bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Strategies for Engineered Vascular Grafts and Vascularized Tissue Engineering
Authors: Chen Jun , Zhang Di , Wu Lin-Ping , Zhao Ming
Published: April 2023

Blood vessels not only transport oxygen and nutrients to each organ, but also play an important role in the regulation of tissue regeneration. Impaired or occluded vessels can result in ischemia, tissue necrosis, or even life-threatening events. Bioengineered vascular grafts have become a promising alternative treatment for damaged or occlusive vessels. Large-scale tubular grafts, which can match arteries, arterioles, and venules, as well as meso- and microscale vasculature to alleviate ischemia or prevascularized engineered tissues, have been developed. In this review, materials and techniques for engineering tubular scaffolds and vasculature at all levels are discussed. Examples of vascularized tissue engineering in bone, peripheral nerves, and the heart are also provided. Finally, the current challenges are discussed and the perspectives on future developments in biofunctional engineered vessels are delineated.
Microwave Modification of an Epoxy Basalt-Filled Oligomer to Improve the Functional Properties of a Composite Based on It
Authors: Bekeshev Amirbek , Vasinkina Ekaterina , Kalganova Svetlana , Kadykova Yulia , Mostovoy Anton , Shcherbakov Andrey , Lopukhova Marina , Aimaganbetova Zukhra
Published: April 2023

The purpose of this work is to study the influence of the electric field strength of an electromagnetic wave with the maximum modifying effect on an epoxy basalt-filled oligomer, which is of great scientific and practical importance for the development of microwave oligomer modification technology. The optimal modes of microwave modification, under which the highest values of the mechanical properties of an epoxy basalt-filled polymer composite material are obtained, are identified: power of 400 W and an exposure time of 24 s. At the same time, the breaking stress in bending increases by 20%, the impact strength increases by 2 times, and hardness increases by 31%. A slight increase of 4.5% in heat resistance is noted compared to the composite obtained on the basis of an oligomer unmodified in the microwave electromagnetic field. The results of resistance to various aggressive environments are obtained, which show that the studied physical and mechanical characteristics of the epoxy basalt-filled material after exposure to an aggressive environment decrease by less than 14%, which corresponds to their good resistance to an aggressive environment. It is established that the effect of the microwave electromagnetic field on an epoxy basalt-filled oligomer is an effective modification method that improves physical and mechanical characteristics with a high level of temporal stability to climatic influences, with a coefficient of property retention of more than 90%.
Preparation and Properties of Poly (butylene adipate-co-terephthalate)/thermoplastic Hydroxypropyl Starch Composite Films Reinforced with Nano-Silica
Authors: Li Zehao , Li Hui , Wang Muxi , Zhang Zhongyan , Yang Liting , Ma Lijun , Liu Hong
Published: April 2023

The use of biodegradable plastics is gradually increasing, but its expensive cost limits promotion. In this study, poly(butylene adipate-co-terephthalate)/thermoplastic hydroxypropyl starch reinforced with nano-silica (PBAT/TPHSg-SiO2) composite films with high hydroxypropyl starch content were prepared in a two-step process. The effect of reinforced thermoplastic hydroxypropyl starch on the mechanical, thermal, processing properties, and micromorphology of the composite films was investigated. The results showed that the tensile strength of the composite films was significantly improved by the addition of nano-silica, with 35% increase in horizontal tensile strength and 21% increase in vertical tensile strength after the addition of 4 phr of nano-silica. When the content of thermoplastic hydroxypropyl starch reinforced with nano-silica (TPHSg-4SiO2) is 40%, the horizontal and vertical tensile strengths of the films are 9.82 and 12.09 MPa, respectively, and the elongation at break of the films is over 500%. Electron micrographs show that TPHSg-4SiO2 is better homogeneously dispersed in the PBAT and exhibits a bi-continuous phase structure at a TPHSg-4SiO2 content of 40%. In this study, the blowing PBAT/TPHSg-4SiO2 composite films effectively reduce the cost and still show better mechanical properties, which are suitable for packaging applications.
A Facile Hydrothermal Synthesis of S-VO2-Cellulose Nanocomposite for Photocatalytic Degradation of Methylene Blue Dye
Authors: Nassar Mostafa Y. , NourEldien Mona S. , Ibrahim Islam M. , Aly Hisham M.
Published: April 2023

A novel UV-light-active MCC/S-VO2 photocatalyst was successfully synthesized by a simple and reliable hydrothermal route. XRD, FT-IR, Raman analysis, XPS, FE-SEM, EDX, TEM, DRS, and thermal analysis techniques were utilized for the characterization of the as-prepared photocatalysts. The photocatalytic activities of the V2O5, doped S-VO2, and MCC/S-VO2 nanostructures were investigated by monitoring the fading out of the methylene blue (MB) concentration under UV-light irradiation. The results revealed that the photocatalytic degradation of MB via MCC/S-VO2 was superior compared with that exhibited by pure V2O5 and doped S-VO2. It was found that 72.3% of MB (100 mL; 20 mg·L−1) was degraded after 6 h in contact with MCC/S-VO2. Interestingly, the photodegradation of MB dye was enhanced dramatically by adding H2O2, while 92.5% of MB was degraded within 55 min. The kinetic studies revealed that the MB degradation followed the pseudo-first-order model with a rate constant (kobs) of 3.9 × 10−2 min−1. The effect of several active species scavengers on the photocatalytic degradation process was investigated. The data exhibited that hydroxyl radicals and positive holes were the key active species during the degradation process. The stability and reusability of the as-prepared nanostructures were examined, and the results displayed its applicability for the removal of MB dye from aqueous media.
The Development and Application of Novel Water-Based Drilling Fluid for Complex Pressure System Formation in the South China Sea
Authors: Liu Hexing , Huang Yi , Liu Zhiqin , Dong Lifei , Wang Miao
Published: April 2023

The Y oilfield reservoir is characterized by ultra-depleted reservoir and multiple pressure systems in each well section. If conventional PLUS/KCL drilling fluid is used in directional drilling, the following problems are found: the rheological stability of drilling fluid is not sufficient, resulting in increased viscosity and thickening after aging, and the single plugging material may cause loss-circulation when the drilling differential pressure is more than 26 MPa, resulting in poor reservoir protection effects. To solve the above problems, based on the conventional PLUS/KCL drilling fluid formula, a PLUS/KCL drilling fluid system suitable for directional drilling in multi-pressure systems was formed by optimizing the addition of a treating agent to improve the rheological stability of the drilling fluid, optimizing the plugging agent and compound combination to improve plugging ability, and optimizing the particle size distribution of a temporary plugging agent to improve the reservoir protection effect. The laboratory test evaluation showed that the optimized PLUS/KCL drilling fluid had fine plugging pressure capacity, and the intrusion depth was only 4.1 cm on the natural core at 120 °C/35 MPa/12 h, effectively reducing the risk of loss-circulation. The permeability recovery rate of the core after cutting off the polluted end was more than 90%, indicating that the reservoir protection effect was good. The drilling fluid performance was stable, and the cuttings rolling recovery rate was over 90%. Field application showed that the optimized PLUS/KCL drilling fluid was used without any loss-circulation or wellbore instability, and the production of all wells was over-matched, effectively solving the problem of ensuring drilling safety and reducing reservoir damage under the differential pressure of multiple pressure systems.
Integrating GIS, Remote Sensing, and Citizen Science to Map Oak Decline Risk across the Daniel Boone National Forest
Authors: Crocker Ellen , Gurung Kumari , Calvert Jared , Nelson C. Dana , Yang Jian
Published: April 2023

Oak decline is a general term used for the progressive dieback and eventual mortality of oak trees due to many compounding stressors, typically a combination of predisposing, inciting, and contributing factors. While pinpointing individual causes of decline in oak trees is a challenge, past studies have identified site and stand characteristics associated with oak decline. In this study, we developed a risk map of oak decline for the Daniel Boone National Forest (DBNF), combining GIS, remote sensing (RS), and public reporting (citizen science, CS). Starting with ground reports of decline (CS), we developed a site-scale model (GIS and RS) for oak decline based on four previously identified predisposing factors: elevation, slope, solar radiation, and topographic wetness. We found that areas identified in the model as having a high oak decline risk also reflected areas of observed oak decline (CS). We then optimized and expanded this risk model to the entire range of the DBNF, based on both site characteristics (as piloted for the case study site) and stand inventory data. The stand inventory data (including species composition and age) further improved the model, resulting in a risk map at the landscape level. This case study can serve as a planning tool and highlights the potential usefulness of integrating GIS, remote sensing, and citizen science.
Analysis of Characteristics for Inter-System Bias on Multi-GNSS Undifferenced and Uncombined Precise Point Positioning
Authors: Lu Yangyang , Yang Hu , Li Bo , Li Jun , Xu Aigong , Zhang Mingze
Published: April 2023

Multi Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) has become the mainstream of PPP technology. Due to the differences in the coordinates and time references of each GNSS, multi-GNSS PPP must include additional Inter-System Bias (ISB) parameters to ensure compatibility between different GNSSs. Therefore, research on the characteristics of ISB is also essential. To analyze the short- and long-term time characteristics of multi-GNSS ISBs, as well as their relationship with receiver type and receiver antenna type, the Undifferenced and Uncombined (UDUC) PPP model of Global Positioning System (GPS), BeiDou navigation satellite system (BDS), and Galileo satellite navigation system (Galileo) was rigorously derived, and the physical of ISBs was elaborated in depth. ISB parameters were estimated and analyzed using 31 days of data from the 31 Multi-GNSS Experimental stations (MGEX). The results indicate that: (1) the ISB value is dependent on the station receiver type, receiver antenna type, analysis center product utilized, and GNSS system. (2) The short-term time characteristics of ISB-COM, ISB-WUM, and ISB-GBM are similar for the same station but not for the long term. In addition, ISBs are more stable in the short term. (3) There is little correlation between the ISB time characteristics, the receiver type, and the receiver antenna type, and the day-boundary discontinuity(DBD) on the ISB can be ignored for the concussive days’ process.
Mapping Growing Stem Volume Using Dual-Polarization GaoFen-3 SAR Images in Evergreen Coniferous Forests
Authors: Ye Zilin , Long Jiangping , Zheng Huanna , Liu Zhaohua , Zhang Tingchen , Wang Qingyang
Published: April 2023

Unaffected by cloud cover and solar illumination, synthetic aperture radar (SAR) images have great capability to map forest growing stem volume (GSV) in complex biophysical environments. Up to now, c-band dual-polarization Gaofen-3 (GF-3) SAR images, acquired by the first Chinese civilian satellite equipped with multi-polarized modes, are rarely applied in mapping forest GSV. To evaluate the capability of dual-polarization GF-3 SAR images in mapping forest GSV, several proposed derived features were initially extracted by mathematical operations and applied to obtain optimal feature sets by different feature sorting methods and feature selection methods. Then, the maps of GSV in an evergreen coniferous forest were inverted by various machine learning algorithms and stacking ensemble learning methods with different strategies. The results implied that backscattering coefficients and partially proposed derived features showed high sensitivity to the forest GSV, and the saturation phenomenon also obviously occurred once the forest GSV was larger than 300 m3/ha. Furthermore, the results showed that the accuracy of the mapped GSV was significantly improved using the stacking ensemble learning methods. Using various optimal feature sets and base models (MLR, KNN, SVM, and RF), the rRMSE values mainly ranged from 30% to 40%. After using the stacking ensemble learning methods, the values of rRMSE ranged from 16.71% to 20.51%. This confirmed that dual-polarization GF-3 images have great potential to map forest GSV in evergreen coniferous forests.
Multilevel Data and Decision Fusion Using Heterogeneous Sensory Data for Autonomous Vehicles
Authors: Ignatious Henry Alexander , El-Sayed Hesham , Kulkarni Parag
Published: April 2023

Autonomous vehicles (AVs) are predicted to change transportation; however, it is still difficult to maintain robust situation awareness in a variety of driving situations. To enhance AV perception, methods to integrate sensor data from the camera, radar, and LiDAR sensors have been proposed. However, due to rigidity in their fusion implementations, current techniques are not sufficiently robust in challenging driving scenarios (such as inclement weather, poor light, and sensor obstruction). These techniques can be divided into two main groups: (i) early fusion, which is ineffective when sensor data are distorted or noisy, and (ii) late fusion, which is unable to take advantage of characteristics from numerous sensors and hence yields sub-optimal estimates. In this paper, we suggest a flexible selective sensor fusion framework that learns to recognize the present driving environment and fuses the optimum sensor combinations to enhance robustness without sacrificing efficiency to overcome the above-mentioned limitations. The proposed framework dynamically simulates early fusion, late fusion, and mixtures of both, allowing for a quick decision on the best fusion approach. The framework includes versatile modules for pre-processing heterogeneous data such as numeric, alphanumeric, image, and audio data, selecting appropriate features, and efficiently fusing the selected features. Further, versatile object detection and classification models are proposed to detect and categorize objects accurately. Advanced ensembling, gating, and filtering techniques are introduced to select the optimal object detection and classification model. Further, innovative methodologies are proposed to create an accurate context and decision rules. Widely used datasets like KITTI, nuScenes, and RADIATE are used in experimental analysis to evaluate the proposed models. The proposed model performed well in both data-level and decision-level fusion activities and also outperformed other fusion models in terms of accuracy and efficiency.
The Dynamic Heterogeneous Relationship between Urban Population Distribution and Built Environment in Xi’an, China: A Case Study
Authors: Yang Xiping , Zhao Zhiyuan , Shi Chaoyang , Luo Lin , Tu Wei
Published: April 2023

The interaction between the population and built environment is a constant topic in urban spaces and is the main driving force of urban evolution. Understanding urban population distribution and its relationship with the built environment could provide guidance for urban planning, traffic, and disaster management. Following this line of thought, this study conducted an empirical analysis in Xi’an, a rapidly developing western city in China. Well-permeated mobile phone location data were used to represent the spatiotemporal dynamics of the population, and the built environment was characterized from five perspectives—transportation, location, building, greenery, and land use—using multisource geospatial data. Finally, the dynamic heterogeneous influence of built environment factors on population distribution was examined using multiscale geographically weighted regression (MGWR). Overall, the influencing coefficients exhibited a significant dynamic changing process from a temporal perspective and simultaneously demonstrated spatial nonstationarity. Moreover, the specific findings about the influence of each built environment factor facilitate a deeper insight into dynamic population distribution and its determinants.
Spatiotemporal Distribution of Afterslip following the 2014 Yutian Mw 6.9 Earthquake Using COSMO-SkyMed and Sentinel-1 InSAR Data
Authors: Huang Zhanhong , Xie Lei , Zhao Lei , Xu Wenbin
Published: April 2023

Spatiotemporal distribution of early afterslip is essential for seismic hazard evaluation and determination of fault friction properties. In this study, we used early post-seismic COSMO-SkyMed (19 February 2014–08 April 2014) and long-term Sentinel-1 (16 October 2014–17 June 2020) observations from multiple platforms over different periods to create a rate decay model driven by post-seismic afterslip. The combined observations provide full coverage of the post-seismic deformation following the 2014 Yutian Mw 6.9 earthquake that occurred at the southwestern end of the Altyn Tagh Fault. The observation and modeling results showed that post-seismic deformation was characterized by left-lateral strike-slip movement with minor normal slip, which was consistent with that of co-seismic rupture. The maximum early afterslip (7–55 days) was as large as approximately 0.09 m with a depth of 7 km in the west of co-seismic rupture, and the maximum long-term afterslip was about 0.24 m. The simulated post-seismic deformation caused by poroelastic rebound and viscoelastic relaxation suggests that the afterslip mechanism controls the post-seismic deformation. The coupling pattern of the aftershock and afterslip indicates that the aftershock was mainly caused by the afterslip. The post-seismic spatiotemporal features of the 2014 Yutian earthquake have significant implications for analyzing seismic hazards at the southwestern end of the Altyn Tagh Fault.
High Precision Measurements of Resonance Frequency of Ozone Rotational Transition J = 61,5–60,6 in the Real Atmosphere
Authors: Kulikov Mikhail Yu. , Krasil’nikov Alexander A. , Belikovich Mikhail V. , Ryskin Vitaly G. , Shvetsov Alexander A. , Skalyga Natalya K. , Kukin Lev M. , Feigin Alexander M.
Published: April 2023

Ground-based passive measurements of downwelling atmospheric radiation at ~110.836 GHz allow extracting the spectra of ozone self-radiation (rotational transition J = 61,5–60,6) coming from the low stratosphere–mesosphere and retrieving vertical profiles of ozone concentration at these altitudes. There is a notable (several hundred kHz) ambiguity in the determination of the resonance frequency of this important ozone line. We carried out long-term ground-based measurements of atmospheric microwave radiation in this range using upgraded apparatus with high technical accuracy and spectral resolution (~12 kHz). The obtained brightness temperature spectra allowed us to determine the frequency of this ozone line to be 110,835.909 ± 0.016 MHz. We verified that the Doppler frequency shift by horizontal wind as well as the variations of the tropospheric absorption had little effect on the obtained result. The found value was 131 ± 16 kHz less than that measured in the laboratory and differed from modern model calculations. At the same time, it was close to the results of early semiempirical calculations made more than 40 years ago. The applications where precise knowledge about the resonance frequency of this ozone line can be important were discussed in this paper.
Spatiotemporal Evolution and Risk Analysis of Land Use in the Coastal Zone of the Yangtze River Delta Region of China
Authors: Yin Lingzhi , Wang Yafei , Sun Chenglai , Ye Yuxuan
Published: April 2023

The extensive accumulation of big data, along with the development of a high-performance platform, bridge the gap between the previous inability to provide long-term time series and broad-scale coastal zone monitoring and risk warnings with remote sensing techniques. Based on 20 years of Landsat images from the Google Earth Engine platform, the time series land cover in the coastal zone of the Yangtze River Delta in China was classified. Then, a spatiotemporal clustering method based on grid segmentation was proposed to analyze the spatiotemporal evolution details of artificial surface expansion and the risks of cropland loss and ecological degradation caused by this. The results showed that significant changes have taken place in the quantitative structure and spatial morphology of coastal land use in the past 20 years. The artificial surface maintained a growth trend, increasing by 229%, while cropland decreased by 19%. Natural land showed a fluctuation pattern of “up→down→up”. The spatiotemporal details of land use obtained through 1km grid segmentation and clustering analysis were more significant. The artificial surface mainly underwent a progressive spatial expansion along the central urban area and important transportation axes (types III and IV), with the most dramatic changes occurring from 2010 to 2013. Type III cropland loss was the most significant, falling from 75.02% in 2000 to 38.23% in 2020. At the same time, the change in type III water body corresponds to the newly increased area of reclamation, which has decreased by 17% in the past 20 years, indicating that the degradation of coastal natural wetlands was significant. This paper provided a comprehensive diagnosis of coastal land use change, which could help policy makers and implementers to propose more targeted and differentiated coastal development and protection policies.
Latest Trends on Tree Classification and Segmentation Using UAV Data—A Review of Agroforestry Applications
Authors: Chehreh Babak , Moutinho Alexandra , Viegas Carlos
Published: April 2023

When it comes to forest management and protection, knowledge is key. Therefore, forest mapping is crucial to obtain the required knowledge towards profitable resource exploitation and increased resilience against wildfires. Within this context, this paper presents a literature review on tree classification and segmentation using data acquired by unmanned aerial vehicles, with special focus on the last decade (2013–2023). The latest research trends in this field are presented and analyzed in two main vectors, namely: (1) data, where used sensors and data structures are resumed; and (2) methods, where remote sensing and data analysis methods are described, with particular focus on machine learning approaches. The study and review methodology filtered 979 papers, which were then screened, resulting in the 144 works included in this paper. These are systematically analyzed and organized by year, keywords, purpose, sensors, and methods used, easily allowing the readers to have a wide, but at the same time detailed, view of the latest trends in automatic tree classification and segmentation using unmanned aerial vehicles. This review shows that image processing and machine learning techniques applied to forestry and segmentation and classification tasks are focused on improving the accuracy and interpretability of the results by using multi-modal data, 3D information, and AI methods. Most works use RGB or multispectral cameras, or LiDAR scanners, individually. Classification is mostly carried out using supervised methods, while segmentation mostly uses unsupervised machine learning techniques.
Machine Learning Algorithms for the Retrieval of Canopy Chlorophyll Content and Leaf Area Index of Crops Using the PROSAIL-D Model with the Adjusted Average Leaf Angle
Authors: Sun Qi , Jiao Quanjun , Chen Xidong , Xing Huimin , Huang Wenjiang , Zhang Bing
Published: April 2023

The canopy chlorophyll content (CCC) and leaf area index (LAI) are both essential indicators for crop growth monitoring and yield estimation. The PROSAIL model, which couples the properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAIL) radiative transfer models, is commonly used for the quantitative retrieval of crop parameters; however, its homogeneous canopy assumption limits its accuracy, especially in the case of multiple crop categories. The adjusted average leaf angle (ALAadj), which can be parameterized for a specific crop type, increases the applicability of the PROSAIL model for specific crop types with a non-uniform canopy and has the potential to enhance the performance of PROSAIL-coupled hybrid methods. In this study, the PROSAIL-D model was used to generate the ALAadj values of wheat, soybean, and maize crops based on ground-measured spectra, the LAI, and the leaf chlorophyll content (LCC). The results revealed ALAadj values of 62 degrees for wheat, 45 degrees for soybean, and 60 degrees for maize. Support vector regression (SVR), random forest regression (RFR), extremely randomized trees regression (ETR), the gradient boosting regression tree (GBRT), and stacking learning (STL) were applied to simulated data of the ALAadj in 50-band data to retrieve the CCC and LAI of the crops. The results demonstrated that the estimation accuracy of singular crop parameters, particularly the crop LAI, was greatly enhanced by the five machine learning methods on the basis of data simulated with the ALAadj. Regarding the estimation results of mixed crops, the machine learning algorithms using ALAadj datasets resulted in estimations of CCC (RMSE: RFR = 51.1 μg cm−2, ETR = 54.7 μg cm−2, GBRT = 54.9 μg cm−2, STL = 48.3 μg cm−2) and LAI (RMSE: SVR = 0.91, RFR = 1.03, ETR = 1.05, GBRT = 1.05, STL = 0.97), that outperformed the estimations without using the ALAadj (namely CCC RMSE: RFR = 93.0 μg cm−2, ETR = 60.1 μg cm−2, GBRT = 60.0 μg cm−2, STL = 68.5 μg cm−2 and LAI RMSE: SVR = 2.10, RFR = 2.28, ETR = 1.67, GBRT = 1.66, STL = 1.51). Similar findings were obtained using the suggested method in conjunction with 19-band data, demonstrating the promising potential of this method to estimate the CCC and LAI of crops at the satellite scale.
Investigating the Impact of Urbanization on Water Ecosystem Services in the Dongjiang River Basin: A Spatial Analysis
Authors: Jia Kai , Huang Ailin , Yin Xiaoling , Yang Ji , Deng Liming , Lin Zhuoling
Published: April 2023

The expansion of urban areas has resulted in a substantial increase in demand for water ecosystem services. To address this issue, this study aims to investigate how the interaction between urbanization and water ecosystem services changed in response to different levels of urbanization in the Dongjiang River Basin from 1985 to 2020. The research examines four water ecosystem services (water yield, soil retention, and water purifications of N and P) and three types of urbanizations (population urbanization, economic urbanization, and land urbanization) to identify spatial heterogeneities among developed urban areas, developing urban areas, and rural regions, as well as their dynamic interactions. The findings indicate that water ecosystem services and urbanizations tend to be spatially polarized, with high values downstream and low values upstream. Although they have become more closely aligned, there is a local mismatch under basin-level homogeneity. Urbanization has migrated and centralized in a southward direction, while water ecosystem services have moved westward. This difference of migration results in an increasing trade-off in the west band of Dongjiang River. In particular, the developing urban area has been strengthening the function of the transition zone between the developed urban area and rural area, resulting in a dramatic decrease in synergy. The synergy of the rural area dominates the increasing synergy of the entire basin, but the developed urban area tends to lower the water ecosystem services that lag behind urbanization. The study recommends that policymakers consider different urban levels when developing urbanization plans and water resource management strategies, and implement measures to maintain the synergy in the rural area and mitigate the trade-off in the developing area.
Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals
Authors: Ortega-Rodríguez Jordan , Gómez-González José Francisco , Pereda Ernesto
Published: April 2023

Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain–machine interfaces (BMIs). In addition, the application of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (one-to-many classification) and authentication (one-to-one or true/false classification). In this article, we establish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order to reduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, one with 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI.
AHY-SLAM: Toward Faster and More Accurate Visual SLAM in Dynamic Scenes Using Homogenized Feature Extraction and Object Detection Method
Authors: Gong Han , Gong Lei , Ma Tianbing , Sun Zhicheng , Li Liang
Published: April 2023

At present, SLAM is widely used in all kinds of dynamic scenes. It is difficult to distinguish dynamic targets in scenes using traditional visual SLAM. In the matching process, dynamic points are incorrectly added to the pose calculation with the camera, resulting in low precision and poor robustness in the pose estimation. This paper proposes a new dynamic scene visual SLAM algorithm based on adaptive threshold homogenized feature extraction and YOLOv5 object detection, named AHY-SLAM. This new method adds three new modules based on ORB-SLAM2: a keyframe selection module, a threshold calculation module, and an object detection module. The optical flow method is used to screen keyframes for each frame input in AHY-SLAM. An adaptive threshold is used to extract feature points for keyframes, and dynamic points are eliminated with YOLOv5. Compared with ORB-SLAM2, AHY-SLAM has significantly improved pose estimation accuracy over multiple dynamic scene sequences in the TUM open dataset, and the absolute pose estimation accuracy can be increased by up to 97%. Compared with other dynamic scene SLAM algorithms, the speed of AHY-SLAM is also significantly improved under a guarantee of acceptable accuracy.
Highlighting Shooting Opportunities in Football
Authors: Loutfi Ilias , Gómez-Jordana Luis Ignacio , Ric Angel , Milho João , Passos Pedro
Published: April 2023

The purpose of the present study was to create a two-dimensional model which illustrates a landscape of shooting opportunities at goal during a competitive football match. For that purpose, we analysed exemplar attacking subphases of each team when the ball was in the last 30 m of the field. The player’s positional data (x and y coordinates) and the ball were captured at 25 fps and processed to create heatmaps that illustrated the shooting opportunities that were available in the first and second half in different field areas. Moreover, the time that the shooting opportunities were available was estimated. Results show that in the observed match, most of the shooting opportunities lasted between 1 and 2 s, with only a few opportunities lasting more than 2 s. The shooting opportunities did not display a homogenous distribution over the field. The obtained heatmaps provide valuable and specific information about each team’s shooting opportunities, allowing the identification of the most vulnerable areas. Additionally, the amount, duration, and location of the shooting opportunities have shown significant differences between teams. This customizable model is sensitive to the features of shooting opportunities and can be used in real-time video analysis for individual and collective performance analysis.
Information Extraction Network Based on Multi-Granularity Attention and Multi-Scale Self-Learning
Authors: Sun Weiwei , Liu Shengquan , Liu Yan , Kong Lingqi , Jian Zhaorui
Published: April 2023

Transforming the task of information extraction into a machine reading comprehension (MRC) framework has shown promising results. The MRC model takes the context and query as the inputs to the encoder, and the decoder extracts one or more text spans as answers (entities and relationships) from the text. Existing approaches typically use multi-layer encoders, such as Transformers, to generate hidden features of the source sequence. However, increasing the number of encoder layers can lead to the granularity of the representation becoming coarser and the hidden features of different words becoming more similar, potentially leading to the model’s misjudgment. To address this issue, a new method called the multi-granularity attention multi-scale self-learning network (MAML-NET) is proposed, which enhances the model’s understanding ability by utilizing different granularity representations of the source sequence. Additionally, MAML-NET can independently learn task-related information from both global and local dimensions based on the learned multi-granularity features through the proposed multi-scale self-learning attention mechanism. The experimental results on two information extraction tasks, named entity recognition and entity relationship extraction, demonstrated that the method was superior to the method based on machine reading comprehension and achieved the best performance on the five benchmark tests.
Development of a Quick-Install Rapid Phenotyping System
Authors: Buelvas Roberto M. , Adamchuk Viacheslav I. , Lan John , Hoyos-Villegas Valerio , Whitmore Arlene , Stromvik Martina V.
Published: April 2023

In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale.
Target Velocity Ghost Imaging Using Slice Difference Method
Authors: Jia Fan , Zhang Zijing , Zhao Yuan
Published: April 2023

Ghost imaging is a technique that uses the correlation between reference and signal arms to obtain intensity images of targets. Compared with the existing laser active imaging methods, ghost imaging can improve the signal-to-noise ratio and resolution. In this paper, through the use of the slice difference method, we propose a new scheme that allows a velocity image of moving targets to be obtained. We conduct a complete theoretical analysis and provide a proof-of-principle experiment. The experimental results are in good agreement with those of the theoretical analysis, and a velocity image with 64 × 64 resolution is obtained. This protocol achieves a great increase in the signal-to-noise ratio over what would be achievable using direct imaging. The results show a fully functional instance of velocity imaging, which is a key advancement on the path towards the multi-dimensional information acquisition of moving targets. Our scheme fulfils an urgent need for the detection of moving targets and may thus find use in fields such as target attitude perception and security monitoring.
Intelligent Video Analytics for Human Action Recognition: The State of Knowledge
Authors: Kulbacki Marek , Segen Jakub , Chaczko Zenon , Rozenblit Jerzy W. , Kulbacki Michał , Klempous Ryszard , Wojciechowski Konrad
Published: April 2023

The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temporal, and deep learning-based approaches, including visual transformers. We also discuss the challenges and limitations of these techniques and the potential of modern edge AI architectures to enable real-time human action recognition in resource-constrained environments.
Stakeholders’ Perspectives on Generative Voluntary Safety Reporting Culture (GVSRC) in the Gulf of Mexico (GOM) Oil and Gas (O&G) Sector Using the Offshore Safety Action Program (OSAP)
Authors: Adjekum Daniel Kwasi , Owusu-Amponsah Nana Yaw , Afari Samuel Asante , Waller Zachary , Rasouli Vamegh , Ullrich Gary , Snyder Paul , Corbin Neal
Published: April 2023

To fill a gap in understanding of the Generative Voluntary Safety Reporting Culture (GVSRC) in the Gulf of Mexico (GOM) Oil and Gas (O&G) sector, perspectives of stakeholders based on their experiences were explored using attributes of a proposed Offshore Safety Action Program (OSAP) modeled after the Aviation Safety Action Program (ASAP). A phenomenological approach encompassing semi-structured interviews (n = 18) and five focus-group sessions (n = 18) was used to collect data from a cross-section of top management, supervisors, regulatory representatives, and subject-matter experts (SME). Four themes emerged from a Thematic Analysis: (1) Voluntary safety reporting culture, (2) Voluntary safety reporting bottlenecks, (3) Universality, and (4) Organizational review of safety events. Most respondents strongly supported the OSAP because it ensures a formalized adjudication of voluntary safety reports by an Event Review Committee (ERC) with representation from employees, management, and regulators. Most respondents supported the non-punitive and confidential attributes of the OSAP as a means to enhance GVSRC. However, there were varying perspectives on defining intentional disregard for safety under the OSAP. Due to the enumerated challenges of cost, respondents agreed that organizations use a scalable process commensurate with the complexity of their operations when adopting the OSAP. A veritable framework for data-driven corrective actions, organizational learning, and enhanced GVSRC in the offshore sector is a potential policy implication of adopting the OSAP.
A New Model for a Sustainable Healthcare Supply Chain Prioritizes Patient Safety: Using the Fuzzy Delphi Method to Identify Healthcare Workers’ Perspectives
Authors: Kanokphanvanich Chayada , Rattanawong Wanchai , Vongmanee Varin
Published: April 2023

The pandemic crisis and the resulting global uncertainties have obviously had a severe impact on the healthcare supply chain (HSC), leading scholars, healthcare executives, and policymakers to focus on the sustainability of the HSC. Technologies have emerged and developed rapidly in recent years, especially in the healthcare industry, for coping with the pandemic crisis and supporting the “new normal” for humankind. Within this context, various new technologies have been implemented to maximize the supply chain process, ensure patient and healthcare worker safety, and improve the quality of care. Hence, the integration of a technological dimension with the traditional three pillars of sustainability may aid in attempts to define the potential attributes of these dimensions of sustainability. Therefore, this study aimed to identify the key attributes of a sustainable healthcare supply chain (SHSC), and this paper presents a new, four-dimensional model for SHSCs, consisting of social, environmental, economic, and technological dimensions. A systematic literature review was conducted, resulting in the identification of 35 potential SHSC attributes. The Fuzzy Delphi Method (FDM) was then applied to determine the appropriateness of these potential attributes according to the consensus of 13 experts, including healthcare workers in a variety of medical specialties, who profoundly understand HSC sustainability. The results yielded 22 appropriate attributes, which were then categorized across the four dimensions. Consequently, a new model of an SHSC, which prioritizes patient safety, was constructed and is proposed here. This SHSC model can be applied strategically to the healthcare industry to enhance the safety of both medical personnel and patients in a sustainable manner.
Carbon Footprint Analysis of Tourism Life Cycle: The Case of Guilin from 2011 to 2022
Authors: Cao Rui , Mo Yanhua , Ma Jiangming
Published: April 2023

Low-carbon tourism is an important way for the tourism industry to achieve the United Nations Sustainable Development Goals and the goals of carbon peaking and carbon neutrality. In order to promote the development of Guilin as a world-class tourism city and ensure the sustainable development of the tourism industry in Guilin, this paper combines the concept of carbon footprint and the theory of life cycle to build a tourists’ carbon footprint life cycle analysis model of Guilin. Taking tourists in Guilin as an example, the composition and changes of tourists’ carbon footprint are dynamically analyzed. The research shows that: (1) The overall tourism carbon footprint of Guilin showed an upward trend during 2011–2019. From 2020 to 2022, due to the impact of COVID-19, Guilin’s tourism carbon footprint has decreased significantly. The per capita carbon footprint of tourism in Guilin showed a downward trend from 2011 to 2022; (2) The order of the size of Guilin’s tourism carbon footprint is tourism transportation > tourism catering > tourism accommodation > tourism activities; (3) From 2011 to 2022, the carbon footprint of tourism transportation in Guilin showed an obvious narrowing state, while the carbon footprint of tourism accommodation, tourism activities, and tourism catering showed an obvious expanding trend. Based on the characteristics of the carbon footprint of Guilin’s tourism and the current situation of the development of Guilin’s tourism, this paper puts forward suggestions on reducing carbon emissions, forms a new tool for evaluating and constructing low-carbon tourism, and provides a scientific basis and practical reference significance for the sustainable development of low-carbon tourism in Guilin.
Methane Emission and Carbon Sequestration Potential from Municipal Solid Waste Landfill, India
Authors: BP Naveen , Tabaroei Abdollah , Garg Ankit
Published: April 2023

Quantities of waste generation are drastically increasing every day, and most of the waste is disposed of through open dumps and landfilling. Methane, carbon dioxide, and nitrous oxide are major greenhouse gases (GHGs) produced from landfill sites. However, the global-warming potential of methane is 21 times higher than that of carbon dioxide. Hence, there is immense concern for its utilization from landfill sites. In developing countries, the composition of municipal solid waste (MSW) has high amounts of biodegradable waste (50–60%). This leads to higher emissions of GHGs a per ton of MSW compared to the developed world. In this study, the attempt will be made to estimate the amount of carbon stored in MSW burial in landfills. Tests were conducted in two different locations at the Mavallipura landfill. MSW samples were collected for every meter interval (1–2 m, 2–3 m and so on) up to 6 m. The result shows that carbon stored in organic matter increases with depth from approximately 2.2% at 1.0 m depth to 4.8% at 6 m depth. Based on MSW’s carbon storage factor and data on MSW generation, global carbon sequestration from MSW burial in the Mavallipura landfill is estimated to be at least 10 million metric tons per year. In additional, the study aims to quantify methane-gas production from the ward levels and the Mavallipura landfill site in India.
Forecasting and Coupled Coordination Analysis of Supply and Demand for Sustainable Talent in Chinese Agriculture
Authors: Wang Shuya , Tian Xinjia , Wang Hui , Liu Chang , Wang Zhilin , Song Qiuhua
Published: April 2023

In recent years, China has achieved notable results with its poverty alleviation program, the focus of which is shifting toward the comprehensive promotion of rural revitalization. The role played by sustainable human resources in agriculture is becoming increasingly prominent. In this context, China’s sustainable talent in agriculture is used as the research object, and a neural network analysis method is applied to construct a prediction model of sustainable agricultural talent to forecast its supply and demand. The prediction aims to provide a scientific basis for the strategic planning of talent development for rural revitalization. Based on the forecast results by region and province, we analyzed the level of coordinated development of talent supply and demand to provide a reference for the coordinated development of supply and demand of sustainable talent in agriculture in China. The results showed that a large sustainable agricultural talent demand gap exists in China. The overall talent supply and demand coupling coordination level is low; we found significant differences among different regions and provinces, characterized by decreasing order of the northeast, central, west, and east. According to the socio-economic development level, agricultural economic foundation, and other factors, we divided the provinces into six types for analysis. To promote the coordinated development of sustainable human agricultural resources, talent policy support at the national level is required to reduce the loss of human resources to other countries; at the regional level, the talent environment for rural revitalization should be optimized to increase the attraction of talent. At the provincial level, agricultural and forestry education resources should be created to increase the supply of sustainable agricultural talent.