Search Results (1,489)

Urban green spaces play a vital role in climate change mitigation through carbon sequestration and storage. However, accurately quantifying their carbon sink capability remains challenging due to complex vertical structures and spatial heterogeneity. This study proposes a comprehensive inventory framework integrating multi-source LiDAR (UAV and Backpack) with a phenology-based complementary strategy to quantify carbon dynamics across three nested scales: green space types, plant communities, and species. Two key indicators—Carbon Sequestration Efficiency (CSE) and Carbon Density (CD)—were used to evaluate both the dynamic and static aspects of carbon sink function. The results reveal a clear asynchrony between CSE and CD across scales. No single plant type performed best in both dimensions, indicating a trade-off between growth efficiency and biomass accumulation. Hierarchical clustering identified distinct plant groups with divergent carbon sink strategies, supporting nuanced vegetation selection. The dual-indicator and dual-platform approach proposed in this study advances our existing understanding of the carbon sequestration capacity of urban green spaces and provides a robust methodological foundation for data-driven low-carbon urban ecological planning. Full article

Background and Objectives: The aim of this study was to evaluate the radiographic progression of non-surgically managed jaw lesions that remained untreated due to patient deferral or refusal of surgery. Radiographic changes were assessed using two panoramic radiographs obtained at different time points, with a focus on dimensional progression, morphological characteristics, and anatomical involvement. Materials and Methods: A total of 85 non-surgically managed intraosseous cystic and cyst-like jaw lesions were evaluated on two panoramic radiographs obtained at least one year apart. Histopathological confirmation was available for 26 of the lesions (30.6%), while the remaining cases were evaluated radiographically due to the absence of surgical intervention or accessible pathology records. Assessments included localization, size, shape, internal structure, borders, association with non-erupted teeth, root resorption, tooth displacement, involvement of anatomical structures, and cortical changes such as thinning, expansion, or destruction. Nonparametric statistical comparisons were used to assess time-dependent changes and differences between follow-up groups. Results: A total of 57 lesions occurred in the mandible and 28 in the maxilla, predominantly in the posterior regions. The mean vertical/horizontal measurements of the intraosseous lesions was found to be 10.9 ± 4.6 mm/12.2 ± 6.5 mm (Mean ± SD) on the initial panoramic radiographs (Med: 10.0–IQR: 6.50/Med: 12.0–IQR: 8.75) and 14.8 ± 5.3 mm/17.5 ± 8.3 mm (Mean ± SD) on the second panoramic radiographs (Med: 14.5–IQR: 6.75/Med: 16.0–IQR: 10.75), respectively. Both vertical and horizontal dimensions showed a statistically significant increase between the two time points (p < 0.05). Initially, 41 lesions exhibited corticated margins; at follow-up, an additional 33 non-corticated lesions developed cortication. Lesions without corticated margins on the initial images exhibited significantly greater vertical and horizontal growth than those with corticated borders (p < 0.05). Lesions followed for 3–5 years showed significantly greater dimensional changes compared with those observed for shorter or longer intervals (p < 0.05). Lesion shape, internal structure, and multilocularity remained largely stable. Conclusions: Within the limitations of this retrospective study, non-surgically managed jaw lesions showed a tendency to increase in size over time. While the development of corticated borders may be associated with reduced growth activity, panoramic radiography alone is insufficient for definitive assessment, and regular radiographic follow-up should be considered within a broader clinical context. Full article

The transportation safety of aviation structural components directly impacts equipment performance and mission success rates, constituting a critical link in modern aviation industry that cannot be overlooked. Traditional methods relying on numerical analysis or structural health monitoring techniques analyze structural stress during transportation to ensure safety. However, they suffer from low computational efficiency, inability to perform real-time online monitoring, or limited coverage to a few measurement points. To address these challenges, this study proposes a metabolic deep learning model based on Gated Recurrent Units (GRU) for predicting stresses in symmetrical vertical tail (SVT) structures during transportation. First, a refined finite element model of the symmetrical vertical tail structure is established using a model ensemble analysis strategy. By integrating modal analysis with transient analysis, the maximum stress loads for different transportation processes are calculated, generating maximum stress data samples under multiple acceleration history conditions. Second, by establishing a mathematical description and deep learning framework, the GRU establishes a mapping relationship between acceleration history and maximum stress, enabling prediction of maximum stress loads associated with different acceleration histories. This method effectively resolves the challenge of exponential mesh growth in complex assembly simulations. This research enables real-time structural stress warnings for drivers during highway transportation, triggering early alerts when stress approaches allowable limits to ensure structural safety and reliability. Full article

Crop row line detection is essential for precision agriculture, supporting autonomous navigation, field management, and growth monitoring. To address the low detection accuracy of rapeseed seedling rows under complex field conditions, this study proposes a detection framework that integrates an improved BiSeNetV2 with a dynamic sliding-window fitting strategy. The improved BiSeNetV2 incorporates the Efficient Channel Attention (ECA) mechanism to strengthen crop-specific feature representation, an Atrous Spatial Pyramid Pooling (ASPP) decoder to improve multi-scale perception, and Depthwise Separable Convolutions (DS Conv) in the Detail Branch to reduce model complexity while preserving accuracy. After semantic segmentation, a Gaussian-filtered vertical projection method is applied to identify crop-row regions by locating density peaks. A dynamic sliding-window algorithm is then used to extract row trajectories, with the window size adaptively determined by the row width and the sliding process incorporating both a lateral inertial-drift strategy and a dynamically adjusted longitudinal step size. Finally, variable-order polynomial fitting is performed within each crop-row region to achieve precise extraction of the crop-row lines. Experimental results indicate that the improved BiSeNetV2 model achieved a Mean Pixel Accuracy (mPA) of 87.73% and a Mean Intersection over Union (MIoU) of 79.40% on the rapeseed seedling dataset, marking improvements of 9.98% and 8.56%, respectively, compared to the original BiSeNetV2. The crop row detection performance for rapeseed seedlings under different environmental conditions demonstrated that the Curve Fitting Coefficient (CFC), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) were 0.85, 1.57, and 1.27 pixels on sunny days; 0.86, 2.05 and 1.63 pixels on cloudy days; 0.74, 2.89, and 2.22 pixels on foggy days; and 0.76, 1.38, and 1.11 pixels during the evening, respectively. The results reveal that the improved BiSeNetV2 can effectively identify rapeseed seedlings, and the detection algorithm can identify crop row lines in various complex environments. This research provides methodological support for crop row line detection in precision agriculture. Full article

Motivation: We aim to study the boundary stability and persistence of positive odes in mathematical epidemiology models by importing structural tools from chemical reaction networks. This is largely a review work, which attempts to congregate the fields of mathematical epidemiology (ME), and chemical reaction networks (CRNs), based on several observations. We started by observing that epidemiologic strains, defined as disjoint blocks in either the Jacobian on the infected variables, or as blocks in the next generating matrix (NGM), coincide in most of the examples we studied, with either the set of critical minimal siphons or with the set of minimal autocatalytic sets (cores) in an underlying CRN. We leveraged this to provide a definition of the disease-free equilibrium (DFE) face/infected set as the union of either all minimal siphons, or of all cores (they always coincide in our examples). Next, we provide a proposed definition of ME models, as models which have a unique boundary fixed point on the DFE face, and for which the Jacobian of the infected subnetwork admits a regular splitting, which allows defining the famous next generating matrix. We then define the interaction graph on minimal siphons (IGMS), whose vertices are minimal siphons, and whose edges indicate the existence of reactions producing species in one siphon from species in another. When this graph is acyclic, we say the model exhibits an Acyclic Minimal Siphon Decomposition (AMSD). For AMSD models whose minimal siphons partition the infection species, we show that the NGM is block triangular after permutation, which implies the classical max structure of the reproduction number $R_0$ for multi-strain models. In conclusion, using irreversible reaction networks, minimal siphons and acyclic siphon decompositions, we provide a natural bridge from CRN to ME. We implement algorithms to compute IGMS and detect AMSD in our Epid-CRN Mathematica package (which already contain modules to identify minimal siphons, criticality, drainability, self-replicability, etc.). Finally, we illustrate on several multi-strain ME examples how the block structure induced by AMSD, and the ME reproduction functions, allow expressing boundary stability and persistence conditions by comparing growth numbers to 1, as customary in ME. Note that while not addressing the general Persistence Conjecture mentioned in the title, our work provides a systematic method for deriving boundary instability conditions for a significant class of structured models. Full article

Two-dimensional (2D) metal halide perovskites have attracted considerable interest for their markedly improved environmental stability and versatile compositional tunability compared to their three-dimensional (3D) counterparts. Nevertheless, the anisotropic charge transport caused by insulating organic spacers often leads to inefficient charge transport and limiting device performance. Precise control over crystallographic orientation, particularly achieving vertical alignment of the inorganic layers, is essential to facilitate out-of-plane charge transport and enhance device efficiency. This review systematically summarizes recent advances in understanding and controlling the crystallographic orientation of 2D perovskites, emphasizing manipulating strategies such as processing optimization, composition engineering, spacer design, solvent selection, and additive assistance to promote vertical alignment of inorganic layers and improve interlayer charge transport. We also discuss the influence of phase distribution, quantum well width, and crystal growth kinetics on device performance. Finally, we outline prevailing challenges and future opportunities for achieving the ideal microstructure and high-efficiency 2D perovskite solar cells. Full article

In this study, we numerically investigate the influence of thermal gradients on the growth and intensification of vortices formed within a rotating cylinder subjected to inhomogeneous cooling at the top or inhomogeneous heating at the bottom. The presence of horizontal thermal inhomogeneities at the upper and lower boundaries determines whether the vortex originates near the top or the bottom of the domain. Moreover, the magnitude of both horizontal and vertical thermal gradients plays a critical role in the vortex’s intensification, vertical stretching, and overall development. The observed phenomena are interpreted through a force balance analysis. Increasing the ambient rotation rate leads to the emergence of periodic structures, such as tilted or double vortices, which also undergo intensification and stretching as thermal gradients increase. These findings highlight the importance of thermal boundary conditions in shaping vortical structures and may contribute to a deeper understanding of the genesis, morphology, and intensification mechanisms of thermoconvective vortices. Full article

Rapid economic development in the past four decades in China has brought about significant consequences for people’s livelihoods. Healthy social mobility is fundamental for equality of opportunity, economic vitality, and socioeconomic sustainability. This paper examines the intragenerational livelihood mobility of urban residents in recent decades based on a case study in Guangzhou City and Foshan City, Guangdong Province, Southeast China. Longitudinal livelihood trajectory surveys have been investigated to gain research data. The primary determinants of livelihood mobility were also elucidated through analysis of muti-logistic regression. The results show that five livelihood trajectories are summarized based on their vertical movements in social status. The results further indicate that class polarization exists in urban residents’ mobility. 48.2% of respondents have experienced upward mobility, and 33.6% of them have even stepped over social classes. Meanwhile, the livelihoods of the others remained unchanged or suffered downward mobility. Respondents with male gender, better educational attainments, positive personality, and lower hierarchies of first occupations are associated with a higher probability of upward mobility. These results suggest that wealth redistribution among different social groups should be implemented to promote the benefits of economic growth being shared more broadly, and ultimately to boost socioeconomic sustainability. Full article

The narrow courtyard houses in the rural areas of Guanzhong region of Shaanxi Province, China, are a spatial representation of the long-term interaction of multiple influencing factors. This study, based on 716 questionnaires and 125 semi-structured interviews, comprehensively employed typology, qualitative analysis, comprehensive fuzzy evaluation, and grey correlation degree analysis methods to analyze the spatial evolution process of 125 typical samples since 1949. The results of research show: (1) In terms of spatial form, the narrow courtyard houses have evolved along a “from single to multiple, from horizontal to vertical, from open to closed” path. Their core has shifted from the symbolic “courtyard” to the functional “hall”, and the value of the main and auxiliary spaces has also undergone reconstruction, reflecting a modern transformation from “priority of etiquette” to “life quality orientation”. (2) The driving path starts from the institutional traction during the “survival stage”, then shifts to the economic dominance during the “growth stage”, and finally turns to the policy guidance and quality pursuit in the “life stage”, which are all coordinated. Policy and industrial structure are the core macro driving forces that run through the entire process. (3) Overall, the modernization transformation of the narrow courtyard houses is a dynamic process driven by external factors, with its path gradually shifting from the traditional endogenous model to external promotion and towards a diversified balance; however, the current “vacuum” state of cultural concepts reveals that the modernization of rural houses is still in the transitional stage between old and new paradigms. Based on this, the core of future rural house construction lies in achieving an internal reshaping from functional form to cultural value, guiding the spatial form to move from “disorderly exploration” to the organic generation of a “new paradigm”, providing a sustainable spatial paradigm for rural revitalization. Full article

Wide band gap (WBG) oxide and metal nanocomposites can possess bifunctionality from combining tightly coupled nanoobjects with different physicochemical properties. Adjusting synthesis conditions tunes these properties through modulating the process–morphology–function relationship. However, the controllable synthesis of such nanocomposites and their related applications are still underexplored. Here, we present a novel process flow to synthesize crystalline ZnO nanosheaves dotted with silver nanoparticles. The uniqueness of our strategy lies in the use of a silver mask for vertical growth of ZnO nanosheaves and thermal evaporating/dewetting Ag film to form a photocatalytic/plasmonic heterostructure. Upon combining a huge specific surface area and nanocrystallinity of ZnO nanosheaves, we enabled its surface-enhanced Raman scattering (SERS)-activity free of plasmonic components, yet their Ag modification resulted in improving detection limit in relation to Ellman’s reagent. Ag/ZnO nanosheaves showed dramatic photocatalytic activity to clean SERS-active surface. The systematic approach to synthesize Ag/ZnO heterostructure holds great promise in practical applications associated with interest in both photocatalytic and plasmonic properties. Full article

While the root architecture of potted crop seedlings directly determines subsequent crop productivity and adaptability, these root systems remain challenging to quantify using conventional methods due to their structural complexity. To investigate the microscopic characteristics of the root systems of pepper seedlings within pots, Micro-CT was employed to scan the seedling pots. After three-dimensional (3D) reconstruction was conducted on the data acquired from the pot scans, the 3D model of the root system was segmented and extracted using the watershed algorithm. Vertically, the three-dimensional root model was divided from top to bottom into four equally spaced regions (a, b, c, and d), showing the volumetric distribution characteristics of pepper seedling roots within the pots. The results showed that region a had the largest average root volume proportion (29.72%), primarily due to the substantial volume contribution of the taproot. Region d followed with an average proportion of 27.26%, resulting from root coiling and entanglement at the pot bottom caused by the spatial constraints of the seedling tray. The middle regions of the pot, b and c, showed average root volume proportions of 23.14% and 19.89%, respectively. To further investigate the influence of root system characteristics on root injury during seedling gripping, the seedlings were categorized into three types based on their taproot growth positions. A gripping experiment was conducted on these three seedling types using spatula-equipped needles. The results showed that the greatest root injury (12.67%) was observed in Type 1 seedlings, which had taproots located closest to the needle insertion point. In contrast, the least injury (4.09%) was found in Type 3 seedlings, characterized by centrally positioned taproots. Type 2 seedlings, with their taproots growing on the side (laterally away from the insertion point), sustained intermediate injury (5.45%). This was because their lateral positioning led to an uneven distribution of mechanical stress during gripping compared with Type 3 seedlings. A validation experiment conducted on an automated seedling retrieval platform confirmed the root injury analysis. The experimental results showed maximum root injury in Type 1 seedlings (14.16%), followed by Type 2 (6.03%) and Type 3 (4.82%) seedlings, with a successful retrieval rate of 95.29%. These findings were consistent with the Micro-CT analysis. This study could provide a theoretical foundation for low-injury seedling gripping in fully automated seedling transplanters. Full article

Soil salinization poses a major threat to agricultural sustainability in arid regions worldwide, where it is intrinsically linked to irrigated agriculture. In these water-scarce environments, the equilibrium of the water and salt balance is easily disrupted, causing salts to accumulate in the root zone and directly constraining crop growth, thereby creating an urgent need for precise water and salt management strategies. While precise water and salt transport models are essential for prediction and control, their accuracy is often compromised by parameter uncertainty. To address this, we developed a lumped water–salt balance model for the Hetao Irrigation District (HID) in China, integrating farmland and non-farmland areas and vertically structured into root zone, transition layer, and aquifer. A novel calibration approach, combining random sampling with Kernel Density Estimation (KDE), was introduced to identify optimal parameter ranges rather than single values, thereby enhancing model robustness. The model was calibrated and validated using data from the Yichang sub-district. Results showed that the water balance module performed satisfactorily in simulating groundwater depth (R² = 0.79 for calibration, 0.65 for validation). The salt balance module effectively replicated the general trends of soil salinity dynamics, albeit with lower R² values, which reflects the challenges of high spatial variability and data scarcity. This method innovatively addresses the common challenge of parameter uncertainty in the model, narrows the parameter value ranges, enhances model reliability, and incorporates sensitivity analysis (SA) to identify key parameters in the water–salt model. This study not only provides a practical tool for managing water and salt dynamics in HID but also offers a methodological reference for addressing parameter uncertainty in hydrological modeling of other data-scarce regions. Full article

Branch characteristics (quantity, morphology, and distribution) are critical determinants of tree growth and wood quality. However, the influence of species mixing, particularly mixing ratios, on branch development remains poorly understood. This study examined the branch attributes of Betula alnoides and Castanopsis hystrix in a six-year-old mixed-species trial plantation including monoculture of each species, and three mixtures at ratios of 1:1, 1:3, and 1:5 (B. alnoides–C. hystrix) in Pingxiang, Guangxi, China. Branch quantity (number, proportion, and density), morphology (diameter, length, and angle), and distribution (vertical and horizontal) were measured or recorded from 40 sampled dominant or codominant trees (20 B. alnoides and 20 C. hystrix). The results showed that mixing significantly increased the number and density of branches over 124.2% and 53.2%, respectively, in the lower crown (below 10 m) of B. alnoides, with these metrics positively correlated to the proportion of C. hystrix, while mixing exerted limited effects on branch quantity and size of C. hystrix. The 1:3 and 1:5 mixtures yielded more small branches (diameter < 10 mm) as well as more large branches (>25 mm) for B. alnoides. Branch distribution was almost uniform in different horizontal directions for both species, while variations in branch quantity and morphology along the stem were primarily species-specific; and both aspects remained consistent across the different mixing ratios. In conclusion, mixing B. alnoides with a low proportion of C. hystrix is proposed to produce high-quality solid wood for both species. Future studies should investigate alternative mixing patterns and higher proportions of B. alnoides in mixture with C. hystrix to optimize large-size and high-quality timber production. Full article

This study presents the design and development of an intelligent end-effector integrated into a custom 7-degree-of-freedom (DOF) robotic arm for monitoring the health status of tomato plants during their growth stages. The robotic system combines five rotational and two prismatic joints, enabling both horizontal reach and vertical adaptability to inspect plants of varying heights without repositioning the robot’s base. The integrated vision module employs a YOLOv5 neural network trained with 7864 images of tomato leaves, including both healthy and diseased samples. Image preprocessing included normalization and data augmentation to enhance robustness under natural lighting conditions. The optimized model achieved a detection accuracy of 90.2% and a mean average precision (mAP) of 92.3%, demonstrating high reliability in real-time disease classification. The end-effector, fabricated using additive manufacturing, incorporates a Raspberry Pi 4 for onboard processing, allowing autonomous operation in agricultural environments. The experimental results validate the feasibility of combining a custom 7-DOF robotic structure with a deep learning-based detector for continuous plant monitoring. This research contributes to the field of agricultural robotics by providing a flexible and precise platform capable of early disease detection in dynamic cultivation conditions, promoting sustainable and data-driven crop management. Full article

Food security and supply networks are becoming an ever-increasing concern requiring innovative practices to deal with the contributing factors. Controlled Environment Agriculture (CEA) offers an alternative to conventional cropping systems for increasing the yields of certain produce types. Crop yields (tons/hectare/year) in CEA are reported to range between 10 and 100 times higher than open-field agriculture, and the water use in CEA is typically about 4.5–16% of that from conventional farms per unit mass of produce. However, these systems can be energy intensive due to temperature regulation requirements, compromising their environmental and economic viability. Energy is the second largest overhead cost in CEA with carbon footprints being reported as 5.6–16.7 times and 2.3–3.3 times greater than that of open-field agriculture for indoor vertical farms and greenhouses, respectively. This can be offset, in part, by reducing the reliance on cooling systems. However, high temperature stress negatively impacts crops at morphological, cellular, metabolic, and molecular levels, reducing produce quality and quantity. Biostimulants are additives which can benefit plant growth through ameliorating stress. This review considers recent research on the effects of heat stress on a variety of crops commonly grown in CEA and the categories of biostimulants that have known thermoprotective qualities. Seaweed extracts, chitin/chitosan, protein hydrolysates and amino acids, inorganic compounds, beneficial microorganisms, and humic substances are explored, alongside the known benefits, limitations, and knowledge gaps. Full article