bluuebunny/crossref_metadata_2025 huggingface dataset:
Sex-Specific Responses of Sexual Reproduction, Clonal Reproduction, and Vegetative Growth to Environmental (Biotic and Abiotic) Factors in the Clonal Dioecious Plant Acer barbinerve
Authors: Liao Dan , Lei Jingjing , Wang Yingni , Bao Yuxin , Zhang Xinna , Wang Juan
Published: February 2025

Sexual dimorphism in dioecious plants serves as a critical adaptive strategy in complex environments. This study systematically investigated the effects of topographic factors (elevation, slope, aspect, and convexity), soil nutrients (C, N, P), and interspecific competition intensity on the reproductive strategies and vegetative growth of the clonal dioecious plant, Acer barbinerve. Using Spearman’s correlation analysis, multiple regression models, and PLS-PM path models, key findings include the following: (1) female sexual reproduction biomass showed a significant positive correlation with the topography principal component (topo_PC1), with a notable gender–topography interaction, whereas male sexual reproduction was negatively regulated by elevation; (2) clonal reproduction in both sexes was significantly suppressed by interspecific competition, but females additionally exhibited positive topographic responses; and (3) male vegetative growth was significantly impacted by environmental stress, while females maintained relative stability. These results demonstrate that females optimize reproductive investment through topography-mediated resource acquisition, whereas males are more susceptible to resource competition constraints. This sex-specific adaptive strategy corroborates the dimorphic niche hypothesis, highlighting how environmental heterogeneity drives divergent life history allocations in dioecious species. The findings provide novel insights into the ecological mechanisms underlying sexual dimorphism and inform gender ratio management in ecological restoration practices.
Environmental Impact of Urban Surface Transportation: Influence of Driving Mode and Drivers’ Attitudes
Authors: Armenta-Déu Carlos
Published: February 2025

This paper focuses on the environmental impact of urban surface transportation and the influence that driving mode and drivers’ attitudes have on it. This article emphasizes the importance of a respectful attitude toward the environment and adopting moderate or conservative driving modes. This study covers driving GHG emissions in urban and peripheral areas for variable driving conditions, evaluating reductions or increases in CO2 emissions depending on the way of driving. The analysis of the different cases shows that pollutant emissions are significantly lower if a car driver reduces the acceleration rate and slows down by 10 to 20 km/h, or 6 to 12 mph, depending on the urban zone, downtown or peripheral, and traffic conditions. The reduction in GHG emissions can be as high as 0.083 kg of CO2 per day, on average, representing a global yearly reduction of 30 kg of GHG emissions per vehicle. This paper analyzes how inadequate driving speed and the above traffic regulation limits have caused a surplus in GHG emissions and a severe impact on urban areas, which are sensitive to pollution, increasing the GHG emission rate by between 28% and 40% depending on driving mode and driver attitude. This study shows that vehicle speed reduction did not significantly increase traveling time, with an average time extension of 0.2 min per km, representing a global extended daily traveled time of 6.4 min for the average daily journey distance in many countries. GHG increases due to inadequate driving increase the early human mortality rate by 0.4%, representing nearly 35 million early deaths per year.
Development and Characterization of Novel Hybrid Particleboard Made from Several Non-Wood Lignocellulosic Materials
Authors: Tarigan Fazilla Oktaviani , Hakim Luthfi , Purwoko Agus , Sucipto Tito , Nasution Halimatuddahliana , Fatriasari Widya , Lubis Muhammad Adly Rahandi , Sutiawan Jajang , Bakhsi Mohammad Irfan , Kim Nam-Hun , Antov Petar , Lee Seng Hua , Selvasembian Rangabhashiyam , Hussin Mohd Hazwan , Aristri Manggar Arum , Iswanto Apri Heri
Published: February 2025

The green transition trend in the wood-based panel industry aims to reduce environmental impact and waste production, and it is a viable approach to meet the increasing global demand for wood and wood-based materials as roundwood availability decreases, necessitating the development of composite products as alternatives to non-wood lignocellulosic raw materials. As a result, the purpose of this study is to examine and assess the physical, mechanical, and acoustic properties of particleboard manufactured from non-wood lignocellulosic biomass. The core layer was composed of non-wood lignocelluloses (banana stem, rice straw, coconut fiber, sugarcane bagasse, and fibrous vascular bundles (FVB) from snakefruit fronds), whereas the surface was made of belangke bamboo (Gigantochloa pruriens) and wood. The chemical characteristics, fiber dimensions and derivatives, and contact angles of non-wood lignocellulosic materials were investigated. The contact angle, which ranged from 44.57 to 62.37 degrees, was measured to determine the wettability of these materials toward adhesives. Hybrid particleboard (HPb) or sandwich particleboard (SPb) samples of 25 cm × 25 cm with a target density of 0.75 g/cm3 and a thickness of 1 cm were manufactured using 7% isocyanate adhesive (based on raw material oven dry weight). The physical parameters of the particleboard, including density, water content, water absorption (WA), and thickness swelling (TS), ranged from 0.47 to 0.79 g/cm3, 6.57 to 13.78%, 16.46 to 103.51%, and 3.38 to 39.91%, respectively. Furthermore, the mechanical properties of the particleboard, including the modulus of elasticity (MOE), bending strength (MOR), and internal bond strength (IB), varied from 0.39 to 7.34 GPa, 6.52 to 87.79 MPa, and 0.03 to 0.69 MPa, respectively. On the basis of these findings, the use of non-wood lignocellulosic raw materials represents a viable alternative for the production of high-performance particleboard.
Impact of Thermal Variations on the Fatigue and Fracture of Bi-Material Interfaces (Polyimide–EMC, Polyimide–SiO2, and Silicon–EMC) Found in Microchips
Authors: Videira Pedro F. C. , Ferreira Renato A. , Maleki Payam , Akhavan-Safar Alireza , Carbas Ricardo J. C. , Marques Eduardo A. S. , Karunamurthy Bala , da Silva Lucas F. M.
Published: February 2025

As the trend towards the densification of integrated circuit (IC) devices continues, the complexity of interfaces involving dissimilar materials and thermo-mechanical interactions has increased. Highly integrated systems in packages now comprise numerous thin layers made from various materials. The interfaces between these different materials represent a vulnerable point in ICs due to imperfect adhesion and stress concentrations caused by mismatches in thermo-mechanical properties such as Young’s modulus, coefficients of thermal expansion (CTE), and hygro-swelling-induced expansion. This study investigates the impact of thermal variations on the fracture behavior of three bi-material interfaces used in semiconductor packaging: epoxy molding compound–silicon (EMC–Si), silicon oxide–polyimide (SiO2–PI), and PI–EMC. Using double cantilever beam (DCB) tests, we analyzed these interfaces under mode I loading at three temperatures: −20 °C, 23 °C, and 100 °C, under both quasi-static and cyclic loading conditions. This provided a comprehensive analysis of the thermal effects across all temperature ranges in microelectronics. The results show that temperature significantly alters the failure mechanism. For SiO2–PI, the weakest point shifts from silicon at low temperatures to the interface at higher temperatures due to thermal stress redistribution. Additionally, the fracture energy of the EMC–Si interface was found to be highly temperature-dependent, with values ranging from 0.136 N/mm at low temperatures to 0.38 N/mm at high temperatures. SiO2–PI’s fracture energy at high temperature was 42% less than that of EMC–Si. The PI–EMC interface exhibited nearly double the crack growth rate compared to EMC–Si. The findings of this study provide valuable insights into the fracture behavior of bi-material interfaces, offering practical applications for improving the reliability and design of semiconductor devices, especially in chip packaging.
Advancing Sustainability in Modern Polymer Processing: Strategies for Waste Resource Recovery and Circular Economy Integration
Authors: Radu Ionut-Cristian , Vadureanu Andreea-Mihaela , Cozorici Derniza-Elena , Blanzeanu Erika , Zaharia Catalin
Published: February 2025

By the late 1970s, plastics had emerged as the most widely used materials globally. The discovery, development, and processing of diverse polymeric materials have profoundly shaped modern life and driven the expansion of numerous industries. Given the widespread interest in the utilization of these materials, it has become increasingly imperative to design their life cycles from the outset. This approach aims to maximize their utility while minimizing their environmental footprint. This review aims to identify and analyze the key challenges in polymer processing applicable to both additive and formative manufacturing methods, emphasizing the relationship between processing and recycling within the framework of sustainability. Modern polymer processing techniques play a crucial role in enhancing the sustainability of polymer products by improving recycling potential (with consideration of polymer type, source, and additives), cost-effectiveness, carbon footprint, and key properties such as durability, lifespan, performance, and environmental impact. It will also explore the concept of the circular economy and its integration into modern processing methods, including extrusion, injection molding, and 3D printing. Additionally, current polymer recycling methods are analyzed with respect to their effectiveness, sustainability, and compatibility with the original materials. Moreover, the discussion emphasizes the benefits of a circular economy compared to a linear one by exploring the concepts of closed-loop and open-loop systems, along with their diverse applications depending on the material and the initial processing method employed. To ensure that humanity continues to benefit from polymer materials while striving for a waste-free environment, it is essential to integrate the principles of sustainable development from the very beginning.
Study on Carbon Emission Reduction Strategy of CCUS Technology in Natural Gas Supply Chain Considering Government Subsidies
Authors: Chen Yuguang , Zhang Jijun , Wang Fuping , Zhang Jianping , Wu Wenjian , Li Hongbing
Published: February 2025

Climate change urgently requires reducing carbon emissions in the energy sector. Natural gas is a cleaner fossil fuel and important for the global shift to sustainable energy. However, its supply chain still produces significant carbon emissions. Carbon capture, utilization, and storage (CCUS) technologies are key to lowering these emissions, but their use is limited by high costs and technical challenges. Despite prior research examining CCUS technologies and government subsidies, few studies have systematically analyzed the differential impacts of various subsidy mechanisms on CCUS adoption in the natural gas supply chain, especially considering consumers’ low-carbon preferences. We compare two types of subsidies: one that helps cover the costs of investing in carbon reduction, and another that reduces the cost of natural gas. Using the Stackelberg game theory model, where the gas supplier leads and the buyer follows, we analyze how these subsidies influence the decisions of both parties, the level of carbon reduction, the amount of natural gas purchased, and their overall profits. Our results show that government subsidies significantly boost carbon reduction, increase natural gas purchases, and enhance profits for both suppliers and buyers compared to no subsidies. Higher subsidies lead to greater carbon reductions and larger market sizes. Specifically, subsidies for carbon reduction investments are more effective in encouraging suppliers to reduce emissions, while subsidies on natural gas costs benefit buyers by increasing their ability to reduce carbon and improve profitability. This research offers important guidance for policymakers in creating effective subsidy programs that promote the adoption of CCUS technologies and support a low-carbon transition in the natural gas supply chain.
Factors Influencing the Sterilization of E. coli in a High-Voltage Electric Field: Electric Field Strength, Temperature and Humidity
Authors: Pan Di , Wen Diya , Guo Xuan , Song Hua , Bai Shupei
Published: February 2025

The application of a high-voltage electric field (HVEF) is crucial for mitigating the growth and reproduction of pathogenic microorganisms on solid surfaces. However, the electric field distribution of HVEF in the undischarged state and bactericidal effect remain largely unexplored. Herein, a unipolar wire–plate HVEF device was developed and its field strength distribution was simulated by COMOSL software (version 6.2). The simulation analysis revealed that the electric field strength and space charge density of HVEF in the undischarged state reached a maximum at the discharge electrode and a minimum at the middle of the two electrode wires. The electric field strength and the inhomogeneity of electric intensity distribution also decreased with the increase in electrode spacing. The sterilization effect of E. coli was positively correlated with voltage and treatment time, but negatively correlated with electrode spacing, which was consistent with the change in the field strength of the simulation results. When the voltage was −12.5 kv and electrode spacing 2.7 cm, the sterilization rate was 98.03% at 60 min. In addition, the increase in treatment temperature and relative humidity also affected the electric field distribution, thereby improving the bactericidal effect. Our results revealed that the electric field distribution was the main factor affecting the sterilization effect of HVEF, which provided theoretical support for the design of HVEF sterilization equipment and the optimization of sterilization performance
Purification of CaF2 from Fluorine-Containing Sludge: Optimization via Calcium Ion Coexistence Effect
Authors: Zhu Danyang , Chen Xiang , Gong Hui , Wang Xiankai , Qiao Xueyuan , Dai Xiaohu
Published: February 2025

The rapid development of the photovoltaic industry has significantly increased fluorine-containing sludge production. Calcium fluoride (CaF2), a vital non-renewable raw material used in optics, metallurgy, and chemical synthesis, holds immense significance for ensuring the sustainable supply of fluoride resources. This study focuses on purifying CaF2 from fluorine-containing sludge using a systematic approach. Through characterization techniques such as XRF, SEM-EDS, XRD, FT-IR, and laser granulometry, the sludge’s composition was thoroughly analyzed. An acid-leaching–alkali-leaching method was proposed and validated for CaF2 purification. A key finding during acid leaching was the “calcium ion coexistence effect”, where the dissolution of other calcium salts influences CaF2 dissolution equilibrium, reducing its loss. Leveraging this phenomenon, an optimized strategy was developed by increasing acid concentration while reducing acid volume. This approach effectively addresses two common challenges in traditional acid-leaching processes: high CaF2 dissolution loss and difficulties in impurity removal. Experimental results revealed that under optimized acid-leaching conditions, the purity of CaF2 increased significantly from an initial 36.7 wt% to 76.1 wt% after acid-leaching–alkali-leaching. This study demonstrates a successful method for purifying CaF2 from fluorine-containing sludge, providing a sustainable solution for fluoride resource recovery.
Harvesting Salinity Gradient Energy by Diffusion of Ions, Liquid Water, and Water Vapor
Authors: Veerman Joost
Published: February 2025

In this paper, we briefly discuss the main points of salinity gradient energy (SGE). First, we discuss the sources of SGE and the methods to harvest it. Then, we calculate, using the laws of physical chemistry, the amount of energy that can be harvested with three selected methods based on the diffusion of ions, liquid water, and water vapor, respectively. Then, we give an overview of the applications, highlighting a number of new developments such as assisted reverse electrodialysis (ARED) and energy storage. It turns out that reverse electrodialysis offers unexpected possibilities such as energy storage, utilizing waste heat, and the administration of transdermal drug delivery, a technique that has been launched very recently.
A Study on the Formation Process of Fe Clusters During Insulation of Cu95Fe5 Alloy
Authors: Wang Xufeng , Zhang Hanyu , Xu Congjing , Gao Xufeng , Zhang Zhenhao , Li Yungang , Liu Lu
Published: February 2025

The molecular dynamics simulation technique was utilized to examine the structural variations of the Cu95Fe5 alloy, as well as the precipitation and aggregation processes of Fe atoms during heat preservation. It is shown that the crystallization temperature of the Cu95Fe5 alloy under the condition of a 2 × 1010 K/s cooling rate is 882 K. Crystallization of the alloy occurs when it is insulated under the temperature conditions of 900 K, 1000 K, and 1100 K. The lower the holding temperature, the shorter the holding time required for the system to initiate crystallization. In other words, lower temperature holding facilitates easier crystallization. The formation process of Fe clusters has been investigated. The size and quantity of Fe clusters at various stages are influenced by the interatomic interaction forces between Fe atoms, the diffusion capabilities of Fe atoms, the local structural rearrangement of nondispersive atoms, and the interfacial energy between Fe clusters and the Cu matrix. The formation of Fe clusters in the insulation process mainly undergoes four stages: First, the initial small clusters are disassembled under the influence of molecular thermal motion and the attractive interactions between Fe atoms. This process leads to the formation and growth of new small clusters. Secondly, the clusters condense and grow under non-diffusive atomic local structure rearrangement. Third, the Fe clusters are sphericalized under Cu-Fe interfacial energy. Fourth, individual Fe atoms are diffused into large Fe clusters under the action of molecular thermal motion.
Olive Tree Twigs as an Attractive Green Source of Antioxidant and Antibiofilm Biomolecules
Authors: Dermeche Samia , Mezoued Kahina , Naib Hinda , Senani Nassima , Chaouche Thinina Afif , Alenazy Rawaf , Alhussaini Mohammed Sanad , Abdulrahman A. I. Alyahya , Alqasmi Mohammed , Almufarriji Fawaz M. , Alturaiki Fahad Mohammed , Bedouhene Samia , Nabti El-hafid , Trabelsi Lamia , Houali Karim
Published: February 2025

Biofilms represent complex three-dimensional microbial communities that can harbor strains highly resistant to antimicrobial agents. These structures, which form on both biotic and abiotic surfaces, are associated with food spoilage and increased complications in hospitalized patients. Consequently, there is significant interest in developing novel biofilm and infection control strategies, particularly those focusing on natural molecules with dual antimicrobial and antibiofilm properties. In this study, olive tree twigs from three varieties of Olea europea chemlal (CH), Azeradj (AZ), and wild-type Olea europaea sylvestris (W) were collected from the Kabylia region in Algeria. The samples underwent systematic extraction and were evaluated for their antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay, antimicrobial properties via disk diffusion assay, minimum inhibitory concentration (MIC), and antibiofilm capabilities. Results demonstrated that olive tree twig extracts exhibited substantial antioxidant activity and significant antibacterial and antibiofilm potential. The antioxidant activity, measured through DPPH radical scavenging, showed IC50 values ranging from 38.12 ± 1.52 µg/mL to 148.7 ± 1.23 µg/mL. When tested against six pathogenic bacterial strains, including both ATCC reference strains and milk isolates, the MIC values ranged from 1.18 mg/mL to 4.71 mg/mL. Notably, sub-inhibitory concentrations significantly reduced biofilm formation across most tested strains, with inhibition rates varying from 21% to 90.43%. The effectiveness of biofilm inhibition was dependent on the bacterial strain, olive tree variety, and extract concentration used. Statistical analysis confirmed the significance of these results (p < 0.05). Given the demonstrated antioxidant, antibacterial, and antibiofilm properties of these olive tree twig extracts, they show promise for further development as surface disinfectants and potential applications in food safety and infection control. Additional research is warranted to fully characterize their mechanisms of action and optimize their practical applications.
A New Approach Using Multi-Layer Films from Food Waste as a Shrink Film
Authors: Plevová Kateřina , Feuchter Michael , Wild Nadine , Resch-Fauster Katharina
Published: February 2025

Multi-layer films are one of the most challenging classes of polymer waste for recycling, as they consist of a mixture of constituent materials like polyethylene (PE), polyamide 6 (PA6), and ethylene vinyl alcohol (EVOH). This study investigates the characterization, washing, and mechanical properties of recycled blends derived from such multi-layer films. Raman spectroscopy and Differential Scanning Calorimetry (DSC) were used to characterize the individual components in single- and multi-layer films, and distinct properties of LDPE, LLDPE, PA6, and EVOH were observed. Mechanical properties enhanced by proper shredding, washing procedures, and multiple combinations of polyethylene blends were investigated to optimize the mechanical characteristics of the recycled materials, especially strain at break. Additionally, the shrinkage behavior of the recycled films was compared to commercial shrink films, demonstrating their potential for use in industry packaging applications. These results highlight a more sustainable possibility for multi-layer packaging applications.
The Hydrodynamics of Electrically Charged Flows
Authors: Pastor-Reyes Octavio , Hernández-Sánchez José Federico , Martínez-Arellano Isadora , Sánchez Salvador , Castillo José , Damián Ricardo , Zenit Roberto , Brito-de la Fuente Edmundo , Ascanio Gabriel
Published: February 2025

Electrically charged flows were investigated using experimental techniques. These flows were visualized and recorded employing high-speed video, which allowed the study of the formation of electrically charged filaments, focusing on the flow characteristics at meniscus rupture and the flow downstream of the atomization region. Experiments were performed following the design-of-experiments methodology, which provided information on the effect of the main factors and their combinations on the response variables, such as spray angle, size distribution, and particle number. Meniscus formation and its rupture were analyzed as a function of competition between forces. Furthermore, the different rupture modes were determined as a function of the electric field intensity (electric Bond number, Boe). The findings reveal that the best atomization condition is defined by a stable Taylor cone jet (at meniscus rupture). However, the results differ downstream of the atomization, since stable jet atomization is characterized by poor particle dispersion. To improve such conditions, it was found that flows with oscillation around the vertical axis and particle detachment (controlled instability) lead to better atomization. This is because a greater dissemination of particles is promoted, and greater homogeneity of the product and smaller particle sizes are generated. A secondary atomization process causes such conditions after the rupture of the meniscus, which is known as Coulomb fission.
Study on the Influence of Crystal Plugging on the Mechanical Behavior of Karst Tunnel Lining Structure
Authors: Wang Yehao , Liu Shiyang , Zhang Li , Guan Shaojie , Li Zongzhi , Cheng Liang , Liu Jie , Chen Jie
Published: February 2025

The blockage of a tunnel drainage system has a significant impact on the stability and operation safety of a tunnel lining structure. In this paper, changes in the pore water pressure, stress and displacement of a tunnel lining under different blocking conditions are studied by means of indoor test and numerical simulation. The results show that the calcium carbonate crystallization phenomenon in the tunnel’s initial support concrete gradually appears with the passage of time, which leads to a decline in concrete quality and has a negative impact on its compressive strength and elastic modulus. The pore water pressure, stress and displacement increase with the precipitation of calcium carbonate crystals and the intensification of drainage system blockage. However, the influence of calcium carbonate crystallization on pore water pressure, stress and displacement is relatively limited within 40 days. The study provides a reference for tunnel construction in complex geological environments.
Quantitative Assessment of the Water Stress in the Tigris–Euphrates River Basin Driven by Anthropogenic Impacts
Authors: Luan Wenfei , Li Xin , Kuang Wenhui , Su Jianbin , Xue Huazhu , Zhang Kaixiang , Zhu Jingyao , Li Ge
Published: February 2025

Water stress has induced many environmental and developmental conflicts in the arid basins in the Middle East region under the context of climate change and increasing anthropogenic influence. Quantifying the anthropogenic influence on water stress at the basin scale is very challenging because of insufficient anthropogenic-related spatial data. Given that climate change is a global impact that is hard to mitigate at the basin scale, quantifying anthropogenic influence is practical to inform strategies for alleviating regional water stress. Thus, this study attempts to quantify the contribution of potential anthropogenic factors driving the water stress in the Tigris–Euphrates river basin (TERB) using pure spatial data. The water stress level in the studied basin was evaluated via the water stress index (WSI), which can be obtained as the ratio of water demand to water availability, from the Aqueduct 4.0 dataset. The driving contributions of social development (population, POP; fine particulate matter, PM2.5), economic development (gross domestic product, GDP; electricity consumption, EC), and landscape modification (urban expansion index, UEI; cultivated land expansion index, CEI) factors were quantitatively evaluated based on a spatial statistical geographical detector model (GDM). Assessment showed that nearly 66.13% of the TERB area was under severe water stress, particularly in Syria, Iraq, Saudi Arabia, and Iran. The q statistic of the GDM, adopted to quantify the contribution of driving factors, revealed that CEI (0.174), EC (0.145), and GDP (0.123) were the dominant factors driving water stress. These individual influences were further enhanced particularly in the interaction between economic development and landscape modification factors such as UEI and CEI (0.566), PM2.5 and UEI (0.350), EC and CEI (0.346), GDP and CEI (0.323), and PM2.5 and GDP (0.312). The findings of this research can provide some beneficial references to alleviate the TERB’s water stress for its future sustainable development.
Optical Remote Sensing Analysis of Exhaust Emissions During Aircraft Taxiing at Hefei Xinqiao International Airport
Authors: Qin Yusheng , Han Xin , Li Xiangxian , Gui Huaqiao , Xue Weiwei , Gao Minguang , Tong Jingjing , Zhang Yujun , Shi Zheng
Published: February 2025

The taxiing stage of an aircraft is characterized by its long duration, low operating thrust, and low combustion efficiency, resulting in substantial emissions of CO, CO2, and VOCs, which adversely affect air quality near airports. This study has developed an open-path Fourier transform infrared spectroscopy (OP-FTIR) monitor with second-level time resolution to enable the optical remote monitoring of pollutants during taxiing. Measurements of CO, CO2, and VOCs were conducted over one month at Hefei Xinqiao International Airport (HXIA). The generalized additive model (GAM) is used for data analysis to reveal complex nonlinear relationships between aircraft emission concentrations and meteorological factors, aircraft models, and their corresponding registration numbers. The GAM analysis shows that among meteorological factors, humidity, and atmospheric pressure have the most significant impact on aircraft exhaust monitoring, with a relative average contribution value as high as approximately six. The explanatory power of aircraft models for emissions is low (R2 < 0.18), whereas that of registration numbers is high (R2 > 0.6), suggesting that individual differences between aircrafts play a crucial role in emission concentration variations. Furthermore, a noticeable correlation was found between the CO/CO2 ratio and volatile organic compound (VOC) concentrations (R2 > 0.63), indicating that combustion efficiency significantly affects VOC emissions. This study not only advances the real-time remote sensing monitoring of pollutants during aircraft taxiing but also underscores the crucial role of the GAM in identifying the key drivers of emissions, providing a scientific basis for precise environmental protection management and policy-making.
Quantifying Uncertainty in Projections of Desertification in Central Asia Using Bayesian Networks
Authors: Liu Jinping , Ren Yanqun , He Panxing , Xiao Jianhua
Published: February 2025

Desertification presents major environmental challenges in Central Asia, driven by climatic and anthropogenic factors. The present study quantifies desertification risk through an integrated approach using Bayesian networks and the ESAS model, offering a holistic perspective on desertification dynamics. Four key variables—vegetation cover, precipitation, land-use intensity, and soil quality—were incorporated into a Bayesian model to evaluate their influence on desertification. A probabilistic model was developed to gauge desertification intensity, with simulations conducted at 200 geospatial points. Hazard maps for 2030–2050 were produced under climate scenarios SSP245 and SSP585, incorporating projected land-use changes. All procedures for desertification risk assessment, land-use mapping, and climate downscaling were performed using the Google Earth Engine platform. The findings suggest a 4% increase in desertification risk under SSP245 and an 11% increase under SSP585 by 2050, with the greatest threats observed in western regions such as Kazakhstan, Uzbekistan, and Turkmenistan. Sensitivity analysis indicated that vegetation quality exerts the strongest influence on desertification, reflected by a Vegetation Quality Index (VQI) ranging from 1.582 (low in Turkmenistan) to 1.692 (very low in Kazakhstan). A comparison of the Bayesian and ESAS models revealed robust alignment, evidenced by an R2 value of 0.82, a Pearson correlation coefficient of 0.76, and an RMSE of 0.18. These results highlight the utility of Bayesian networks as an effective tool for desertification assessment and scenario analysis, underscoring the urgency of targeted land management and proactive climate adaptation. Although reclaimed land presents opportunities for afforestation and sustainable agriculture, carefully considering potential trade-offs with biodiversity and ecosystem services remains essential.
The Potential Impact of the Three Gorges Reservoir on Regional Extreme Precipitation—A Sensitivity Study
Authors: Huang Ya , Xiao Weihua , Zhou Yuyan
Published: February 2025

Understanding the potential impact of the Three Gorges Reservoir (TGR) on regional extreme precipitation and its mechanisms is critical for the safe operation of the reservoir and the efficient management of regional water resources. This study uses the regional climate model RegCM4 to conduct a double-nested simulation experiment (50 km to 10 km) from 1989 to 2012, evaluated against the CN5.1 observation dataset. Sensitivity experiments with three different lake area ratios (0%, 20% and 100%) were performed using the sub-grid partitioning method in the Community Land Model Version 4.5 to analyze the spatiotemporal distribution, intensity, and frequency of precipitation under varying TGR water areas. The results show that with a 20% lake area ratio, precipitation slightly decreases, but the impact on extreme precipitation indices is not statistically significant. However, with a 100% lake area ratio, significant decreases in both total and extreme precipitation indices occur. The reduction is primarily driven by the formation of anomalous mountain-valley circulation between the TGR and surrounding mountains, which leads to atmospheric subsidence and reduced convective activity. These findings indicate that while the TGR has a negligible impact on extreme precipitation under its current configuration, the exaggerated sensitivity experiments reveal potential mechanisms and localized effects. This research enhances the understanding of the TGR’s influence on regional extreme precipitation and provides valuable insights for water resource management and reservoir operation.
Three-Stage Up-Scaling and Uncertainty Estimation in Forest Aboveground Biomass Based on Multi-Source Remote Sensing Data Considering Spatial Correlation
Authors: Ding Xiangyuan , Chen Erxue , Zhao Lei , Fan Yaxiong , Wang Jian , Ma Yunmei
Published: February 2025

Airborne LiDAR (ALS) data have been extensively utilized for aboveground biomass (AGB) estimation; however, the high acquisition costs make it challenging to attain wall-to-wall estimation across large regions. Some studies have leveraged ALS data as intermediate variables to amplify sample sizes, thereby reducing costs and enhancing sample representativeness and model accuracy, but the cost issue remains in larger-scale estimations. Satellite LiDAR data, offering a broader dataset that can be acquired quickly with lower costs, can serve as an alternative intermediate variable for sample expansion. In this study, we employed a three-stage up-scaling approach to estimate forest AGB and introduced a method for quantifying estimation uncertainty. Based on the established three-stage general-hierarchical-model-based estimation inference (3sGHMB), an RK-3sGHMB inference method is proposed to make use of the regression-kriging (RK) method, and then it is compared with conventional model-based inference (CMB), general hierarchical model-based inference (GHMB), and improved general hierarchical model-based inference (RK-GHMB) to estimate forest AGB and uncertainty at both the pixel and forest farm levels. This study was carried out by integrating plot data, sampled ALS data, wall-to-wall Sentinel-2A data, and airborne P-SAR data. The results show that the accuracy of CMB (Radj2 = 0.37, RMSE = 33.95 t/ha, EA = 63.28%) is lower than that of GHMB (Radj2 = 0.38, RMSE = 33.72 t/ha, EA = 63.53%), while it is higher than that of 3sGHMB (Radj2 = 0.27, RMSE = 36.58 t/ha, EA = 60.43%). Notably, RK-GHMB (Radj2 = 0.60, RMSE= 27.07 t/ha, EA = 70.72%) and RK-3sGHMB (Radj2 = 0.55, RMSE = 28.55 t/ha, EA = 69.13%) demonstrate significant accuracy enhancements compared to GHMB and 3sGHMB. For population AGB estimation, the precision of the proposed RK-3sGHMB (p = 94.44%) is the highest, providing that there are sufficient sample sizes in the third stage, followed by RK-GHMB (p = 93.32%) with sufficient sample sizes in the second stage, GHMB (p = 90.88%), 3sGHMB (p = 88.91%), and CMB (p = 87.96%). Further analysis reveals that the three-stage model, considering spatial correlation at the third stage, can improve estimation accuracy, but the prerequisite is that the sample size in the third stage must be sufficient. For large-scale estimation, the RK-3sGHMB model proposed herein offers certain advantages.
The VMD-Informer-BiLSTM-EAA Hybrid Model for Predicting Zenith Tropospheric Delay
Authors: Yuan Zhengdao , Lin Xu , Xu Yashi , Dai Ruiting , Yang Cong , Zhao Lunwei , Han Yakun
Published: February 2025

Zenith Tropospheric Delay (ZTD) is a significant source of atmospheric error in the Global Navigation Satellite System (GNSS). Developing a high-accuracy ZTD prediction model is essential for both GNSS positioning and GNSS meteorology. To address the challenges of incomplete information extraction and gradient explosion present in current single and combined neural network models that utilize serial ensemble learning, this study proposes a VMD-Informer-BiLSTM-EAA hybrid model based on a parallel ensemble learning strategy. Additionally, it takes into account the non-stationarity of the ZTD sequence. The model employs the Variational Mode Decomposition (VMD) method to address the non-stationarity of ZTD. It utilizes both the informer and Bidirectional Long Short-Term Memory (BiLSTM) architectures to learn ZTD data in parallel, effectively capturing both long-term trends and short-term dynamic changes. The features are then fused using the Efficient Additive Attention (EAA) mechanism, which assigns weights to create a more comprehensive representation of the ZTD data. This enhanced representation ultimately leads to improved predictions of ZTD values. We fill in the missing parts of the GNSS-derived ZTD using the ZTD data from ERA5, sourced from the IGS stations in the Australian region, specifically at 12 IGS stations. These interpolated data are then used to develop a VMD-Informer-BiLSTM-EAA hybrid model for ZTD predictions with a one-year forecast horizon. We applied this model to predict the ZTD for each IGS station in our study area for the year 2021. The numerical results indicate that our model outperforms several comparative models, such as VMD–Informer, Transformer, BiLSTM and GPT3, based on the following key metrics: a Root Mean Square Error (RMSE) of 1.43 cm, a Mean Absolute Error (MAE) of 1.15 cm, a Standard Deviation (STD) of 1.33 cm and a correlation coefficient (R) of 0.96. Furthermore, our model reduces the training time by 8.2% compared to the Transformer model, demonstrating superior prediction performance and robustness in long-term ZTD forecasting. This study introduces a novel approach for high-accuracy ZTD modeling, which is significantly beneficial for precise GNSS positioning and the detection of water vapor content.
High-Accuracy Mapping of Soil Organic Carbon by Mining Sentinel-1/2 Radar and Optical Time-Series Data with Super Ensemble Model
Authors: Cui Zhibo , Chen Songchao , Hu Bifeng , Wang Nan , Zhai Jiaxiang , Peng Jie , Bai Zijin
Published: February 2025

Accurate digital soil organic carbon mapping is of great significance for regulating the global carbon cycle and addressing climate change. With the advent of the remote sensing big data era, multi-source and multi-temporal remote sensing techniques have been extensively applied in Earth observation. However, how to fully mine multi-source remote sensing time-series data for high-accuracy digital SOC mapping remains a key challenge. To address this challenge, this study introduced a new idea for mining multi-source remote sensing time-series data. We used 413 topsoil organic carbon samples from southern Xinjiang, China, as an example. By mining multi-source (Sentinel-1/2) remote sensing time-series data from 2017 to 2023, we revealed the temporal variation pattern of the correlation between Sentinel-1/2 time-series data and SOC, thereby identifying the optimal time window for monitoring SOC using Sentinel-1/2 data. By integrating environmental covariates and a super ensemble model, we achieved high-accuracy mapping of SOC in Southern Xinjiang, China. The results showed the following aspects: (1) The optimal time windows for monitoring SOC using Sentinel-1/2 data were July–September and July–August, respectively; (2) the modeling accuracy using multi-source sensor data integrated with environmental covariates was superior to using single-source sensor data integrated with environmental covariates alone. In the optimal model based on multi-source data, the cumulative contribution rate of Sentinel-2 data is 51.71% higher than that of Sentinel-1 data; (3) the stacking super ensemble model’s predictive performance outperformed the weight average and simple average ensemble models. Therefore, mining the optimal time windows of multi-source remote sensing data and environmental covariates, driven a super ensemble model, represents a high-accuracy strategy for digital SOC mapping.
A High-Precision Virtual Central Projection Image Generation Method for an Aerial Dual-Camera
Authors: Luo Xingzhou , Zhao Haitao , Liu Yaping , Liu Nannan , Chen Jiang , Yang Hong , Pan Jie
Published: February 2025

Aerial optical cameras are the primary method for capturing high-resolution images to produce large-scale mapping products. To improve aerial photography efficiency, multiple cameras are often used in combination to generate large-format virtual central projection images. This paper presents a high-precision method for directly transforming raw images obtained from a dual-camera system mounted at an oblique angle into virtual central projection images, thereby enabling the construction of low-cost, large-format aerial camera systems. The method commences with an adaptive sub-block in the overlapping regions of the raw images to extract evenly distributed feature points, followed by iterative relative orientation to improve accuracy and reliability. A global projection transformation matrix is constructed, and the sigmoid function is employed as a weighted distance function for image stitching. The results demonstrate that the proposed method produces more evenly distributed feature points, higher relative orientation accuracy, and greater reliability. Simulation analysis of image overlap indicates that when the overlap exceeds 7%, stitching accuracy can be better than 1.25 μm. The aerial triangulation results demonstrate that the virtual central projection images satisfy the criteria for the production of 1:1000 scale mapping products.
SPDC-YOLO: An Efficient Small Target Detection Network Based on Improved YOLOv8 for Drone Aerial Image
Authors: Bi Jingxin , Li Keda , Zheng Xiangyue , Zhang Gang , Lei Tao
Published: February 2025

Target detection in UAV images is of great significance in fields such as traffic safety, emergency rescue, and environmental monitoring. However, images captured by UAVs usually have multi-scale features, complex backgrounds, uneven illumination, and low target resolution, which makes target detection in UAV images very challenging. To tackle these challenges, this paper introduces SPDC-YOLO, a novel model built upon YOLOv8. In the backbone, the model eliminates the last C2f module and the final downsampling module, thus avoiding the loss of small target features. In the neck, this paper proposes a novel feature pyramid, SPC-FPN, which employs the SBA (Selective Boundary Aggregation) module to fuse features from two distinct scales. In the head, the P5 detection head is eliminated, and a new detection head, Dyhead-DCNv4, is proposed, replacing DCNv2 in the original Dyhead with DCNv4 and utilizing three attention mechanisms for dynamic feature weighting. In addition, the model uses the CGB (Context Guided Block) module for downsampling, which can learn and fuse local features with surrounding contextual information, and the PPA (Parallelized Patch-Aware Attention) module replacing the original C2f module to further improve feature expression capability. Finally, SPDC-YOLO adopts EIoU as the loss function to optimize target localization accuracy. On the public dataset VisDrone2019, the experimental results show that SPDC-YOLO improves mAP50 by 3.4% compared to YOLOv8n while reducing the parameters count by 1.03 M. Compared with other related methods, SPDC-YOLO demonstrates better performance.
A Performance Study of Mobility Speed Effects on Vehicle Following Control via V2V MIMO Communications
Authors: Sopajarn Jerawat , Booranawong Apidet , Chumpol Surachate , Jindapetch Nattha , Yuichi Okuyama , Saito Hiroshi
Published: February 2025

Vehicle-to-vehicle (V2V) communications are important for intelligent transportation system (ITS) development for driving safety, traffic efficiency, and the development of autonomous vehicles. V2V communication channels, environments, mobility patterns, and mobility speed significantly affect the accuracy of autonomous vehicle control. In this paper, we propose a versatile system-level framework that can be used for investigation, experimentation, and verification to expedite the development of autonomous vehicles. Once vehicle functionality, communication channels, and driving scenarios were modelled, experiments with different mobility speeds and communication channels were set up to measure the communication quality and the effects on the vehicle’s following control. In our experiment, the leader vehicle was set to travel through a high-building environment with a constant speed of 36 km/h and suddenly changed lanes in front of the follower vehicle. The speed of the follower vehicle ranged from 40 km/h to 80 km/h. The experimental results show that the quality of single-input and single-output (SISO) communication is less efficient than multiple-input and multiple-output (MIMO) communication. The quality of SISO communication between vehicles with a speed difference of 4 km/h (leader 36 km/h and follower 40 km/h) had a link quality worse than 0.85, which caused unstable control in the follower vehicle speed. However, it was also found that if the speed of the follower vehicle increased to 80 km/h, the link quality of SISO communication was better, close to 0.95, due to the decreased distance between the vehicles, resulting in better control. Moreover, it was found that the impact of SISO communication can be overcome by using the MIMO communication technique and selecting the best input signal at each time. MIMO communication has less signal loss, allowing the follower vehicle to make correct decisions throughout the movement.
Rapid Evaluation Method to Vertical Bearing Capacity of Pile Group Foundation Based on Machine Learning
Authors: Cao Yanmei , Ni Jiangchuan , Chen Jianguo , Geng Yefan
Published: February 2025

With the continuous increase in bridge lifespans, the rapid check and evaluation of the vertical bearing capacity for the pile foundations of existing bridges have been in greater demand. The usual practice is to carry out compression bearing tests under static loads in order to obtain the accurate ratio of the dynamic to static stiffness. However, it is difficult and costly to conduct in situ experiments for each pile foundation. Herein, a rapid evaluation method to measure the vertical bearing capacity of bridge pile foundations is proposed. Firstly, a 3D-bearing cap–pile group–soil interaction model was established to simulate a bearing test of a pile foundation that was subject to static loads and dynamic loads, and then the numerical results were validated by in situ dynamic and static loading tests on an abandoned bridge pier with the same pile group foundation; the dataset for machine learning was constructed using the numerical results, and finally, the bearing capacity of the pile foundation could be predicted rapidly. The results show the following outcomes: the established numerical model can effectively simulate dynamic and static loading tests of pile foundations; the intelligent prediction model based on machine learning can predict the ratio of static stiffness to dynamic stiffness and can thus rapidly evaluate the vertical residual bearing capacity and the designed ultimate loading capacity, allowing for the nondestructive testing and evaluation of the pile foundations of existing bridges.