bluuebunny/crossref_metadata_2025 huggingface dataset:
Applications and Life Cycle Assessment of Shape Memory Polyethylene Terephthalate in Concrete for Crack Closure
Authors: Maddalena Riccardo , Sweeney John , Winkles Jack , Tuinea-Bobe Cristina , Balzano Brunella , Thompson Glen , Arena Noemi , Jefferson Tony
Published: February 2022

Shape memory polymer (SMP) products have been developed for application as crack closure devices in concrete. They have been made from PET in the form of both fibres and hollow tubes. Here, manufacturing methods using die-drawing and mandrel-drawing to induce shape memory are reported. The fibre-based devices are incorporated into concrete and, upon triggering, exert shrinkage restraint forces that close cracks in the concrete. The evolution of shrinkage restraint force in the fibres as manufactured was measured as a function of temperature, showing stresses in excess of 35 MPa. Tendons consisting of fibre bundles are incorporated into concreate beams subjected to controlled cracking. When activated, the tendons reduce the crack widths by 80%. The same fibres are used to produce another class of device known as knotted fibres, which have knotted ends that act as anchor points when they incorporated directly into concrete. Upon activation within the cracked concrete, these devices are shown to completely close cracks. The tubes are used to enclose and restrain prestressed Kevlar fibres. When the tubes are triggered, they shrink and release the prestress force in the Kevlar, which is transferred to the surrounding concrete in the form of a compressive force, thereby closing cracks. The Kevlar fibres also provide substantial reinforcement after activation. The devices are shown to be able to partially and fully close cracks that have been opened to 0.3 mm and achieve post-activation flexural strengths comparable to those of conventional reinforced and prestressed structural elements. Finally, a preliminary life cycle assessment study was used to assess the carbon footprint a nominal unit of concrete made with SMPs fibres compared to conventional concrete.
Effect of PVOH/PLA + Wax Coatings on Physical and Functional Properties of Biodegradable Food Packaging Films
Authors: Apicella Annalisa , Barbato Antonio , Garofalo Emilia , Incarnato Loredana , Scarfato Paola
Published: February 2022

Biodegradable polymers suffer from inherent performance limitations that severely limit their practical application. Their functionalization by coating technology is a promising strategy to significantly improve their physical properties for food packaging. In this study, we investigated the double coating technique to produce multifunctional, high barrier and heat-sealable biodegradable films. The systems consisted of a web layer, made of poly(lactide) (PLA) and poly(butylene-adipate-co-terephthalate) (PBAT), which was first coated with a poly(vinyl) alcohol based layer, providing high barrier, and then with a second layer of PLA + ethylene-bis-stereamide (EBS) wax (from 0 to 20%), to provide sealability and improve moisture resistance. The films were fully characterized in terms of chemical, thermal, morphological, surface and functional properties. The deposition of the PVOH coating alone, with a thickness of 5 μm, led to a decrease in the oxygen transmission rate from 2200 cm3/m2 d bar, for the neat substrate (thickness of 22 μm), to 8.14 cm3/m2 d bar (thickness of 27 μm). The deposition of the second PLA layer did not affect the barrier properties but provided heat sealability, with a maximum bonding strength equal to 6.53 N/25 mm. The EBS wax incorporation into the PLA slightly increased the surface hydrophobicity, since the water contact angle passed from 65.4°, for the neat polylactide layer, to 71° for the 20% wax concentration. With respect to the substrate, the double-coated films exhibited increased stiffness, with an elastic modulus ca. three times higher, and a reduced elongation at break, which, however still remained above 75%. Overall, the developed double-coated films exhibited performances comparable to those of the most common synthetic polymer films used in the packaging industry, underlining their suitability for the packaging of sensitive foods with high O2-barrier requirements.
Electrospun Fibrous Membrane with Confined Chain Configuration: Dynamic Relaxation and Glass Transition
Authors: Zhang Nuozi , Wang Chenhong , Chen Hao , Wu Jiaen , Han Charles C. , Xu Shanshan
Published: February 2022

Thermodynamic glass transition processes of electrospun membranes were first introduced to study their dynamic relaxation nature, which is not constantly in equilibrium. The relaxation modes of electrospun membranes are slow but measurable near and above the Tg, given the stretched chain over long distances. Based on differential scanning calorimetry (DSC) experiments and the general principle of mode-coupling theory (MCT), endothermic peak temperature and relaxation enthalpy were used to analyze the relaxation process by capturing these instantaneous “arrested” structures. The short- and long-wavelength relaxation modes could be identified with different annealing times and temperatures relative to DSC-measured Tg for electrospun membranes with different molecular weights. Results clearly showed the dynamic nature of a glass transition in polymeric materials. Tp and enthalpy loss initially increased and then directly decreased with the increase in annealing time. When Ta > Tg, regardless of the size of the molecular weight, the Tp and enthalpy loss of the PLGA fibers would directly decrease, and the curves would shift toward the melted one. Combination of electrospinningand normal DSC instrument can be used to investigating the dynamic relax process through an adequately designed kinetic scanning procedure. This result can be explained by the general principle of MCT-type dynamic theory.
A Vibration Analysis of the Rubber Inertial Dampers Used in Electrical Vehicles
Authors: Itu Calin , Vlase Sorin , Marin Marin
Published: February 2022

The development of electric vehicle manufacturing, which is considered a useful new popular propulsion system, has major design differences compared to conventional vehicles. This requires a reconsideration of the main components of vehicles and an analysis of them to determine the optimal design and solutions for the new models of cars. Among the many systems that need to be reconsidered is the suspension. A cheaper solution for reducing the car’s vibrations is suspension where the damping is ensured by elastic rubber elements, which are very simple, as they have significant structural damping and a much lower price than the classic solution. The main advantage of this solution is the simplicity. The paper presents and analyzes such an element, analyzing the vibrations of this element and the way in which inertial masses (metal spheres) inserted into the volume of the rubber influence the behavior of this element. The transmissibility of such an element, and how the number of balls and the level of structural damping influences this property, is also analyzed. The results suggest possible applications in the automotive industry.
Cetylpyridinium Bromide/Polyvinyl Chloride for Substantially Efficient Capture of Rare Earth Elements from Chloride Solution
Authors: Allam Eman M. , Lashen Taysser A. , Abou El-Enein Saeyda A. , Hassanin Mohamed A. , Sakr Ahmed K. , Hanfi Mohamed Y. , Sayyed M. I. , Al-Otaibi Jamelah S. , Cheira Mohamed F.
Published: February 2022

A new sorbent cetylpyridinium bromide/polyvinylchloride (CPB/PVC) was prepared and tested to extract rare earth elements (REEs) from their chloride solutions. It was identified by FTIR, TGA, SEM, EDX, and XRD. The impact of various factors such as pH, RE ion initial concentration, contacting time, and dose amount via sorption process was inspected. The optimum pH was 6.0, and the equilibrium contact time was reached at 60 min at 25 °C. The prepared adsorbent (CPB/PVC) uptake capacity was 182.6 mg/g. The adsorption of RE ions onto the CPB/PVC sorbent was found to fit the Langmuir isotherm as well as pseudo-second-order models well. In addition, the thermodynamic parameters of RE ion sorption were found to be exothermic and spontaneous. The desorption of RE ions from the loaded CPB/PVC sorbent was investigated. It was observed that the optimum desorption was achieved at 1.0 M HCl for 60 min contact time at ambient room temperature and a 1:60 solid: liquid phase ratio (S:L). As a result, the prepared CPB/PVC sorbent was recognized as a competitor sorbent for REEs.
A Simple and Effective Modeling Method for 3D Porous Irregular Structures
Authors: Ren Lijing , Zhang Denghui
Published: February 2022

Porous structures are kinds of structures with excellent physical properties and mechanical characteristics through components and internal structure. However, the irregular internal morphology of porous structures poses new challenges to product modeling techniques. Traditional computer-aided design (CAD) modeling methods can only represent the external geometric and topological information of models, lacking the description of the internal structure and conformation, which limits the development of new porous products. In this paper, a new simple and effective modeling method for 3D irregular porous structures is proposed, which improves the controllability of pore shape and porosity, thus overcoming the limitations of existing methods in 3D and concave structures. The key idea is to solve isothermal for modeling the porosity of porous units. Experimental results show that the method can easily obtain smooth and approximate porous structures from arbitrary irregular 3D surfaces.
Faba Bean Fractions for 3D Printing of Protein-, Starch- and Fibre-Rich Foods
Authors: Johansson Mathias , Nilsson Klara , Knab Fanny , Langton Maud
Published: February 2022

Food 3D printing allows for the production of personalised foods in terms of shape and nutrition. In this study, we examined whether protein-, starch- and fibre-rich fractions extracted from faba beans can be combined to produce fibre- and protein-rich printable food inks for extrusion-based 3D printing. Small amplitude oscillatory shear measurements were used to characterise the inks while compression tests and scanning electron microscopy were used to characterise the freeze-dried samples. We found that rheological parameters such as storage modulus, loss tangent and yield stress were related to ink printability and shape stability. Investigations on the effect of ink composition, infill pattern (honeycomb/grid) and direction of compression on textural and microstructural properties of freeze-dried 3D-printed objects revealed no clear effect of infill pattern, but a strong effect of direction of compression. Microstructure heterogeneity seemed to be correlated with the textural properties of the printed objects.
Grape Pomace Valorization by Extraction of Phenolic Polymeric Pigments: A Review
Authors: Castellanos-Gallo Lilisbet , Ballinas-Casarrubias Lourdes , Espinoza-Hicks José C. , Hernández-Ochoa León R. , Muñoz-Castellanos Laila Nayzzel , Zermeño-Ortega Miriam R. , Borrego-Loya Alejandra , Salas Erika
Published: February 2022

In recent years there has been a growing concern about environmental pollution linked to the generation of agroindustrial waste. The wine industry generates approximately 8.49 million tons of grape pomace per year worldwide; this residue can be used to obtain compounds with biological activity. Grape pomace is a source of anthocyanins, pigments that have antioxidant properties and help prevent cardiovascular disease. The development of sustainable extraction, purification and identification techniques constitutes an important step in adding value to this waste. Therefore, the present research has focused on presenting a review of works carried out in the last years.
Recent Developments in Voltammetric Analysis of Pharmaceuticals Using Disposable Pencil Graphite Electrodes
Authors: David Iulia Gabriela , Buleandra Mihaela , Popa Dana Elena , Cheregi Mihaela Carmen , David Vasile , Iorgulescu Emilia Elena , Tartareanu Georgiana Oana
Published: February 2022

The even growing production of both well-known and new derivatives with pharmaceutical action involves the need for developing facile and reliable methods for the analysis of these compounds. Among the widely used instrumental techniques, the electrochemical ones are probably the simplest and the most rapid, also having good performance characteristics. However, the key tool in electroanalysis is the working electrode. Due to the inherent electrochemical and economic advantages of the pencil graphite electrode (PGE), the interest in its applicability in the analysis of different analytes has continuously increased in recent years. Thus, this paper aims to review the scientific reports published in the last 10 years on the use of the disposable eco- and user-friendly PGEs in the electroanalysis of compounds of pharmaceutical importance in different matrices. The PGE characteristics and designs (bare or modified with various types of materials), along with their applications and performance parameters (e.g., linear range, limit of detection, and reproducibility), will be discussed, and their advantages and limitations will be critically emphasized.
A Spatio-Temporal Deep Learning Network for the Short-Term Energy Consumption Prediction of Multiple Nodes in Manufacturing Systems
Authors: Guo Jianhua , Han Mingdong , Zhan Guozhi , Liu Shaopeng
Published: February 2022

Short-term energy prediction plays an important role in green manufacturing in the industrial internet environment and has become the basis of energy wastage identification, energy-saving plans and energy-saving control. However, the short-term energy prediction of multiple nodes in manufacturing systems is still a challenging issue owing to the fuzzy material flow (spatial relationship) and the dynamic production rhythm (temporal relationship). To obtain the complex spatial and temporal relationships, a spatio-temporal deep learning network (STDLN) method is presented for the short-term energy consumption prediction of multiple nodes in manufacturing systems. The method combines a graph convolutional network (GCN) and a gated recurrent unit (GRU) and predicts the future energy consumption of multiple nodes based on prior knowledge of material flow and the historical energy consumption time series. The GCN is aimed at capturing spatial relationships, with the material flow represented by a topology model, and the GRU is aimed at capturing dynamic rhythm from the energy consumption time series. To evaluate the method presented, several experiments were performed on the power consumption dataset of an aluminum profile plant. The results show that the method presented can predict the energy consumption of multiple nodes simultaneously and achieve a higher performance than methods based on the GRU, GCN, support vector regression (SVR), etc.
Particle Swarm Optimization Algorithm-Tuned Fuzzy Cascade Fractional Order PI-Fractional Order PD for Frequency Regulation of Dual-Area Power System
Authors: Shouran Mokhtar , Alsseid Aleisawee
Published: February 2022

This study proposes a virgin structure of Fuzzy Logic Control (FLC) for Load Frequency Control (LFC) in a dual-area interconnected electrical power system. This configuration benefits from the advantages of fuzzy control and the merits of Fractional Order theory in traditional PID control. The proposed design is based on Fuzzy Cascade Fractional Order Proportional-Integral and Fractional Order Proportional-Derivative (FC FOPI-FOPD). It includes two controllers, namely FOPI and FOPD connected in cascade in addition to the fuzzy controller and its input scaling factor gains. To boost the performance of this controller, a simple and powerful optimization method called the Particle Swarm Optimization (PSO) algorithm is employed to attain the best possible values of the suggested controller’s parameters. This task is accomplished by reducing the Integral Time Absolute Error (ITAE) of the deviation in frequency and tie line power. Furthermore, to authenticate the excellence of the proposed FC FOPI-FOPD, a comparative study is carried out based on the obtained results and those from previously published works based on classical PID tuned by the Losi Map-Based Chaotic Optimization Algorithm (LCOA), Fuzzy PID Optimized by Teaching Learning-Based Optimization (TLBO) algorithm and Fuzzy PID with a filtered derivative mode tuned by PSO, which is employed in the same interconnected power system. The robustness of the suggested fuzzy structure is investigated against the parametric uncertainties of the testbed system. The simulation results revealed that the proposed FC FOPI-FOPD is robust, and it outperformed the other investigated controllers. For example, the drops in the frequency in area one and area two were improved by 89.785% and 97.590%, respectively, based on employing the proposed fuzzy configuration compared with the results obtained from the traditional PID.
Surface Characterization and Performance Evaluation of Nitrogen Implanted Coinage Dies
Authors: Cruz João , Corregidor Victoria , Nunes Bruno , Alves Luís , Colaço Rogério , Alves Eduardo
Published: February 2022

Very high fluence implantation of 14N+ ions was used to promote the formation of a nitride layer on the surface of steel coinage dies used by the Portuguese Mint. Die steel samples were studied before and after implantation at 90 keV plus 30 keV for two nominal fluences, 5.0 × 1017 at/cm2 and 1.0 × 1018 at/cm2, and for two implantation temperatures, room and liquid nitrogen temperature. Surface characterization was carried out by ultramicrohardness indentation, Atomic Force Microscopy (AFM), and Rutherford Backscattering Spectrometry (RBS). A significant increase (factor two) in depth reached by the implanted nitrogen was observed for LN2 implantations, which, to our knowledge, was never reported before. Results point out the benefit of nitrogen implantation at room temperature with a nominal fluence of 5.0 × 1017 at/cm2, which raised the hardness of the dies by ∼40% (from 6.2 ± 0.5 to 8.7 ± 0.7 GPa). Nitrogen implanted dies were used to mint circulating two cents euro coins and their performance was assessed, showing that when compared to Vautrol coated dies (standard coating used by the Portuguese Mint), the crack density was lower and similar wear resistance was observed.
Numerical Simulation Model for Evaluating Protection Measures of Blast Furnace Hearth
Authors: Wang Lei , Chen Liangyu , Li Yang , Ma Jiaocheng
Published: February 2022

The blast furnace (BF) hearth is critical for determining the life of a BF. Irreversibly eroded hearths can be caused by high-temperature molten iron erosion, alkali metal corrosion, and thermal stress. When serious depression erosion occurs in the hearth, furnace protection measures can prevent the erosion from expanding and ensure the safe operation of the BF. At present, furnace protection measures and furnace protection strength are mostly selected based on engineering experience. In this paper, a three-dimensional (3D) computational fluid dynamics (CFD) numerical model of BF hearth with elephant-type depression erosion was established to predict and evaluate the effect of furnace protection measures. At the same time, the phase change behavior of hot iron solidification was also considered. A numerical model was used to analyze common furnace protection measures such as increasing furnace hearth cooling, closing the tuyere, reducing the tapping productivity, and reducing the tapping temperature. The calculation results are consistent with actual furnace protection experience.
Mathematical Simulation of Forest Fuel Pyrolysis and Crown Forest Fire Impact for Forest Fire Danger and Risk Assessment
Authors: Baranovskiy Nikolay Viktorovich , Kirienko Viktoriya Andreevna
Published: February 2022

In order to predict and assess the danger from crown forest fires, it is necessary to study the thermal degradation of different forest fuels in a high-temperature environment. In this paper, the main characteristics of pyrolysis accompanied by moisture evaporation in a foliage sample of angiosperms (birch) were investigated within conditions typical for a crown forest fire. The heat and mass transfer in the forest fuel element is described by the system of non-stationary non-linear heat conduction equations with corresponding initial and boundary conditions. The considered problem is solved within the framework of the three-dimensional statement by the finite difference method. The locally one-dimensional method was used to solve three-dimensional equations for heat conduction. The simple iteration method was applied to solve nonlinear effects caused by the forest fuel pyrolysis and moisture evaporation. The fourth kind of boundary conditions are applicable at the interface between the sub-areas. Software implementation of the mathematical model is performed in the high-level programming language Delphi in the RAD Studio software. The characteristic changes in the sample temperature field and the phase composition under high-temperature exposure from a forest fire are presented. The induction period of the thermal decomposition of dry organic matter in the sample was determined. Recommendations are made about key features of accounting for the pyrolysis and evaporation processes when predicting forest fire danger. The research results can be used in the development and improvement of systems for predicting forest fire danger and environmental consequences of the forest fires.
Introduction to Special Issue: Organ Transplantation in Islam: Perspectives and Challenges
Authors: Ali Jan A.
Published: February 2022

It has been suggested that tissue and organ transplantation, as a surgical procedure to restore lost or mutilated body parts, is a centuries-old practice confirmed in an ancient Sanskrit text on medicine and surgery called the Sushruta Samhita in circa 600 B [...]
Establishing Intergenerational Relationships in Unlikely Collaborative Educational Contexts
Authors: Moreno Abellán Pedro , Martínez de Miguel López Silvia , Salmerón Aroca Juan Antonio
Published: February 2022

This work presents a project called “ESium Project”, whose main objective is to create new spaces for social participation and a relationship between two different age groups: the young Social Education students and the elderly. It has a double purpose: to promote that reciprocal space for cooperation and interaction in a way that avoids the risk of disengagement and to promote a professional perspective. Target students will become future professionals who will also work with the elderly collective. Furthermore, a research-action method aims to make the participants protagonists of the educational actions that are carried out by using qualitative techniques as essential strategies for the work of collectives. Not only do the results regard the intergenerational stereotypes, they also show the assessment of the satisfaction by the involved participants, the international acknowledgement of the educational activity developed, and the possibility to use this in other contexts. In this way, it can be concluded that there is a need to tackle intergenerationally in teacher education from a wide-open inclusive perspective to take advantage of senior talent and innovation.
High-Speed Magnetic Surveying for Unexploded Ordnance Using UAV Systems
Authors: Kolster Mick Emil , Wigh Mark David , Lima Simões da Silva Eduardo , Bjerg Vilhelmsen Tobias , Døssing Arne
Published: February 2022

Using Uncrewed Aerial vehicles (UAVs) to rapidly scan areas for potential unexploded ordnance (UXO) can provide an efficiency increase while minimizing detonation risks. We present a complete overview of how such mappings can be performed using scalar magnetometers, including initial sensor testing, time stamping validation, data positioning, noise removal, and source model inversion. A test survey was performed across disarmed UXO targets, during which three scalar magnetometers were towed in an airframe (“bird”) 10 m below a small (<25 kg) high speed (∼10 m/s) UAV to avoid magnetic disturbances from the UAV itself. Data were collected across ∼58 min of flight, with each sensor traversing ∼31.7 km to acquire dense data coverage across a 600 m × 100 m area. By using three individual magnetometers in the bird, UXO detection results across single-sensor data and several different multi-sensor configurations can be compared. The data obtained exhibited low apparent noise floors (on the order of tens of picoTesla) and retained a precision that enabled targeted modelling and removal of high-frequency noise with amplitudes of ±5 picoTesla. All of the different gradiometer configurations tested enabled recovery of most targets (including all major targets), although the horizontal configuration performed significantly worse in comparison.
Enhanced Chlorophyll-a in the Coastal Waters near the Eastern Guangdong during the Downwelling Favorable Wind Period
Authors: Yang Chaoyu , Ye Haibin
Published: February 2022

Phytoplankton dynamics, which are highly sensitive to the ecosystem condition and change, are different in coastal waters and open ocean. Previous researches mainly focused on the open ocean dynamic in the South China Sea (SCS), but few research on the Eastern Guangdong (EGD), especially during a period of downwelling-favorable winds. In fact, the influence of topographic irregularities, downwelling, continental outflows, and cold, nutrient-rich currents from Fujian-Zhejiang coastal waters (ZFC) of the East China Sea (ECS) on the spatial distribution of Chlorophyll a (Chl a) in EGD coastal waters could be significant. This study utilized ocean color time series and meteorological and hydrographic data jointly from July 2002 to June 2020 to investigate the heterogeneous regional specific distribution pattern of Chl a in EGD and the environmental determinants in different regions subdivided by water depth. Then the temporal and spatial distribution pattern of Chl a in EGD in winter were discussed in detail by applying an Empirical Orthogonal Function (EOF) analysis, GaoFeng-1 (GF-1) satellite data and in situ measured dataset. The sea surface temperature (SST) in February was negatively related with Chl a in shallow coastal waters less than 60 m deep due to the nutrient-rich, cold waters from ZFC. The monthly mean photosynthetically active radiation (PAR) and precipitation showed significant effects on the phytoplankton growth over regions with a depth less than 10 m. An area with higher Chl a concentration in the downwelling zone were detected in winter. By an examining the temporal variability in meridional distribution of the mean Chl a at 22.41°N and 22.21°N, a symmetrical peak was observed. The coastal fronts extended southwestward from the southeastern coast of Guangdong Province to Dangan Island in the SCS. In addition, a cross-shelf filament was detected near the coast of Shanwei, Guangdong, China on 8 January 2020.
Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery
Authors: Narmilan Amarasingam , Gonzalez Felipe , Salgadoe Arachchige Surantha Ashan , Kumarasiri Unupen Widanelage Lahiru Madhushanka , Weerasinghe Hettiarachchige Asiri Sampageeth , Kulasekara Buddhika Rasanjana
Published: February 2022

The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05) spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R2) and root mean square error (RMSE). Spectral indices such as RVI and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R2) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R2) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content.
Change Analysis on the Spatio-Temporal Patterns of Main Crop Planting in the Middle Yangtze Plain
Authors: Jiang Luguang , Wu Si , Liu Ye
Published: February 2022

As a traditional agricultural production base in China, the Middle Yangtze Plain (MYP) is a typical region to explore the intensification, large-scale, and agglomeration of agricultural land, and its crop planting situation is sensitive to changes in national agricultural policy and economic development. So far, the research of crop remote sensing extraction mainly has focused on the areas with simple crops rotation patterns, by using short-time sequence remote sensing data with low spatial resolution. The objective of this study was to address how to accurately map the spatial distribution of main crops considering their spectral and phenological features, and what characteristics of spatio-temporal patterns dynamics of crops occurred in the MYP in 1990–2020. Based on Landsat and MODIS data, using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) as well as the raster-based spectral and phenological differential change method (RSPDCM), this study mapped the spatial distribution of main crops (rice, cotton, maize, soybean, rapeseed and winter wheat) in the MYP during 1990–2020 and analyzed their planting characteristics. The RSPDCM has a good overall accuracy of more than 89%. The planting characteristics of the main crops were highly intensive and agglomerate double-cropping rotation in the MYP’s paddy field. Rice and rapeseed were the two most important crops, accounting for 74.75% of the annual planting area. The highly intensive and large-scale areas were mainly distributed in the Dongting Lake Plain (DTLP) and Poyang Lake Plain (PYLP), while the highly agglomerate areas of main crops were mainly distributed in the Jianghan Plain (JHP). This study innovatively provides a high-precision multi-cropping spatial dynamic mapping method and basic information, which is helpful to realize high-precision remote sensing extraction of crops in different regions of the world and provide basic data for optimizing the allocation of agricultural production resources in top grain-producing areas.
Effect of Aerosol Vertical Distribution on the Modeling of Solar Radiation
Authors: Fountoulakis Ilias , Papachristopoulou Kyriakoula , Proestakis Emmanouil , Amiridis Vassilis , Kontoes Charalampos , Kazadzis Stelios
Published: February 2022

Default aerosol extinction coefficient profiles are commonly used instead of measured profiles in radiative transfer modeling, increasing the uncertainties in the simulations. The present study aimed to determine the magnitude of these uncertainties and contribute towards the understanding of the complex interactions between aerosols and solar radiation. Default, artificial and measured profiles of the aerosol extinction coefficient were used to simulate the profiles of different radiometric quantities in the atmosphere for different surface, atmospheric, and aerosol properties and for four spectral bands: ultraviolet-B, ultraviolet-A, visible, and near-infrared. Case studies were performed over different areas in Europe and North Africa. Analysis of the results showed that under cloudless skies, changing the altitude of an artificial aerosol layer has minor impact on the levels of shortwave radiation at the top and bottom of the atmosphere, even for high aerosol loads. Differences of up to 30% were, however, detected for individual spectral bands. Using measured instead of default profiles for the simulations led to more significant differences in the atmosphere, which became very large during dust episodes (10–60% for actinic flux at altitudes between 1 and 2 km, and up to 15 K/day for heating rates depending on the site and solar elevation).
Nightlights and Subnational Economic Activity: Estimating Departmental GDP in Paraguay
Authors: McCord Gordon Carlos , Rodriguez-Heredia Mario
Published: February 2022

Subnational measures of economic activity are crucial for analyzing inequalities that persist across subnational regions and for tracking progress towards sustainable development within a country. Eighteen of the Sustainable Development Goals (SDG) indicators require having estimates of Gross Domestic Product (GDP), making subnational GDP estimates crucial for local SDG monitoring. However, many countries do not produce official subnational GDP estimates. Using Paraguay as an example, we show how nightlights imagery from the Visible Infrared Imaging Radiometer Suite’s Day-Night Band (VIIRS-DNB) and data from neighboring countries can be used to produce subnational GDP estimates. We first estimate the relationship between VIIRS and economic activity in South American countries at the first subnational administrative level, employing various econometric models. Results suggest that nightlights are strongly predictive of subnational GDP variation in South American countries with available data. We assess various models’ goodness-of-fit using both cross-validation against other countries’ subnational GDP data and comparing predictions against an input–output accounting of Paraguay’s subnational GDP. Finally, we use the preferred model to produce a time series of department-level GDP in Paraguay.
Analysis of Ecological Blockage Pattern in Beijing Important Ecological Function Area, China
Authors: Xu Jiangqi , Wang Jia , Xiong Nina , Chen Yuhan , Sun Lu , Wang Yutang , An Likun
Published: February 2022

With the implementation of human activities, such as logging, reclamation, and construction, the increasing fragmentation of ecological space and the increasing blockage of biological migration corridors cause many threats to biodiversity conservation. In this study, we used the Northwest Beijing Ecological Containment Area as the research area. Based on an integrated circuit theoretical model, we identified functional connectivity networks and analyzed the spatial and temporal changes of ecological blockage patterns in the region from 1998–2018 in terms of the landscape connectivity, ecological breakpoints, pinch points, and barriers, respectively. The results show that the average remote sensing ecological index had a trend of decreasing and then increasing, and a total of 33, 34, and 63 habitat core areas and 70, 74, and 152 ecological corridors were identified in 1998, 2010, and 2018, respectively. The regions with high ecological blockage were mainly in the central part of Yanqing District, the southwest corner of the study area, and the eastern urban area. Although the number of potential ecological corridors gradually increases with the probability of migration in the study area, the blockage status and vulnerability of the ecological corridors continue to increase due to the conflict between land uses. The ecological status of the study area reflects the comprehensive effectiveness of the capital’s high-quality development under the strategic deployment of ecological civilization. In the context of habitat fragmentation, the effective protection and restoration of the ecological conditions in the ecological function areas is of great importance in guaranteeing the ecological quality and sustainable development of the country.
A Ship Detection Method via Redesigned FCOS in Large-Scale SAR Images
Authors: Zhu Mingming , Hu Guoping , Zhou Hao , Wang Shiqiang , Feng Ziang , Yue Shijie
Published: February 2022

Ship detection in large-scale synthetic aperture radar (SAR) images has achieved breakthroughs as a result of the improvement of SAR imaging technology. However, there still exist some issues due to the scattering interference, sparsity of ships, and dim and small ships. To address these issues, an anchor-free method is proposed for dim and small ship detection in large-scale SAR images. First, fully convolutional one-stage object detection (FCOS) as the baseline is applied to detecting ships pixel by pixel, which can eliminate the effect of anchors and avoid the missing detection of small ships. Then, considering the particularity of SAR ships, the sample definition is redesigned based on the statistical characteristics of ships. Next, the feature extraction is redesigned to improve the feature representation for dim and small ships. Finally, the classification and regression are redesigned by introducing an improved focal loss and regression refinement with complete intersection over union (CIoU) loss. Experimental simulation results show that the proposed R-FCOS method can detect dim and small ships in large-scale SAR images with higher accuracy compared with other methods.
Spectral-Based Classification of Plant Species Groups and Functional Plant Parts in Managed Permanent Grassland
Authors: Britz Roland , Barta Norbert , Schaumberger Andreas , Klingler Andreas , Bauer Alexander , Pötsch Erich M. , Gronauer Andreas , Motsch Viktoria
Published: February 2022

Grassland vegetation typically comprises the species groups grasses, herbs, and legumes. These species groups provide different functional traits and feed values. Therefore, knowledge of the botanical composition of grasslands can enable improved site-specific management and livestock feeding. A systematic approach was developed to analyze vegetation of managed permanent grassland using hyperspectral imaging in a laboratory setting. In the first step, hyperspectral images of typical grassland plants were recorded, annotated, and classified according to species group and plant parts, that is, flowers, leaves, and stems. In the second step, three different machine learning model types—multilayer perceptron (MLP), random forest (RF), and partial least squares discriminant analysis (PLS-DA)—were trained with pixel-wise spectral information to discriminate different species groups and plant parts in individual models. The influence of radiometric data calibration and specific data preprocessing steps on the overall model performance was also investigated. While the influence of proper radiometric calibration was negligible in our setting, specific preprocessing variants, including smoothening and derivation of the spectrum, were found to be beneficial for classification accuracy. Compared to extensively preprocessed data, raw spectral data yielded no statistically decreased performance in most cases. Overall, the MLP models outperformed the PLS-DA and RF models and reached cross-validation accuracies of 96.8% for species group and 88.6% for plant part classification. The obtained insights provide an essential basis for future data acquisition and data analysis of grassland vegetation.
Correlation-Guided Ensemble Clustering for Hyperspectral Band Selection
Authors: Wang Wenguang , Wang Wenhong , Liu Hongfu
Published: February 2022

Hyperspectral band selection is a commonly used technique to alleviate the curse of dimensionality. Recently, clustering-based methods have attracted much attention for their effectiveness in selecting informative and representative bands. However, the single clustering algorithm is used in most of the clustering-based methods, and the neglect of the correlation among adjacent bands in their clustering procedure is prone to resulting in the degradation of the representativeness of the selected band set. This may, consequently, adversely impact hyperspectral classification performance. To tackle such issues, in this paper, we propose a correlation-guided ensemble clustering approach for hyperspectral band selection. By exploiting ensemble clustering, more effective clustering results are expected based on multiple band partitions given by base clustering with different parameters. In addition, given that adjacent bands are most probably located in the same cluster, a novel consensus function is designed to construct the final clustering partition by performing an agglomerative clustering. Thus, the performance of our addressed task (band selection) is further improved. The experimental results on three real-world datasets demonstrate that the performance of our proposed method is superior compared with those of state-of-the-art methods.
Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
Authors: Buongiorno Nardelli Bruno , Cavaliere Davide , Charles Elodie , Ciani Daniele
Published: February 2022

Surface ocean dynamics play a key role in the Earth system, contributing to regulate its climate and affecting the marine ecosystem functioning. Dynamical processes occur and interact in the upper ocean at multiple scales, down to, or even less than, few kilometres. These scales are not adequately resolved by present observing systems, and, in the last decades, global monitoring of surface currents has been based on the application of geostrophic balance to absolute dynamic topography maps obtained through the statistical interpolation of along-track satellite altimeter data. Due to the cross-track distance and repetitiveness of satellite acquisitions, the effective resolution of interpolated data is limited to several tens of kilometres. At the kilometre scale, sea surface temperature pattern evolution is dominated by advection, providing indirect information on upper ocean currents. Computer vision techniques are perfect candidates to infer this dynamical information from the combination of altimeter data, surface temperature images and observing-system geometry. Here, we exploit one class of image processing techniques, super-resolution, to develop an original neural-network architecture specifically designed to improve absolute dynamic topography reconstruction. Our model is first trained on synthetic observations built from a numerical general-circulation model and then tested on real satellite products. Provided concurrent clear-sky thermal observations are available, it proves able to compensate for altimeter sampling/interpolation limitations by learning from primitive equation data. The algorithm can be adapted to learn directly from future surface topography, and eventual surface currents, high-resolution satellite observations.
Spatiotemporal Patterns and Driving Force of Urbanization and Its Impact on Urban Ecology
Authors: Zhang Meng , Du Huaqiang , Zhou Guomo , Mao Fangjie , Li Xuejian , Zhou Lv , Zhu Di’en , Xu Yanxin , Huang Zihao
Published: February 2022

Urbanization inevitably poses a threat to urban ecology by altering its external structure and internal attributes. Nighttime light (NTL) has become increasingly extensive and practical, offering a special perspective on the world in revealing urbanization. In this study, we applied the Normalized Impervious Surface Index (NISI) constructed by NTL and MODIS NDVI to examine the urbanization process in the Yangtze River Delta (YRD). Geographical detectors combined with factors involving human and natural influences were utilized to investigate the drive mechanism. Urban ecology stress was evaluated based on changes in urban morphological patterns and fractional vegetation cover (FVC). The results showed that the NISI can largely overcome the obstacle of directly coupling NTL data in performing urbanization and has efficient applicability in the long-term pixel scale. Built-up areas in the YRD increased by 2.83 times during the past two decades, from 2053.5 to 7872.5 km2. Urbanization intensity has saturated the city center and is spilling over into the suburbs, which show a “cold to hot” spatial clustering distribution. Economic factors are the primary forces driving urbanization, and road network density is becoming essential as factor that reflects urban infrastructure. Urban geometry pattern changes in fractal dimension (FD) and compactness revealed the ecological stress from changing urban external structure, and internal ecological stress was clear from the negative effect on 63.4% FVC. This impact gradually increased in urban expanded area and synchronously decreased when urbanization saturated the core area. An analysis of ecological stress caused by urbanization from changing physical structure and social attributes can provide evidence for urban management and coordinated development.
Graph-Based Embedding Smoothing Network for Few-Shot Scene Classification of Remote Sensing Images
Authors: Yuan Zhengwu , Huang Wendong , Tang Chan , Yang Aixia , Luo Xiaobo
Published: February 2022

As a fundamental task in the field of remote sensing, scene classification is increasingly attracting attention. The most popular way to solve scene classification is to train a deep neural network with a large-scale remote sensing dataset. However, given a small amount of data, how to train a deep neural network with outstanding performance remains a challenge. Existing methods seek to take advantage of transfer knowledge or meta-knowledge to resolve the scene classification issue of remote sensing images with a handful of labeled samples while ignoring various class-irrelevant noises existing in scene features and the specificity of different tasks. For this reason, in this paper, an end-to-end graph neural network is presented to enhance the performance of scene classification in few-shot scenarios, referred to as the graph-based embedding smoothing network (GES-Net). Specifically, GES-Net adopts an unsupervised non-parametric regularizer, called embedding smoothing, to regularize embedding features. Embedding smoothing can capture high-order feature interactions in an unsupervised manner, which is adopted to remove undesired noises from embedding features and yields smoother embedding features. Moreover, instead of the traditional sample-level relation representation, GES-Net introduces a new task-level relation representation to construct the graph. The task-level relation representation can capture the relations between nodes from the perspective of the whole task rather than only between samples, which can highlight subtle differences between nodes and enhance the discrimination of the relations between nodes. Experimental results on three public remote sensing datasets, UC Merced, WHU-RS19, and NWPU-RESISC45, showed that the proposed GES-Net approach obtained state-of-the-art results in the settings of limited labeled samples.