bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical Study on Chemical Kinetic Characteristics of Counterflow Diffusion Flame Extinction of Methane/Ammonia/Air Flame under High Pressure or Air Preheating Temperature
Authors: Chen Ying , Wang Jingfu , Zhang Jian , Li Yi
Published: July 2024

Green ammonia has become an increasingly popular fuel in recent years because of its combustion process without carbon oxide release. Adding ammonia to methane fuel for co-combustion has become one of the important research topics in the current combustion field. In the present study, the CH4/NH3/Air counterflow diffusion flame was taken as the research object, and Chemkin-2019 R3 software was used to explore and analyze the flame extinction limit and chemical kinetics characteristics under different ammonia mixing ratios, initial pressures, and air preheating temperatures. It was obtained that the flame extinction stretch rate was decreased by increasing the NH3 mole fraction in the CH4/NH3 mixed fuel. The increase in pressure or air preheating temperature would accelerate the chemical reaction rate of each component in the combustion process, increase the flame extinction limit, and counteract the “stretching effect” of the flame, thus restraining the flame extinguishing phenomenon. The results of a path analysis show that the formation and consumption of OH had an important influence on flame extinction in the chain reaction. The net reaction rate of OH increases with increasing the initial pressure or air preheating temperature, which leads to an increase in flame intensity, combustion stability, and the extinction limit. Furthermore, the function curve between the reaction influences the RIF factor and the stretch rate of the first-to-ten reactions, affected by the heat release of flame combustion, was drawn and quantitatively analyzed. Eventually, a sensitivity analysis of the flame under different working conditions was completed, which found that promoting the forward reaction R39 H + O2<=>O + OH also promotes the positive combustion as a whole when the flame was near extinction. The sensitivity coefficient of R39 in the CH4/NH3/Air flame increases with the growing initial pressure. The increasing air preheating temperature was capable of switching the reaction of R248 NH2 + OH<=>NH + H2O in the CH4/NH3/Air flame from an inhibiting reaction to a promoting reaction, while decreasing the sensitivity coefficient of inhibiting the forward reaction R10 O + CH3<=>H + CH2O, R88 OH + HO2<=>O2 + H2O, and R271 H + NO + M<=>HNO + M. Thus, the inhibition effect of flame extinction was weakened, and the positive progress of combustion was promoted.
Activation of Peroxymonosulfate by Co-Ni-Mo Sulfides/CNT for Organic Pollutant Degradation
Authors: You Shihao , Di Jing , Zhang Tao , Chen Yufeng , Yang Ruiqin , Gao Yesong , Li Yin , Gai Xikun
Published: July 2024

To explore advanced oxidation catalysts, peroxymonosulfate (PMS) activation by Co-Ni-Mo/carbon nanotube (CNT) composite catalysts was investigated. A compound of NiCo2S4, MoS2, and CNTs was successfully prepared using a simple one-pot hydrothermal method. The results revealed that the activation of PMS by Co-Ni-Mo/CNT yielded an exceptional Rhodamine B decolorization efficiency of 99% within 20 min for the Rhodamine B solution. The degradation rate of Co-Ni-Mo/CNT was 4.5 times higher than that of Ni-Mo/CNT or Co-Mo/CNT, and 1.9 times as much than that of Co-Ni/CNT. Additionally, radical quenching experiments revealed that the principal active groups were 1O2, surface-bound SO4•−, and •OH radicals. Furthermore, the catalyst exhibited low metal ion leaching and favorable stability. Mechanism studies revealed that Mo4+ on the surface of MoS2 participated in the oxidation of PMS and the transformation of Co3+/Co2+ and Ni3+/Ni2+. The synergism between MoS2 and NiCo2S4 reduces the charge transfer resistance between the catalyst and solution interface, thus accelerating the reaction rate. Interconnected structures composed of metal sulfides and CNTs can also enhance the electron transfer process and afford sufficient active reaction sites. Our work provides a further understanding of the design of multi-metal sulfides for wastewater treatment.
Associations between Long-Term Dietary Coenzyme Q10 Intake and New-Onset Hypertension in Adults: Insights from a Nationwide Prospective Cohort Study
Authors: Zhao Dan , Tian Zezhong , Kuang Huiying , Xu Yixuan , Zheng Yiqi , Zhong Zepei , Liang Lihan , Yang Yan
Published: July 2024

Coenzyme Q10 (CoQ10) supplementation appears to be associated with a lower blood pressure. Nevertheless, it remains unclear whether food-sourced CoQ10 will affect new-onset hypertension in general adults. This study investigated the relationship between dietary CoQ10 intake and new-onset hypertension among the general population. Participants without hypertension at baseline from the China Health and Nutrition Survey (CHNS) prospective cohort study were included (n = 11,428). Dietary CoQ10 intake was collected by validated dietary recalls and the food weighing method. Linear and non-linear relationships between dietary CoQ10 intake and new-onset hypertension were analyzed using multivariable Cox proportional hazards models and restricted cubic splines. During follow-up (median: 6 years), 4006 new-onset hypertension cases were documented. Compared with non-consumers, the hazard ratio (HR) and 95% confidence interval (CI) from quintile 2 to 4 total dietary CoQ10 were 0.83 (0.76, 0.91), 0.86 (0.78, 0.94) and 1.01 (0.92, 1.11); total plant-derived CoQ10 were 0.80 (0.73, 0.88), 1.00 (0.91, 1.09) and 1.10 (1.00, 1.20); and animal-derived CoQ10 were 0.65 (0.59, 0.71), 0.58 (0.53, 0.64) and 0.68 (0.62, 0.75). The lowest risk was found at moderate intake, with a non-linear relationship (P nonlinearity < 0.05). Furthermore, the overall inverse association was stronger among individuals without alcohol consumption or eating a low-fat diet. Moderate long-term dietary CoQ10 intake might be protective against new-onset hypertension. However, it follows a non-linear relationship and excessive intake may increase the risk of new-onset hypertension in the Chinese population.
Effects of Oral Nutritional Supplement with β-Hydroxy-β-methylbutyrate (HMB) on Biochemical and Hematological Indices in Community-Dwelling Older Adults at Risk of Malnutrition: Findings from the SHIELD Study
Authors: Tey Siew Ling , Huynh Dieu Thi Thu , Kong Sing Teang , Oliver Jeffery , Baggs Geraldine , Low Yen Ling , How Choon How , Cheong Magdalin , Chow Wai Leng , Tan Ngiap Chuan , Aw Tar Choon , Chew Samuel Teong Huang
Published: July 2024

Malnutrition may result in abnormal biochemical and hematological indices. This planned prespecified analysis investigated the effects of a specialized oral nutritional supplement (ONS) on biochemical and hematological indices in community-dwelling older adults at risk of malnutrition. In the Strengthening Health in ELDerly through nutrition (SHIELD) study, 811 older adults aged 65 years and above took part in this randomized, double-blind, placebo-controlled, multi-center study. Participants were randomly allocated to either a complete and balanced specialized ONS (each serving provides 262 kcal, 10.5 g protein, 7.75 µg vitamin D3, and 0.74 g calcium β-hydroxy-β-methylbutyrate) and dietary counselling (intervention group) or a placebo and dietary counselling (placebo group). Both groups consumed study products twice a day for 180 days. Data were collected at baseline, day 90, and day 180. Blood analysis results at follow-up visits were analyzed using repeated measures analysis of covariance with adjustments for confounders. Overall, when compared with the placebo group, the intervention group showed significantly greater urea (6.0 mmol/L vs. 5.4 mmol/L, p < 0.001), urea to creatinine ratio (4.39 vs. 4.26, p < 0.001), prealbumin (24.9 mg/dL vs. 24.0 mg/dL, p < 0.001), vitamin B12 (480.0 pmol/L vs. 420.1 pmol/L, p < 0.001), and globulin levels (26.8 g/L vs. 26.5 g/L, p = 0.032). The intervention group also had a significantly higher absolute reticulocyte count (62.0 × 103/µL vs. 58.2 × 103/µL, overall p < 0.001) and mean platelet volume (10.0 fL vs. 9.9 fL, overall p = 0.003). Furthermore, significant improvements were seen in total protein at day 90 (71.7 g/L vs. 71.1 g/L, p = 0.017) and in absolute monocyte count at day 90 (0.50 × 103/µL vs. 0.47 × 103/µL, p = 0.009) in the intervention group. In conclusion, daily consumption of a specialized ONS for six months led to significant improvements in biochemical and hematological indices in community-dwelling older adults at risk of malnutrition.
A Comprehensive Review Exploring the Protective Role of Specific Commensal Gut Bacteria against Salmonella
Authors: Singh Saloni , Koo Ok Kyung
Published: July 2024

Gut microbiota is a diverse community of microorganisms that constantly work to protect the gut against pathogens. Salmonella stands out as a notorious foodborne pathogen that interacts with gut microbes, causing an imbalance in the overall composition of microbiota and leading to dysbiosis. This review focuses on the interactions between Salmonella and the key commensal bacteria such as E. coli, Lactobacillus, Clostridium, Akkermansia, and Bacteroides. The review highlights the role of these gut bacteria and their synergy in combating Salmonella through several mechanistic interactions. These include the production of siderophores, which compete with Salmonella for essential iron; the synthesis of short-chain fatty acids (SCFAs), which exert antimicrobial effects and modulate the gut environment; the secretion of bacteriocins, which directly inhibit Salmonella growth; and the modulation of cytokine responses, which influences the host’s immune reaction to infection. While much research has explored Salmonella, this review aims to better understand how specific gut bacteria engage with the pathogen, revealing distinct defense mechanisms tailored to each species and how their synergy may lead to enhanced protection against Salmonella. Furthermore, the combination of these commensal bacteria could offer promising avenues for bacteria-mediated therapy during Salmonella-induced gut infections in the future.
Navigating Preclinical Models and Medications for Peripheral Neuropathy: A Review
Authors: Jali Abdulmajeed M. , Banji David , Banji Otilia J. F. , Hurubi Khalid Y. , Tawhari Faisal Y. , Alameer Atheer A. , Dohal Atyaf S. , Zanqoti Raha A.
Published: July 2024

Peripheral neuropathy (PN) is a multifaceted disorder characterised by peripheral nerve damage, manifesting in symptoms like pain, weakness, and autonomic dysfunction. This review assesses preclinical models in PN research, evaluating their relevance to human disease and their role in therapeutic development. The Streptozotocin (STZ)-induced diabetic rat model is widely used to simulate diabetic neuropathy but has limitations in faithfully replicating disease onset and progression. Cisplatin-induced PN models are suitable for studying chemotherapy-induced peripheral neuropathy (CIPN) and closely resemble human pathology. However, they may not fully replicate the spectrum of sensory and motor deficits. Paclitaxel-induced models also contribute to understanding CIPN mechanisms and testing neuroprotective agents. Surgical or trauma-induced models offer insights into nerve regeneration and repair strategies. Medications such as gabapentin, pregabalin, duloxetine, and fluoxetine have demonstrated promise in these models, enhancing our understanding of their therapeutic efficacy. Despite progress, developing models that accurately mirror human PN remains imperative due to its complex nature. Continuous refinement and innovative approaches are critical for effective drug discovery. This review underscores the strengths and limitations of current models and advocates for an integrated approach to address the complexities of PN better and optimise treatment outcomes.
Advances in Light-Responsive Smart Multifunctional Nanofibers: Implications for Targeted Drug Delivery and Cancer Therapy
Authors: Agiba Ahmed M. , Elsayyad Nihal , ElShagea Hala N. , Metwalli Mahmoud A. , Mahmoudsalehi Amin Orash , Beigi-Boroujeni Saeed , Lozano Omar , Aguirre-Soto Alan , Arreola-Ramirez Jose Luis , Segura-Medina Patricia , Hamed Raghda Rabe
Published: July 2024

Over the last decade, scientists have shifted their focus to the development of smart carriers for the delivery of chemotherapeutics in order to overcome the problems associated with traditional chemotherapy, such as poor aqueous solubility and bioavailability, low selectivity and targeting specificity, off-target drug side effects, and damage to surrounding healthy tissues. Nanofiber-based drug delivery systems have recently emerged as a promising drug delivery system in cancer therapy owing to their unique structural and functional properties, including tunable interconnected porosity, a high surface-to-volume ratio associated with high entrapment efficiency and drug loading capacity, and high mass transport properties, which allow for controlled and targeted drug delivery. In addition, they are biocompatible, biodegradable, and capable of surface functionalization, allowing for target-specific delivery and drug release. One of the most common fiber production methods is electrospinning, even though the relatively two-dimensional (2D) tightly packed fiber structures and low production rates have limited its performance. Forcespinning is an alternative spinning technology that generates high-throughput, continuous polymeric nanofibers with 3D structures. Unlike electrospinning, forcespinning generates fibers by centrifugal forces rather than electrostatic forces, resulting in significantly higher fiber production. The functionalization of nanocarriers on nanofibers can result in smart nanofibers with anticancer capabilities that can be activated by external stimuli, such as light. This review addresses current trends and potential applications of light-responsive and dual-stimuli-responsive electro- and forcespun smart nanofibers in cancer therapy, with a particular emphasis on functionalizing nanofiber surfaces and developing nano-in-nanofiber emerging delivery systems for dual-controlled drug release and high-precision tumor targeting. In addition, the progress and prospective diagnostic and therapeutic applications of light-responsive and dual-stimuli-responsive smart nanofibers are discussed in the context of combination cancer therapy.
Chaperone Copolymer-Assisted Catalytic Hairpin Assembly for Highly Sensitive Detection of Adenosine
Authors: Liao Yazhen , Yin Xiaoxue , Liu Wenqian , Du Zhenrui , Du Jie
Published: July 2024

Adenosine is an endogenous molecule that plays a vital role in biological processes. Research indicates that abnormal adenosine levels are associated with a range of diseases. The development of sensors capable of detecting adenosine is pivotal for early diagnosis of disease. For example, elevated adenosine levels are closely associated with the onset and progression of cancer. In this study, we designed a novel DNA biosensor utilizing chaperone copolymer-assisted catalytic hairpin assembly for highly sensitive detection of adenosine. The functional probe comprises streptavidin magnetic beads, an aptamer, and a catalytic chain. In the presence of adenosine, it selectively binds to the aptamer, displacing the catalytic chain into the solution. The cyclic portion of H1 hybridizes with the catalytic strand, while H2 hybridizes with the exposed H1 fragment to form an H1/H2 complex containing a G-quadruplex. Thioflavin T binds specifically to the G-quadruplex, generating a fluorescent signal. As a nucleic acid chaperone, PLL-g-Dex expedites the strand exchange reaction, enhancing the efficiency of catalytic hairpin assembly, thus amplifying the signal and reducing detection time. The optimal detection conditions were determined to be a temperature of 25 °C and a reaction time of 10 min. Demonstrating remarkable sensitivity and selectivity, the sensor achieved a lowest limit of detection of 9.82 nM. Furthermore, it exhibited resilience to interference in complex environments such as serum, presenting an effective approach for rapid and sensitive adenosine detection.
A Modeling Framework for the Thermoforming of Carbon Fiber Reinforced Thermoplastic Composites
Authors: Tan Long Bin , Teo Wern Sze , Cheah Yi Wen , Narayanaswamy Sridhar
Published: July 2024

A comprehensive modeling framework for the thermoforming of polymer matrix woven laminate composite was developed. Two numerical indicators, the slip path length and traction magnitude, have been identified to be positively correlated to matrix smearing and wrinkling defects. The material model has been calibrated with picture-frame experimental results, and the prediction accuracy for intra-ply shear and thickness distribution was examined with measurements of the physically formed parts. Specifically, thickness prediction for most locations on the formed parts was accurate within an 11.6% error margin. However, at two points with significant intra-ply shear, the prediction errors increased to around 20%. Finally, a parametric study was conducted to determine the relationship between various process parameters and the quality of the formed part. For the trapezoidal part, orienting the laminate at 45 degrees to the mold axis reduces the likelihood of matrix smear and wrinkling defects. Although this laminate orientation yielded a greater spatial variation in part thickness, the thickness deviation is lower than that for the 0-degree orientation case. Two forming analyses were conducted with ramp rates of 25 mm/s and 80 mm/s to match the equipment’s operational limits. It was observed that higher forming rates led to a greater likelihood of defects, as evidenced by a 15% and 10% increase in the formed part areas with longer slip paths and higher traction magnitudes, respectively. It was discovered that shallower molds benefit from faster ramp rates, while deeper molds require slower rates to manage extensive shearing, stretching and bending. Faster forming rates lead to smaller thickness increases at high intra-ply shear regions, indicating a shift from intra-ply shear to out-of-plane bending due to the visco-plastic effect of the molten laminate and can negatively impact part quality. Lastly, it was shown that a well-conceived strategy using darts could improve the part quality by reducing the magnitude of the defect indicators.
Corrosion Characteristics of Polymer-Modified Oil Well Cement-Based Composite Materials in Geological Environment Containing Carbon Dioxide
Authors: Zhang Yan , Xie Junyu , Zhao Weiming , Dai Jie , Gao Fei
Published: July 2024

Oil well cement is easily damaged by carbon dioxide (CO2) corrosion, and the corrosion of oil well cement is affected by many factors in complex environments. The anti-corrosion performance of oil well cement can be improved by polymer materials. In order to explore the influence of different corrosion factors on the corrosion depth of polymer-modified oil well cement, the influence of different corrosion factors on corrosion depth was studied based on the Box–Behnken experimental design. The interaction of different influencing factors and the influence of multiple corrosion depths were analyzed based on the response surface method, and a response surface model was obtained for each factor and corrosion depth. The results indicate that within the scope of the study, the corrosion depth of polymer-modified oil well cement was most affected by time. The effects of temperature and the pressure of CO2 decreased sequentially. The response surface model had good significance, with a determination coefficient of 0.9907. The corrosion depth was most affected by the interaction between corrosion time and the pressure of CO2, while the corrosion depth was less affected by the interaction between corrosion temperature and corrosion time. Improving the CO2 intrusion resistance of cement slurry in an environment with a high concentration of CO2 gas can effectively ensure the long-term structural integrity of cement.
Efficient Preparation and Optimization of Activated Carbon Monoliths from Resorcinol-Formaldehyde Resins for CO2 Capture
Authors: Mosquera José E. , Delbecq Frédéric , Daouk Elias , Drelich Audrey , Saleh Khashayar , Gautier Rémi , Leturia Mikel
Published: July 2024

Activated carbon monoliths with developed porosity, high surface area and excellent adsorption properties were successfully prepared from resorcinol-formaldehyde resins using a physical activation method. The primary objective of this study was to examine the impact of key parameters, namely hexamethylenetetramine content (0.08–0.2 g), pyrolysis heating rate (5–20 °C/min) and activation time (1–7 h), on the final characteristics of the activated carbon in order to identify the optimal operating conditions to achieve the desired properties. All the cured resin samples were pyrolyzed at 900 °C under a nitrogen atmosphere, while the activation process took place in the presence of CO2. The evaluation of the activated carbon materials was based on the CO2 adsorption capacity and BET surface area, micropore area and total pore volume, which were employed as the criteria for selecting the optimal activated carbon. The synthesized porous carbon monoliths exhibited good properties: high BET surface area (900 m2/g), high CO2 adsorption capacity (5.33 mmol/g at 0 °C and 1 bar, 3.8 mmol/g at 25 °C and 1 bar) and good CO2 selectivity for CO2/N2 and CO2/CH4 mixtures. These results were obtained with a pyrolysis heating rate of 5 °C/min and a 3 h activation period.
Production and Application of a New Biosurfactant for Solubilisation and Mobilisation of Residual Oil from Sand and Seawater
Authors: Silva Ivison Amaro , Fortunato José Gabriel Lima Alcântara , Almeida Fabíola Carolina Gomes , Alves Romulo Nepomuceno , Cunha Maristela Casé Costa , Rufino Raquel Diniz , Fernandes Mucio Luiz Banja , Sarubbo Leonie Asfora
Published: July 2024

Significant research has been conducted to minimise environmental impacts and promote the sustainable use of resources and raw materials. Microbial surfactants are an example of advanced materials obtained from sustainable production processes. In the present study, a biosurfactant was produced by the yeast Starmerella bombicola ATCC 22214 grown in a previously selected low-cost mineral medium containing 10% sucrose, 1.2% canola oil, and 0.5% corn steep liquor. The biosurfactant reduced surface tension from 72 ± 0.1 to 32.76 ± 0.3 mN/m. The yield was 23 g/L, and the critical micelle concentration was 0.6 g/L. The biosurfactant emulsified 96.25 ± 0.08% of used motor oil, was characterised as a sophorolipid, and exhibited stability under extreme conditions with no significant loss of its properties. Toxicity was assessed by exposing the microcrustacean Artemia salina and the zebrafish (Danio rerio) to the biosurfactant. The biosurfactant proved efficient for use in remediation processes, removing 97.8% and 69.2% of the petroleum derivative from sand in kinetic and static tests, respectively, and removed 91.5% of the contaminant from seawater. The results indicate the potential of this new biosurfactant for the mobilisation and solubilisation of hydrocarbons in the marine environment. This green biomolecule is a promising technology for the replacement of chemical dispersants in the remediation of aquatic and soil systems.
Evaluation and Improvement of a CALIPSO-Based Algorithm for Cloud Base Height in China
Authors: Li Ruolin , Ma Xiaoyan
Published: July 2024

Clouds are crucial in regulating the Earth’s energy budget. Global cloud top heights have been easily retrieved from satellite measurements, but there are few methods for determining cloud base height (CBH) from satellite measurements. The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm was proposed to derive the height of the lower-troposphere liquid cloud base by using the Cloud-Aerosol Lidar with Orthogonal polarization cloud aerosol LiDAR (CALIOP) profiles and weather observations at airports from aviation routine and special weather report (METARs and SPECIs, called METAR) observation data in the United States. A modification to the CBASE algorithm over China (CNMETAR-CBASE) is presented in this paper. In this paper, the ability of the CBASE algorithm to calculate CBH in China is evaluated, and METAR observations over China (CNMETAR) were then used to modify the CBASE algorithm. The results including CNMETAR observation data in China can better retrieve CBH over China compared with the results using the original CBASE algorithm, and the accuracy of the global CBH results has been improved. Overestimations of CBH with the original algorithm range from 500 to 800 m in China, which have been reduced to about 300 m with an improved algorithm. The deviations calculated by the algorithm also have a significant reduction, from 480 m (CBASE) to 420 m (CNMETAR-CBASE). In conclusion, the modified CBASE algorithm not only calculates the CBH more accurately in China but also improves the results of the global CBH retrieved from satellites.
Exploring Distributed Scatterers Interferometric Synthetic Aperture Radar Attributes for Synthetic Aperture Radar Image Classification
Authors: Wei Mingxuan , Liu Yuzhou , Zhu Chuanhua , Wang Chisheng
Published: July 2024

Land cover classification of Synthetic Aperture Radar (SAR) imagery is a significant research direction in SAR image interpretation. However, due to the unique imaging methodology of SAR, interpreting SAR images presents numerous challenges, and land cover classification using SAR imagery often lacks innovative features. Distributed scatterers interferometric synthetic aperture radar (DS-InSAR), a common technique for deformation extraction, generates several intermediate parameters during its processing, which have a close relationship with land features. Therefore, this paper utilizes the coherence matrix, the number of statistically homogeneous pixels (SHPs), and ensemble coherence, which are involved in DS-InSAR as classification features, combined with the backscatter intensity of multi-temporal SAR imagery, to explore the impact of these features on the discernibility of land objects in SAR images. The results indicate that the adopted features improve the accuracy of land cover classification. SHPs and ensemble coherence demonstrate significant importance in distinguishing land features, proving that these proposed features can serve as new attributes for land cover classification in SAR imagery.
Research on Multi-Source Data Fusion and Satellite Selection Algorithm Optimization in Tightly Coupled GNSS/INS Navigation Systems
Authors: Yu Xuyang , Guo Zhiming , Wu Liaoni
Published: July 2024

With the increase in the number of Global Navigation Satellite System (GNSS) satellites and their operating frequencies, richer observation data are provided for the tightly coupled Global Navigation Satellite System/Inertial Navigation System (GNSS/INS). In this paper, we propose an efficient and robust combined navigation scheme to address the key issues of system accuracy, robustness, and computational efficiency. The tightly combined system fuses multi-source data such as the pseudo-range, the pseudo-range rate, and dual-antenna observations from the GNSS and the horizontal attitude angle from the vertical gyro (VG) in order to realize robust navigation in a sparse satellite observation environment. In addition, to cope with the high computational load faced by the system when the satellite observation conditions are good, we propose a weighted quasi-optimal satellite selection algorithm that reduces the computational burden of the navigation system by screening the observable satellites while ensuring the accuracy of the observation data. Finally, we comprehensively evaluate the proposed system through simulation experiments. The results show that, compared with the loosely coupled navigation system, our system has a significant improvement in state estimation accuracy and still provides reliable attitude estimation in regions with poor satellite observation conditions. In addition, in comparison experiments with the optimal satellite selection algorithm, our proposed satellite selection algorithm demonstrates greater advantages in terms of computational efficiency and engineering practicability.
Multi-Scale Dynamics and Spatial Consistency of Economy and Population Based on NPP/VIIRS Nighttime Light Data and Population Imagery: A Case Study of the Yangtze River Delta
Authors: Xu Yucheng , Chen Shengbo , Wang Zibo , Liu Bin , Wang Linfeng
Published: July 2024

Population and economy are crucial factors contributing to regional disparities. Studying the patterns and relationships between these two elements is essential for promoting sustainable development in regions and cities. This study constructs multi-scale geographic concentration indices and inconsistency indices, utilizing NPP/VIIRS and LandScan data to quantitatively analyze the spatial pattern changes of population and economy in the Yangtze River Delta across various spatial scales, revealing the matching relationships between population and economic elements within cities. The results indicate that the economy in the Yangtze River Delta is spreading outward from the core areas, with the average population–nightlight inconsistency index decreasing from 1.57 to 1.33. This suggests that the imbalance between population and economy within the urban agglomeration is gradually improving, consistent with trends observed in statistical survey data. Within individual cities, there is a noticeable spatial mismatch between population and nightlight intensity, with the population primarily concentrated in urban core areas. As urban spaces expand, the areas where population concentration is significantly lower than nightlight concentration are gradually diminishing. By 2022, the land area where population and economic concentration are coordinated within the Yangtze River Delta urban areas increased from 9.13% to 16.24%. Population concentration in these coordinated regions rose from 11.33% to 16.33%, while nightlight concentration increased from 9.98% to 13.63%. The improved geographic concentration and inconsistency indices are effective indicators for multi-scale monitoring of population and economic spatial changes. The integration of NPP/VIIRS nighttime light data and LandScan data provides an effective method for uncovering different spatial patterns of population and socio-economic element aggregation in urban structures. This can offer insights for promoting sustainable regional and urban development.
Synergizing a Deep Learning and Enhanced Graph-Partitioning Algorithm for Accurate Individual Rubber Tree-Crown Segmentation from Unmanned Aerial Vehicle Light-Detection and Ranging Data
Authors: Zhu Yunfeng , Lin Yuxuan , Chen Bangqian , Yun Ting , Wang Xiangjun
Published: July 2024

The precise acquisition of phenotypic parameters for individual trees in plantation forests is important for forest management and resource exploration. The use of Light-Detection and Ranging (LiDAR) technology mounted on Unmanned Aerial Vehicles (UAVs) has become a critical method for forest resource monitoring. Achieving the accurate segmentation of individual tree crowns (ITCs) from UAV LiDAR data remains a significant technical challenge, especially in broad-leaved plantations such as rubber plantations. In this study, we designed an individual tree segmentation framework applicable to dense rubber plantations with complex canopy structures. First, the feature extraction module of PointNet++ was enhanced to precisely extract understory branches. Then, a graph-based segmentation algorithm focusing on the extracted branch and trunk points was designed to segment the point cloud of the rubber plantation. During the segmentation process, a directed acyclic graph is constructed using components generated through grey image clustering in the forest. The edge weights in this graph are determined according to scores calculated using the topologies and heights of the components. Subsequently, ITC segmentation is performed by trimming the edges of the graph to obtain multiple subgraphs representing individual trees. Four different plots were selected to validate the effectiveness of our method, and the widths obtained from our segmented ITCs were compared with the field measurement. As results, the improved PointNet++ achieved an average recall of 94.6% for tree trunk detection, along with an average precision of 96.2%. The accuracy of tree-crown segmentation in the four plots achieved maximal and minimal R2 values of 98.2% and 92.5%, respectively. Further comparative analysis revealed that our method outperforms traditional methods in terms of segmentation accuracy, even in rubber plantations characterized by dense canopies with indistinct boundaries. Thus, our algorithm exhibits great potential for the accurate segmentation of rubber trees, facilitating the acquisition of structural information critical to rubber plantation management.
Weed Species Identification: Acquisition, Feature Analysis, and Evaluation of a Hyperspectral and RGB Dataset with Labeled Data
Authors: Ronay Inbal , Lati Ran Nisim , Kizel Fadi
Published: July 2024

Site-specific weed management employs image data to generate maps through various methodologies that classify pixels corresponding to crop, soil, and weed. Further, many studies have focused on identifying specific weed species using spectral data. Nonetheless, the availability of open-access weed datasets remains limited. Remarkably, despite the extensive research employing hyperspectral imaging data to classify species under varying conditions, to the best of our knowledge, there are no open-access hyperspectral weed datasets. Consequently, accessible spectral weed datasets are primarily RGB or multispectral and mostly lack the temporal aspect, i.e., they contain a single measurement day. This paper introduces an open dataset for training and evaluating machine-learning methods and spectral features to classify weeds based on various biological traits. The dataset comprises 30 hyperspectral images, each containing thousands of pixels with 204 unique visible and near-infrared bands captured in a controlled environment. In addition, each scene includes a corresponding RGB image with a higher spatial resolution. We included three weed species in this dataset, representing different botanical groups and photosynthetic mechanisms. In addition, the dataset contains meticulously sampled labeled data for training and testing. The images represent a time series of the weed’s growth along its early stages, critical for precise herbicide application. We conducted an experimental evaluation to test the performance of a machine-learning approach, a deep-learning approach, and Spectral Mixture Analysis (SMA) to identify the different weed traits. In addition, we analyzed the importance of features using the random forest algorithm and evaluated the performance of the selected algorithms while using different sets of features.
North American Circum-Arctic Permafrost Degradation Observation Using Sentinel-1 InSAR Data
Authors: Guan Shaoyang , Wang Chao , Tang Yixian , Zou Lichuan , Yu Peichen , Li Tianyang , Zhang Hong
Published: July 2024

In the context of global warming, the accelerated degradation of circum-Arctic permafrost is releasing a significant amount of carbon. InSAR can indirectly reflect the degradation of permafrost by monitoring its deformation. This study selected three typical permafrost regions in North America: Alaskan North Slope, Northern Great Bear Lake, and Southern Angikuni Lake. These regions encompass a range of permafrost landscapes, from tundra to needleleaf forests and lichen-moss, and we used Sentinel-1 SAR data from 2018 to 2021 to determine their deformation. In the InSAR process, due to the prolonged snow cover in the circum-Arctic permafrost, we used only SAR data collected during the summer and applied a two-stage interferogram selection strategy to mitigate the resulting temporal decorrelation. The Alaskan North Slope showed pronounced subsidence along the coastal alluvial plains and uplift in areas with drained thermokarst lake basins. Northern Great Bear Lake, which was impacted by wildfires, exhibited accelerated subsidence rates, revealing the profound and lasting impact of wildfires on permafrost degradation. Southern Angikuni Lake’s lichen and moss terrains displayed mild subsidence. Our InSAR results indicate that more than one-third of the permafrost in the North American study area is degrading and that permafrost in diverse landscapes has different deformation patterns. When monitoring the degradation of large-scale permafrost, it is crucial to consider the unique characteristics of each landscape.
Interrupted-Sampling Repeater Jamming Countermeasure Based on Intrapulse Frequency–Coded Joint Frequency Modulation Slope Agile Waveform
Authors: Wang Xiaoge , Li Binbin , Chen Hui , Liu Weijian , Zhu Yongzhe , Luo Jun , Ni Liuliu
Published: July 2024

Interrupted-sampling repeater jamming (ISRJ) is widely used in the field of electronic countermeasures, and can severely affect radar detection. Therefore, the problem of ISRJ suppression is a compelling task. In this paper, we propose an ISRJ suppression method based on an intrapulse frequency-coded joint frequency modulation (FM) slope agile waveform. The intrapulse frequency-coded joint FM slope agile waveform is first designed. The delay inserted between subpulses makes the waveform easy to implement in engineering, and the ambiguity function diagram of the waveform approximates the ideal thumbtack type. Next, the echo slices are classified in the fractional domain utilizing the discontinuity of ISRJ and the focusing property of fractional Fourier transform for chirp signals. Then, the target and interference in the interfered echo slices are reconstructed by compressed sensing, and a time-domain filter is constructed based on interference-free echo slices. Finally, the echo signal after interference suppression is further filtered in the time domain to degrade range sidelobes. Simulation results show that the proposed method can effectively suppress three typical types of ISRJ. Moreover, the probability of target detection after interference suppression exceeds 90% when the jamming-to-signal ratio equals 50 dB.
Investigating Intra-Pulse Doppler Frequency Coupled in the Radar Echo Signal of a Plasma Sheath-Enveloped Target
Authors: Bai Bowen , Pu Bailiang , Zhang Ke , Yang Yilin , Li Xiaoping , Liu Yanming
Published: July 2024

In detecting hypersonic vehicles, the radar echo signal is coupled with an intra-pulse Doppler frequency (I-D frequency) component caused by relative motion of a plasma sheath (PSh) and the vehicle, which can induce the phenomenon of a ghost target in a one-dimensional range profile. In order to investigate the I-D frequency generated by the relative motion of a PSh, this study transforms a linear frequency modulated (LFM) signal into a single carrier frequency signal based on echo signal equivalent time delay-dechirp processing and realizes high resolution and fast extraction of the I-D frequency coupled with the frequency-domain echo signal. Furthermore, by relying on the computation of the surface flow field of the RAMC-II Blunt Cone Reentry Vehicle, the coupled I-D frequency in the radar echo signal of a PSh-enveloped target under circumstances of typical altitudes and carrier frequencies is extracted and further investigated, revealing the variation law of I-D frequency. The key findings of this study provide a novel approach for suppressing anomalies in radar detection of PSh-enveloped targets as well as effective detecting and as robust target tracking.
Aerial SfM–MVS Visualization of Surface Deformation along Folds during the 2024 Noto Peninsula Earthquake (Mw7.5)
Authors: Yoshida Kazuki , Endo Ryo , Iwahashi Junko , Sasagawa Akira , Yarai Hiroshi
Published: July 2024

This study aimed to map and analyze the spatial pattern of the surface deformation associated with the 2024 Noto Peninsula earthquake (Mw7.5) using structure-from-motion/multi-view-stereo (SfM–MVS), an advanced photogrammetric technique. The analysis was conducted using digital aerial photographs with a ground pixel dimension of 0.2 m (captured the day after the earthquake). Horizontal locations of GCPs were determined using pre-earthquake data to remove the wide-area horizontal crustal deformation component. The elevations of the GCPs were corrected by incorporating quasi-vertical values derived from a 2.5-dimensional analysis of synthetic aperture radar (SAR) results. In the synclinorium structure area, where no active fault had previously been identified, we observed a 5 km long uplift zone (0.1 to 0.2 km in width), along with multiple scarps that reached a maximum height of 2.2 m. The area and shape of the surface deformation suggested that the induced uplift and surrounding landslides were related to fold structures and their growth. Thus, our study shows the efficacy of SfM–MVS with respect to accurately mapping earthquake-induced deformations, providing crucial data for understanding seismic activity and informing disaster-response strategies.
Impact of Tile Size and Tile Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification
Authors: Cira Calimanut-Ionut , Manso-Callejo Miguel-Ángel , Yokoya Naoto , Sălăgean Tudor , Badea Ana-Cornelia
Published: July 2024

Popular geo-computer vision works make use of aerial imagery, with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near the image boundaries. In this work, the impact of three tile sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km2 of the Spanish territory that was labelled with binary road information) under twelve different scenarios, with each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on data from novel areas covering approximately 825 km2. The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of distinct levels of the fixed factors (tile size, tile overlap, and neural network architecture) on them. Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and a higher amount of tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap, and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set.
LWSDNet: A Lightweight Wheat Scab Detection Network Based on UAV Remote Sensing Images
Authors: Yin Ning , Bao Wenxia , Yang Rongchao , Wang Nian , Liu Wenqiang
Published: July 2024

Wheat scab can reduce wheat yield and quality. Currently, unmanned aerial vehicles (UAVs) are widely used for monitoring field crops. However, UAV is constrained by limited computational resources on-board the platforms. In addition, compared to ground images, UAV images have complex backgrounds and smaller targets. Given the aforementioned challenges, this paper proposes a lightweight wheat scab detection network based on UAV. In addition, overlapping cropping and image contrast enhancement methods are designed to preprocess UAV remote-sensing images. Additionally, this work constructed a lightweight wheat scab detection network called LWSDNet using mixed deep convolution (MixConv) to monitor wheat scab in field environments. MixConv can significantly reduce the parameters of the LWSDNet network through depthwise convolution and pointwise convolution, and different sizes of kernels can extract rich scab features. In order to enable LWSDNet to extract more scab features, a scab feature enhancement module, which utilizes spatial attention and dilated convolution, is designed to improve the ability of the network to extract scab features. The MixConv adaptive feature fusion module is designed to accurately detect lesions of different sizes, fully utilizing the semantic and detailed information in the network to enable more accurate detection by LWSDNet. During the training process, a knowledge distillation strategy that integrates scab features and responses is employed to further improve the average precision of LWSDNet detection. Experimental results demonstrate that the average precision of LWSDNet in detecting wheat scab is 79.8%, which is higher than common object detection models and lightweight object detection models. The parameters of LWSDNet are only 3.2 million (M), generally lower than existing lightweight object detection networks.
RSSI-WSDE: Wireless Sensing of Dynamic Events Based on RSSI
Authors: Tian Xiaoping , Wu Song , Zhang Xiaoyan , Du Lei , Fan Sencao
Published: July 2024

Wireless sensing is a crucial technology for building smart cities, playing a vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless sensing enables the formulation of more scientific decisions. The wireless sensing of dynamic events is a significant branch of wireless sensing. Sensing the specific times and durations of dynamic events is a challenging problem due to the dynamic event information is concealed within static environments. To effectively sense the relevant information of event occurrence, we propose a wireless sensing method for dynamic events based on RSSI, named RSSI-WSDE. RSSI-WSDE utilizes variable-length sliding windows and statistical methods to process original RSSI time series, amplifying the differences between dynamic events and static environments. Subsequently, z-score normalization is employed to enhance the comparability of the sensing effects for different dynamic events. Furthermore, by setting the adaptive threshold, the occurrence of dynamic event is sensed and the relevant information is marked on the original RSSI time series. In this study, the sensing performance of RSSI-WSDE was tested in indoor corridors and outdoor urban road environments. The wireless sensing of dynamic events, including walking, running, cycling, and driving, was conducted. The experimental results demonstrate that RSSI-WSDE can accurately sense the occurrence of dynamic events, marking the specific time and duration with millisecond-level precision. Moreover, RSSI-WSDE exhibits robust performance in wireless sensing of dynamic events in both indoor and outdoor environments.