bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy
Authors: Falcioni Renan , Gonçalves João Vitor Ferreira , de Oliveira Karym Mayara , de Oliveira Caio Almeida , Reis Amanda Silveira , Crusiol Luis Guilherme Teixeira , Furlanetto Renato Herrig , Antunes Werner Camargos , Cezar Everson , de Oliveira Roney Berti , Chicati Marcelo Luiz , Demattê José Alexandre M. , Nanni Marcos Rafael
Published: September 2023

Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms—such as PLS, VIP, iPLS-VIP, GA, RF, and CARS—the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.
Transcriptome Analysis Reveals Candidate Genes Involved in Gibberellin-Induced Fruit Development in Rosa roxburghii
Authors: Huang Xiaolong , Wu Xiaoai , Sun Guilian , Jiang Yu , Yan Huiqing
Published: September 2023

Gibberellins (GAs) play indispensable roles in the fruit development of horticultural plants. Unfortunately, the molecular basis behind GAs regulating fruit development in R. roxburghii remains obscure. Here, GA3 spraying to R. roxburghii ‘Guinong 5’ at full-bloom promoted fruit size and weight, prickle development, seed abortion, ascorbic acid accumulation, and reduction in total soluble sugar. RNA-Seq analysis was conducted to generate 45.75 Gb clean reads from GA3- and non-treated fruits at 120 days after pollination. We obtained 4275 unigenes belonging to differently expressed genes (DEGs). Gene ontology and the Kyoto Encyclopedia of Genes and Genomes displayed that carbon metabolism and oxidative phosphorylation were highly enriched. The increased critical genes of DEGs related to pentose phosphate, glycolysis/gluconeogenesis, and citrate cycle pathways might be essential for soluble sugar degradation. Analysis of DEGs implicated in ascorbate revealed the myoinositol pathway required to accumulate ascorbic acid. Finally, DEGs involved in endogenous phytohormones and transcription factors, including R2R3 MYB, bHLH, and WRKY, were determined. These findings indicated that GA3-trigged morphological alterations might be related to the primary metabolites, hormone signaling, and transcription factors, providing potential candidate genes that could be guided to enhance the fruit development of R. roxburghii in practical approaches.
Influence of Particle Size on the Mechanical Performance and Sintering Quality of Peanut Husk Powder/PES Composites Fabricated through Selective Laser Sintering
Authors: Idriss Aboubaker I. B. , Yang Chun-Mei , Li Jian , Guo Yanling , Liu Jiuqing , Abdelmagid Alaaeldin A. A. , Ahmed Gafer A. , Zhang Hao
Published: September 2023

This study intends to enhance the mechanical strength of wood–plastic composite selective laser sintering (SLS) parts by using a sustainable composite, peanut husk powder (PHP)/poly ether sulfone (PES) (PHPC). The study aims to address agricultural waste pollution by encouraging the eco-friendly utilization of such waste in SLS technology. To ensure the sintering quality and mechanical properties and prevent deformation and warping during sintering, the thermo-physical properties of PHP and PES powders were analyzed to determine a suitable preheating temperature for PHPC. Single-layer sintering tests were conducted to assess the formability of PHPC specimens with varying PHP particle sizes. The study showed the effects of different PHP particle sizes on the mechanical performance of PHPC parts. The evaluation covered various aspects of PHPC SLS parts, including mechanical strength, density, residual ash content, dimensional accuracy (DA), and surface roughness, with different PHP particle sizes. The mechanical analysis showed that PHPC parts made from PHP particles of ≤0.125 mm were the strongest. Specifically, the density bending strength, residual ash content, tensile, and impact strength were measured as 1.1825 g/cm3, 14.1 MPa, 1.2%, 6.076 MPa, and 2.12 kJ/cm2, respectively. Notably, these parameters showed significant improvement after the wax infiltration treatment. SEM was used to examine the PHP and PES powder particles, PHPC specimen microstructure, and PHPC SLS parts before and after the mechanical tests and waxing. Consequently, SEM analysis wholly confirmed the mechanical test results.
Inhibitory Effects of Some Hydrocolloids on the Formation of Advanced Glycation End Products and Heterocyclic Amines in Chemical Models and Grilled Beef Patties
Authors: Du Hongfei , Huang Tiantian , Zeng Maomao , Shen Qingwu , Jiao Ye , Quan Wei
Published: September 2023

Effectively inhibiting the formation of heterocyclic amines (HAs) and advanced glycation end products (AGEs) is crucial to human health. In the present study, chemical model systems were used to evaluate the inhibitory effects of seven hydrocolloids on HA and AGE formation. The results showed that hydrocolloids effectively inhibited the formation of two major AGEs. However, their inhibitory action against HA formation showed unexpected results, wherein alginic acid, carrageenan and konjac glucomannan promoted the formation of 2-Amino-1-methyl-6-phenylimidazo [4,5-b]pyridine (PhIP), harmane, norharmane and 2-amino-3,8-dimethyl-imidazo [4,5-f]-quinoline (MeIQx). Only chitosan and pectin showed significant inhibitory effects on HAs, reducing HA levels by 34.5–56.3% and 30.1–56.6%, respectively. In grilled beef patties, the addition of 1.5% chitosan and pectin significantly decreased AGE and HA content by 53.8–67.0% and 46.9–68.1%, respectively. Moreover, it had a limited impact on quality and sensory properties. Further mechanism studies conducted in model systems revealed that chitosan and pectin decreased the formation of key intermediates of AGEs and HAs. These findings suggest that chitosan and pectin are powerful inhibitors against AGE and HA formation with minimal impact on food quality. Therefore, their application in meat preparation and processing could effectively decrease human dietary exposure to HAs and AGEs.
Biological Macromolecule Hydrogel Based on Recombinant Type I Collagen/Chitosan Scaffold to Accelerate Full-Thickness Healing of Skin Wounds
Authors: Kang Duo , Wang Wenhai , Li Yanmei , Ma Yi , Huang Yadong , Wang Jufang
Published: September 2023

The development of biological macromolecule hydrogel dressings with fatigue resistance, sufficient mechanical strength, and versatility in clinical treatment is critical for accelerating full-thickness healing of skin wounds. Therefore, in this study, multifunctional, biological macromolecule hydrogels based on a recombinant type I collagen/chitosan scaffold incorporated with a metal–polyphenol structure were fabricated to accelerate wound healing. The resulting biological macromolecule hydrogel possesses sufficient mechanical strength, fatigue resistance, and healing properties, including antibacterial, antioxygenic, self-healing, vascularization, hemostatic, and adhesive abilities. Chitosan and recombinant type I collagen formed the scaffold network, which was the first covalent crosslinking network of the hydrogel. The second physical crosslinking network comprised the coordination of a metal–polyphenol structure, i.e., Cu2+ with the catechol group of dopamine methacrylamide (DMA) and stacking of DMA benzene rings. Double-crosslinked networks are interspersed and intertwined in the hydrogel to reduce the mechanical strength and increase its fatigue resistance, making it more suitable for clinical applications. Moreover, the biological macromolecule hydrogel can continuously release Cu2+, which provides strong antibacterial and vascularization properties. An in vivo full-thickness skin defect model confirmed that multifunctional, biological macromolecule hydrogels based on a recombinant type I collagen/chitosan scaffold incorporated with a metal–polyphenol structure can facilitate the formation of granulation tissue and collagen deposition for a short period to promote wound healing. This study highlights that this biological macromolecule hydrogel is a promising acute wound-healing dressing for biomedical applications.
Polycaprolactone/Starch/Agar Coatings for Food-Packaging Paper: Statistical Correlation of the Formulations’ Effect on Diffusion, Grease Resistance, and Mechanical Properties
Authors: Lo Faro Emanuela , Bonofiglio Angela , Barbi Silvia , Montorsi Monia , Fava Patrizia
Published: September 2023

Paper is one of the most promising materials for food packaging and wrapping due to its low environmental impact, but surface treatments are often needed to improve its performance, e.g., the resistance to fats and oils. In this context, this research is focused on the formulation of a new paper bio-coating. Paper was coated with liquids containing poly(hexano-6-lactone) (PCL), glycerol and variable percentages of starch (5–10% w/w PCL dry weight), agar-agar (0–1.5% w/w PCL dry weight), and polyethylene glycol (PEG) (5% or 15% w/w PCL dry weight) to improve coating uniformity and diffusion. A design of experiments approach was implemented to find statistically reliable results in terms of the best coating formulation. Coated paper was characterized through mechanical and physical properties. Results showed that agar content (1.5% w/w PCL dry weight) has a beneficial effect on increasing the resistance to oil. Furthermore, the best coating composition has been calculated, and it is 10% w/w PCL dry weight of starch, 1.5% w/w PCL dry weight of agar, and 15% w/w PCL dry weight of PEG.
Chemorheology of a Si/Al > 3 Alkali Activated Metakaolin Paste through Parallel Differential Scanning Calorimetry (DSC) and Dynamic Mechanical Analysis (DMA)
Authors: Aversa Raffaella , Ricciotti Laura , Perrotta Valeria , Apicella Antonio
Published: September 2023

Although geopolymers, as structural materials, should have superior engineering properties than traditional cementitious materials, they often need to improve their final characteristics’ reproducibility due to the need for more control of the complex silico-aluminate decomposition and polymerisation stages. Thermosetting of a reactive geopolymeric paste involves tetrahedral Silicate and Aluminate precursor condensation into polyfunctional oligomers of progressively higher molecular weight, transforming the initial liquid into a gel and a structural solid. Viscosity and gelation control become particularly critical when the geopolymer is processed with 3D printing additive technology. Its physical state modification kinetics should match the flow and setting characteristics required by the deposition process. The reaction kinetics and the elastic and viscous characteristics preceding gelation and hardening have been investigated for an alkali-activated Metakaolin/Sodium Silicate-Sodium Hydroxide paste with a Si/Al ratio > 3. A chemoreological approach has been extended to these inorganic polymerisable systems, as already utilised for organic thermosetting polymers. Differential scanning calorimetry and Oscillatory DMA were carried out to monitor the advancement of the polymerisation reaction and the associated variations of the rheological viscoelastic properties. Dynamic thermal scans were run at 1 °C/min and a frequency of 10 Hz for the dynamic mechanical tests. The observed kinetics of polymerisation and variations of the elastic and viscous components of the complex viscosities and shear moduli are described in terms of polycondensation of linear and branched chains of oligomeric macromolecules of increasing complexity and molecular weight up to gelation (Gel1) and cross-linking of the gelled macrostructure (Gel2) and final glassy state. Geopolymerization can be allocated into two main behavioural zones: a viscoelastic liquid paste below 32.5% of reaction advancement and a viscoelastic solid above. Initial complex viscosities range from 2.3 ± 0.9  × 10−5 MPa*s to 6.8 ± 0.9  × 10−2 in the liquid-like state and from 1.9 ± 0.1 MPa to 9.6 ± 2.1 × 102 MPa in the solid-like state.
A Practical Approach for Biochemical Modeling in the CFD Evaluation of Novel Anaerobic Digester Concepts for Biogas Production
Authors: Miana Mario , Santamaría Ana Martínez , Carbajo Jose B. , Bengoechea Cristina , García Gorka , Izquierdo Salvador
Published: September 2023

The detailed physics-based description of anaerobic digesters is characterized by their multiscale and multiphysics nature, with Computational Fluid Dynamics (CFD) simulations being the most comprehensive approach. In practice, difficulties in obtaining a detailed characterization of the involved biochemical reactions hinder its application in the design of novel reactor concepts, where all physics interplays in the reactor must be considered. To solve this limitation, a practical approach is introduced where a calibration step using actual process data was applied for the simplified biochemical reactions involved, allowing us to efficiently manage uncertainties arising when characterizing biochemical reactions with lab scale facilities. A complete CFD modeling approach is proposed for the anaerobic digestion of wastewater, including heat transfer and multiphasic flow. The proposed multiphase model was verified using reference data and, jointly with the biochemical modeling approach, applied to a lab-scale non-conventional anaerobic digester for winery wastewater treatment. The results showed qualitative improvement in predicting methane production when the diameter of the particles was reduced, since larger particles tend to move downwards. The biochemistry of the process could be simplified introducing a preexponential factor of 380 (kmol/m3)(1 – n)/s for each considered chemical reaction. In general, the proposed approach can be used to overcome limitations when using CFD to scale-up optimization of non-conventional reactors involving biochemical reactions.
Unraveling Anaerobic Digestion Instability: A Simple Index Based on the Kinetic Balance of Biochemical Reactions
Authors: Jia Ru , Song Young-Chae , An Zhengkai , Kim Keugtae , Oa Seong-Wook
Published: September 2023

Restoration potential (RP) and deterioration potential (DP) were formulated to shed light on the kinetic balance between anaerobic biochemical reactions. RP is gauged by the ratio of the methanogenesis rate (MR) to the acidogenesis rate (AR), while the DP is the sum of the accumulation rate (AcR) and dilution rate (DR) of total VFAs, normalized using the AR. In an anaerobic digester for a mixture of pulverized food waste and liquified sewage sludge, an RP above 1.0 signifies a restorative state in the kinetic balance of anaerobic biochemical reactions across various operational phases, including startup and steady state, and shifts in organic loading rate. Conversely, a DP value of 0.0 or higher denotes a deterioration in the kinetic balance. The instability index (ISI), calculated as the DP to RP ratio, serves as an indicator of an anaerobic digestion state. When the standard deviation of ISI surpasses 0.2, it signifies instability in biochemical reactions; however, an average ISI below 0.05 indicates a stable digestion process. The study underscores the efficacy of RP, DP, and ISI as robust indicators for assessing the stability of anaerobic digestion based on the kinetics of biochemical reactions.
Optimization Study of Injection and Production Parameters for Shallow- and Thin-Layer Heavy Oil Reservoirs with Nitrogen Foam-Assisted Steam Flooding
Authors: Gong Yugang , Xin Xiankang , Yu Gaoming , Ni Mingcheng , Xu Peifu
Published: September 2023

Shallow- and thin-layer heavy oil reservoirs are characterized by their shallow burial, thin thickness, high viscosity, and scattered distribution. After years of steam injection development, several issues have emerged, including a highly comprehensive water cut in the reservoir and serious steam channeling. Therefore, there is an urgent need to change the development approach to enhance crude oil recovery. It has been discovered that developing heavy oil reservoirs through nitrogen foam-assisted steam flooding can effectively address the challenges encountered in pure steam development. This paper takes H Oilfield Block A as a case study, analyzes the geological characteristics and development status of the reservoir in this block, and predicts the recovery of steam injection development in this block using the injection-production characteristic curve method. Furthermore, by establishing a reservoir geological model and fitting it to the historical behavior of the target reservoir, the nitrogen foam-assisted steam flooding injection and production parameters were optimized. The optimal parameters are as follows: optimal steam injection intensity of 2.0 t/(d·ha·m), optimal production/injection ratio of 1.2:1, optimal nitrogen foam slug injection volume of 0.15 PV, optimal nitrogen/steam ratio of 2:1, and intermittent injection between 3 and 4 foam slugs. It is anticipated that this optimized scheme will result in a predicted increase in final recovery of 13.55%. The findings of this study hold significant importance in guiding the application of nitrogen foam-assisted steam flooding in shallow and thin heavy oil reservoirs.
An Advisor-Based Architecture for a Sample-Efficient Training of Autonomous Navigation Agents with Reinforcement Learning
Authors: Wijesinghe Rukshan Darshana , Tissera Dumindu , Vithanage Mihira Kasun , Xavier Alex , Fernando Subha , Samarawickrama Jayathu
Published: September 2023

Recent advancements in artificial intelligence have enabled reinforcement learning (RL) agents to exceed human-level performance in various gaming tasks. However, despite the state-of-the-art performance demonstrated by model-free RL algorithms, they suffer from high sample complexity. Hence, it is uncommon to find their applications in robotics, autonomous navigation, and self-driving, as gathering many samples is impractical in real-world hardware systems. Therefore, developing sample-efficient learning algorithms for RL agents is crucial in deploying them in real-world tasks without sacrificing performance. This paper presents an advisor-based learning algorithm, incorporating prior knowledge into the training by modifying the deep deterministic policy gradient algorithm to reduce the sample complexity. Also, we propose an effective method of employing an advisor in data collection to train autonomous navigation agents to maneuver physical platforms, minimizing the risk of collision. We analyze the performance of our methods with the support of simulation and physical experimental setups. Experiments reveal that incorporating an advisor into the training phase significantly reduces the sample complexity without compromising the agent’s performance compared to various benchmark approaches. Also, they show that the advisor’s constant involvement in the data collection process diminishes the agent’s performance, while the limited involvement makes training more effective.
Vector Decomposition-Based Arbitrary-Oriented Object Detection for Optical Remote Sensing Images
Authors: Zhou Kexue , Zhang Min , Dong Youqiang , Tan Jinlin , Zhao Shaobo , Wang Hai
Published: September 2023

Arbitrarily oriented object detection is one of the most-popular research fields in remote sensing image processing. In this paper, we propose an approach to predict object angles indirectly, thereby avoiding issues related to angular periodicity and boundary discontinuity. Our method involves representing the long edge and angle of an object as a vector, which we then decompose into horizontal and vertical components. By predicting the two components of the vector, we can obtain the angle information of the object indirectly. To facilitate the transformation between angle-based representation and the proposed vector-decomposition-based representation, we introduced two novel techniques: angle-to-vector encode (ATVEncode) and vector-to-angle decode (VTADecode). These techniques not only improve the efficiency of data processing, but also accelerate the training process. Furthermore, we propose an adaptive coarse-to-fine positive–negative-sample-selection (AdaCFPS) method based on the vector-decomposition-based representation of the object. This method utilizes the Kullback–Leibler divergence loss as a matching degree to dynamically select the most-suitable positive samples. Finally, we modified the YOLOX model to transform it into an arbitrarily oriented object detector that aligns with our proposed vector-decomposition-based representation and positive–negative-sample-selection method. We refer to this redesigned model as the vector-decomposition-based object detector (VODet). In our experiments on the HRSC2016, DIOR-R, and DOTA datasets, VODet demonstrated notable advantages, including fewer parameters, faster processing speed, and higher precision. These results highlighted the significant potential of VODet in the context of arbitrarily oriented object detection.
Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China
Authors: Ma Qiumei , Gui Xu , Xiong Bin , Li Rongrong , Yan Lei
Published: September 2023

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan’an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE.
Assessing the Potential Impacts of Climate Change on Current Coastal Ecosystems—A Canadian Case Study
Authors: Dau Quan Van , Wang Xiuquan , Shah Mohammad Aminur Rahman , Kinay Pelin , Basheer Sana
Published: September 2023

Understanding how climate change affects coastal ecosystems is one of the most important elements in determining vulnerability and resilience for long-term ecosystem management in the face of the increasing risk of coastal hazards (e.g., sea level rise, coastal flooding, and storm surge). This research attempts to undertake a study on the ecosystem–climate nexus in the Canadian province of Prince Edward Island (PEI). Cloud-based remote sensing techniques with Google Earth Engine (GGE) are utilized to identify ecosystem changes over time. In addition, the effects of coastal flooding and storm surge ecosystems under different climate scenarios are examined. The results suggest a reduction in the forest (3%), open water or marsh component (9%), salt water (5%), no open water or marsh component (3%), and salt or brackish marsh (17%) ecosystems from 2013 to 2022. Dune and beach exhibit a non-uniform distribution across the period because of variations in natural processes, with an upward trend ranging from 0% to 11%. Approximately 257 km2 (9.4%) of PEI’s ecosystems would be affected by extreme coastal flooding (scenario 4), compared to 142 km2 (5.2%), 155 km2 (5.7%), and 191 km2 (7%) in scenarios 1, 2, and 3, respectively. Under a 4 m storm surge scenario, around 223 km2 (8.2%) of PEI’s ecosystems would be flooded, compared to 61 km2 (2.2%), 113 km2 (4.1%), and 168 km2 (6.1%) under 1 m, 2 m, and 3 m scenarios, respectively. The findings from this research would enable policymakers to take necessary actions to sustain ecosystem services in PEI while confronting the impacts of climate change.
Identifying the Effects of Vegetation on Urban Surface Temperatures Based on Urban–Rural Local Climate Zones in a Subtropical Metropolis
Authors: Zhou Siyu , Zheng Hui , Liu Xiao , Gao Quan , Xie Jing
Published: September 2023

Many studies have observed the crucial role of vegetated local climate zone (LCZ) types in mitigating the surface urban heat island (SUHI) effect. However, research analyzing the spatial variations in land surface temperature (LST) in a metropolis based on an urban–rural LCZ scheme and exploring the cooling effects of different vegetation types is still lacking. Here, our study focuses on the Guangzhou–Foshan metropolis and aims to elucidate the spatial variations in LST in subtropical cities and the regulating effect of vegetation on LST changes. We used a normalized difference vegetation index (NDVI) and LST data from space-borne MODIS products for the years 2000, 2009, and 2019, as well as LCZ maps, urban–rural gradient data, and land use and land cover (LULC) maps. Urban–rural, seasonal, daytime, nighttime, and diurnal comparative analyses were conducted using logarithmic regression, Pearson partial correlation, and comparison analysis. The results showed that LST values for built LCZ types were generally higher than those of land cover LCZ types, showing a positive correlation with building density and height. The LST decreased logarithmically across the urban–rural gradients, with a rapid decrease initially in the near-gradient urban area, followed by a flattening trend in farther-gradient suburban and rural areas. Regarding vegetated LCZ types, the NDVI metrics showed a significant negative correlation with the LST during the daytime but a positive correlation during the nighttime. The cooling effect of vegetated LCZ types was evident, with an average cooling amplitude of 1.92 °C over the three investigated years. In conclusion, urban LST changes are closely associated with LCZ types, urban–rural gradients, NDVI values, and vegetation types. The cooling ability of vegetation exhibited seasonal and diurnal variations, with a special emphasis on the cooling effect of dense evergreen broadleaf forests. Our findings offer valuable insights and can guide urban ecological construction and management by comprehensively assessing the impact of vegetation on urban surface temperatures.
Preliminary Evaluation of Geometric Positioning Accuracy of C-SAR Images Based on Automatic Corner Reflectors
Authors: Jiao Yanan , Zhang Fengli , Liu Xiaochen , Wang Qi , Huang Qiqi , Huang Zhiwei
Published: September 2023

C-SAR/01 and C-SAR/02 serve as successors to the GF-3 satellite. They are designed to operate in tandem with GF-3, collectively forming a C-band synthetic aperture radar (SAR) satellite constellation. This constellation aims to achieve 1 m resolution imaging with a revisit rate of one day. It can effectively cater to various applications such as marine disaster prevention, monitoring marine dynamic environments, and supporting marine scientific research, disaster mitigation, environmental protection, and agriculture. Geometric correction plays a pivotal role in acquiring highly precise geographic location data for ground targets. The geometric positioning accuracy without control points signifies the SAR satellite’s geometric performance. However, SAR images do not exhibit a straightforward image-point–object-point correspondence, unlike optical images. In this study, we introduce a novel approach employing high-precision automatic trihedral corner reflectors as ground control points (GCPs) to assess the geometric positioning accuracy of SAR images. A series of satellite-ground synchronization experiments was conducted at the Xilinhot SAR satellite calibration and validation site to evaluate the geometric positioning accuracy of different C-SAR image modes. Firstly, we calculated the azimuth and elevation angles of the corner reflectors based on satellite orbit parameters. During satellite transit, these corner reflectors were automatically adjusted to align with the radar-looking direction. We subsequently measured the exact longitude and latitude coordinates of the corner reflector vertex in situ using a high-precision real-time kinematics instrument. Next, we computed the theoretical image coordinates of the corner reflectors using the rational polynomial coefficients (RPC) model. After that, we determined the accurate position of the corner reflector in the Single Look Complex (SLC) SAR image using FFT interpolation and the sliding window method. Finally, we evaluated and validated the geometric positioning accuracy of C-SAR images by comparing the two coordinates. The preliminary results indicate that the positioning accuracy varies based on the satellite, imaging modes, and orbital directions. Nevertheless, for most sample points, the range positioning accuracy was better than 60 m, and the azimuth positioning accuracy was better than 80 m. These findings can serve as a valuable reference for subsequent applications of C-SAR satellites.
Analysis of Mass Wasting Processes in the Slumgullion Landslide Using Multi-Track Time-Series UAVSAR Images
Authors: Cai Jiehua , Wang Changcheng , Zhang Lu
Published: September 2023

The Slumgullion landslide is a large translational debris slide whose currently active part has likely been sliding for approximately 300 years. Its permanent motion and evolutionary processes have attracted the attention of many researchers. In order to study its mass wasting processes and evolution trend, the spatial–temporal displacement of the Slumgullion landslide was retrieved using an adaptive pixel offset tracking (POT) method with multi-track Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images. Based on three-dimensional displacement and slope information, we then revealed the spatial–temporal distribution of surface mass depletion or accumulation in the landslide, which provides a new perspective to analyze the evolutionary processes of landslides. The results indicate that the Slumgullion landslide had a spatially variable displacement, with a maximum displacement of 35 m. The novel findings of this study mainly include two parts. First, we found that the surface mass accumulated in the toe of the landslide and depleted in the top and middle area during the interval, which could increase the resisting force and decrease the driving force of the Slumgullion landslide. This result is compelling evidence which indicates the Slumgullion landslide should eventually tend to be stable. Second, we found that the distribution of geological structures can well explain some of the unique mass wasting in the Slumgullion landslide. The larger local mass depletion in the landslide neck area verifies that the sharp velocity increase in this region is not only caused by the reduction in width but is also significantly affected by the local normal faults. In summary, this study provides an insight into the relation between the landslide motion, mass volume change, and geological structure. The results demonstrate the great potential of multi-track airborne SAR for displacement monitoring and evolutionary analysis of landslides.
Improved Deep Learning-Based Vehicle Detection for Urban Applications Using Remote Sensing Imagery
Authors: Ragab Mahmoud , Abdushkour Hesham A. , Khadidos Adil O. , Alshareef Abdulrhman M. , Alyoubi Khaled H. , Khadidos Alaa O.
Published: September 2023

Remote sensing (RS) data can be attained from different sources, such as drones, satellites, aerial platforms, or street-level cameras. Each source has its own characteristics, including the spectral bands, spatial resolution, and temporal coverage, which may affect the performance of the vehicle detection algorithm. Vehicle detection for urban applications using remote sensing imagery (RSI) is a difficult but significant task with many real-time applications. Due to its potential in different sectors, including traffic management, urban planning, environmental monitoring, and defense, the detection of vehicles from RS data, such as aerial or satellite imagery, has received greater emphasis. Machine learning (ML), especially deep learning (DL), has proven to be effective in vehicle detection tasks. A convolutional neural network (CNN) is widely utilized to detect vehicles and automatically learn features from the input images. This study develops the Improved Deep Learning-Based Vehicle Detection for Urban Applications using Remote Sensing Imagery (IDLVD-UARSI) technique. The major aim of the IDLVD-UARSI method emphasizes the recognition and classification of vehicle targets on RSI using a hyperparameter-tuned DL model. To achieve this, the IDLVD-UARSI algorithm utilizes an improved RefineDet model for the vehicle detection and classification process. Once the vehicles are detected, the classification process takes place using the convolutional autoencoder (CAE) model. Finally, a Quantum-Based Dwarf Mongoose Optimization (QDMO) algorithm is applied to ensure an optimal hyperparameter tuning process, demonstrating the novelty of the work. The simulation results of the IDLVD-UARSI technique are obtained on a benchmark vehicle database. The simulation values indicate that the IDLVD-UARSI technique outperforms the other recent DL models, with maximum accuracy of 97.89% and 98.69% on the VEDAI and ISPRS Potsdam databases, respectively.
Application of Model-Based Time Series Prediction of Infrared Long-Wave Radiation Data for Exploring the Precursory Patterns Associated with the 2021 Madoi Earthquake
Authors: Zhang Jingye , Sun Ke , Zhu Junqing , Mao Ning , Ouzounov Dimitar
Published: September 2023

Taking the Madoi MS 7.4 earthquake of 21 May 2021 as an example, this paper proposes using time series prediction models to predict the outgoing long-wave radiation (OLR) anomalies and study short-term pre-earthquake signals. Five time series prediction models, including autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM), were trained with the OLR time series data of the aseismic moments in the 5° × 5° spatial range around the epicenter. The model with the highest prediction accuracy was selected to retrospectively predict the OLR values during the aseismic period and before the earthquake in the area. It was found, by comparing the predicted time series values with the actual time series value, that the similarity indexes of the two time series before the earthquake were lower than the index of the aseismic period, indicating that the predicted time series before the earthquake significantly differed from the actual time series. Meanwhile, the temporal and spatial distribution characteristics of the anomalies in the 90 days before the earthquake were analyzed with a 95% confidence interval as the criterion of the anomalies, and the following was found: out of 25 grids, 18 grids showed anomalies—the anomalies of the different grids appeared on similar dates, and the anomalies of high values appeared centrally at the time of the earthquake, which supports the hypothesis that pre-earthquake signals may be associated with the earthquake.
Age Identification of Farmland Shelterbelt Using Growth Pattern Based on Landsat Time Series Images
Authors: Zhang Xing , Li Jieling , Li Ying , Deng Rongxin , Yang Gao , Tang Jing
Published: September 2023

Farmland shelterbelt, as a category of shelterbelt in forestry ecological engineering, has an important influence on agricultural sustainability in agricultural systems. Timely and accurate acquisition of farmland shelterbelt age is not only essential to understanding their shelter effects but also directly relates to the adjustment of subsequent shelterbelt projects. In this study, we developed an age identification method using growth pattern to extract the age of shelterbelt (i.e., years after planting) based on Landsat time series images. This method was applied to a typical area of shelterbelt construction in the north of Changchun, China. The results indicated that the accuracy of age identification reached a stable situation when the permissible age error exceeded 3 years, achieving an accuracy of approximately 90%. Moreover, the accuracy at different growth phases (1–3 years, 4–15 years, 16–30 years, and >30 years) decreased with increasing age, and the accuracy of each growth phase can reach more than 80% when the permissible age error is beyond 7 years. Compared to building the typically weak statistical relationship between the shelterbelt age and remote sensing characteristic information to derive age, this method presented a direct age identification method for fine-scale age extraction of the shelterbelt. It introduced a novel perspective for shelterbelt age identification and the assessment of shelterbelt project advancement on the regional scale.
EddyDet: A Deep Framework for Oceanic Eddy Detection in Synthetic Aperture Radar Images
Authors: Zhang Di , Gade Martin , Wang Wensheng , Zhou Haoran
Published: September 2023

This paper presents a deep framework EddyDet to automatically detect oceanic eddies in Synthetic Aperture Radar (SAR) images. The EddyDet has been developed using the Mask Region with Convolutional Neural Networks (Mask RCNN) framework, incorporating two new branches: Edge Head and Mask Intersection over Union (IoU) Head. The Edge Head can learn internal texture information implicitly, and the Mask IoU Head improves the quality of predicted masks. A SAR dataset for Oceanic Eddy Detection (SOED) is specifically constructed to evaluate the effectiveness of the EddyDet model in detecting oceanic eddies. We demonstrate that the EddyDet is capable of achieving acceptable eddy detection results under the condition of limited training samples, which outperforms a Mask RCNN baseline in terms of average precision. The combined Edge Head and Mask IoU Head have the ability to describe the characteristics of eddies more correctly, while the EddyDet shows great potential in practice use accurately and time efficiently, saving manual labor to a large extent.
Investigating High-Resolution Spatial Wave Patterns on the Canadian Beaufort Shelf Using Synthetic Aperture Radar Imagery at Herschel Island, Qikiqtaruk, Yukon, Canada
Authors: Brembach Kerstin , Pleskachevsky Andrey , Lantuit Hugues
Published: September 2023

The Arctic is experiencing the greatest increase in air temperature on Earth. This significant climatic change is leading to a significant positive trend of increasing wave heights and greater coastal erosion. This in turn effects local economies and ecosystems. Increasing wave energy is one of the main drivers of this alarming trend. However, the data on spatial and temporal patterns of wave heights in the Arctic are either coarse, interpolated or limited to point measurements. The aim of this study is to overcome this shortcoming by using remote sensing data. In this study, the Synthetic Aperture Radar (SAR) satellite TerraSAR-X (TS-X) and TanDEM-X (TD-X) imagery are used to obtain sea state information with a high spatial resolution in Arctic nearshore waters in the Canadian Beaufort Sea. From the entire archive of the TS-X/TD-X StripMap mode with coverage around 30 km × 50 km acquired between 2009 and 2020 around Herschel Island, Qikiqtaruk (HIQ), all the ice-free scenes were processed. The resulting dataset of 175 collocated scenes was used to map the significant wave height (Hs) and to link spatial and temporal patterns to local coastal processes. Sea state parameters are estimated in raster format with a 600 m step using the empirical algorithm CWAVE_EX. The statistics of the Hs were aggregated according to spatial variability, seasonality and wind conditions. The results show that the spatial wave climate is clearly related to the dominant wind regime and seasonality. For instance, the aggregation of all the scenes recorded in July between 2009 and 2020 results in an average of 0.82 m Hs, while in October the average Hs is almost 0.40 m higher. The analysis by wind direction shows that fetch length and wind speed are likely the most important variables influencing the spatial variability. A larger fetch under NW conditions results in a mean wave height of 0.92 m, while waves generated under ESE conditions are lower at 0.81 m on average.
Research on Landslide Trace Recognition by Fusing UAV-Based LiDAR DEM Multi-Feature Information
Authors: Han Lei , Duan Ping , Liu Jiajia , Li Jia
Published: September 2023

Landslide traces are crucial geomorphological features of landslides. Through the recognition of landslide traces, a better grasp of the topographical features of landslides can be achieved, thereby aiding in the enhancement of capabilities for the prevention, response, and management of landslides. Aiming at the complex topographic features of landslide traces, only using a single DEM product could provide a complete and comprehensive recognition of landslide traces. A method of landslide tracing recognition based on the fusion of multi-feature information from the Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-based LiDAR) Digital Elevation Model (DEM) is proposed. First, a high-precision DEM is constructed by using the LiDAR point cloud data. Based on the DEM, four multi-feature images that can enhance the landslide geomorphology are generated: hillshading, slope, positive openness, and sky-view factor. Furtherore, the DEM multi-feature images were fused using the Visualization for Archaeological Topography (VAT) method to obtain the DEM Multi-Feature Fusion Image (DEM-DFFI). Finally, the landslide traces were extracted from the DEM-DFFI based on fractal theory. The method presented in this paper makes full use of DEM multi-feature images and fuses them, which can accurately and clearly show the topographic and geomorphological features of landslides. Based on this, it helps improve landslide trace recognition accuracy.
EMO-MoviNet: Enhancing Action Recognition in Videos with EvoNorm, Mish Activation, and Optimal Frame Selection for Efficient Mobile Deployment
Authors: Hussain Tarique , Memon Zulfiqar Ali , Qureshi Rizwan , Alam Tanvir
Published: September 2023

The primary goal of this study is to develop a deep neural network for action recognition that enhances accuracy and minimizes computational costs. In this regard, we propose a modified EMO-MoviNet-A2* architecture that integrates Evolving Normalization (EvoNorm), Mish activation, and optimal frame selection to improve the accuracy and efficiency of action recognition tasks in videos. The asterisk notation indicates that this model also incorporates the stream buffer concept. The Mobile Video Network (MoviNet) is a member of the memory-efficient architectures discovered through Neural Architecture Search (NAS), which balances accuracy and efficiency by integrating spatial, temporal, and spatio-temporal operations. Our research implements the MoviNet model on the UCF101 and HMDB51 datasets, pre-trained on the kinetics dataset. Upon implementation on the UCF101 dataset, a generalization gap was observed, with the model performing better on the training set than on the testing set. To address this issue, we replaced batch normalization with EvoNorm, which unifies normalization and activation functions. Another area that required improvement was key-frame selection. We also developed a novel technique called Optimal Frame Selection (OFS) to identify key-frames within videos more effectively than random or densely frame selection methods. Combining OFS with Mish nonlinearity resulted in a 0.8–1% improvement in accuracy in our UCF101 20-classes experiment. The EMO-MoviNet-A2* model consumes 86% fewer FLOPs and approximately 90% fewer parameters on the UCF101 dataset, with a trade-off of 1–2% accuracy. Additionally, it achieves 5–7% higher accuracy on the HMDB51 dataset while requiring seven times fewer FLOPs and ten times fewer parameters compared to the reference model, Motion-Augmented RGB Stream (MARS).
A Facile Graphene Conductive Polymer Paper Based Biosensor for Dopamine, TNF-α, and IL-6 Detection
Authors: Rahman Md Ashiqur , Pal Ramendra Kishor , Islam Nazmul , Freeman Robert , Berthiaume Francois , Mazzeo Aaron , Ashraf Ali
Published: September 2023

Paper-based biosensors are a potential paradigm of sensitivity achieved via microporous spreading/microfluidics, simplicity, and affordability. In this paper, we develop decorated paper with graphene and conductive polymer (herein referred to as graphene conductive polymer paper-based sensor or GCPPS) for sensitive detection of biomolecules. Planetary mixing resulted in uniformly dispersed graphene and conductive polymer ink, which was applied to laser-cut Whatman filter paper substrates. Scanning electron microscopy and Raman spectroscopy showed strong attachment of conductive polymer-functionalized graphene to cellulose fibers. The GCPPS detected dopamine and cytokines, such as tumor necrosis factor-alpha (TNF-α), and interleukin 6 (IL-6) in the ranges of 12.5–400 µM, 0.005–50 ng/mL, and 2 pg/mL–2 µg/mL, respectively, using a minute sample volume of 2 µL. The electrodes showed lower detection limits (LODs) of 3.4 µM, 5.97 pg/mL, and 9.55 pg/mL for dopamine, TNF-α, and IL-6 respectively, which are promising for rapid and easy analysis for biomarkers detection. Additionally, these paper-based biosensors were highly selective (no serpin A1 detection with IL-6 antibody) and were able to detect IL-6 antigen in human serum with high sensitivity and hence, the portable, adaptable, point-of-care, quick, minute sample requirement offered by our fabricated biosensor is advantageous to healthcare applications.