bluuebunny/crossref_metadata_2025 huggingface dataset:
The Optimized Preparation Conditions of Cellulose Triacetate Hollow Fiber Reverse Osmosis Membrane with Response Surface Methodology
Authors: Yang Shu , Chen Kaikai , Xiang Hongming , Wang Yingwen , Huang Chenyan
Published: August 2023

Reverse osmosis (RO) membrane materials play a key role in determining energy consumption. Currently, CTA is regarded as having one of the highest degrees of chlorine resistance among materials in the RO process. The hollow fiber membrane has the advantages of a large membrane surface area and a preparation process without any redundant processes. Herein, response surface methodology with Box–Behnken Design (BBD) was applied for optimizing the preparation conditions of the cellulose triacetate (CTA) hollow fiber RO membrane. There were four preparation parameters, including solid content, spinning temperature, post-treatment temperature, and post-treatment time, which could affect the permeability of the membrane significantly. In this study, the interaction between preparation parameters and permeability (permeate flux and salt rejection) was evaluated by regression equations. Regression equations can be applied to obtain the optimized preparation parameters of hollow fiber RO membranes and reasonably predict and optimize the permeability of the RO membranes. Finally, the optimized preparation conditions were solid content (44%), spinning temperature (167 °C), post-treatment temperature (79 °C), and post-treatment time (23 min), leading to a permeability of 12.029 (L·m−2·h−1) and salt rejection of 90.132%. This study of reinforced that CTA hollow fiber membrane may promote the transformation of the RO membrane industry.
Research on Metallurgical Saw Blade Surface Defect Detection Algorithm Based on SC-YOLOv5
Authors: Meng Lili , Cui Xi , Liu Ran , Zheng Zhi , Shao Hongli , Liu Jinxiang , Peng Yao , Zheng Lei
Published: August 2023

Under the background of intelligent manufacturing, in order to solve the complex problems of manual detection of metallurgical saw blade defects in enterprises, such as real-time detection, false detection, and the detection model being too large to deploy, a study on a metallurgical saw blade surface defect detection algorithm based on SC-YOLOv5 is proposed. Firstly, the SC network is built by integrating coordinate attention (CA) into the Shufflenet-V2 network, and the backbone network of YOLOv5 is replaced by the SC network to improve detection accuracy. Then, the SIOU loss function is used in the YOLOv5 prediction layer to solve the angle problem between the prediction frame and the real frame. Finally, in order to ensure both accuracy and speed, lightweight convolution (GSConv) is used to replace the ordinary convolution module. The experimental results show that the mAP@0.5 of the improved YOLOv5 model is 88.5%, and the parameter is 31.1M. Compared with the original YOLOv5 model, the calculation amount is reduced by 56.36%, and the map value is increased by 0.021. In addition, the overall performance of the improved SC-YOLOv5 model is better than that of the SSD and YOLOv3 target detection models. This method not only ensures the high detection rate of the model, but also significantly reduces the complexity of the model and the amount of parameter calculation. It meets the needs of deploying mobile terminals and provides an effective reference direction for applications in enterprises.
Lipid Fractionation and Physicochemical Characterization of Carapa guianensis Seed Oil from Guyana
Authors: James Stacy O. , Bouzidi Laziz , Emery R. J. Neil , Narine Suresh S.
Published: August 2023

The seed oil of Carapa guianensis, known as crabwood oil (CWO), is distinguished for its medicinal and cosmetics applications, attributed to its bioactive components and lipid profile. CWO and its dry and solvent fractionation were studied, with a focus on physicochemical functionality and the partitioning of known bioactive compounds, such as limonoids and sterols. Important bioactive components, including limonoids and sterols, were partitioned depending on the fractionation method; in particular, there is a direct dependence on solvent polarity. There was a very strong solid fraction yield–solvent polarity with a high linear slope of −121.3%. The partitioning of the lipids is significant enough to drive measurable and predictable changes in the physical properties. Palmitic (P: C16:0) and oleic (O: C18:1) fatty acids account for about 60% of the total fatty acid composition of the TAGs of CWO and its fractions. The most abundant limonoid is methyl angolensate (from 28 to 39%), followed by Trichilin A (from 13% to 22%). Gedunin and Andirobin were more abundant in the liquid fractions, whereas Carapanolides (less than 1.3%) were more present in the olein fractions. The crystallization and melting temperatures of the solid fractions were up to 26 °C, compared to 11 °C for CWO, and were particularly strongly correlated to the polarity of the solvents. The SFC profile indicated semi-solid fats, with the solid fractions showing up to 19% at 18 °C, twice the SFC in CWO. The fractions demonstrated a wide range of distinguishable microstructures. The shapes include well-organized spherulites and needle-like and rod-like crystals with sizes varying from 5 to 250 µ, suggesting that they are likely to have different flow characteristics and feel to the skin and mouth. There is a potential to make unique compositions with significantly different properties, with antimicrobial and antifungal efficacy due to the bioactive components of CWO through fractionation, using polarity as a predictive tool.
The Recovery of TiO2 from Ilmenite Ore by Ammonium Sulfate Roasting–Leaching Process
Authors: Abdelgalil Mahmoud S. , El-Barawy K. , Ge Yang , Xia Longgong
Published: August 2023

TiO2 production is a key part of Ti metallurgy and Ti recycling, and the process itself has turned out to be energy-consuming and material-consuming. New technologies are needed to utilize complex Ti ores, such as ilmenite, and reduce the carbon footprint of TiO2 extraction. Ammonium sulfate roasting has been revealed as an efficient way to carry out phase transformations of complex minerals. A low-temperature sulfation roasting approach was studied to chemically breaking down the crystal structure of ilmenite and generate metal soluble sulfates simultaneously. These roasted products were introduced to water leaching, then the residue of the water leaching was leached by diluted HCl acid, and the TiO2 product was enriched in the leaching residue. The effects of roasting temperature, roasting time, ilmenite-to-ammonium sulfate mass ratio, ilmenite particle size, and second-stage roasting on iron removal and titanium loss leaching efficiency were systematically studied. The results show that the optimum roasting conditions were a roasting temperature of 500 °C, a roasting time of 210 min, an ilmenite-to-(NH4)2SO4 mass ratio of 1:7, and an ilmenite particle size of below 43 µm. Under optimized conditions, the TiO2 grade in the obtained synthetic rutile reached 75.83 wt.%. Furthermore, the phase transformation and reaction mechanism during roasting are discussed and interpreted.
Separation of VOC Gaseous Mixtures Using an Adsorption–Desorption Device
Authors: Gabrišová Ľudmila , Macho Oliver , Juriga Martin , Gábrišová Zuzana , Valent Ivan , Kabát Juraj , Blaško Jaroslav , Kubinec Róbert
Published: August 2023

The aim of this work was to separate ethanol in an experimental adsorption–desorption device. We focused on concentrating ethanol by adsorption onto granulated activated carbon in its gaseous phase, which was produced by stripping a matrix with different ethanol concentrations (2, 5, 10, and 15% v/v). For adsorption, three kinds of granulated activated carbon (GAC) were used, marked as GAC1, GAC2, and GAC3. The separation product had a higher ethanol concentration than the initial ethanol concentration before the adsorption–desorption process. The enrichment factor was, in the case of the initial ethanol concentration, 2% v/v at the level of 10. With our new adsorption–desorption device, it is possible to achieve a product with an ethanol concentration of 59.0% v/v with stripping, adsorption, desorption, and condensation. To verify the separation efficiency, a real matrix (fermentation broth) was used. The ethanol concentration in the real matrix was, at the beginning of the separation process, 0.65% v/v; after using our separation device, it was 11.35% v/v. Using a real matrix, the enrichment factor was at the level of 18. The main advantage is the use of our new adsorption–desorption device for the continuous separation of ethanol from fermentation broth. A mathematical model was created, based on which it is possible to calculate the ethanol concentration in the product of the separation process with high accuracy.
Study on Efficient Removal Method of Fine Particulate Dust in Green Metallurgy Process
Authors: Li Haiying , Xue Hairui , Zhang Junya , Zhang Guijie
Published: August 2023

In order to solve the problem of the low removal efficiency of fine particles in the flue gases of the metallurgy process, a chemical agglomeration pretreatment method was studied. The coagulant solution of xanthan gum, konjac gum, and their mixtures was selected to research the reunion effects of and the efficiency of gravitational dust removal of fine dust in the gas of the converter flue using a self-built experimental platform. Moreover, the effects of wetting agent type, dust concentration, pressure, and flue gas velocity on the fine grain removal efficiency were investigated. The results showed that the mixed solution of 1 g/L mixed gum and 0.5 g/L SDS had the most obvious effect on the particle size increasing of fine dust particles and the best removal effect when the flue gas velocity was 10 m/s. There was a peak particle size of 85.32 μm increased about eight times larger, and the removal efficiencies reached 51.46% for PM2.5 and 53.13% for PM10. The Box–Behnken experimental design combined with a response surface analysis method was used to optimize the parameters of the mixed gum concentration, pressure, and flue gas velocity. The optimal removal conditions were 1 g/L, 0.4 MPa, and 10 m/s. The results of this study can provide efficient methods and technical support for pre-processing and efficient removal of fine particles in heavy-polluting industries such as steel making. This will promote the green development of the metallurgical industry.
Study of the Effect of Modification of Asphalt on the Rheological Properties Employing Microwave Radiation—An Aging Study
Authors: Owaid Khalid Ahmed , Ghazal Raghed Y. , Abdelzaher M. A.
Published: August 2023

This study focuses on producing asphalt with improved rheological properties that differ from the original asphalt and are less affected by aging conditions. The rheological properties of Qayara asphalt were enhanced by modifying the asphalt using spent rubber tire (SRT) with different percentages of anhydrous aluminum chloride. Percentages ranging from 1.0% by weight of the spent tire rubber were added after proceeding with the thermal crushing process. The percentages of anhydrous aluminum chloride catalyst were 0.4 and 0.8%, respectively. This mixture was microwaved at 270 watt of power for 4, 8, and 12 min, respectively. The measurements performed are plasticity, penetration, softening point, and penetration index. The previously mentioned measurements were also made on the modified asphalt one year after the modification process to understand the effect of aging conditions. The microstructure and thermodynamics have been characterized by FE-SEM and EDX measurements. This study provides good rheological properties of the modified bitumen binder that is aging-resistant.
Religious Heritage Complex and Authenticity: Past and Present Assemblages of One Cypriot Icon
Authors: Zaprzalska Dorota
Published: August 2023

Drawing on data from ethnographic fieldwork, this study focuses on an icon known as Panagia Amirou kept at the Amirou Monastery in Cyprus. Instead of analyzing its unusual form, consisting of two icons from different times inserted one into another, this paper uses the concept of “religious heritage complex” to understand the special status of the icon, the complexity of the assemblages it has joined over time, and the relationships between religion and heritage. The Panagia Amirou icon has become the symbol of the monastery and appears to be crucial for the creation of a shared identity by giving the community the feeling of a connection to the past and continuity of monastic tradition, for as they believe, the icon and the monastery are from the same time. Particular attention is paid to the process of authenticating the icon’s special status. The legend recounts not only the icon’s old age but also its miraculous appearance and the healing power inherent in it from the very beginning, so it authenticates, together with processions, not only the alleged connection of the icon with the beginning of the monastery but also the status of the icon as being miracle-working.
Revivalism and Decoloniality: The Paradox of Modernization without Westernization in the Political Theology of Israr Ahmad
Authors: Rehman Mohammad Adnan
Published: August 2023

This article explores the contribution of modern Muslim revivalism to Muslims’ political decolonization, and the paradoxical role the West plays in that process. On the one hand, revivalism rejects the founding principles of liberal political theory, and on the other hand, it readily adopts the salient structures and mechanisms of the modern polity with a view to Islamize them, all the while insisting on the Muslims’ need to de-Westernize. Toward revealing the hitherto neglected dimensions of revivalism, my analysis adopts an unconventional route by subjecting revivalism to a semiotic analysis in conversation with the archetypal theories of Mircea Eliade and Carl G. Jung. The analysis unveils the universal psychological structures of revival, and their specific Muslim symbolization. I conclude (a) that depth psychology makes modern Muslim revival inevitable, which will only grow stronger and gain wider appeal while the Muslims continue to suffer decline; (b) that among the different forms of Muslim revival, revivalism ventures the farthest in decolonizing Muslim political imagination; (c) that the revivalist imagination makes their espoused caliphate imperative for the purpose of ritual participation in Islam’s sacred origins; and (d) that a critical reconstruction and evolution of revivalism holds out the promise of a greater contribution to Muslim decolonization. For my analysis, I largely turn to the Pakistani political theologian Israr Ahmad (d. 2010), whose ideas have been disseminated widely across the Muslim world, yet who has not received the requisite academic scrutiny. Moreover, intra-revivalist critique of revivalism has been a neglected aspect in the study of revival, and its careful scrutiny should become a topic of investigation in its own right. In that regard, Ahmad offers a most important critique of earlier revival efforts and their entanglement with certain aspects of coloniality.
Optimal Cyber Security Investment in a Mixed Risk Management Framework: Examining the Role of Cyber Insurance and Expenditure Analysis
Authors: Mazzoccoli Alessandro
Published: August 2023

Cyber security importance has escalated globally, driven by its pivotal role in shaping daily life, encompassing both personal and non-personal aspects. Cyber security breach probability functions play a crucial role in comprehending how cyber security investments affect vulnerability to cyber attacks. These functions employ mathematical models to guide decision making in cyber risk management. Thus, studying and improving them is useful in this context. In particular, using these models, this article explores the effectiveness of an integrated risk management strategy that merges insurance and security investments, aiming to minimize overall security expenses. Within this strategy, security investments contribute to reducing the insurance premium. This research investigates the optimal investment for this blended approach under total insurance coverage. When the integrated risk management strategy combining insurance and security investments is deemed the optimal choice, this paper reveals that the insurance premium tends to be the dominant component in the overall security expense in the majority of cases. This implies that the cost of insurance outweighs the cost of security investments.
Satellite Imagery for Rapid Detection of Liquefaction Surface Manifestations: The Case Study of Türkiye–Syria 2023 Earthquakes
Authors: Taftsoglou Maria , Valkaniotis Sotiris , Papathanassiou George , Karantanellis Efstratios
Published: August 2023

The 6 February 2023 earthquake doublet (Mw 7.7 and Mw 7.6) that occurred on the East Anatolian Fault Zone (EAFZ) triggered a significant amount of soil liquefaction phenomena in SE Türkiye and NW Syria. The great areal extent of the affected area and the necessity of rapid response led to the adoption and improvement of a workflow for mapping liquefaction phenomena based on remote sensing data. Using satellite imagery, we identified 1850 sites with liquefaction manifestation and lateral spreading deformation. We acquired a thorough map of earthquake-triggered liquefaction based on visual mapping with optical satellite imagery (high and very high-resolution) and the aid of radar satellite imagery and interferometry. The majority of sites are found along meandering sections of river valleys, coastal plains, drained lakes, swamps, and lacustrine basins along the East Anatolian Fault, highlighting once again the influence of geomorphology/surficial geology on the distribution of liquefaction phenomena. A total of 95% of the liquefaction occurrences were mapped within 25 km from the surface trace of the fault, confirming the distance from fault rupture as a more effective tool for predicting the distribution of liquefaction than epicentral distance. Thus, taking into consideration the rapid documentation of these phenomena without the limitations in terms of time, cost, and accessibility of the field investigation techniques, this desktop-based approach can result in a rapid and comprehensive map of liquefaction from a strong earthquake, and can also be used as a future guide for subsequent field investigations for liquefaction hazard mapping.
Lake Altimetry Using Long Coherent Integration Carrier Phase Measurements from Airborne GNSS Reflectometry
Authors: Varais Nolan , Verdun Jérôme , Cali José , Lestarquit Laurent
Published: August 2023

Today, land and ocean observations are crucial for monitoring climate change. The method of GNSS reflectometry is an opportunistic way to provide low-cost observations of many geophysical parameters. However, although this method has been the subject of numerous research studies, work is still in progress to improve its possibilities and fields of application. This paper focuses on GNSS reflectometry using carrier phase measurements for water altimetry measurements. The difficulties in implementing such a method lie in the need to collect a coherent signal and to solve the integer ambiguity value. In this context, the implementation of innovative signal processing is described, including the correlation of the reflected signals in dedicated software and the prolongation of the coherent integration time to enhance signal coherency. These processes were applied to data collected over Carcans-Hourtin Lake in France to compute the height of the reflection surface which was then compared to in situ GNSS buoy height measurements. The results show that at 300 ft (91.44 m), the differences between the lake heights measured with the buoy and with the reflectometry data can reach less than 1 cm for the L1, E1 and E5 GNSS signals. In addition, the slope of the geoid estimated with the reflectometry data is very consistent with that of the RAF20 geoid model, with a difference of up to less than 2 mm/km.
Coastal Dynamics at Kharasavey Key Site, Kara Sea, Based on Remote Sensing Data
Authors: Kazhukalo Georgii , Novikova Anna , Shabanova Natalya , Drugov Mikhail , Myslenkov Stanislav , Shabanov Pavel , Belova Nataliya , Ogorodov Stanislav
Published: August 2023

In recent decades, acceleration of coastal erosion has been observed at many key sites of the Arctic region. Coastal dynamics of both erosional and accretional stretches at Kharasavey, Kara Sea, was studied using multi-temporal remote sensing data covering the period from 1964 to 2022. Cross-proxy analyses of the interplay between coastal dynamics and regional (wave and thermal action) and local (geomorphic and lithological features; technogenic impact) drivers were supported by cluster analysis and wind–wave modelling via the Popov–Sovershaev method and WaveWatch III. Ice-rich permafrost bluffs and accretional sandy beaches exhibited a tendency towards persistent erosion (−1.03 m/yr and −0.42 m/yr, respectively). Shoreline progradation occurred locally near Cape Burunniy (6% of the accretional stretch) and may be due to sediment flux reversals responding to sea-ice decline. Although the mean rates of erosion were decreasing at a decadal scale, cluster analysis captured a slight increase in the retreat for 71% of the erosional stretch, which is apparently related to the forcing of wind–wave and thermal energy. Erosional hotspots (up to −7.9 m/yr) occurred mainly in the alignment of Cape Kharasavey and were predominantly caused by direct human impact. The presented study highlights the non-linear interaction of the Arctic coastal change and environmental drivers that require further upscaling of the applied models and remote sensing data.
Analysis of Deformation Dynamics in Guatemala City Metropolitan Area Using Persistent Scatterer Interferometry
Authors: García-Lanchares Carlos , Marchamalo-Sacristán Miguel , Fernández-Landa Alfredo , Sancho Candela , Krishnakumar Vrinda , Benito Belén
Published: August 2023

The analysis of deformation dynamics in Guatemala city and its surrounding region presented in this paper holds significant relevance due to the high vulnerability of this area to natural disasters, combined with its rapid urbanization, similar to most Central American cities, contrasting with a lack of InSAR and deformation studies in the region. A total of 226 SAR images from Sentinel-1 A and B satellites in both ascending and descending geometries were processed with the Persistent Scatterer Interferometry (PSI) technique employing the SNAP-StaMPS integrated processing chain. The study area encompasses the Metropolitan Region of Guatemala, which is characterized by a diverse and active geological framework, with a historical record of earthquakes, intense groundwater extraction, and local subsidence phenomena, causing fissures and sinkholes. Four active areas were identified in the study area, each covering more than 50 hectares, with subsidence velocities greater than 10 mm/yr. This study provides valuable insights into fostering the sustainable development of this region by identifying deformation patterns, characterizing main active areas, and evaluating associated risks for disaster management and prevention. The results can also aid informed decision-making processes and guide urban planning and resource management strategies in other Central American countries. The application of InSAR studies is crucial for improving safety and sustainability in urban environments and natural resource management in vulnerable regions.
Machine Learning for Predicting Forest Fire Occurrence in Changsha: An Innovative Investigation into the Introduction of a Forest Fuel Factor
Authors: Wu Xin , Zhang Gui , Yang Zhigao , Tan Sanqing , Yang Yongke , Pang Ziheng
Published: August 2023

Affected by global warming and increased extreme weather, Hunan Province saw a phased and concentrated outbreak of forest fires in 2022, causing significant damage and impact. Predicting the occurrence of forest fires can enhance the ability to make early predictions and strengthen early warning and responses. Currently, fire prevention and extinguishing in China’s forests and grasslands face severe challenges due to the overlapping of natural and social factors. Existing forest fire occurrence prediction models mostly take into account vegetation, topographic, meteorological and human activity factors; however, the occurrence of forest fires is closely related to the forest fuel moisture content. In this study, the traditional driving factors of forest fire such as satellite hotspots, vegetation, meteorology, topography and human activities from 2004 to 2021 were introduced along with forest fuel factors (vegetation canopy water content and evapotranspiration from the top of the vegetation canopy), and a database of factors for predicting forest fire occurrence was constructed. And a forest fire occurrence prediction model was built using machine learning methods such as the Random Forest model (RF), the Gradient Boosting Decision Tree model (GBDT) and the Adaptive Augmentation Model (AdaBoost). The accuracy of the models was verified using Area Under Curve (AUC) and four other metrics. The RF model with an AUC value of 0.981 was more accurate than all other models in predicting the probability of forest fire occurrence, followed by the GBDT (AUC = 0.978) and AdaBoost (AUC = 0.891) models. The RF model, which has the best accuracy, was selected to predict the monthly forest fire probability in Changsha in 2022 and combined with the Inverse Distance Weight Interpolation method to plot the monthly forest fire probability in Changsha. We found that the monthly spatial and temporal distribution of forest fire probability in Changsha varied significantly, with March, April, May, September, October, November and December being the months with higher forest fire probability. The highest probability of forest fires occurred in the central and northern regions. In this study, the core drivers affecting the occurrence of forest fires in Changsha City were found to be vegetation canopy evapotranspiration and vegetation canopy water content. The RF model was identified as a more suitable forest fire occurrence probability prediction model for Changsha City. Meanwhile, this study found that vegetation characteristics and combustible factors have more influence on forest fire occurrence in Changsha City than meteorological factors, and surface temperature has less influence on forest fire occurrence in Changsha City.
A Cartographic Perspective on the Planetary Geologic Mapping Investigation of Ceres
Authors: Naß Andrea , van Gasselt Stephan
Published: August 2023

The NASA Dawn spacecraft visited asteroid 4 Vesta between 2011 and 2012 and dwarf planet 1 Ceres between 2015 and 2018 to investigate their surfaces through optical and hyperspectral imaging and their composition through gamma-ray and neutron spectroscopy. For the global mapping investigation of both proto-planets, geologic mappers employed Geographic Information System (GIS) software to map 15 quadrangles using optical and hyperspectral data and to produce views of the geologic evolution through individual maps and research papers. While geologic mapping was the core motivation of the mapping investigation, the project never aimed to produce homogeneous and consistent map representations. The chosen mapping approach and its implementation led to a number of inconsistencies regarding cartographic representation, including differential generalization through varying mapping scales, topologic inconsistencies, lack of semantic integrity, and scale consistency, and ultimately, to the management of reusable research data. Ongoing data acquisition during the mapping phase created additional challenges for the homogenization of mapping results and a potential derivation of a global map. This contribution reviews cartographic and data perspectives on the mapping investigation of Ceres and highlights (a) data sources, (b) the cartographic concept, (c) mapping conduct, and (d) dissemination as well as research-data management arrangements. It furthermore discusses decisions and experiences made during mapping and finishes with a set of recommendations from the viewpoint of the cartographic sciences.
RemainNet: Explore Road Extraction from Remote Sensing Image Using Mask Image Modeling
Authors: Li Zhenghong , Chen Hao , Jing Ning , Li Jun
Published: August 2023

Road extraction from a remote sensing image is a research hotspot due to its broad range of applications. Despite recent advancements, achieving precise road extraction remains challenging. Since a road is thin and long, roadside objects and shadows cause occlusions, thus influencing the distinguishment of the road. Masked image modeling reconstructs masked areas from unmasked areas, which is similar to the process of inferring occluded roads from nonoccluded areas. Therefore, we believe that mask image modeling is beneficial for indicating occluded areas from other areas, thus alleviating the occlusion issue in remote sensing image road extraction. In this paper, we propose a remote sensing image road extraction network named RemainNet, which is based on mask image modeling. RemainNet consists of a backbone, image prediction module, and semantic prediction module. An image prediction module reconstructs a masked area RGB value from unmasked areas. Apart from reconstructing original remote sensing images, a semantic prediction module of RemainNet also extracts roads from masked images. Extensive experiments are carried out on the Massachusetts Roads dataset and DeepGlobe Road Extraction dataset; the proposed RemainNet improves 0.82–1.70% IoU compared with other state-of-the-art road extraction methods.
Mid-Long-Term Prediction of Surface Seawater Organic Carbon in the Southern South China Sea Based on Multi-Applicability CNN-LSTM Prediction Model
Authors: Liu Na , Zhang Kuncheng , Yu Jing , Chen Shaoyang , Zheng Hao
Published: August 2023

The organic carbon pool is a crucial component of the ocean carbon cycle. The study of organic carbon distribution and interannual variability in the land-sea interface can contribute to understanding the global ocean carbon cycle and ecological effects in the context of the Anthropocene and help achieve the Sustainable Development Goals (SDGs). At present, there has been a certain amount of research on the source and flux of carbon in the ocean carbon cycle, but the prediction of marine carbon is still in its infancy. In this paper, a CNN-LSTM deep learning model that takes into account spatio-temporal features was used to make a 5-year mid-long-term rolling prediction of particulate organic carbon (POC) and yellow matter (CDOM) using MODIS Level 2 semimonthly synthetic data from the official website of NASA from January 2002 to June 2020. The model uses chlorophyll-a data to adjust the parameters. The results showed that the model could also be applied to the mid-long-term rolling prediction of POC and CDOM. The model was capable of accurately predicting POC and CDOM over periods of three and two years, respectively (R > 0.5). Meanwhile, the 5-year trends of the predicted and actual values were verified using the least squares method and the Mann-Kendall trend test. The results showed that the predicted and actual values of sea surface POC and CDOM in 2015–2020 showed an overall upward trend. The surface-level POC and CDOM in the ocean are considered to be related to primary production. The mid-long-term prediction of surface seawater organic carbon in the southern South China Sea helps humans explore the regional characteristics of organic carbon in the coral reef waters of the South China Sea and study the changing trend of surface seawater organic carbon.
Dense Convolutional Neural Network for Identification of Raman Spectra
Authors: Zhou Wei , Qian Ziheng , Ni Xinyuan , Tang Yujun , Guo Hanming , Zhuang Songlin
Published: August 2023

The rapid development of cloud computing and deep learning makes the intelligent modes of applications widespread in various fields. The identification of Raman spectra can be realized in the cloud, due to its powerful computing, abundant spectral databases and advanced algorithms. Thus, it can reduce the dependence on the performance of the terminal instruments. However, the complexity of the detection environment can cause great interferences, which might significantly decrease the identification accuracies of algorithms. In this paper, a deep learning algorithm based on the Dense network has been proposed to satisfy the realization of this vision. The proposed Dense convolutional neural network has a very deep structure of over 40 layers and plenty of parameters to adjust the weight of different wavebands. In the kernel Dense blocks part of the network, it has a feed-forward fashion of connection for each layer to every other layer. It can alleviate the gradient vanishing or explosion problems, strengthen feature propagations, encourage feature reuses and enhance training efficiency. The network’s special architecture mitigates noise interferences and ensures precise identification. The Dense network shows more accuracy and robustness compared to other CNN-based algorithms. We set up a database of 1600 Raman spectra consisting of 32 different types of liquid chemicals. They are detected using different postures as examples of interfered Raman spectra. In the 50 repeated training and testing sets, the Dense network can achieve a weighted accuracy of 99.99%. We have also tested the RRUFF database and the Dense network has a good performance. The proposed approach advances cloud-enabled Raman spectra identification, offering improved accuracy and adaptability for diverse identification tasks.
Automatic Suppression Method for Water Surface Glints Using a Division of Focal Plane Visible Polarimeter
Authors: Wang Meishu , Qiu Su , Jin Weiqi , Yang Jie
Published: August 2023

To address the problem of water surface detection imaging equipment being susceptible to water surface glints, this study demonstrates a method called De-Glints for suppressing glints and obtaining clear underwater images using a division of focal plane (DoFP) polarimeter. Based on the principle of polarization imaging, the best polarization angle and the image corresponding to the minimal average gray level of each pixel are calculated. To evaluate the improvement in image quality, the index E was designed. The results of indoor and outdoor experiments show that the error of the angle calculation of this method is within 10%, and the minimum error is only 3%. The E index is positively improved and can be relatively improved by 8.00 under the interference of strong outdoor glints, and the method proposed in this paper shows a good adaptive ability to the dynamic scene.
Freight Wagon Digitalization for Condition Monitoring and Advanced Operation
Authors: Moya Iker , Perez Alejandro , Zabalegui Paul , de Miguel Gorka , Losada Markos , Amengual Jon , Adin Iñigo , Mendizabal Jaizki
Published: August 2023

Traditionally, freight wagon technology has lacked digitalization and advanced monitoring capabilities. This article presents recent advancements in freight wagon digitalization, covering the system’s definition, development, and field tests on a commercial line in Sweden. A number of components and systems were installed on board on the freight wagon, leading to the intelligent freight wagon. The digitalization includes the integration of sensors for different functions such as train composition, train integrity, asset monitoring and continuous wagon positioning. Communication capabilities enable data exchange between components, securely stored and transferred to a remote server for access and visualization. Three digitalized freight wagons operated on the Nässjo–Falköping line, equipped with strategically placed monitoring sensors to collect valuable data on wagon performance and railway infrastructure. The field tests showcase the system’s potential for detecting faults and anomalies, signifying a significant advancement in freight wagon technology, and contributing to an improvement in freight wagon digitalization and monitoring. The gathered insights demonstrate the system’s effectiveness, setting the stage for a comprehensive monitoring solution for railway infrastructures. These advancements promise real-time analysis, anomaly detection, and proactive maintenance, fostering improved efficiency and safety in the domain of freight transportation, while contributing to the enhancement of freight wagon digitalization and supervision.
Organic Electrochemical Transistor with MoS2 Nanosheets Modified Gate Electrode for Sensitive Glucose Sensing
Authors: Hu Jin , Dai Jiajia , Huang Caiping , Zeng Xierong , Wei Weiwei , Wang Zhezhe , Lin Peng
Published: August 2023

An organic electrochemical transistor (OECT) with MoS2 nanosheets modified on the gate electrode was proposed for glucose sensing. MoS2 nanosheets, which had excellent electrocatalytic performance, a large specific surface area, and more active sites, were prepared by liquid phase ultrasonic exfoliation to modify the gate electrode of OECT, resulting in a large improvement in the sensitivity of the glucose sensor. The detection limit of the device modified with MoS2 nanosheets is down to 100 nM, which is 1~2 orders of magnitude better than that of the device without nanomaterial modification. This result manifests not only a sensitive and selective method for the detection of glucose based on OECT but also an extended application of MoS2 nanosheets for other biomolecule sensing with high sensitivity.
Improved Strong Tracking Cubature Kalman Filter for UWB Positioning
Authors: Pu Yuxiang , Li Xiaolong , Liu Yunqing , Wang Yanbo , Wu Suhang , Qu Tianshuai , Xi Jingyi
Published: August 2023

For the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate predictive dynamics model in wireless ultra-wideband (UWB) positioning systems, an improved strong tracking cubature Kalman filter (ISTCKF) positioning algorithm is proposed in this paper. The main idea of the algorithm is as follows. First, the observations are reconstructed based on the weighted positioning results obtained from the predictive dynamics model and the least squares algorithm. Second, the difference in statistical properties between the observation noise and the NLOS errors is utilized to identify the NLOS observations by the corresponding judgment statistics obtained from the operation between the original observations and the reconstructed observations. The main positioning error of the UWB positioning system at the current moment is then judged by the NLOS identification results, and the corresponding fading factors are calculated according to the judgment results. Finally, the corresponding ISTCKF is constructed based on the fading factors to mitigate the main positioning error and obtain accurate positioning result in the UWB positioning system. In this paper, the reconstructed observations mitigate the observation noise in the original observation, and then the ISTCKF mitigates the main errors in the UWB positioning system. The experimental results show that the ISTCKF algorithm reduces the positioning error by 55.2%, 32.3% and 28.9% compared with STCKF, ACKF and RSTCKF, respectively. The proposed ISTCKF algorithm significantly improves the positioning accuracy and stability of the UWB system.
Sensors on the Internet of Things Systems for Urban Disaster Management: A Systematic Literature Review
Authors: Zeng Fan , Pang Chuan , Tang Huajun
Published: August 2023

The occurrence of disasters has the potential to impede the progress of sustainable urban development. For instance, it has the potential to result in significant human casualties and substantial economic repercussions. Sustainable cities, as outlined in the United Nations Sustainable Development Goal 12, prioritize the objective of disaster risk reduction. According to the Gesi Smarter 2030, the Internet of Things (IoT) assumes a pivotal role in the context of smart cities, particularly in domains including smart grids, smart waste management, and smart transportation. IoT has emerged as a crucial facilitator for the management of disasters, contributing to the development of cities that are both resilient and sustainable. This systematic literature analysis seeks to demonstrate the sensors utilized in IoT for the purpose of urban catastrophe management. The review encompasses both the pre-disaster and post-disaster stages, drawing from a total of 72 articles. During each stage, we presented the characteristics of sensors employed in IoT. Additionally, we engaged in a discourse regarding the various communication technologies and protocols that can be utilized for the purpose of transmitting the data obtained from sensors. Furthermore, we have demonstrated the methodology for analyzing and implementing the data within the application layer of IoT. In conclusion, this study addresses the existing research deficiencies within the literature and presents potential avenues for future exploration in the realm of IoT-enabled urban catastrophe management, drawing upon the findings of the evaluated publications.
Soccer and Disability, Is It Possible? Analysis of the Learning and Coaching Context in Spain
Authors: Burgos-García Antonio
Published: August 2023

Coaching a youth soccer player is important, and the coach’s role is key. Actually, there is no profile or coaching program for grassroots-soccer coaches that favor the practice of soccer and disability, according to different research and experts. The main purpose of this study is to identify and analyze the professional profile of the grassroots-soccer coach who has soccer players with disabilities (learning and coaching context). This research applies a quantitative method, specifically, non-experimental, cross-sectional, descriptive, and inferential methodology. The sample of analysis is the staff members of the professional soccer clubs of LaLigaSantander Genuine (Spain). An important result is that half of the grassroots-soccer coaches have not received specific education to coach youth soccer players with disabilities. Finally, one important conclusion of this research is that by generating a climate of trust and empathy, grassroots-soccer coaches improve the performance of their soccer players with disabilities by recognizing and understanding their emotional states.
Optimal Configuration of Wind-PV and Energy Storage in Large Clean Energy Bases
Authors: Liu Mingyi , Zhang Bin , Wang Jiaqi , Liu Han , Wang Jianxing , Liu Chenghao , Zhao Jiahui , Sun Yue , Zhai Rongrong , Zhu Yong
Published: August 2023

The installed capacity of energy storage in China has increased dramatically due to the national power system reform and the integration of large scale renewable energy with other sources. To support the construction of large-scale energy bases and optimizes the performance of thermal power plants, the research on the corporation mode between energy storage and thermal energy, including the optimization of energy-storage capacity and its operation in large-scale clean energy bases. In this paper, a large-scale clean energy base system is modeled with EBSILON and a capacity calculation method is established by minimizing the investment cost and energy storage capacity of the power system and constraints such as power balance, SOC, and power fluctuations. The research proposed a method of using coupled system of thermal energy storage systems primarily based on molten salt thermal storage and thermal power generation for rough modulation and using battery energy storage system for fine modulation tasks. Example of fine modulation includes frequency modulation and heating demand of the district, which significantly reduces the energy storage investment by more than 95%. A case study of a 10 MW clean energy base is conducted. The result shows that the overall pre-tax internal rate of return of the base project is 8%, which has good economic benefits.
Promoting the Sustainability of Artisanal Fishing through Environmental Education with Game-Based Learning
Authors: Torralba-Burrial Antonio , Dopico Eduardo
Published: August 2023

Oceans provide a wide range of ecosystem services, including food and cultural ecosystem services derived from fisheries. The sustainability of fisheries is addressed by United Nations Sustainable Development Goal 14 on ocean conservation, and education strategies should include ways to achieve it. In this paper, we describe a game-based learning environmental education experience for the promotion of artisanal fishing developed in northwest Spain, in which more than a thousand primary education students (aged 6–12) participated. Following a qualitative methodology, we analyze our own generated games, their formative assessment, and the results of their implementation in schools and informal education events. Moreover, we compare the educational games generated with other game-based learning experiences on ocean literacy and the sustainability of fisheries. These educational experiences provide play-based learning opportunities in which students show great motivation and increase their knowledge about marine biodiversity, the socio-environmental effects of fisheries, and ocean conservation. Some differences were identifiable in the choice and design of the games, including their type and duration, the dimensions of sustainability analyzed, the specific learning objectives sought, the historical and cultural references used, cooperative peer learning, and the generation of shared knowledge. In this experience, the inclusion of education for responsible consumption (SDG 12) within the sustainability of fisheries represents a remarkable innovation, empowering students in their role as fish consumers.
Research on the Impact of Event Sustainability on Brand Equity in Event Activities: A Case Study of Hainan Expo
Authors: Cheng Xiaoxi , Cheng Ao
Published: August 2023

This study investigates sustainability practices from the event participants’ perspective. Taking the China International Consumer Products Expo as a case study, this study adopts a quantitative research method to examine the effects of the sustainability practices of events on brand equity via structural equation modeling. The results of the empirical analysis are as follows: (1) the sustainability practices of events is a multi-dimensional concept, and the four determinants of sustainability practices of events examined in this study are environmental sustainability practices, economic sustainability practices, social sustainability practices, and policy sustainability practices; (2) the four dimensions of sustainability practices have positive effects on brand image; (3) social sustainability, environmental sustainability, and policy sustainability practices have a positive impact on willingness to pay a price premium; (4) brand image and attendees’ willingness to pay a price premium are significantly related to brand loyalty. The findings will be useful not only in improving the new structure of sustainability in the event field but also in providing new ideas and new ways for event organizers to improve event brand equity.