Search Results (12,273)

Recreational resources, fundamental to ecological experiences, are critical in balancing conservation with development. Effective ecotourism planning is especially vital for newly established protected areas such as the Hainan Tropical Rainforest National Park in China’s developing system of natural conservation areas. Targeting Hainan Tropical Rainforest National Park, this study applies nearest neighbor index, kernel density analysis, and exploratory spatial data analysis (ESDA) to study the spatial pattern of 274 recreational resource points. Results indicate a clustered spatial pattern with significant differences in resource density among municipalities. Specifically, 98% of these resources can be reached in 3 h, with an average travel time of 91 min, and cultural resources exhibit greater accessibility than natural resources. Natural resource availability and ethnic culture are major factors of resource distribution and accessibility. This research offers a theoretical basis and practical guidance for optimizing recreational resource allocation and promoting ecotourism in the park, contributing to the ongoing discussion of sustainable tourism development. Full article

Land use imbalances are a critical driving factor contributing to regional disparities in carbon storage (CS). As a significant component of China’s Yangtze River Economic Belt, Hunan Province has undergone substantial shifts in land use types, resulting in an uneven distribution of ecosystem CS and sequestration capacity. Therefore, within the framework of the “dual carbon” strategy, examining the effects of land use changes driven by regional resource imbalances on CS holds practical importance for advancing regional sustainable development. This study focuses on Hunan Province, utilizing the PLUS-InVEST model to assess the spatiotemporal evolution of CS under land use changes from 1990 to 2020. Additionally, multiple scenario-based development modes were employed to predict county-level CS. The results indicate the following: (1) From 1990 to 2020, Hunan Province experienced continuous urban expansion, with forest land and cultivated land, which are core ecological land types, being converted into construction land. (2) Over these 30 years, the province’s total CS increased by 2.47 × 10⁸ t, with significant spatial differentiation. High-value zones were concentrated in bands along the province’s borders, while lower values were observed in the central and northern regions. The highest CS values were recorded in forested areas at the province’s periphery, whereas the lowest values were observed in the northern water bodies. (3) The scenario-based predictions revealed notable differences, with the ecological protection scenario demonstrating a substantial carbon sink effect. By prioritizing forest and cultivated land, CS could be maximized. This research provides valuable insights for enhancing CS and optimizing land use structures in regions facing resource imbalances. Full article

Building clustering is a key problem that needs to be solved in the realization of the automatic synthesis of large-scale maps. The selection of different feature and spatial distance calculation methods has a great impact on the clustering results, and the need to manually select appropriate feature and distance metrics leads to the problem of not being able to fully consider the complexity and diversity of buildings. In this paper, we propose a multi-scale clustering method for buildings that takes visual perception into account using the Gestalt principle to simulate how humans classify buildings through visual perception. Moreover, by analyzing the spatial features and texture attributes of buildings, a visual distance is designed to be used as a condition for building classification to assess the similarity between buildings, solving the complexity of manually selecting feature vectors and spatial distances and realizing the adaptive selection of features. Through experimental validation at different scales (macro, meso and micro), the present method is able to achieve the accurate clustering of buildings, and a frequency threshold of 91% is found, which is able to determine the optimal clustering results. The experimental results show that the proposed method can not only fully consider the complexity and diversity of buildings but also effectively support the understanding and analysis of urban spatial structure and provide a scientific decision-making basis for urban planning and management. Full article

With the increasing development of productivity, new materials that allow for the efficient use of energy are slowly becoming a sought-after goal, as well as a challenge that is currently being faced. For this reason, we have made aerogels as the target of our research and prepared different series (CLPI (1–5)) of cross-linked polyimide aerogels by mixing and cross-linking the heat-insulating cross-linking agent 1,3,5-tris(4-aminobenzylamino)benzene (TAB) with polyamic acid solution. We created a three-dimensional spatial organization by using vacuum freeze-drying and programmed high-temperature drying, then controlled the concentration of the polyamidate solution to investigate the concentration and TAB’s influence on aerogel-related properties. Among them, the shrinkage is reduced from 40% in CLPI-1 to 28% in CLPI-5, and it also shows excellent mechanical characteristics, the highest compression strength (CLPI-5) reaches 0.81 MPa and specific modulus reaches 41.95 KN m/Kg. In addition, adding TAB improves the aerogel thermal resistance, T₅ in N₂ from PI-2 519 °C to CLPI-2 556 °C. The three-dimensional network-type structure of the aerogel shows an excellent thermal insulation effect, where the thermal conductivity can be as low as 24.4 mWm⁻¹ K⁻¹. Compared with some protective materials, cross-linked polyimide aerogel presents better flame-retardant properties, greatly improving the scope of its application in the industrial protection. Full article

Analyzing the spatiotemporal changes and influencing factors of carbon emissions generated by land use is of great importance for improving land use structure and promoting regional low-carbon economic development. This study, based on remote sensing and statistical yearbook data from 1995 to 2020, calculated the carbon emissions from land use in Jiangxi Province, China. Multiple spatial analysis methods and the logarithmic mean Divisia index were used to elucidate the spatiotemporal evolution and driving factors of carbon emissions, and the findings revealed the following: (1) The spatiotemporal changes in land use in Jiangxi Province during 1995–2020 were substantial as forest land accounted for 65% of the entire land area, while construction land increased by 98.1%. Cultivated land decreased the most, followed by forest land. (2) There was a fourfold rise in carbon emissions in Jiangxi Province, driven primarily by construction land, and northern areas produced higher carbon emissions compared with central and southern regions. Forest land was the main carbon sink. (3) Economic development (257.36%) and the impact of the proportion of construction land (211.31%) were the primary factors contributing to the increase in carbon emissions from land use, while other factors had inhibitory effects. This study transformed the macroscale low-carbon development strategy of cities into targeted local policies, and the research theories and methods adopted could provide scientific reference for other regions in urgent need of carbon reduction worldwide. Full article

The constructs of lipid molecules follow self-assembly, driven by intermolecular interactions, forming stacking of lipid bilayer films. Achieving designed geometry at nano- to micro-levels with packing deviating from the near-equilibrium structure is difficult to achieve due to the strong tendency of lipid molecules to self-assemble. Using ultrasmall (<fL) droplets containing designed molecules, our prior work has demonstrated that molecular assembly, in principle, is governed mainly by transient inter-molecular interactions under their dynamic spatial confinement, i.e., tri-phase boundaries during drying. As a result, the assemblies can deviate, sometimes significantly, from the near-equilibrium structures of self-assembly. The present work applies the approach and concept to lipid molecules using 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC). Taking advantage of the high spatial precision and the minute size of the delivery probe in our combined atomic force microscopy and microfluidic delivery, the transient shape of each liquid droplet is regulated. In doing so, the final geometry of the POPC assemblies has been regulated to the designed geometry with nanometer precision. The results extend the concept of controlled assembly of molecules to amphiphilic systems. The outcomes exhibit high potential in lipid-based biomaterial science and biodevice engineering. Full article

Structural composite materials have gained significant appeal because of their ability to be customized for specific mechanical qualities for various applications, including avionics, wind turbines, transportation, and medical equipment. Therefore, there is a growing demand for effective and non-invasive structural health monitoring (SHM) devices to supervise the integrity of materials. This work introduces a novel sensor design, consisting of three spiral resonators optimized to operate at distinct frequencies and excited by a feeding strip line, capable of performing non-destructive structural strain monitoring via frequency coding. The initial discussion focuses on the analytical modeling of the sensor, which is based on a circuital approach. A numerical test case is developed to operate across the frequency range of 100 to 400 MHz, selected to achieve a balance between penetration depth and the sensitivity of the system. The encouraging findings from electromagnetic full-wave simulations have been confirmed by experimental measurements conducted on printed circuit board (PCB) prototypes embedded in a fiberglass-based composite sample. The sensor shows exceptional sensitivity and cost-effectiveness, and may be easily integrated into composite layers due to its minimal cabling requirements and extremely small profile. The particular frequency-coded configuration enables the suggested sensor to accurately detect and distinguish various structural deformations based on their severity and location. Full article

The solubilization behavior of medroxyprogesterone (MP) within gemini surfactant micelles (14-6-14,2Br⁻) was investigated and compared with that of progesterone to uncover distinct solubilization mechanisms. We employed ¹H-NMR and 2D ROESY spectroscopy to elucidate the spatial positioning of MP within the micelle, revealing that MP integrates more deeply into the micellar core. This behavior is linked to the unique structural features of MP, particularly its 17β-acetyl group, which promotes enhanced interactions with the hydrophobic regions of the micelle, while the 6α-methyl group interacts with the hydrophilic regions of the micelle. The 2D ROESY correlations specifically highlighted interactions between the hydrophobic chains of the surfactant and two protons of MP, H22 and H19. Complementary machine learning and electron density analyses supported these spectroscopic findings, underscoring the pivotal role of the molecular characteristics of MP in its solubilization behavior. These insights into the solubilization dynamics of MP not only advance our understanding of hydrophobic compound incorporation in gemini surfactant micelles but also indicate the potential of 14-6-14,2Br⁻ micelles for diverse drug delivery applications. Full article

China aims to reduce its carbon emissions to achieve carbon peaking and neutrality. Measuring the carbon emission efficiency of three urban agglomerations in China, exploring their spatiotemporal characteristics, and investigating the main influencing factors are crucial for achieving regional sustainable development and dual carbon goals. Using the super-slack-based measurement (super-SBM) model, we calculated the carbon emission efficiency of the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations from 2011 to 2021 and explored the spatiotemporal non-equilibrium characteristics of carbon emission efficiency and its influencing factors. The results indicated that: (1) Overall, the carbon emission efficiency showed an N-type trend, with the PRD having the highest average efficiency. Regional differences between the YRD and BTH regions gradually increased. (2) The efficiency hotspots shifted from the PRD to the YRD, whereas the cold spots were mainly concentrated in the BTH region. The variation in the standard deviation ellipse radius of carbon emission efficiency in the urban agglomerations was clear, and the spatial disequilibrium was significant. (3) Economic level and opening up had positive impacts on carbon emission efficiency, whereas energy intensity and industrial structure had negative impacts. The effects of population size, government intervention, and technological level varied among the regions. Full article

In the context of achieving the goal of carbon neutrality, exploring the changes in land demand and ecological carbon stocks under future scenarios at the urban level is important for optimizing regional ecosystem services and developing a land-use structure consistent with sustainable development strategies. We propose a framework of a coupled system dynamics (SD) model, patch generation land-use simulation (PLUS) model, and integrated valuation of ecosystem services and trade-offs (InVEST) model to dynamically simulate the spatial and temporal changes of land use and land-cover change (LUCC) and ecosystem carbon stocks under the NDS (natural development scenario), EPS (ecological protection scenario), RES (rapid expansion scenario), and HDS (high-quality development scenario) in Nanjing from 2020 to 2040. From 2005 to 2020, the expansion rate of construction land in Nanjing reached 50.76%, a large amount of ecological land shifted to construction land, and the ecological carbon stock declined dramatically. Compared with 2020, the ecosystem carbon stocks of the EPS and HDS increased by 2.4 × 10⁶ t and 1.5 × 10⁶ t, respectively, with a sizable ecological effect. It has been calculated that forest and cultivated land are the two largest carbon pools in Nanjing, and the conservation of both is decisive for the future carbon stock. It is necessary to focus on enhancing the carbon stock of forest ecosystems while designating differentiated carbon sink enhancement plans based on the characteristics of other land types. Fully realizing the carbon sink potential of each ecological functional area will help Nanjing achieve its carbon neutrality goal. The results of the study not only reveal the challenges of ecological conservation in Nanjing but also provide useful guidance for enhancing the carbon stock of urban terrestrial ecosystems and formulating land-use planning in line with sustainable development strategies. Full article

Diode-pumped multi-mode graded-index (GRIN) fiber Raman lasers provide prominent brightness enhancement both in linear and half-open cavities with random distributed feedback via natural Rayleigh backscattering. Femtosecond laser-inscribed random refractive index structures allow for the sufficient reduction in the Raman threshold by means of Rayleigh backscattering signal enhancement by +50 + 66 dB relative to the intrinsic fiber level. At the same time, they offer an opportunity to generate Stokes beams with a shape close to fundamental transverse mode (LP₀₁), as well as to select higher-order modes such as LP₁₁ with a near-1D longitudinal random structure shifted off the fiber axis. Further development of the inscription technology includes the fabrication of 3D ring-shaped random structures using a spatial light modulator (SLM) in a 100/140 μm GRIN multi-mode fiber. This allows for the generation of a multi-mode diode-pumped GRIN fiber random Raman laser at 976 nm with a ring-shaped output beam at a relatively low pumping threshold (~160 W), demonstrated for the first time to our knowledge. Full article

It is necessary to extract and recognize the cloud regions presented in imagery to generate satellite imagery as analysis-ready data (ARD). In this manuscript, we proposed a new deep learning model to detect cloud areas in very-high-resolution (VHR) satellite imagery by fusing two deep learning architectures. The proposed UNet3+ model with a hybrid Swin Transformer and EfficientNet (UNet3+STE) was based on the structure of UNet3+, with the encoder sequentially combining EfficientNet based on mobile inverted bottleneck convolution (MBConv) and the Swin Transformer. By sequentially utilizing convolutional neural networks (CNNs) and transformer layers, the proposed algorithm aimed to extract the local and global information of cloud regions effectively. In addition, the decoder used MBConv to restore the spatial information of the feature map extracted by the encoder and adopted the deep supervision strategy of UNet3+ to enhance the model’s performance. The proposed model was trained using the open dataset derived from KOMPSAT-3 and 3A satellite imagery and conducted a comparative evaluation with the state-of-the-art (SOTA) methods on fourteen test datasets at the product level. The experimental results confirmed that the proposed UNet3+STE model outperformed the SOTA methods and demonstrated the most stable precision, recall, and F1 score values with fewer parameters and lower complexity. Full article

Scientific and reasonable satisfaction evaluation is an excellent way to understand the effects of building (structure) renovation and public satisfaction. This study presents a satisfaction evaluation of civil air defense engineering renovated into cooling place from the perspective of public experience. First, a satisfaction evaluation indicator system was constructed for civil air defense engineering renovated into cooling place from the perspective of public experience, focusing on 5 first-level indicators—location condition, spatial status, physical environment, service management, and emotional experience—and 27 second-level indicators. Second, the matter-element extension and combined weighting method were introduced to establish a satisfaction evaluation model for civil air defense engineering renovated into cooling place from the perspective of public experience. Finally, the rationality of the index system and feasibility of the evaluation model were verified by considering civil air defense cooling centers as examples. This research can provide a basis for the further optimization of such projects and the development and management of cities, as well as new ideas or methods for the satisfaction evaluation of similar old buildings (structures) after renovation. Full article

Impulse radio ultra-wideband (IR-UWB) radar, operating in the low-frequency band, can penetrate walls and utilize its high range resolution to recognize different human activities. Complex deep neural networks have demonstrated significant performance advantages in classifying radar spectrograms of various actions, but at the cost of a substantial computational overhead. In response, this paper proposes a lightweight model named TG2-CAFNet. First, clutter suppression and time–frequency analysis are used to obtain range–time and micro-Doppler feature maps of human activities. Then, leveraging GhostV2 convolution, a lightweight feature extraction module, TG2, suitable for radar spectrograms is constructed. Using a parallel structure, the features of the two spectrograms are extracted separately. Finally, to further explore the correlation between the two spectrograms and enhance the feature representation capabilities, an improved nonlinear fusion method called coordinate attention fusion (CAF) is proposed based on attention feature fusion (AFF). This method extends the adaptive weighting fusion of AFF to a spatial distribution, effectively capturing the subtle spatial relationships between the two radar spectrograms. Experiments showed that the proposed method achieved a high degree of model lightweightness, while also achieving a recognition accuracy of 99.1%. Full article

Flint Hall Farm in Godstone, Surrey, UK, is situated adjacent to the London Orbital Motorway, or M25, and contains several landslide systems which pose a significant geohazard risk to this critical infrastructure. The site has been routinely monitored by geotechnical engineers following a landslide that encroached onto the hard shoulder in December 2000; current in situ instrumentation includes inclinometers and piezoelectric sensors. Interferometric Synthetic Aperture Radar (InSAR) is an active remote sensing technique that can quantify millimetric rates of Earth surface and structural deformation, typically utilising satellite data, and is ideal for monitoring landslide movements. We have developed the hardware and software for an Unmanned Aerial Vehicle (UAV), or drone radar system, for improved operational flexibility and spatial–temporal resolutions in the InSAR data. The hardware payload includes an industrial-grade DJI drone, a high-performance Ettus Software Defined Radar (SDR), and custom Copper Clad Laminate (CCL) radar horn antennas. The software utilises Frequency Modulated Continuous Wave (FMCW) radar at 5.4 GHz for raw data collection and a Range Migration Algorithm (RMA) for focusing the data into a Single Look Complex (SLC) Synthetic Aperture Radar (SAR) image. We present the first SAR image acquired using the drone radar system at Flint Hall Farm, which provides an improved spatial resolution compared to satellite SAR. Discrete targets on the landslide slope, such as corner reflectors and the in situ instrumentation, are visible as bright pixels, with their size and positioning as expected; the surrounding grass and vegetation appear as natural speckles. Drone SAR imaging is an emerging field of research, given the necessary and recent technological advancements in drones and SDR processing power; as such, this is a novel achievement, with few authors demonstrating similar systems. Ongoing and future work includes repeat-pass SAR data collection and developing the InSAR processing chain for drone SAR data to provide meaningful deformation outputs for the landslides and other geotechnical hazards and infrastructure. Full article