Search Results (201)

Willow stands (Salix spp.) are an essential part of riparian ecosystems, as they sustain biodiversity and provide bioenergy solutions. The present review synthesizes the global scientific literature about the management of willow stands. In order to achieve this goal, we used a dual approach combining bibliometric analysis with traditional literature review. As such, we consulted 416 publications published between 1978 and 2024. This allowed us to identify key species, ecosystem services, conservation strategies, and management issues. The results we have obtained show a diversity of approaches, with an increase in short-rotation coppice (SRC) systems and the multiple roles covered by willow stands (carbon sequestration, biomass production, riparian restoration, and habitat provision). The key trends we have identified show a shift toward topics such as climate resilience, ecological restoration, and precision forestry. This trend has become especially pronounced over the past decade (2014–2024), as reflected in the increasing use of these keywords in the literature. However, as willow systems expand in scale and function—from biomass production to ecological restoration—they also raise complex challenges, including invasive tendencies in non-native regions and uncertainties surrounding biodiversity impacts and soil carbon dynamics over the long term. The present review is a guide for forest policies and, more specifically, for future research, linking the need to integrate and use adaptive strategies in order to maintain the willow stands. Full article

Quantum finance, as a key application scenario of quantum computing, showcases multiple significant advantages of quantum machine learning over traditional machine learning methods. This paper first aims to overcome the limitations of the hidden quantum Markov model (HQMM) in handling continuous data and proposes an innovative method to convert continuous data into discrete-time sequence data. Second, a hybrid quantum computing model is developed to forecast stock market trends. The model was used to predict 15 stock indices from the Shanghai and Shenzhen Stock Exchanges between June 2018 and June 2021. Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. This validation of quantum advantage in financial forecasting enables the practical deployment of quantum-inspired prediction models by investors and institutions in trading environments. This quantum-enhanced model empowers investors to predict market regimes (bullish/bearish/range-bound) using real-time data, enabling dynamic portfolio adjustments, optimized risk controls, and data-driven allocation shifts. Full article

The water-yield function in the Yellow River Basin (YRB) of China for maintaining the basin’s ecological water balance plays a crucial role. Understanding its spatiotemporal variation and the underlying drivers in the basin is crucial for the management, utilization, and development of water resources. Thus, we used the InVEST model to explore its spatiotemporal dynamics across multiple scales (“basin–county–pixel”). Then, we integrated socio-economic and natural factors to elucidate the driving forces and spatial heterogeneity of water-yield dynamics. Our findings indicated that water-yield trends increased in 71.76% of the YRB, and significant water-yield increases were detected in 13.9% of the basin over the past 40 years. A phase-wise comparison revealed a shift in water yield from a decreasing trend in the first two decades to a significant increasing trend in the last two decades. Hotspot analysis revealed that hotspots of increasing water-yield trends have shifted from the downstream section of the basin toward the southwest, while hotspots of decreasing water-yield trends first concentrated in the basin’s southern part and then disappeared. Both natural and socioeconomic factors have exerted positive and negative impacts on water-yield dynamics. Among them, the dynamics of water yield have been predominantly driven by natural variables. Full article

Urban–rural integration realises the coordinated development and prosperity of urban and rural areas as a whole by optimising the allocation of resources and the flow of factors, and its connotations have been extended from a single dimension to multiple dimensions such as people, land and industry. The Beijing–Tianjin–Hebei Region has a typical “Core–Periphery Structure”, and this paper took the 187 county units within the region as the research object, taking into account indicators of development and coordination to construct an evaluation index system of urban–rural integration of the Beijing–Tianjin–Hebei region counties in the dimensions of “people–land–industry”. Global principal component analysis was used to measure the evolutionary pattern of the urban–rural integration level between 2005 and 2020, and its spatiotemporal drivers were analysed by using the Geographical and Temporal Weighted Regression model (GTWR). The results of the study show that (1) the level of urban–rural integration in the Beijing–Tianjin–Hebei region showed an increasing trend during the 15-year study period, the high-value areas of urban–rural integration were mainly distributed in Beijing and the Bohai Rim region in the eastern part of the Tianjin–Hebei region, and the level of urban–rural integration of the peri-urban county units of the city was better than that of the remote counties and cities as a whole. (2) In terms of spatial agglomeration, all dimensions were characterised by significant spatial agglomeration. The degree of agglomeration was categorised as urban–rural comprehensive integration (U-RCI) > urban–rural industry integration (U-RII) > urban–rural land integration (U-RLI) > urban–rural people integration (U-RPI). (3) In terms of spatial and temporal driving factors for urban–rural integration, the driving role of U-RPI, U-RLI and U-RII for U-RCI has gradually weakened during the past 15 years, and urban–rural integration in the counties shifted from a single role to a more central coordinated and multidimensional driving role. Full article

This study investigates the use of a custom-built 10 GHz continuous wave micro-Doppler radar system to analyze external vibrations of idling vehicles under various conditions. Scenarios included different gear engagements with one occupant and parked gear with up to four occupants. Motivated by security concerns, such as the threat posed by idling vehicles with multiple occupants, the research explores how micro-Doppler signatures can indicate vehicle readiness to move. Experiments focused on a mid-size SUV, with similar trends seen in other vehicles. Radar data were compared to in situ accelerometer measurements, confirming that the radar system can detect subtle frequency changes, especially during gear shifts. The system’s sensitivity enables it to distinguish variations tied to gear state and passenger load. Extracted features like frequency and magnitude show strong potential for use in machine learning models, offering a non-invasive, remote sensing method for reliably identifying vehicle operational states and occupancy levels in security or monitoring contexts. Spectrogram and PSD analyses reveal consistent tonal vibrations around 30 Hz, tied to engine activity, with harmonics at 60 Hz and 90 Hz. Gear shifts produce impulse signatures primarily below 20 Hz, and transient data show distinct peaks at 50, 80, and 100 Hz. Key features at 23 Hz and 45 Hz effectively indicate engine and gear states. Radar and accelerometer data align well, supporting the potential for remote sensing and machine learning-based classification. Full article

Historic districts of ancient towns serve as significant carriers of historical and cultural heritage while also being popular tourist destinations. Within the context of urbanization and organic renewal, the protection and development of historic districts have become crucial research topics. This study collects literature from the Web of Science database and applies manual screening to ensure relevance to the research theme. Using CiteSpace as an analytical tool, the study conducts a visual analysis from multiple perspectives, including keywords, writing time, authors, centrality, keyword clustering analysis, and timeline visualization. By constructing a knowledge graph, this research explores the key pathways and knowledge nodes in the organic renewal of spatial environments in historic districts of ancient towns. Based on literature clustering, the study categorizes research into four major aspects: heritage conservation, cultural and tourism development, spatial planning and design, and environmental enhancement. Based on this, universal strategies for the cultural and tourism development of historic districts in ancient towns are proposed. The research focus shifts from emphasizing cultural heritage preservation to the integrated development of culture and tourism. In the spatial development of historic districts, everyday life scenes should be incorporated while new technologies should be utilized to enhance environmental comfort. This paper summarizes the current research frontiers in this field and proposes future research trends, providing valuable references for scholars in related areas. Full article

This study demonstrates that the ripeness of avocado fruits can be analyzed using frequency-dependent electrical conductivity and permittivity through a non-invasive Magnetic Induction Spectroscopy (MIS) method. Utilizing an MIS system for conductivity and permittivity measurements of a large sample set ($N=60$) of avocado fruits across multiple frequencies from 100 kHz to 3 MHz enables clear observation of their dispersion behavior and the evolution of their spectra over ripening time in a completely non-contact manner. For the entire sample batch, the conductivity spectrum exhibits a general upward shift and spectral flattening over ripening time. To further quantify these features, normalized gradient analysis and equivalent circuit modeling were employed, and statistical analysis confirmed the correlations between electrical parameters and ripening stages. The trend characteristics of the normalized gradient parameter $P_y$ provide a basis for defining the three ripening stages within the 22-day period: early pre-ripe stage (0–5 days), ripe stage (5–15 days), and overripe stage (after 15 days). The equivalent circuit model, which is both physically interpretable and fitted to experimental data, revealed that the ripening process of avocado fruits is characterized by a weakening of capacitive structures and an increase in extracellular solution conductivity, suggesting changes in cellular integrity and extracellular composition, respectively. The results also highlight significant inter-sample variability, which is inherent to biological samples. To further investigate individual conductivity variation trends, Gaussian Mixture Model (GMM) clustering and Principal Component Analysis (PCA) was conducted for exploratory sample classification and visualization. Through this approach, the sample set was classified into three categories, each corresponding to distinct conductivity variation patterns. Full article

Background/Objectives: Cardiovascular diseases (CVDs) are a leading cause of mortality globally. Growing studies have been conducted to examine the diet–CVD association to alleviate the health and economic burdens associated with CVDs, but beneficial dietary factors may vary by study region and cohort. There was a need to identify the trends in diet–CVD research by study region and current emerging dietary exposures of interest, which could inform areas for future research and the regions where evidence is relatively limited. Methods: A comprehensive search of multiple databases was performed to identify eligible prospective cohorts examining diet–CVD associations published between 2019 and 2024. Trends in dietary exposure, including dietary patterns, food groups, and nutrients, were analyzed by publication year and geographical distribution. Results: A total of 298 studies were included in the review. While the United States continued to lead in the number of CVD–diet cohort studies, China has significantly increased its contributions over the past five years, increasing from 2.1% to 14.3%. The cohorts that contributed the most to the literature included the Nurses’ Health Study and the Danish Diet, Cancer, and Health cohort. Although food groups accounted for the highest number with respect to dietary exposure overall, there was a notable shift in diet–CVD cohort studies from a focus on nutrients to dietary patterns. Plant-based and Mediterranean diets were the most frequently investigated, while ultra-processed foods and country-specific dietary indices also gained prominence. Conclusions: This systematic review highlighted the shift towards dietary patterns in nutritional epidemiology, emphasizing the importance of understanding the role of nutrition in health through holistic dietary approaches. The observed trends in dietary exposure research suggested the need for future studies to delve deeper into the complexities of dietary patterns, including how cultural and socioeconomic elements defined the nuances of country-specific dietary patterns. Full article

The Ma’anshan section of the lower Yangtze River features a complex multi-anabranching system, where the river divides into several branches around mid-channel sandbars, with distinct point bars alternately developing along both banks. Within this morphologically active system, Zhengpu Harbor suffered severe operational disruptions by accelerated siltation at its approach channel, primarily due to its vulnerable location downstream of the expanding Niutun River point-bar on the left bank. To systematically diagnose the mechanisms of siltation, this study integrates multi-method investigations: decadal-scale morphodynamic analysis using long-term bathymetric surveys, numerical modeling to quantify engineering impacts on flow dynamics, and multiple linear regression analysis for the contributions of key influencing factors. The result identifies three primary drivers of siltation, collectively responsible for 70% of the sediment accumulation, including the rightward shift of the thalweg in the Ma’anshan left branch, reduced flow diversion of the left Branch of Central bar, and the expansion of the Niutun River point bar. River engineering structures, such as bridges, contribute approximately 12%, while changes in upstream flow-sediment supply account for approximately 18%. To mitigate siltation at Zhengpu Harbor’s approach channel, this study proposes targeted engineering interventions to enhance local hydrodynamic conditions. The spur dikes were designed to enhance the morphological stabilization of the Central bar head to regulate flow distribution. A diversion channel could also be excavated at the tail of the Niutun River shoal, and emergency dredging was recommended at the harbor front. Numerical modeling indicates that these measures will increase flow velocity by over 0.1 m/s at the harbor front, mitigating the siltation situation. The study concludes that the proposed engineering measures can reduce annual siltation by approximately 30% under normal-year hydrological conditions, demonstrating their feasibility in mitigating siltation trends in multi-anabranching river systems. This research provides a reference for addressing siltation issues in harbors within complex anabranching river systems. Full article

The global shipping industry is evolving towards deep integration of digital transformation, intelligent upgrading, and green development. Meanwhile, recent geopolitical shifts have introduced heightened uncertainties into international shipping, compounding the challenges and escalating the demands for weather routing services for ocean-going ships. This paper provides a systematic review and expert perspective on China’s current status and key challenges in ocean-going weather routing services. Based on operational insights from China’s national meteorological service synthesized with a review of current trends and the literature, it further explores the future development of China’s ocean-going weather routing services and technologies from multiple dimensions: enhancing maritime weather observation capabilities, developing advanced weather routing service models, upgrading autonomous and controllable global satellite communication systems, promoting intelligent navigation technologies to facilitate shipping’s low-carbon transition, and expanding meteorological support capabilities for Arctic shipping routes. The analysis identifies critical gaps and proposes strategic directions, offering a unique contribution to understanding the trajectory of weather routing services within China’s specific national context from the perspective of its primary national service provider. Full article

Understanding the spatiotemporal patterns of cropland carbon and carbon water use efficiency (CWUE) and its driving factors is essential for sustainable agricultural development. Based on a multi-source remote sensing dataset, this study applies a trend analysis (Sen + Mann–Kendall), a dual-type randomized extraction algorithm, and an optimized XGBoost model to examine the spatiotemporal variations in cropland CWUE, including the water use efficiency of net primary production (WUE_NPP), water use efficiency of gross primary production (WUE_GPP), and carbon use efficiency (CUE) in Henan Province from 2001 to 2019. This study further quantifies the impact of irrigation on the cropland CWUE and explores the synergistic effects of its driving factors in irrigated areas. Results reveal significant regional differences in cropland CWUE across Henan Province. Higher multi-year average values of CUE and WUE_NPP were observed in the western region, while the WUE_GPP was more prominent in the south-central region. Over 76% of cropland areas showed a general downward trend in three indicators, with significant interannual declines. Non-irrigated cropland exhibited higher CWUE values than irrigated ones. The average values over multiple years of the WUE_GPP, WUE_NPP, and CUE of irrigated cropland were 2.51 ${\mathrm{g} \mathrm{C} \mathrm{m}}^{-2}{ \mathrm{mm}}^{-1}$, 1.08 ${\mathrm{g} \mathrm{C} \mathrm{m}}^{-2}{ \mathrm{mm}}^{-1}$, and 0.43, respectively. Sunlight was the dominant factor influencing the WUE_GPP in irrigated areas, while precipitation primarily regulated the WUE_NPP and CUE. The influence of the gross domestic product (GDP) was found to be minimal. Notably, both the leaf area index (LAI) and precipitation exhibited a shift from a positive to negative influence on CUE once their values exceeded optimal thresholds, indicating that resource overabundance can lead to physiological limitations. This study offers valuable insights into how irrigated cropland responds to the combined effects of multiple environmental and socio-economic drivers. Full article

The number of server failures, including those in radiotherapy, has dramatically increased over the past 5 years, primarily due to cyberattacks. Despite this trend, many clinics remain unprepared to handle such situations effectively. While it is possible to resolve these issues with thorough preparation and dedicated effort without causing significant interruptions to patient treatments, the process is considerably easier if numerous steps and analyses, both technical and clinical, have already been undertaken. This preemptive work allows for quicker responses and a faster resumption of patient treatments. There are established guidelines on how to prioritize patients and manage total dose in the event of multiple missed treatment sessions. However, many radiotherapy departments in Germany still lack individualized plans for handling software failures. In this article, we describe a failure of the radiotherapy OIS (ARIA by Varian) caused by an interface failure in the Central IT department of the clinic. From this event, we developed a clinical guideline for addressing issues during the outage and identified clinical processes that can be implemented in advance. Our focus was particularly on handling the large volumes of data involved in organizing patient treatments and scheduling. Overall, there needs to be a cultural shift in both the development of technical server infrastructures and the approach to managing OIS failures, as the likelihood of such events increases along with the negative impacts due to increasingly complex treatment plans and software landscapes. Full article

Capturing complex temporal dependencies across multiple scales remains a fundamental challenge in time series forecasting. Transformer-based models have achieved impressive performance on sequence tasks, but vanilla designs often struggle to integrate information from both local fluctuations and global trends, especially in non-stationary sequences. We propose iTransformer-FFC, a novel forecasting framework that addresses these issues through frequency-domain analysis and multi-scale feature fusion. In particular, iTransformer-FFC introduces a Fast Fourier Convolution (FFC) module to transform time series data into the frequency domain, isolating dominant periodic components and attenuating noise before attention is applied. A hierarchical feature fusion mechanism that integrates features extracted at multiple temporal resolutions then jointly models global and local temporal patterns, while a factorized self-attention architecture reduces the quadratic complexity of standard Transformers, improving efficiency while maintaining accuracy. Together, these innovations enable more effective long-range dependency modeling and adaptability to regime shifts in the data. Extensive experiments on five public benchmark datasets demonstrate that iTransformer-FFC consistently outperforms state-of-the-art models, including the vanilla Transformer, an earlier iTransformer variant, and PatchTST. Notably, our model achieves on average an 8.73% lower MSE and 6.95% lower MAE than the best performing baseline, confirming its superior predictive accuracy and generalization in multi-scale time series forecasting through its innovative integration of frequency-domain analysis, hierarchical feature fusion, and factorized attention mechanisms. Full article

The exploration of the coupling trend between urban agglomeration development (UAD) and land surface temperature (LST) expansion is of great significance, and it is of scientific value for the regulation of the thermal environment of urban agglomerations, the optimization of urban spatial planning, and the achievement of sustainable urban development. This study employs an array of remote sensing datasets from multiple sources—employing a multi-faceted approach encompassing an overall coupling situation analysis model, a coordination and evaluation system, a geographically weighted spatial autocorrelation algorithm, and landscape pattern quantification indicators—to explore the mutual feedback mechanism and spatial coupling characterization of LST and UAD in the Guanzhong Plain Urban Agglomeration (GZPUA). The results of the study can provide data support for urban spatial planning and thermal environment regulation. The results indicate the following findings: (1) In the GZPUA, the nighttime light (NTL) and land surface temperature (LST) centroids show a significant tendency toward approaching one another, with a spatial offset decreasing from 45.0 km to 9.1 km at the end, indicating a strengthening trend in the photothermal system’s coupling synergy. (2) The coordination of light and heat in the study area exhibits significant non-equilibrium development, with a dynamic trend of urban development space shifting towards the southwest. It confirms the typical regional response law of rapid urbanization. (3) The Moran’s I index of the photothermal system in the study area increased from 0.289 to 0.335, an increase of 15.9%. The proportion of “high–high” (H-H)/“low–low” (L-L)-type regions with clustering distribution of cold and hot spots reaches 58.01%, and their spatial continuity characteristics are significantly enhanced, indicating a significant trend of spatial structural integration between urban heat island effect and construction land expansion. Full article

Phytoplankton size classes (PSC), which categorize phytoplankton into pico- (<2 µm), nano- (2–20 µm), and microphytoplankton (>20 µm), have been widely used to describe functional group responses to environmental variability. Distribution of PSCs heavily influences marine ecosystems and biogeochemical processes. Despite the importance of PSC distributions, especially in the face of climate change, long-term studies on PSC variability and its driving factors are lacking. This study aimed to identify the key environmental drivers affecting summer PSC variability in the northern East China Sea (NECS) by analyzing 27 years (1998–2024) of satellite-derived data. Statistical analyses using random forest and multiple linear regression models revealed that euphotic depth (Z_eu) and suspended particulate matter (SPM) were the primary factors influencing PSC variation; deeper Z_eu values favored smaller picophytoplankton, whereas higher SPM concentrations supported larger PSCs. Long-term trend analysis showed a clear shift toward increasing picophytoplankton contributions (+2.4% per year), with corresponding declines in nano- and microphytoplankton levels (2.2% and 0.4% annually, respectively). These long-term changes are hypothesized to result from a persistent decline in SPM concentrations, which modulate light attenuation and nutrient dynamics in the euphotic zone. Marine heat waves intensify these shifts by promoting picophytoplankton dominance through enhanced stratification and reduced nutrient availability. These findings underscore the need for continuous monitoring to inform ecosystem management and predict the impacts of climate change in the NECS. Full article