP) test is a procedure to ensure the anaerobic digestion process performance and digestate stability. Standard protocols for RBP require a significant time for sample preparation, characterisation and testing of the rig setup followed by batch experiments of a minimum of 28 days. To reduce the experimental time to obtain the RBP result, four biogas kinetic models were evaluated for their strength of fit for biogas production data from RBP tests. It was found that the pseudo-parallel first-order model and the first-order autoregressive (AR (1)) model provide a high strength of fit and can predict the RBP result with good accuracy (absolute percentage errors < 10%) using experimental biogas production data of 15 days. Multivariate regression with decision trees (DTs) was adopted in this study to predict model parameters for the AR (1) model from substrate physicochemical parameters. The mean absolute percentage error (MAPE) of the predicted AR (1) model coefficients, the constants and the RBP test results at day 28 across DTs with 20 training set samples are 4.76%, 72.04% and 52.13%, respectively. Using five additional data points to perform the leave-one-out cross-validation method, the MAPEs decreased to 4.31%, 59.29% and 45.62%. This indicates that the prediction accuracy of DTs can be further improved with a larger training dataset. A Gaussian Process Regressor was guided by the DT-predicted AR (1) model to provide probability distribution information for the biogas yield prediction.
Crude Oil Bioremediation: From Bacteria to Microalgae
Authors: Radice Rosa Paola , De Fabrizio Vincenzo , Donadoni Antonietta , Scopa Antonio , Martelli Giuseppe
Published: February 2023

Crude oil is one of the major pollutants present. Its extraction and processing generate processing waters contaminated by hydrocarbons which are harmful to both human health and the flora and fauna that come into contact with it. Hydrocarbon contamination can involve soil and water, and several technologies are used for recovery. The most used techniques for the recovery of spilt oil involve chemical-physical methods that can remove most of the pollutants. Among these, must consider the bioremediation by microorganisms, mostly bacterial capable of degrading many of the toxic compounds contained within the petroleum. Microalgae participate in bioremediation indirectly, supporting the growth of degrading bacteria, and directly acting on contaminants. Their direct contribution is based on the activation of various mechanisms ranging from the production of enzymes capable of degrading hydrocarbons, such as lipoxygenases, to the attack through the liberation of free radicals. The following review analyzed all the works published in the last ten years concerning the ability of microalgae to remove hydrocarbons, intending to identify in these microorganisms an alternative technology to the use of bacteria. The advantages of using microalgae concern not only their ability to remove toxic compounds and release oxygen into the atmosphere but their biomass could then be used in a circular economy process to produce biofuels.
Intensification of endo-1,4-Xylanase Extraction by Coupling Microextractors and Aqueous Two-Phase System
Authors: Božinović Marko , Vičević Renata , Zekić Nikolina , Šalić Anita , Jurinjak Tušek Ana , Zelić Bruno
Published: February 2023

The extraction of xylanase was performed using an aqueous two-phase system (ATPS) based on polyethylene glycol (PEG1540) and various salts. Preliminary studies in a batch extractor showed that the highest extraction efficiency, E = 79.63 ± 5.21%, and purification factor, PF = 1.26 ± 0.25, were obtained with sodium citrate dihydrate-H2O-PEG1540-based ATPS for an extraction time of 10 min. The process was optimized using the experimental Box-Behnken design at three levels with three factors: extraction time (t), xylanase concentration (γ), and mass fraction of PEG in the ATPS (wPEG). Under optimal process conditions (γ = 0.3 mg/mL, wPEG = 0.21 w/w, and t = 15 min), E = 99.13 ± 1.20% and PF = 6.49 ± 0.05 were achieved. In order to intensify the process, the extraction was performed continuously in microextractors at optimal process conditions. The influence of residence time, different feeding strategies, and channel diameter on extraction efficiency and purification factor was further examined. Similar results were obtained in the microextractor for a residence time of τ = 1.03 min (E = 99.59 ± 1.22% and PF = 6.61 ± 0.07) as in the experiment carried out under optimal conditions in the batch extractor. In addition, a batch extractor and a continuous microextractor were used for the extraction of raw xylanase produced by Thermomyces lanuginosus on solid supports.
Design Considerations of the DUCK Detector System
Authors: Beznosko Dmitriy , Aseykin Valeriy , Dyshkant Alexander , Iakovlev Alexander , Krivosheev Oleg , Krivosheev Tatiana , Zhukov Valeriy
Published: February 2023

The article describes the development, design, and upcoming construction and deployment of core modules of DUCK (Detector system of Unusual Cosmic-ray casKades), a cosmic-rays detector system aimed to verify and further study the latest advances in the cosmic-rays field and participate in the international collaborations searching for new types of events. The primary scientific goal for the DUCK project will be an independent verification of the detection of ‘unusual’ cosmic ray events by the Horizon-T detector system. A detailed study of events of this type is a vital step towards understanding the nature of cosmic rays, their origins, and details of interaction with the nuclei in the atmosphere. Further operations as part of the CREDO collaboration will contribute to the continued monitoring of the cosmic events. Additional intellectual value includes the design of the fast detection system with high timing resolution for cosmic events detection and the study of the temporal structure of extensive air showers that would also contribute to the current simulations. All the steps are conducted with student involvement and advance excellence in providing students with real research experience and competitive knowledge.
Early Childhood Teachers’ Confidence to Teach Religious Education and the Influences Which Impact Their Teaching of Religious Education in Catholic Primary Schools
Authors: Law-Davis Sharon
Published: February 2023

This article reports findings from a study that examines the factors which influence early childhood and care (ECEC) early career teachers’ confidence in teaching Religious Education (RE) and how it impacts their teaching of RE in Catholic primary schools in Western Australia. Teachers’ confidence is a teacher’s belief in their ability to accomplish a goal and refers to strength of belief. Research in this area has shown that confidence is correlated with the sense of teaching efficacy and responsibility for student learning. Whilst there has been some research into teacher confidence, there has been insufficient research into early childhood teachers’ confidence to teach RE in Catholic schools in an Australian context. Literature supports the notion that if early childhood teachers have a strong teacher confidence for a subject, they are more motivated to teach the subject and this has a positive correlation with positive student outcomes. The epistemological approach underpinning the research is constructivist in nature; therefore, it is based on understanding the constructed realities of what humans know of the world and themselves as produced by communications and systems of meaning. Three case studies follow teachers from their early career to second-year experience in Catholic schools. This study investigates support structures and aspects that contribute to teacher confidence in the teaching of RE. The main themes identified by the data that contributed to confidence or lack of confidence included training, family and religious backgrounds, teaching and learning, mentoring and support in the first year of teaching. The implications of the results for pre-service teacher training and support for graduate teachers are discussed and some suggestions are offered about the courses provided by universities and what schools and education systems can do to support early career teachers to teach RE effectively in Catholic schools.
Finding God in All Things: Indirect Evangelization and Acculturation of Université l’Aurore in Modern China
Authors: Ren Yi , Zhu Mingzhe
Published: February 2023

The current literature on Christian mission universities in Modern China (1840–1949) pays specific attention to their efforts to adapt to the intellectual and political context of their time. Through extensive archival works, we contribute to this research orientation by documenting the academic activities of the first Catholic university in China, Université l’Aurore (1903–1952) in Shanghai. Established and managed by the French Jesuits, Université l’Aurore exemplified the mission’s tradition of evangelization through science education. Its pedagogical arrangements, selection of teachers, and moral education showed high levels of professionalism and almost no religious influence. The Jesuits, who took as their motto, “to find God in all things”, believed that their scientific excellence could indirectly promote the Catholic spirit among future Chinese intellectuals and elites. Thanks to their strategy of indirect evangelization, not only did Université l’Aurore survive in a period when the government imposed draconic restrictions on mission entities in the name of “educational sovereignty”, it also contributed to the modernization of China’s education and society.
Benefits and Limitations of Indicators for Monitoring the Transformation towards a Circular Economy in Poland
Authors: Nowaczek Agnieszka , Dziobek Ewa , Kulczycka Joanna
Published: February 2023

More and more attention is paid to the circular economy (CE) and indicators that enable the monitoring and verification of the progress of transformation at various levels (the European Union, countries, regions, companies, etc.). Many analyses of CE indicators for different levels have been presented in the literature, but the benefits of their implementation and their limitations have not been discussed individually. The aim of the work is to identify and verify, based on the research conducted, CE indicators relating to sustainable production, along with their advantages and limitations. The article presents the selection process as well as the potential benefits and limitations of using indicators to assess the transformation towards a CE as identified for the Polish economy. The practical aim is to apply the indicators identified to different sectors of the economy. An important element of the work is the proposal for the process of selecting CE indicators that monitor the CE transformation based on their advantages and limitations, taking into account the goals and obligations set out in the main strategic documents of Poland and the EU, the process of consultations with industry, and the structure of the economy.
Sentinel-1 Response to Canopy Moisture in Mediterranean Forests before and after Fire Events
Authors: Pirotti Francesco , Adedipe Opeyemi , Leblon Brigitte
Published: February 2023

This study investigates the sensibility of Sentinel-1 C-band backscatter to the moisture content of tree canopies over an area of about 500 km2 in north-western Portugal, with specific analysis over burnt areas. Sentinel-1 C-VV and C-VH backscatter values from 276 images acquired between January 2018 and December 2020 were assigned to five classes depending on the Drought Code (DC) scenario over several unburned and burned sites with total (>90%) forest canopy cover. Confounding variables such as tree cover and incidence angle were accounted for by masking using specific thresholds. The following results are discussed: (a) C-VV and C-VH backscatter values are inversely correlated (R2 = 0.324 to 0.438 −p < 0.001) with local incidence angle over canopies; (b) correlation is significantly stronger over very wet scenarios (DC class = 0 to 1); (c) C-VV and C-VH backscatter values can discriminate wet to dry forest environments, but they are less sensitive to the transition between dry (DC classes = 1 to 10, 10 to 100) and extremely dry environments (DC classes = 100 to 1000); (d) C-VH is more sensible than C-VV to capture burnt canopy; and (e) the C-VH polarization captures post-fire recovery after an average minimum period of 360 days after the fire event, although with less distinction for extremely wet soils. We conclude that C-band VH backscatter intensity decreases from wet to dry canopy conditions, that this behavior of the backscatter signal with respect to canopy dryness is lost after a fire event, and that after one year it is recovered.
Remote Sensing Image Segmentation Based on Hierarchical Student’s-t Mixture Model and Spatial Constrains with Adaptive Smoothing
Authors: Shi Xue , Wang Yu , Li Yu , Dou Shiqing
Published: February 2023

Image segmentation is an important task in image processing and analysis but due to the same ground object having different spectra and different ground objects having similar spectra, segmentation, particularly on high-resolution remote sensing images, can be significantly challenging. Since the spectral distribution of high-resolution remote sensing images can have complex characteristics (e.g., asymmetric or heavy-tailed), an innovative image segmentation algorithm is proposed based on the hierarchical Student’s-t mixture model (HSMM) and spatial constraints with adaptive smoothing. Considering the complex distribution of spectral intensities, the proposed algorithm constructs the HSMM to accurately build the statistical model of the image, making more reasonable use of the spectral information and improving segmentation accuracy. The component weight is defined by the attribute probability of neighborhood pixels to overcome the influence of image noise and make a simple and easy-to-implement structure. To avoid the effects of artificially setting the smoothing coefficient, the gradient optimization method is used to solve the model parameters, and the smoothing coefficient is optimized through iterations. The experimental results suggest that the proposed HSMM can accurately model asymmetric, heavy-tailed, and bimodal distributions. Compared with traditional segmentation algorithms, the proposed algorithm can effectively overcome noise and generate more accurate segmentation results for high-resolution remote sensing images.
Observed Quasi 16-Day Wave by Meteor Radar over 9 Years at Mengcheng (33.4°N, 116.5°E) and Comparison with the Whole Atmosphere Community Climate Model Simulation
Authors: Yang Chengyun , Lai Dexin , Yi Wen , Wu Jianfei , Xue Xianghui , Li Tao , Chen Tingdi , Dou Xiankang
Published: February 2023

In this study, we present nearly 9 years of the quasi16-day wave (Q16DW) in the mesosphere and lower thermosphere (MLT) wind at middle latitudes based on long-term wind observations between April 2014 and December 2022 by the Mengcheng (33.4°N, 116.5°E) meteor radar. There are two maxima in the Q16DW amplitude in the winter and early spring (near the equinox) and a minimum during the summer. The Q16DWs are relatively weak in meridional winds with no obvious seasonal variations. On average, the phase of the zonal Q16DW is larger than the meridional components with a mean difference that is slightly less than 90°, which suggests that there are orthogonal relationships between them. During the bursts of Q16DW, the periods in winter range between 15 and 18 d, whereas in summer, the periods of the planetary waves have a wider range. The wintertime Q16DW anomalies are, on average, amplified when the zonal wind shear anomalies increase, suggesting that barotropic instability may be a source of the Q16DW. Although the interannual variability of Q16DW amplitudes has been suggested observationally, there is no significant relationship between the interannual wind shear variability and Q16DW at most altitudes.
Integrated HBIM-GIS Models for Multi-Scale Seismic Vulnerability Assessment of Historical Buildings
Authors: Sammartano Giulia , Avena Marco , Fillia Edoardo , Spanò Antonia
Published: February 2023

The complexity of historical urban centres progressively needs a strategic improvement in methods and the scale of knowledge concerning the vulnerability aspect of seismic risk. A geographical multi-scale point of view is increasingly preferred in the scientific literature and in Italian regulation policies, that considers systemic behaviors of damage and vulnerability assessment from an urban perspective according to the scale of the data, rather than single building damage analysis. In this sense, a geospatial data sciences approach can contribute towards generating, integrating, and making virtuous relations between urban databases and emergency-related data, in order to constitute a multi-scale 3D database supporting strategies for conservation and risk assessment scenarios. The proposed approach developed a vulnerability-oriented GIS/HBIM integration in an urban 3D geodatabase, based on multi-scale data derived from urban cartography and emergency mapping 3D data. Integrated geometric and semantic information related to historical masonry buildings (specifically the churches) and structural data about architectural elements and damage were integrated in the approach. This contribution aimed to answer the research question supporting levels of knowledge required by directives and vulnerability assessment studies, both about the generative workflow phase, the role of HBIM models in GIS environments and toward user-oriented webGIS solutions for sharing and public use fruition, exploiting the database for expert operators involved in heritage preservation.
A Seagrass Mapping Toolbox for South Pacific Environments
Authors: Bremner Julie , Petus Caroline , Dolphin Tony , Hawes Jon , Beguet Benoît , Devlin Michelle J.
Published: February 2023

Seagrass beds provide a range of ecosystem services but are at risk from anthropogenic pressures. While recent progress has been made, the distribution and condition of South Pacific seagrass is relatively poorly known and selecting an appropriate approach for mapping it is challenging. A variety of remote sensing tools are available for this purpose and here we develop a mapping toolbox and associated decision tree tailored to the South Pacific context. The decision tree considers the scale at which data are needed, the reason that monitoring is required, the finances available, technical skills of the monitoring team, data resolution, site safety/accessibility and whether seagrass is predominantly intertidal or subtidal. Satellite mapping is recommended for monitoring at the national and regional scale, with associated ground-reference data where possible but without if time and funds are limiting. At the local scale, satellite, remotely piloted aircraft (RPA), kites, underwater camera systems and in situ surveys are all recommended. In the special cases of community-based initiatives and emergency response monitoring, in situ or satellite/RPA are recommended, respectively. For other types of monitoring the primary driver is funding, with in situ, kite and satellite recommended when finances are limited and satellite, underwater camera, RPA or kites otherwise, dependent on specific circumstances. The tools can be used individually or in combination, though caution is recommended when combining tools due to data comparability.
Deep Learning-Based Emulation of Radiative Transfer Models for Top-of-Atmosphere BRDF Modelling Using Sentinel-3 OLCI
Authors: Ojaghi Saeid , Bouroubi Yacine , Foucher Samuel , Bergeron Martin , Seynat Cedric
Published: February 2023

The Bidirectional Reflectance Distribution Function (BRDF) defines the anisotropy of surface reflectance and plays a fundamental role in many remote sensing applications. This study proposes a new machine learning-based model for characterizing the BRDF. The model integrates the capability of Radiative Transfer Models (RTMs) to generate simulated remote sensing data with the power of deep neural networks to emulate, learn and approximate the complex pattern of physical RTMs for BRDF modeling. To implement this idea, we used a one-dimensional convolutional neural network (1D-CNN) trained with a dataset simulated using two widely used RTMs: PROSAIL and 6S. The proposed 1D-CNN consists of convolutional, max poling, and dropout layers that collaborate to establish a more efficient relationship between the input and output variables from the coupled PROSAIL and 6S yielding a robust, fast, and accurate BRDF model. We evaluated the proposed approach performance using a collection of an independent testing dataset. The results indicated that the proposed framework for BRDF modeling performed well at four simulated Sentinel-3 OLCI bands, including Oa04 (blue), Oa06 (green), Oa08 (red), and Oa17 (NIR), with a mean correlation coefficient of around 0.97, and RMSE around 0.003 and an average relative percentage error of under 4%. Furthermore, to assess the performance of the developed network in the real domain, a collection of multi-temporals OLCI real data was used. The results indicated that the proposed framework has a good performance in the real domain with a coefficient correlation (R2), 0.88, 0.76, 0.7527, and 0.7560 respectively for the blue, green, red, and NIR bands.
Electromagnetic Scattering and Doppler Spectrum Simulation of Land–Sea Junction Composite Rough Surface
Authors: Chai Shui-Rong , Zhu Fang-Yin , Li Juan , He Zhen-Xiang , Zou Yu-Feng , Wei Yi-Wen , Li Ke , Guo Li-Xin , Li Long
Published: February 2023

In this paper, a weighted arctangent function is used in conjunction with the spectral method to generate a land–sea junction composite rough surface under the spatially homogeneous and time-stationary hypotheses. The exponential correlation function and the Joint North Sea Wave Project (JONSWAP) spectrum, combined with an experiment-verified shoaling coefficient, are applied to model the land surfaces and the time-varying sea surfaces separately. The second-order small slope approximation (SSA-II) with tapered wave incidence is utilized for evaluating the electromagnetic scattering characteristics and Doppler characteristics of the generated composite rough surface. The influence of land–sea interface factors on radar cross-section (RCS) and Doppler shift of radar echoes is investigated in detail by comparing the RCS and Doppler spectra of the land–sea junction composite rough surfaces with those of finite-depth sea surfaces. It can be found that the Doppler spectra of the land–sea junction composite rough surface is narrower than that of the finite-depth sea surface under upwind directions and wider than that of the finite-depth sea surface under crosswind directions.
Public Acceptance of the Use of Drones in City Logistics: A Citizen-Centric Perspective
Authors: Melo Sandra , Silva Flavia , Abbasi Mohammad , Ahani Parisa , Macedo Joaquim
Published: February 2023

The specific use of drones for city logistics has been increasingly studied and analysed by research and industry. An examination of the findings in the literature indicates that drones have proven to be a useful and added-value tool in the most diverse fields. However, the importance of the citizen’s perspective has still not been sufficiently incorporated into the deployment of urban air mobility systems. This paper seeks to contribute to a better understanding of the interaction between public knowledge and the awareness of, and engagement with, drones, alongside the concerns and support for their use in city logistics. A survey was carried out in Portugal of the citizens with a view to better understanding their attitude towards such a goal. The survey revealed a positive attitude towards the use of drones in city logistics and that socio-demographic characteristics, namely gender, education level, job occupation, age, and home location are not directly correlated with citizens’ attitudes. Moreover, citizens revealed that they favour a potential environmental benefit over a reduction in delivery time, which they would be willing to pay for. The policy implications derived can help develop the knowledge of public perception about drone usage for transport-related tasks.
A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots
Authors: Wang Weiwei , Zhang Biao , Jia Baoxian
Published: February 2023

Hybrid flowshop scheduling problems are encountered in many real-world manufacturing scenarios. With increasingly fierce market competition, the production mode of multiple varieties and small batches has gradually been accepted by enterprises, where the technology of lot streaming is widely used. Meanwhile, green criteria, such as energy consumption and carbon emissions, have attracted increasing attention to improving protection awareness. With these motivations, this paper studies a multiobjective hybrid flowshop green scheduling problem with consistent sublots (MOHFGSP_CS), aiming to minimize two objectives, i.e., makespan and total energy consumption, simultaneously. To solve this complex problem, we first formulate a novel multiobjective optimization model. However, due to the NP-hard nature of the problem, the model is computationally prohibitive as the problem scale increases. Thus, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is proposed. There are three phases in this algorithm: the VND-based employed bee phase, the adjustment weight onlooker bee phase, and the population interaction scout bee phase. In the experimental study, various small-scale and large-scale instances are collected to verify the effectiveness of the multiobjective optimization model and the MDABC. Comprehensive computational comparisons and statistical analysis show that the developed strategies and MDABC show superior performance.
Controlling Arsenic Accumulation in Rice Grain under Nanomaterials-Assisted Optimal Greenhouse Set-Up
Authors: Uda Muhammad Nur Aiman , Gopinath Subash C. B. , Hashim Uda , Uda Muhammad Nur Afnan , Adam Tijjani , Parmin Nor Azizah , Subramaniam Sreeramanan , Chinni Suresh V. , Lebaka Veeranjaneya Reddy , Gobinath Ramachawolran
Published: February 2023

Rice is being increasingly exposed to inorganic arsenic and this affects half of the world population because they are rice consumers. In this study, pot experiments were carried out to investigate the effect of two dose-dependent nanomaterials (silica and graphene) treatment on varied arsenic levels (2, 7 and 12 mg/kg). The results showed that both nanomaterials were affected significantly with 1 mg/mL of nanomaterial. Arsenic adversely affected the plant height, tillering, number of grains, and grain weight and when high concentrations of arsenic were applied at 12 mg/kg, the plant could not withstand it and died before 75 days even in the presence of graphene. Based on inductively coupled plasma mass spectrometry analysis, silica nanoparticles showed the highest inhibition on the total accumulation of arsenic as 93% (control plant), 84% (2 mg/kg), 67% (7 mg/kg) to 35 % (12 mg/kg), whereas graphene showed lower inhibition percentages. This outcome confirms that silica nanoparticles prevent arsenic uptake, because they translocate from the root to the grains and are able to offer a promising way to reduce consumer health risk.
Re-Thinking Industry 4.0 Effect on Competitive Forces: Empirical Study on Innovation
Authors: Kohnová Lucia , Salajová Nikola
Published: February 2023

Sustaining a competitive position on the market has become crucial in order to survive in the dynamic environment under the influence of Industry 4.0. Industrial and service sectors have been affected by new technologies, such as automation, robotization, VR, AR or Big data, and their strategies towards future innovation and focus need to be reevaluated. This requires a revision of theoretical models that assess the external environment and following opportunities and threats. Our original study of 573 Slovak companies presents an analysis of Porter’s five competitive forces in the context of innovation activities and perceived impact of Industry 4.0 on companies. The research results provide a deep insight on the relevance of company size, maturity or sector to the different perspectives of the P5F model, as well as make clear the impact of each force on company measures and activities conducted in recent years. Our findings point to the strong technology push and main focus on product innovation, while reveal a paradigm shift in perceiving the power of suppliers. Results of our study lead to practical implications and enrich the theory on the relevance of strategic analysis in current era.
Impact of COVID-19 on Smallholder Aquaculture Farmers and Their Response Strategies: Empirical Evidence from Bangladesh
Authors: Alam G. M. Monirul , Sarker Md Nazirul Islam , Kamal Md Abdus Samad , Khatun Most Nilufa , Bhandari Humnath
Published: February 2023

Bangladesh’s aquaculture sector has contributed progressively to the nation’s economy over the years, but the COVID-19 pandemic has impeded fish farmers’ access to markets, reduced their production and sales capacity, resulted in lower income, and increased food security vulnerability. This study assesses how COVID-19 affects smallholder fish farmers and their response strategies by employing data collected from 250 fish farmers and traders from intensive fish-growing areas of Bangladesh. The results reveal that most farmers experienced difficulty obtaining inputs, and the price of those inputs skyrocketed during the COVID-19 period, resulting in several months of decreased production and operations. As a result of COVID-19, farm gate prices for silver carp, ruhu, common carp, grass carp, and tilapia fish dropped by 25%, 23%, 23%, 22%, 23%, and 40%, respectively. On the other hand, fish feed prices were found to increase significantly. Reduced income from fish farming and other sources has triggered a significant drop in capital for farming operations and production capacity improvement, leading to food insecurity. The most common coping strategies include reduced buying from the market (vegetables, fruits, meat, milk, etc.), relying on less expensive or less preferred food, purchasing food on credit, and selling assets. Notably, due to COVID-19, a new mode of marketing has evolved as an adaptation strategy in the fish marketing system, such as the use of the mobile phone (18%) and Facebook/internet to sell fish directly to the customer (16%). The sector requires short-term financial assistance to assist fish actors with production and marketing challenges.
The Impact of Officials’ Off-Office Accountability Audit of Natural Resource Assets on Firms’ Green Innovation Strategies: A Quasi-Natural Experiment in China
Authors: Chen Xihui , Ou Juan , Tang Xuemei , Yang Qinghe
Published: February 2023

It is important to assess the factors that affect firms’ strategies for environmental improvement. Taking China’s pilot of officials’ off-office accountability audit of natural resource assets (hereafter accountability audit) that commenced in 2014 as an exogenous shock to government audits of leading officials, we use a difference-in-differences method (DID) to examine the impact of government audits on firms’ green innovation strategies. Our results show that the accountability audit increases the proximity between firms’ previous and present green innovation fields and enhances incremental rather than radical green innovation. Furthermore, these influences are stronger in the case of pressure from local governments for firms to adopt environmental protection measures, government control of firms, and market performance pressures than in other cases. In addition, the accountability audit drives investment in environmental protection toward green innovation in existing fields. Finally, the accountability audit increases firms’ economic value added and disclosure of social responsibility information. Overall, our study provides evidence that firms conduct similar and known green innovations in response to government audits.
Modify the Injection Machine Mechanism to Enhance the Recycling of Plastic Waste Mixed with MHD Nanoparticles
Authors: Abed Ahmed M. , AlArjani Ali , Seddek Laila F. , ElAttar Samia
Published: February 2023

Recycling plastic waste (RPW) benefits the ecological footprint. Therefore, the authors test its mixing by magnetic hydrodynamic MHD nanofluid materials such as alumina in two sizes (βcore, βskin) prepared into a new device called the incubator installed in the desktop injection machine to enhance its solubility by taking advantage of the mixture’s heat via defining the oblique stagnation-point slip flow (OSPSF) of a nanofluid in two dimensions. The paper has been innovative in mathematically identifying the operating parameters’ values for the injection flow mechanism (IFM) via controlling in Riga magnetic field and piston orifice pressure using a meta-heuristic algorithm called WSA. The proposed (IFM) is used to experimentally enhance the mixture properties via parameters’ control to meet the output quality and predict the control equation for the Riga plate. IFM controls the amount of pushed nanoparticles in the mixture with a ratio of plastic to aluminum approximate by 96.1%: 3.9%. The defects were reduced by approximately 23.21%, with an increasing system performance of 70.98%.
Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model
Authors: Wang Xianbin , Zhao Yuqi , Li Weifeng
Published: February 2023

In this paper, we propose a multilayer perceptron-based recognition method for driving cycles of commercial vehicles. Our method solves the problem of identifying the type of driving cycle for commercial vehicles, and improves the efficiency and sustainability of road traffic. We collect driving condition data of 106,200 km long-distance commercial vehicles to validate our method. We pre-proceed six kinds of quantitative features as the data description; these are average speed, gear ratio, and accelerator pedal opening. Our model includes an input layer, hidden layers, and an output layer. The input layer receives and processes the input as low-dimensional features. The hidden layers consist of the feature extraction module and class regression module. The output layer projects extracted features to the classification space and computes the likelihood for each type. We achieve 99.83%, 97.85%, and 99.40% on the recognition accuracy for the expressway driving cycle, the suburban road driving cycle, and the urban road driving cycle, respectively. The experimental results demonstrate that our model achieves better results than the statistical method using Naive Bayes. Moreover, our method utilizes the data more efficiently and thus gains a better generalization performance.
Leveraging Digital Twins to Support Industrial Symbiosis Networks: A Case Study in the Norwegian Wood Supply Chain Collaboration
Authors: Liu Zhenyuan , Hansen Daniel Wilhelm , Chen Ziyue
Published: February 2023

Despite the powerful potentials of digital twins as regards achieving sustainable operations and supply chain management, there is currently very little research on using digital twins for industrial symbiosis, and even less research investigating user needs. Therefore, it is necessary to conduct sufficient research on the market and user needs before setting the framework of digital twins for industrial symbiosis. We interviewed six companies in the Norwegian wood industry that could potentially share one symbiosis network. Based on the interviews, we analyzed the needs of potential digital twins for industrial symbiosis, aiming to understand the user’s point of view on digital twins for industrial symbiosis. The research is expected to provide intellectual support for future digital twins’ design from the user perspective. This paper not only promotes the design of digital twins for industrial symbiosis from the user perspective, but also provides an analytical framework for the user perspective analysis before the development of digital twins-based supply chain collaboration in the industrial symbiosis network.
Research on the Detection and Measurement of Roughness of Dam Concrete Layers Using 3D Laser Scanning Technology
Authors: Hu Nuo , Chen Zhuo , Ma Rui , Liu Haiyang
Published: February 2023

The roughness of the interlayer concrete, which is an important index for the construction of dams, affects the mechanical properties of the interface between new and old concrete. The traditional monitoring method cannot satisfy field measurements. Therefore, this paper proposes a concrete surface roughness measurement method based on 3D laser scanning technology and implements 3D point cloud calculations. Two layers of specimens with same concrete mix proportion were poured 28 days apart, and a splitting tensile strength test of the superimposed specimen was carried out. Four groups of experiments (MR-S, MR-N, MR-15, and MR-35) tested the interlaminar tensile splitting strength of nine 150 mm cubic specimens on the 3rd, 7th, 14th, and 28th days, respectively. Filling volume, mean amplitude, surface area expansion rate, normal angle std. dev., and curvature std. dev. were measured in the study. The results showed that as the surface roughness of the concrete increased, as did the flushing pressure. The splitting strength of the concrete interface after it solidified increased with age. At 28 days, the splitting strength of MR-15 was higher than that of MR-35. The mean amplitude and the normal angle std. dev. of the rough surface could replace the traditional filling volume index. The new 3D point cloud calculation method for calculating concrete surface roughness was proven by experiments to have good performance.
Three-Dimensional Modeling and Analysis of Virtual Test Pavements for Automotive Test Sites
Authors: Jin Hongjie , Dong Zhonghong , Zang Liguo , Li Yaowei
Published: February 2023

The three-dimensional modeling and analysis of virtual pavement are conducted for the complex and diverse test pavements in the vehicle reliability driving test. Firstly, a complete pavement unevenness acquisition system is constructed, and secondly, the classification and processing methods of different pavements are determined according to the actual working conditions and load spectrum data of the test pavement in the test site to provide a theoretical basis for the virtual pavement modeling. At the same time, considering that the test pavement also includes various complex pavement conditions such as curves, slopes and lateral inclination, the structural composition and differences of plane curves, longitudinal ramps and cross-sections are analyzed to provide a guarantee for the further establishment of an accurate 3D pavement model. Finally, the 3D virtual pavement model of typical test pavements is established according to the theory and methods of the above research, and the accuracy of random pavement reconstruction is analyzed, which provides accurate and effective technical support for the simulation of automobile reliability driving tests.