Search Results (16,687)

Changing precipitation patterns in the Arctic is a key indicator of climate change, in addition to increasing land and ocean temperatures, but these patterns are not uniform across the circumpolar region. This regional analysis focuses on the Yukon–Kuskokwim Delta in southwestern Alaska and addresses the following questions: (1) What is the baseline hydroclimatology during the growing season on the Yukon–Kuskokwim Delta? (2) What are the seasonal and intraseasonal trends of the hydroclimate variables in the YKD? (3) What are the implications of documented trends for the study region? Utilizing ECMWF’s ERA5 reanalysis dataset, we conducted a seasonal analysis for May through September for the years 1982–2022. While no strong trend emerged for total precipitation over the 41-year study period, differing trends were observed for large-scale and convective precipitation. The decline in large-scale precipitation is supported by a decrease in storm counts in the Bering Sea, as well as declining vertically integrated moisture convergence and moisture flux. By contrast, the increase in convective precipitation underscores the growing importance of the local hydrologic cycle, further supported by a significant rise in evaporation. These enhanced local hydroclimatological cycles have significant implications for wildfires and subsistence activities. Full article

Enhancing carbon sequestration capacity through effective forest management is a critical strategy for mitigating climate change. China has established public administrations, known as state-owned forest farms (SFFs), primarily to manage state-owned forests. This study examines the carbon sequestration effects of forestry investment made by 211 SFFs in Shaanxi Province from 2000 to 2018, using a panel fixed effects model and a panel threshold model. The findings reveal that SFF investment has a significant time-lag effect on carbon sequestration, with the marginal contribution peaking three years after the initial investment. Additionally, the impact of investment exhibits spatial heterogeneity, varying across regions due to differences in environmental and ecological conditions. Threshold effects are also identified, indicating that the effectiveness of carbon sequestration is constrained by the scale and structure of investment, with diminishing returns observed beyond optimal levels. Furthermore, we found that investment increases carbon sequestration mainly by expanding forest area and improving forest quality. These findings underscore the importance of cost-effectiveness analyses to optimize forestry investment decisions. SFFs are advised to prioritize appropriate investment timing, regions, scales, and structures to achieve optimal carbon sequestration benefits and maximize resource utilization, supporting sustainable forest management and climate change mitigation efforts. Full article

Rapid urbanization has led to severe fragmentation of ecological spaces in high-density metropolitan regions, threatening urban ecological security and environmental well-being. While cities explore various restoration strategies, the systematic identification of ecological priority areas remains an urgent challenge, particularly due to the limitations of multi-scale evaluation methods. This study develops an integrated nested-scale analytical approach to examine ecological elements at metropolitan and central urban levels, using Metropolitan Nanjing as a case study. The framework combines Morphological Spatial Pattern Analysis (MSPA), Landscape Connectivity Analysis, and INVEST Habitat Quality Assessment to identify ecological sources while employing a multi-dimensional ecological resistance evaluation system and Circuit Theory Model for critical node assessment. The findings reveal a notable spatial overlap between ecological pinch points and barrier points across scales, demonstrating the importance of nested-scale coupling in maintaining network stability. Through this analysis, 3297 ecological priority areas are identified and classified into three hierarchical categories, offering a practical framework for optimizing ecological networks in high-density metropolitan regions. Full article

Cities in cold regions face significant challenges, including high carbon emissions, intense energy use, and outdated energy structures, making them critical areas for achieving carbon neutrality and sustainable development. While studies have explored the impact of spatial structures on urban carbon emissions, the effects of multi-scale spatial structures remain insufficiently understood, limiting effective spatial planning strategies. This research examines Changchun, a city in a severe cold region, using data from 2012 to 2021, including road networks, land use, nighttime light, and energy statistics. Employing spatial syntax, landscape pattern indices, random forests, and segmented linear regression, this research establishes a carbon emission translation pathway to analyze the nonlinear effects of multi-scale spatial structures. Findings reveal a 26.70% annual decrease in carbon emissions, with winter emissions 1.84 times higher than summer ones. High-emission zones have shifted from industrial areas to transportation, commercial, and residential zones, reflecting growing seasonal variability and structural changes. Spatial complexity increased while connectivity declined. Multi-scale analysis identified a “decrease–increase–decrease” pattern, with macro-scale centrality declining and micro-scale hierarchy rising. These results provide both theoretical and practical guidance for urban planning in cold regions, supporting early carbon neutrality and long-term sustainable development goals. Full article

Understanding the historical patterns of drought changes is important to effectively manage and mitigate drought. This paper aims to provide a quantitative assessment of the spatiotemporal drought patterns in Saudi Arabia from 1985 to 2022. The study used the Standardized Precipitation Index (SPI) to examine drought patterns on both monthly and yearly timescales. The findings indicate a significant trend of increasing drought conditions in certain regions of the Kingdom from 1985 to 2022. The average rates of change for SPI-03, SPI-06, and SPI-12 were found to be −0.003 yr⁻¹, −0.0034 yr⁻¹, and −0.0099 yr⁻¹, respectively. Droughts were more frequent and persistent in the northern regions of the country, while the western region experienced severe and intense droughts. There were fewer drought occurrences before 2000, but droughts became more frequent after 2000, with large-scale impacts occurring during 2007–2008 and 2013–2014. These findings have important implications for water management strategies and can help mitigate the effects of drought, as they identify hotspot regions across Saudi Arabia at different timescales. Overall, it is important to implement province-specific efforts to reduce environmental vulnerabilities to droughts. Full article

One of the predominant technologies for multi-source navigation in vehicles involves the fusion of GNSS/IMU/ODO through a factor graph. To address issues such as the asynchronous sampling frequencies between the IMU and ODO, as well as diminished accuracy during GNSS signal loss, we propose a GNSS/IMU/ODO integrated navigation method based on an adaptive sliding window factor graph. The measurements from the ODO are utilized as observation factors to mitigate prediction interpolation errors associated with traditional ODO pre-integration methods. Additionally, online estimation and compensation for both installation angle deviations and scale factors of the ODO further enhance its ability to constrain pose errors during GNSS signal loss. A multi-state marginalization algorithm is proposed and then utilized to adaptively adjust the sliding window size based on the quality of GNSS observations, enhancing pose optimization accuracy in multi-source fusion while prioritizing computational efficiency. Tests conducted in typical urban environments and mountainous regions demonstrate that our proposed method significantly enhances fusion navigation accuracy under complex GNSS conditions. In a complex city environment, our method achieves a 55.3% and 29.8% improvement in position and velocity accuracy and enhancements of 32.0% and 61.6% in pitch and heading angle accuracy, respectively. These results match the precision of long sliding windows, with a 75.8% gain in computational efficiency. In mountainous regions, our method enhances the position accuracy in the three dimensions by factors of 89.5%, 83.7%, and 43.4%, the velocity accuracy in the three dimensions by factors of 65.4%, 32.6%, and 53.1%, and reduces the attitude errors in roll, pitch, and yaw by 70.5%, 60.8%, and 26.0%, respectively, demonstrating strong engineering applicability through an optimal balance of precision and efficiency. Full article

Efficient logistics is a key factor in the competitiveness of seaports, especially in regions such as the Baltic Sea, where ports play important roles as hubs in the European Union’s Trans-European transport network (TEN-T). However, there are a lack of comprehensive studies focusing on the logistics efficiency of Baltic Sea ports, especially those integrating technical and technological factors. This study aimed to assess changes in the logistics efficiency of 15 major ports in the Baltic Sea region between 2019 and 2023, taking into account the technological and infrastructure-related elements that influence port performance. The model developed by the authors integrates the nearest neighbour method for cluster identification, data envelopment analysis using the Banker, Charnes, and Cooper (DEA-BCC) model to assess the overall technical, pure technical, and scale logistics efficiency, and spatial autocorrelation analysis to explore spatial interactions. For the DEA-BCC model, constraints were defined for each port based on inputs (number and length of berths) and outputs (cargo and container volumes for 2019–2023). The spatial autocorrelation analysis examined the relationships among the Baltic Sea ports, container volumes, and logistic efficiency values derived from the DEA model. Recognizing the sensitivity of the weight matrix in previous studies, this paper introduced an enhanced two-factor weighting matrix that incorporated geographical distance and the port connectivity index, calculated by the United Nations Conference on Trade and Development (UNCTAD). The statistical reliability of the results was validated using z-scores and p-values. The results showed that the overall technical efficiency of the ports analysed during the period considered was 47.2%, the pure technical efficiency was 61.0%, and the average scale efficiency was around 76%, indicating that diminishing returns to scale dominated. The spatial analysis showed a strong correlation between port connectivity and efficiency, indicating that well-connected ports, such as Gdańsk and Gdynia, had a higher efficiency. The findings make a significant contribution to the understanding of the logistics efficiency of Baltic Sea ports and highlights the importance of regional cooperation, infrastructure improvements, and better connectivity strategies to improve the overall efficiency of seaports in the region. Full article

The starch content in rice grains is a key factor in determining their quality. An optimal starch level not only ensures grain fullness, improving storage stability, but also enhances the stickiness and viscosity of cooked rice, thereby boosting its palatability and nutritional value. However, traditional methods for monitoring starch content are expensive and lack the capability to provide rapid spatial distribution information across large areas. To address this limitation, this study focuses on mature rice grains in the Yingjiang region, leveraging multispectral data from the Sentinel-2 satellite. First and second derivative transformations were applied to the multispectral reflectance data, followed by the use of three feature selection algorithms to identify key spectral bands. BP neural networks and ELM neural network regression models were then integrated to quantitatively estimate starch content across the study area. As a result, high-precision spatial distribution maps of starch content were generated, providing a novel and efficient method for large-scale rapid monitoring. The results demonstrate that, compared to full-band data, the use of SPA feature selection significantly improved the predictive accuracy of both BP and ELM models, despite a slight increase in the models’ MSE. Similarly, CARS feature selection also contributed substantially to enhancing the accuracy of the BP and ELM models. In contrast, UVE feature selection significantly reduced the MSE of the BP model, improving predictive precision, with the model achieving an R² of 0.8061 and an MSE of 0.3896. This study highlights that the inversion method, which combines feature selection algorithms with machine learning models, can effectively enhance the predictive accuracy of starch content estimation. Among the tested approaches, the combination of UVE feature selection and BP neural networks delivered the best performance. These findings confirm the feasibility of utilizing Sentinel-2 satellite multispectral data for the quantitative inversion of agronomic parameters across large agricultural areas, providing robust technical support for precision agriculture. Full article

The Global Navigation Satellite System (GNSS) provides critical positioning, navigation, and timing (PNT) services worldwide, enabling a wide range of applications from everyday use to advanced scientific and military operations. The importance of Low Earth Orbit (LEO) PNT systems lies in their ability to enhance the GNSS by implementing signals in additional frequency bands, offering increased signal strength, reduced latency, and improved accuracy and coverage, particularly in challenging environments such as urban canyons or polar regions, thereby addressing the limitations of the traditional Medium Earth Orbit (MEO) GNSS. This paper details the system engineering of a novel CubeSat-based multi-regional PNT system tailored for deployment in LEO. The proposed system leverages on a miniaturized CubeSat-compatible PNT payload that includes a chip-scale atomic clock (CSAC) and relies on MEO GNSS technologies to deliver positioning and timing information across multiple regions. The findings indicate that the proposed CubeSat-based PNT system offers a viable solution for enhancing global navigation and timing services, with potential commercial and scientific applications. This work contributes to the growing body of knowledge on LEO-based PNT systems and lays the groundwork for future research and development in this rapidly evolving field. Full article

Six degrees of freedom (6-DoF) object pose estimation is essential for robotic grasping and autonomous driving. While estimating pose from a single RGB image is highly desirable for real-world applications, it presents significant challenges. Many approaches incorporate supplementary information, such as depth data, to derive valuable geometric characteristics. However, the challenge of deep neural networks inadequately extracting features from object regions in RGB images remains. To overcome these limitations, we introduce the Geometry-Focused Attention Network (GFA-Net), a novel framework designed for more comprehensive feature extraction by analyzing critical geometric and textural object characteristics. GFA-Net leverages Point-wise Feature Attention (PFA) to capture subtle pose differences, guiding the network to localize object regions and identify point-wise discrepancies as pose shifts. In addition, a Geometry Feature Aggregation Module (GFAM) integrates multi-scale geometric feature maps to distill crucial geometric features. Then, the resulting dense 2D–3D correspondences are passed to a Perspective-n-Point (PnP) module for 6-DoF pose computation. Experimental results on the LINEMOD and Occlusion LINEMOD datasets indicate that our proposed method is highly competitive with state-of-the-art approaches, achieving 96.54% and 49.35% accuracy, respectively, utilizing the ADD-S metric with a 0.10d threshold. Full article

To elucidate the potential roles of presynaptic and postsynaptic serotonergic activity in impulsivity traits, we investigated the relationship between self-reported impulsiveness and serotonin transporter (5-HTT) and 5-HT2A receptors in healthy individuals. In this study, 26 participants completed 3-Tesla magnetic resonance imaging and positron emission tomography with [¹¹C]DASB and [¹¹C]MDL100907. To quantify 5-HTT and 5-HT2A receptor availability, the binding potential (BP_ND) of [¹¹C]DASB and [¹¹C]MDL100907 was derived using the simplified reference tissue model with cerebellar gray matter as the reference region. The participants’ impulsivity levels were assessed using the Barratt Impulsiveness Scale-11 (BIS-11). The region of interest (ROI)-based partial correlation analysis with age, sex, and temperament traits as covariates revealed a significant positive correlation between non-planning impulsiveness and [¹¹C]MDL100907 BP_ND in the caudate (CAU) at Bonferroni-corrected p < 0.0045. Non-planning impulsiveness was also positively correlated with [¹¹C]MDL100907 BP_ND in the prefrontal cortex (PFC), ventromedial PFC, orbitofrontal cortex (OFC), insula (INS), amygdala (AMYG), putamen, ventral striatum, and thalamus, and the total score of BIS-11 was positively correlated with [¹¹C]MDL100907 BP_ND in the OFC, INS, AMYG, and CAU at uncorrected p < 0.05. Motor impulsiveness had a positive correlation with [¹¹C]DASB BP_ND in the CAU at uncorrected p < 0.05. Our results suggest that impulsivity traits, characterized by focusing on the present moment without considering future consequences, may be involved in serotonergic neurotransmission, particularly 5-HT2A receptor-mediated postsynaptic signaling in the CAU, which plays an important role in cognitive processes related to executive function, judgment of alternative outcomes, and inhibitory control. Full article

Efficient mobile detection equipment plays a vital role in ensuring maritime safety, and accurate ship identification is crucial for maritime traffic. Recently, the most advanced learning-based methods have markedly improved the accuracy of ship detection, but these models often face huge challenges on resource limited mobile devices due to their large size and high computational requirements. Thus, we propose a lightweight ship detection network based on attentional feature fusion, called AFF-LightNet. To enhance the fusion of multi-scale features and semantically inconsistent features, we introduce iterative attentional feature fusion (IAFF) into the proposed neck network, improving the efficiency of feature fusion by introducing a multi-scale channel attention module. Also, deep and cross network version 2 (DCNv2) is replaced by Convolution (Conv) in the backbone network to further improve the detection accuracy of the proposed network. It enhances the spatial sampling position in convolution and Region of Interest (Rol) pooling by introducing offsets. Moreover, a lightweight convolution GhostConv was introduced into the head network to reduce the number of parameters and computation cost. Last, Scalable Intersection over Union (SIOU) loss was leveraged to improve the convergence speed of the model. We conduct extensive experiments on the publicly available dataset SeaShips and compare it with existing methods. The experimental results show that compared with the standard YOLOv8n, the improved network has an average accuracy of 98.8%, an increase of 0.4%, a reduction of 1.9 G in computational complexity, and a reduction of 0.19 M in parameter count. Full article

Background/Objectives: The development of culinary tourism offers not only unique culinary travel experiences but also allows for the exploration of various aspects related to food. The main aim of this study was to assess the food preferences of a selected group of female consumers regarding world cuisine and to analyze the sensory quality of selected world cuisine products: ayran, rice noodles, tempeh, and chorizo. Methods: Sensory evaluation of utility characteristics, including color, aroma, texture, appearance, and taste, was conducted using a five-point scale. A custom questionnaire was used to collect data on respondents’ preferences and demographic characteristics. This study included 51 sensory panelists and 356 survey participants. Results: Among the evaluated products, rice noodles received the highest median rating (Me = 4.8), while tempeh scored the lowest (Me = 3.8). Statistical analysis revealed significant differences in sensory perceptions depending on prior product familiarity. Italian (67.5%) and Polish (65.8%) cuisines were most frequently preferred, whereas Indian cuisine (4.3%) was the least popular. Additionally, over 83% of respondents indicated they regularly patronize food establishments offering regional dishes during travel. Conclusion: This study highlights a strong preference for familiar cuisines, such as Italian and Polish, among Polish female consumers, with implications for targeted marketing strategies in the gastronomy sector. The sensory analysis provides actionable insights into product acceptance, emphasizing the importance of cultural adaptation in promoting international food products. Full article

Empirical studies are valuable for assessing soil and water pollution, as they can reduce costs and save time. The present study discusses previous research results using a questionnaire to gather experts’ judgments on technical issues and potential pollution related to the vulnerability of Wastewater Treatment Plants (WWTPs) in Greece. The questionnaire included 44 closed-type questions based on the Likert Scale. It was distributed to a representative sample of 116 operators over seven (7) months (April–November 2021). Geographical Information Systems (GISs) were employed to visualize the spatial distribution of the seismic vulnerability of WWTPs. The study outputs include eight (8) maps depicting the spatial distribution of seismic vulnerability, both with and considering soil–water pollution, by calculating the existence of seismic hazards and identifying potentially affected regions. Additionally, eight (8) tables support this analysis. The survey findings highlight the most vulnerable regions and WWTPs in the country. The results suggest that after excluding Zone III, the WWTPs of Zone II of the national Seismic Hazard Map (SHM) are estimated to be the most vulnerable. This study spatially visualizes the indicator of seismic vulnerability (ISV) and the seismic vulnerability index concerning potential soil–water pollution (ISV-REF), according to the SHM and regions. Most WWTPs have low ISV-REF, while maps illustrate the exceedance of that parameter, identifying the safest units and indicating that Zone I has the safest units according to the exceedance percentages. Integrating data on regions, ISV, ISV-REF, and their exceedance in GIS could lead to authorities’ and technicians’ decisions to implement quick measures. Researchers should also focus their studies more precisely, mitigating the seismic vulnerability of critical infrastructure, such as WWTPs. Full article

The expansion of renewable energy sources (RES) is essential to achieving regional sustainability in alignment with global climate goals. This study investigates the dynamics and projected growth of RES in West Pomerania, Poland, a region with significant potential due to its geographical characteristics and supportive policy frameworks. Historical data from 2010 to 2023 were used to perform a time series analysis that evaluated the annual growth rate (AGR) of various RES technologies, including wind, solar, biomass, and biogas. The analysis revealed a consistent upward trend in RES capacity, particularly in wind and solar energy, demonstrating effective resource mobilisation in the region. Subsequently, a forecasting model was employed to project the growth of the RES capacity through 2033 based on historical trends and technological advancements. The results indicate significant anticipated increases in RES capacity, highlighting West Pomerania’s potential to reduce its reliance on fossil fuels. This growth supports increased energy security and environmental sustainability. This study addresses a notable gap in the literature by linking regional renewable energy development with broader policy frameworks, such as the European Green Deal, and exploring the specific challenges of grid integration and economic disparities in the context of local energy transitions. These findings highlight the importance of sustained investment and policy support to scale renewable infrastructure while aligning regional initiatives with international sustainability goals. By bridging this gap, this study concludes that the West Pomerania strategy can serve as a model for other regions aiming to enhance their renewable energy portfolios and effectively meet the climate goals of the EU. Full article