Search Results (575)

Hyperloop ultra-high-speed transport presents a transformative opportunity for future mobility systems in smart cities. However, assessing its socio-economic impact remains challenging due to Hyperloop’s unique technological, modal, and operational characteristics. As a novel, fifth mode of transportation—distinct from both aviation and rail—Hyperloop requires tailored evaluation tools for policymakers. This study proposes a custom-designed framework to quantify its macroeconomic effects through changes in gross domestic product (GDP) at the city level. Unlike traditional economic models, the proposed approach is specifically adapted to Hyperloop’s multimodality, infrastructure, speed profile, and digital-green footprint. A Poisson pseudo-maximum likelihood (PPML) model is developed and applied at two technology readiness levels (TRL-6 and TRL-9). Case studies of Glasgow, Berlin, and Busan are used to simulate impacts based on geo-spatial features and city-specific trade and accessibility indicators. Results indicate substantial GDP increases driven by factors such as expanded 60 min commute catchment zones, improved trade flows, and connectivity node density. For instance, under TRL-9 conditions, GDP uplift reaches over 260% in certain scenarios. The framework offers a scalable, reproducible tool for policymakers and urban planners to evaluate the economic potential of Hyperloop within the context of sustainable smart city development. Full article

Refining the land use structure can boost land utilization efficiency and curtail regional carbon emissions. Nevertheless, prior research has predominantly concentrated on static linear planning analysis. It has failed to account for how future dynamic alterations in driving factors (such as GDP and population) affect simulation outcomes and how the land use spatial configuration impacts the attainment of the carbon-neutrality goal. In this research, 1 km spatial resolution LULC products were employed to meticulously simulate multiple land use scenarios across China at the national level from 2030 to 2060. This was performed by taking into account the dynamic changes in driving factors. Subsequently, an analysis was carried out on the low-carbon land use spatial structure required to reach the carbon-neutrality target. The findings are as follows: (1) When employing the PLUS (Patch—based Land Use Simulation) model to conduct simulations of various land use scenarios in China by taking into account the dynamic alterations in driving factors, a high degree of precision was attained across diverse scenarios. The sustainable development scenario demonstrated the best performance, with kappa, OA, and FoM values of 0.9101, 93.15%, and 0.3895, respectively. This implies that the simulation approach based on dynamic factors is highly suitable for national-scale applications. (2) The simulation accuracy of the PLUS and GeoSOS-FLUS (Systems for Geographical Modeling and Optimization, Simulation of Future Land Utilization) models was validated for six scenarios by extrapolating the trends of influencing factors. Moreover, a set of scenarios was added to each model as a control group without extrapolation. The present research demonstrated that projecting the trends of factors having an impact notably improved the simulation precision of both the PLUS and GeoSOS-FLUS models. When contrasted with the GeoSOS-FLUS model, the PLUS model attained superior simulation accuracy across all six scenarios. The highest precision indicators were observed in the sustainable development scenario, with kappa, OA, and FoM values reaching 0.9101, 93.15%, and 0.3895, respectively. The precise simulation method of the PLUS model, which considers the dynamic changes in influencing factors, is highly applicable at the national scale. (3) Under the sustainable development scenario, it is anticipated that China’s land use carbon emissions will reach their peak in 2030 and achieve the carbon-neutrality target by 2060. Net carbon emissions are expected to decline by 14.36% compared to the 2020 levels. From the perspective of dynamic changes in influencing factors, the PLUS model was used to accurately simulate China’s future land use. Based on these simulations, multi-scenario predictions of future carbon emissions were made, and the results uncover the spatiotemporal evolution characteristics of China’s carbon emissions. This study aims to offer a solid scientific basis for policy-making related to China’s low-carbon economy and high-quality development. It also intends to present Chinese solutions and key paths for achieving carbon peak and carbon neutrality. Full article

This paper outlines a methodology to compile geo-referenced observational data of Australian birds acting as pollinators of Strelitzia sp. (Bird of Paradise) flowers and dispersers of their seeds. Given the absence of systematic published records, a crowdsourcing approach was employed, combining data from natural history platforms (e.g., iNaturalist, eBird), image hosting websites (e.g., Flickr) and, in particular, social media. Facebook emerged as the most productive channel, with 61.4% of the 301 usable observations sourced from 43 ornithology-related groups. The strategy included direct solicitation of images and metadata via group posts and follow-up communication. The holistic, snowballing search strategy yielded a unique, behavior-focused dataset suitable for analysis. While the process exposed limitations due to user self-censorship on image quality and completeness, the approach demonstrates the viability of crowdsourced behavioral ecology data and contributes a replicable methodology for similar studies in under-documented ecological contexts. Full article

With the growing impact of environmental challenges, the need for well-planned and effectively executed actions to support progress and sustainable social development has become increasingly evident. Geoparks play a vital role in this endeavor by fostering the development of products that celebrate local heritage and promote its conservation, utilizing the natural and cultural resources unique to each region in sustainable ways. Geoproducts, in particular, aim to enrich cultural identity and elevate the value of the landscape and geodiversity by integrating communities into innovative approaches and technologies, engaging them in commercialization, and ensuring sustainability alongside social inclusion. Within the framework of the Safi Geopark Project, this article delves into the concept of geoproducts, their definitions, and their potential to bolster local identity and social and economic development. Leveraging the abundant geological and cultural resources of Safi province, the study presents both tangible and intangible geoproducts that merge traditional craftsmanship with modern sustainability practices. Notable examples include ammonite-inspired ceramics, educational materials, and eco-friendly cosmetics, each carefully designed to reflect and celebrate the region’s geoheritage. This article underscores the crucial role of community involvement in the creation of geoproducts, highlighting their impact on conservation, education, and the promotion of sustainable tourism. By proposing actionable strategies, this study not only broadens the understanding of geoproducts within geoparks but also reinforces their importance as instruments for regional development, heritage conservation, and sustainable economic growth. Full article

The worldwide expansion of geotourism and its ongoing development have captured the interest of numerous scholars, prompting them to investigate various theoretical dimensions within this emerging field. This paper explores the concept of geotourism products and the importance of comprehensively understanding this topic. A thorough review of the existing literature indicates that different researchers offer diverse definitions and interpretations of geotourism products. However, analysis of relevant publications reveals that these perspectives do not adequately incorporate the fundamental principles of geotourism alongside the well-defined and broadly accepted concept of tourism products in general. To facilitate the accurate analysis of research outcomes and to support the effective advancement of geotourism, this paper advocates for a clear and well-defined understanding of what constitutes a geotourism product. Full article

Wind erosion poses a major threat to ecosystem stability and land productivity in arid and semi-arid regions. Accurate identification of its spatiotemporal dynamics and underlying driving mechanisms is a critical prerequisite for effective risk forecasting and targeted erosion control. This study applied the Revised Wind Erosion Equation (RWEQ) model to assess the spatial distribution, interannual variation, and seasonal dynamics of the Soil Wind Erosion Modulus (SWEM) across Inner Mongolia from 1990 to 2022. The GeoDetector model was further employed to quantify dominant drivers, key interactions, and high-risk zones via factor, interaction, and risk detection. The results showed that the average SWEM across the study period was 35.65 t·ha⁻¹·yr⁻¹ and showed a decreasing trend over time. However, localised increases were observed in the Horqin and Hulun Buir sandy lands and central grasslands. Wind erosion was most intense in spring (17.64 t·ha⁻¹·yr⁻¹) and weakest in summer (5.57 t·ha⁻¹·yr⁻¹). Gale days, NDVI, precipitation, and wind speed were identified as dominant drivers. Interaction detection revealed non-linear synergies between gale days and temperature (q = 0.40) and wind speed and temperature (q = 0.36), alongside a two-factor interaction between NDVI and precipitation (q = 0.19). Risk detection indicated that areas with gale days > 58, wind speed > 3.01 m/s, NDVI < 0.2, precipitation of 30.17–135.59 mm, and temperatures of 3.01–4.23 °C are highly erosion-prone. Management should prioritise these sensitive and intensifying areas by implementing site-specific strategies to enhance ecosystem resilience. Full article

The BeiDou-3 Navigation Satellite System (BDS-3) precise point positioning (PPP) service through the B2b signal (PPP-B2b) leverages precise correction data disseminated by satellites to eliminate or mitigate key error sources, including satellite orbit errors, clock biases, and ionospheric delays, thereby enabling high-precision timing and positioning. This paper investigates the disparities in time comparison and positioning capabilities associated with the PPP-B2b signals transmitted by the BDS-3 Geostationary Earth Orbit (GEO) satellites (C59 and C61). Three stations in the Asia–Pacific region were selected to establish two time comparison links. The study evaluated the time transfer accuracy of PPP-B2b signals by analyzing orbit and clock corrections from BDS-3 GEO satellites C59 and C61. Using multi-GNSS final products (GBM post-ephemeris) as a reference, the performance of PPP-B2b-based time comparison was assessed. The results indicate that while both satellites achieve comparable time transfer accuracy, C59 demonstrates superior stability and availability compared to C61. Additionally, five stations from the International GNSS Service (IGS) and the International GNSS Monitoring and Assessment System (iGMAS) were selected to assess the positioning accuracy of PPP-B2b corrections transmitted by BDS-3 GEO satellites C59 and C61. Using IGS/iGMAS weekly solution positioning results as a reference, the analysis demonstrates that PPP-B2b enables centimeter-level static positioning and decimeter-level simulated kinematic positioning. Furthermore, C59 achieves higher positioning accuracy than C61. Full article

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. Full article

Background/Objectives: Inflammatory breast carcinoma (IBC) is an aggressive and rare neoplasm, accounting for 1–5% of all breast cancers. Toll-like receptor type 4 (TLR4) and Advanced Glycation End Products Receptor (AGER/RAGE) have been implicated in breast cancer, and have been shown to promote tumor growth, metastasis, and resistance to therapy by modulating the tumor microenvironment and inflammatory pathways. However, the role of TLR4 and AGER in IBC has not been elucidated. Methods: TLR4 and AGER immunofluorescence expression were evaluated in 27 IBC and 24 non-IBC samples. The expression data and clinicopathological parameters, including the prognostic values of these biomarkers, were compared. TLR4 and AGER gene expression were investigated using the microarray transcriptomic dataset of IBC and non-IBC samples (Gene Expression Omnibus repository—GEO). Results: IBC samples showed higher TLR4 and AGER immunoexpression than the non-IBC group and were associated with obesity and Ki-67 expression (p < 0.05). AGER expression in IBC versus non-IBC was also statistically associated with triple-negative molecular subtypes. Non-IBC subjects with AGER immunoexpression above the cutoff (106.1%, sensitivity of 92.3%, and specificity of 56.2%) showed reduced metastasis-free survival (p = 0.032). In the multivariate analysis, high TLR4 immunostaining increased the risk of metastasis-free survival by 1.029-fold. Analyzing three external GEO datasets confirmed that TLR4 and AGER expression increased in IBC compared to non-IBC samples. Conclusions: Overall, IBC samples showed higher TLR4 and AGER expressions than other breast cancer types, shedding light on the significance of these markers on IBC biology. Full article

Intensified human activities and changes in land-use patterns have led to numerous eco-environmental challenges. A comprehensive understanding of the eco-environmental effects of land-use transitions and their driving mechanisms is essential for developing scientifically sound and sustainable environmental management strategies. However, existing studies often lack a comprehensive analysis of these mechanisms due to methodological limitations. This study investigates the eco-environmental effects of land-use transitions in the Poyang Lake Region over the past 30 years from the perspective of the production-living-ecological space (PLES) framework. Additionally, a geographically explainable artificial intelligence (GeoXAI) framework is introduced to further explore the mechanisms underlying these eco-environmental effects. The GeoXAI framework effectively addresses the challenges of integrating nonlinear relationships and spatial effects, which are often not adequately captured by traditional models. The results indicate that (1) the conversion of agricultural space to forest and lake spaces is the primary factor contributing to eco-environmental improvement. Conversely, the occupation of forest and lake spaces by agricultural and residential uses constitutes the main driver of eco-environmental degradation. (2) The GeoXAI demonstrated excellent performance by incorporating geographic variables to address the absence of spatial causality in traditional machine learning. (3) High-altitude and protected water areas are more sensitive to human activities. In contrast, geographic factors have a greater impact on densely populated urban areas. The results and methodology presented here can serve as a reference for eco-environmental assessment and decision-making in other areas facing similar land-use transformation challenges. Full article

This research examines the performance of the affordable Xiaomi 11T smartphone in static positioning mode. Static Global Navigation Satellite System (GNSS) measurements are acquired over a two-hour period with a known reference point, spanning three consecutive days. The acquired data are processed, employing both real-time and post-processing Precise Point Positioning (PPP) solutions using GPS-only, Galileo-only, and the combined GPS/Galileo datasets. To correct the satellite and clock errors, the instantaneous Centre National d’Études Spatiales (CNES), the final Le Groupe de Recherche de Géodésie Spatiale (GRG), GeoForschungsZentrum (GFZ), and Wuhan University (WUM) products were applied. The results demonstrate that sub-30 cm positioning accuracy is achieved in the horizontal direction using real-time and final products. Additionally, sub-50 cm positioning accuracy is attained in the vertical direction for the real-time and post-processed solutions. Furthermore, the real-time products achieved three-dimensional (3D) position accuracies of 40 cm, 29 cm, and 20 cm using GPS-only, Galileo-only, and the combined GPS/Galileo observations, respectively. The final products achieved 3D position accuracies of 24 cm, 26 cm, and 28 cm using GPS-only, Galileo-only, and the combined GPS/Galileo measurements, respectively. The attained positioning accuracy can be used in some land use and urban planning applications. Full article

The Yellow River Basin (YRB) is a critical ecological zone in China now confronting growing tensions between land conservation and development. This study combines land use, climate, and socio-economic data with spatial–statistical models (GeoDetector [GD]–Patch-generating Land Use Simulation [PLUS]–Integrated Valuation of Ecosystem Services and Trade-Offs [InVEST]) to analyze land use changes (2000–2020), evaluate habitat quality, and simulate scenarios to 2040. Key results include the following: (1) Farmland was decreased by the conversion to forests (+3475 km²) and grasslands (+4522 km²), while construction land expanded rapidly (+11,166 km²); (2) the population and Gross Domestic Product (GDP) pressures drove the farmland loss (q = 0.148 for population, q = 0.129 for GDP), while synergies between evapotranspiration (ET) and the Normalized Difference Vegetation Index (NDVI) promoted forest/grassland recovery (q = 0.155); and (3) ecological protection scenarios increased the grassland area by 12.94% but restricted the construction land growth (−13.84%), with persistent unused land (>3.61% in Inner Mongolia) indicating arid-zone risks. The Habitat Quality-Autocorrelated Coupling Index (HQACI) declined from 0.373 (2020) to 0.345–0.349 (2040), which was linked to drought, groundwater loss, and urban expansion. Proposed strategies including riparian corridor protection, adaptive urban zoning, and gradient-based restoration aim to balance ecological and developmental needs, supporting spatial planning and enhancing the basin-wide habitat quality. Full article

Background: The relationship between medicinal efficacy and the geographical environment in Thesium chinense Turcz. (T. chinense Turcz.), a traditional Chinese herb, remains systematically unexplored. This study integrates metabolomics, machine learning, and ecological factor analysis to elucidate the geographical variation patterns and regulatory mechanisms of secondary metabolites in T. chinense Turcz. from Anhui, Henan, and Shanxi Provinces. Methods: Metabolomic profiling was conducted on T. chinense Turcz. samples collected from three geographical origins across Anhui, Henan, and Shanxi Provinces. Machine learning algorithms (Random Forest, LASSO regression) identified region-specific biomarkers through intersection analysis. Metabolic pathway enrichment employed MetaboAnalyst 5.0 with target prediction. Antioxidant activity (DPPH/hydroxyl radical scavenging) was quantified spectrophotometrically. Environmental correlation analysis incorporated 19 WorldClim variables using redundancy analysis, Mantel tests, and Pearson correlations. Results: We identified 43 geographical marker compounds (primarily flavonoids and alkaloids). Random forest and LASSO regression algorithms determined core markers for each production area: Anhui (4 markers), Henan (6 markers), and Shanxi (3 markers). Metabolic pathway enrichment analysis revealed these markers exert pharmacological effects through neuroactive ligand–receptor interaction and PI3K-Akt signaling pathways. Redundancy analysis demonstrated Anhui samples exhibited significantly higher antioxidant activity (DPPH and hydroxyl radical scavenging rates) than other regions, strongly correlating with stable low-temperature environments (annual mean temperature) and precipitation patterns. Conclusions: This study established the first geo-specific molecular marker system for T. chinense Turcz., demonstrating that the geographical environment critically influences metabolic profiles and bioactivity. Findings provide a scientific basis for quality control standards of geo-authentic herbs and offer insights into plant–environment interactions for sustainable cultivation practices. Full article

Shale plays with pre-existing natural fractures can yield significant production when operating horizontal wells with multi-stage hydraulic fracturing (HWMHF). This work proposes a general, robust, and integrated framework for estimating optimal HWMHF design parameters in an unconventional naturally fractured oil reservoir. This work considers uncertainty in both the distribution of the natural fractures and uncertainty in three geo-mechanical parameters: the internal friction factor, the cohesion coefficient, and the tensile strength. Because a maximum of five design variables is considered, it is appropriate to apply derivative-free algorithms. This work considers versions of the genetic algorithm (GA), particle swarm optimization (PSO), and general pattern search (GPS) algorithms. The forward model consists of two linked software programs: a geo-mechanical simulator and an unconventional shale oil simulator. The two simulators run sequentially during the optimization process without human intervention. The in-house geo-mechanical simulator model provides sufficient computational efficiency so that it is feasible to solve the robust optimization problem. An embedded discrete fracture model (EDFM) is implemented to model large-scale fractures. Two cases strongly verified the feasibility of the framework for the optimization of HWMHF, and the average comprehensive NPV increases by 35% and 102.4%, respectively. By comparison, the pattern search algorithm is more suitable for HWMHF optimization. In this way, oil and gas scientists are contributing to the energy industry more accurately and resolutely. Full article

Androgenetic alopecia (AGA) is marked by the progressive miniaturization of hair follicles (HFs) and hair thinning, driven by a decline in the progenitor cells critical for hair regeneration. Despite this, the mechanisms responsible for progenitor cell depletion remain largely unclear. To investigate transcriptional alterations in the progenitor cell regions of AGA patients while maintaining the spatial tissue context, we employed the GeoMX Digital Spatial Profiling (DSP) platform, which enables a precise comparison with healthy controls. Our analysis revealed the significant upregulation of genes associated with extracellular matrix (ECM) organization and the epithelial–mesenchymal transition (EMT), including FN1, TWIST1, and TGFB2 in the progenitor cell region of the HFs. Correspondingly, protein expression data confirmed increased levels of the protein products of these genes in the affected areas, underscoring their roles in the disease’s progression. These molecular changes suggest an environment conducive to the EMT, potentially contributing to the loss of progenitor cells and indicating a fibrogenic shift within the HF microenvironment. Additionally, our study highlights the influence of peri-infundibular immune cell infiltration on these molecular changes, suggesting that immune-mediated microinflammation may contribute to the fibrogenic environment and progenitor cell loss in the AGA. These findings demonstrate the utility of spatial transcriptomics in identifying potential therapeutic targets and advancing our understanding of AGA’s molecular mechanisms, offering avenues for developing targeted treatment strategies. Full article