Search Results (65)

Image segmentation and survival prediction for nasopharyngeal carcinoma (NPC) are crucial for clinical diagnosis and treatment decisions. This study presents an improved 3D-UNet-based model for NPC GTV segmentation, referred to as LG-UNet. The encoder introduces deep strip convolution and channel attention mechanisms to enhance feature extraction while avoiding spatial feature loss and anisotropic constraints. The decoder incorporates Dynamic Large Convolutional Kernel (DLCK) and Global Feature Fusion (GFF) modules to capture multi-scale features and integrate global contextual information, enabling precise segmentation of the tumor GTV in NPC MRI images. Risk prediction is performed on the segmented multi-modal MRI images using the Lung-Net model, with output risk factors combined with clinical data in the Cox model to predict metastatic probabilities for NPC lesions. Experimental results on 442 NPC MRI scans from Sun Yat-sen University Cancer Center showed DSC of 0.8223, accuracy of 0.8235, recall of 0.8297, and HD95 of 1.6807 mm. Compared to the baseline model, the DSC improved by 7.73%, accuracy increased by 4.52%, and recall improved by 3.40%. The combined model’s risk prediction showed C-index values of 0.756, with a 5-year AUC value of 0.789. This model can serve as an auxiliary tool for clinical decision-making in NPC. Full article

Extreme precipitation events have become a central focus of the scientific community due to their increased occurrence in recent years. This study aims to analyze the variability and trends in aspects associated with the rainy seasons in the South American Monsoon System (SAMS) area from 1979 to 2022. The dates for the onset and demise of the rainy season (ONR and DER, respectively) were determined using antisymmetric outgoing longwave radiation (OLR) data relative to the equator (AOLR) for the clustered regions defined in a previous work. Based on these dates, the duration of the rainy seasons and the total precipitation for each rainy season were also calculated. The main advantage of this study is the analysis of trends within homogeneous regions derived from cluster analysis, which enables a more reliable assessment of precipitation patterns across the spatially heterogeneous SAMS domain. The non-parametric Mann–Kendall test and Sen’s slope estimator were applied to the ONR, DER, rainy season length, and total precipitation time series for each group over the 1979–2022 period. Quartile analysis was performed on the total precipitation time series to identify the most and least intense rainy seasons in the SAMS’s regions. These analyses revealed a trend of shortening of the SAMS rainy season over the 44 years of analysis, with a positive trend in the ONR dates and a negative trend in the DER dates, which is further confirmed by the decreasing trends in rainy season length and accumulated precipitation in most analyzed regions. The most (above the third quartile) and least (below the first quartile) intense rainy seasons were found to be concentrated at the beginning and end of the study period, respectively, for all monsoon regions. After removing the linear trend, the distribution of events appeared more uniform over time, yet the major droughts that occurred after 2010 remained clear. The results of this study contribute to a better understanding of the precipitation characteristics in the SAMS area, and these findings may assist climate forecasting and monitoring centers in improving regional precipitation assessments. Full article

The rapid industrialization and urbanization of the modern era caused widespread deforestation and ecological degradation, raising global concerns about sustainable planning, urban green space, and environmental governance. Around the turn of the 20th century, Guangdong Province in China suffered severe environmental decline due to extensive deforestation, threatening public health, ecological resilience, and urban livability. In response, returning Chinese intellectuals and foreign forestry experts introduced advanced Western forestry theories and practices to address these crises and promote green urban development. This study examines how these transnational forestry ideas were ideologically embraced, locally adapted, and institutionally embedded in modern Guangdong’s afforestation and urban greening efforts. Drawing on a systematic review of historical literature, forestry journals, and government archives, it identifies three key developments. (1) In ideology, figures such as Yat-sen Sun and German forester Fenzel played vital roles in raising public awareness of afforestation. (2) In practice, Guangdong developed a diversified greening model integrating commemorative, ecological, and aesthetic functions. This included transforming Arbor Day into a civic ritual honoring Yat-sen Sun, establishing nurseries and forest farms for large-scale afforestation, systematically planting street trees in urban centers, and creating forest parks that combined conservation, recreation, and historical commemoration. (3) In regulation, Guangdong formulated forestry laws inspired by Western models. By this way, Guangdong effectively addressed the management challenges in urban greening practices. It should also be emphasized that these modern-era practices have persisted in Guangdong, and their historical experience provides a valuable reference for present-day urban greening. Additionally, Fenzel’s methods for planning nurseries and forest farms can be seen as early prototypes of “evidence-based planning”. By highlighting a historically grounded yet under-explored case, this research offers new insights into the long-term evolution of urban greening strategies and provides lessons for current global efforts in sustainable land use and resilient urban design. Full article

Early prediction of lymph node metastasis (LNM) following neoadjuvant therapy (NAT) is crucial for timely treatment optimization in esophageal squamous cell carcinoma (ESCC). This study developed and validated a computed tomography-based radiomic model for predicting pathologically confirmed LNM status at the time of surgery in ESCC patients after NAT. A total of 469 ESCC patients from Sun Yat-sen University Cancer Center were retrospectively enrolled and randomized into a training cohort (n = 328) and a test cohort (n = 141). Three signatures were constructed: the tumor-habitat-based signature (Habitat_Rad), derived from radiomic features of three tumor subregions identified via K-means clustering; the multiple instance learning-based signature (MIL_Rad), combining features from 2.5D deep learning models; and the clinicoradiological signature (Clinic), developed through multivariate logistic regression. A combined radiomic nomogram integrating these signatures outperformed the individual models, achieving areas under the curve (AUCs) of 0.929 (95% CI, 0.901–0.957) and 0.852 (95% CI, 0.778–0.925) in the training and test cohorts, respectively. The decision curve analysis confirmed a high net clinical benefit, highlighting the nomogram’s potential for accurate LNM prediction after NAT and guiding individualized therapy. Full article

This study presents an analysis of England’s 2023 national assessment of assistive technology (AT) access and use, with a particular focus on the qualitative impact of AT as described by users. It aims to address limitations in conventional AT impact assessments, which often prioritize clinical outcomes or user satisfaction, by offering a deeper account of how impact is experienced in everyday life. Drawing on data from a nationally representative survey of 7000 disabled adults and children, as well as six focus group discussions and 28 semi-structured interviews with stakeholders across the WHO 5Ps framework (People, Providers, Personnel, Policy, and Products), the study applies Amartya Sen and Martha Nussbaum’s Capability Approach to explore these experiences. Using inductive thematic analysis, we identify three main domains of user-reported impact: Functions and Activities (e.g., mobility, communication, vision, leisure, daily routines, and cognitive support), Outcomes (e.g., autonomy, quality of life, safety, social participation, wellbeing, and work and learning), and Lived Experience (e.g., access barriers, essentiality, identity and emotional connection, peace of mind, and sense of control and confidence). These findings offer a more user-centered understanding of AT impact and can inform the development of future measurement tools, research design, and government-led interventions to improve AT provision. Full article

This paper investigates the distributed state estimation problem for the linear system against non-Gaussian noise, where every sensor commutates information only within its adjacent sensors without the need for a fusion center. Correntropy is a similarity metric based on a kernel function that has symmetry. Symmetry means that for any two data points, the output value of the kernel function does not depend on the order of the data points. By adopting a correntropy cost function based on the rational quadratic kernel function approximation to restrain non-Gaussian heavy-tailed noise, a centralized maximum correntropy Kalman filter is first derived for the linear sens+or network system at first. Then the corresponding centralized maximum correntropy information filter is attained by employing the information matrices, which is a foundation for further designing distributed information algorithms under multi-sensor networks. Thirdly, the distributed rational quadratic maximum correntropy information filter and distributed adaptive rational quadratic maximum correntropy information filter are designed by exploiting the weighted census average to solve the non-Gaussian heavy-tailed noise interference in sensor networks. Finally, the performance of the proposed algorithms is illustrated through numerical simulations on the sensor network system. Full article

Purpose: This bi-centric pilot study investigates the predictive value of pre-treatment [¹⁸F]FDG PET/CT radiomics for assessing therapy response in primary mediastinal B-cell lymphoma (PMBCL). Methods: All PMBCL patients underwent PET/CT with [¹⁸F]FDG between January 2011 and January 2022 at Policlinico Tor Vergata University Hospital of Rome (70% training and 30% internal validation cohort) and Sant’Anna University Hospital of Ferrara (external validation cohort). The Deauville score (DS) was used as a predictor of therapy response (DS1-DS3 vs. DS4/DS5). A total of 121 quantitative radiomics features (RFts) were extracted from manually segmented volumes of interest (VOIs) in PET and CT images, according to IBSI. ComBat harmonization was applied to correct the center variability of features, followed by class balancing with SMOTE. Two machine learning (ML) prediction models, the PET model and the CT model, were independently developed using robust RFts. For each ML model, two different algorithms were trained (i.e., Random Forest, RF, and Support Vector Machine, SVM) using 10-fold cross validation, tested on the internal/external validation set. Receiver operating characteristic (ROC) curves, area under the curve (AUC), classification accuracy (CA), precision (Prec), sensitivity (Sen), specificity (Spec), true positive (TP) scores, and true negative (TN) scores were computed. Results: The entire dataset was composed of 29 samples for the Rome cohort (23 from D1–D3 and 6 from D4/D5) and 9 samples for the Ferrara cohort (4 from D1–D3 and 5 from D4/D5). A total of 27 RFts were identified as robust for each imaging modality. Both the CT and PET models effectively predicted the Deauville score. The performance metrics of the best classifier (SVM) for the CT and PET models in external validation were AUC = 0.75/0.80, CA = 0.85/0.77, Prec = 0.97/0.67, Sen = 0.60/0.80, Spec = 0.98/0.75, TP = 75.0%/66.7%, and TN = 77.8%/85.7%, respectively. Conclusions: ML models trained on [¹⁸F]FDG PET/CT radiomic features in PMBLC patients could predict the Deauville score. Full article

During the continuous casting process, the submerged entry nozzle (SEN) should be maintained at the geometric center of the mold. However, in actual production, factors such as deformation of the tundish bottom and inaccurate positioning of the traversing car occasionally cause SEN offset. SEN offset can make the molten steel flow field in the mold asymmetric, increasing the risks of slag entrainment on the surface of the casting blank and breakout accidents. To evaluate the influence of different SEN offsets on the mold flow field, this study uses a slab continuous casting mold with a cross-section of 920 mm × 200 mm from a specific factory as the research object. Mathematical simulations were used to investigate the influence of SEN offsets (including width-direction and thickness-direction offsets) on the flow behavior of molten steel in the mold. A physical water model at a 1:1 scale was established for verification. Two parameters, the symmetry index (S) and the bias flow index (N), were introduced to quantitatively evaluate the symmetry of the flow field, and the rationality of the liquid-level fluctuation under this flow field was verified using the F-number (proposed by Japanese experts for mold level fluctuation control) from the index model. The results show the following: when the SEN offset in the thickness direction increases from 0 to 50 mm, the longitudinal symmetry index (Sy) of the molten steel flow field in the mold decreases from 0.969 to 0.704—a reduction of 27.4%; the longitudinal bias flow index (Ny) of molten steel level fluctuation increases from 0.007 to 0.186, representing a 25.6-fold increase, and the F-number rises from 4.297 to 8.482; when the SEN offset in the width direction increases from 0 to 20 mm, the transverse-axis symmetry index (Sx) of the flow field decreases gradually from 0.969 to 0.753 at a 20 mm offset, which is a reduction of approximately 22.29%; the transverse-axis bias flow index (Nx) increases from 0.015 to 0.174 at a 20 mm offset—an increase of 10.6 times; and the F-number increases from 4.297 to 5.548. Considering the comprehensive evaluation of horizontal/vertical symmetry indices, bias flow indices, and F-numbers under the two working conditions, the width-direction SEN offset has the most significant impact on the symmetry of the molten steel flow field. Full article

As a dominant ecological stress factor of climate change, soil drought has become a key challenge restricting food security. Based on soil moisture data, this paper uses the cumulative anomaly method, coefficient of variation, Sen + Mann–Kendall trend analysis, and center of gravity shift model to study the spatiotemporal changes in soil drought in Hebei Province from 2001 to 2020 and uses the optimal parameter geographic detector model to analyze the key factors affecting soil drought. The results show the following: (1) over the past 20 years, soil drought in Hebei Province has shown a trend of “first intensifying and then easing”, experiencing two turning points, and its spatial distribution showed significant agglomeration characteristics. (2) Soil moisture showed single-peak seasonal fluctuation, with severe drought from January to May, peak soil moisture from June to August, soil moisture balance from September to October, and soil moisture deficit intensified in winter. (3) Soil moisture stability showed spatial differentiation, being high in the northeast and low in the southwest. Soil drought in about 70% of the region has improved, and the center of gravity of drought-prone areas has moved to the southwest. (4) NDVI and altitude are the main drivers of soil drought spatial differentiation, and the multi-factor interaction shows a nonlinear enhancement effect. Among them, the parameter thresholds such as NDVI > 0.512 and altitude −32~16 m have a significant inhibitory effect on soil drought. This study can make a contribution to improving water resource management and increasing agricultural productivity in the region. Full article

This study aims to explore the spatio-temporal changes in terrestrial water storage (TWS) in the five provinces of Northwest China and to assess the influences of various driving factors on the changes in TWS. Based on the Gravity Recovery and Climate Experiment (GRACE) satellite data of the five provinces from April 2002 to December 2022, combined with datasets of various driving factors (precipitation, evapotranspiration, runoff, and anthropogenic water use) from 1980 to 2022, a trend analysis was conducted using Sen’s slope method and Mann–Kendall (M-K) tests to characterize the spatial–temporal changes in TWS. The water balance method and quantification of contribution rates were used to analyze the spatio-temporal response of the change in TWS to driving factors and the contributions of driving factors thereto. The results showed that the eastern part of the Xinjiang Uygur Autonomous Region and the northern parts of Shaanxi Province and Ningxia Hui Autonomous Region belonged to the decreasing centers of TWS, while the northern part of the Qinghai–Tibet Plateau belonged to the enriching center of TWS, with a decreasing trend at a rate of 2.86 mm/yr. Precipitation contributed positively to the change in TWS and had a high spatio-temporal response, while the other driving factors (evapotranspiration, runoff, and anthropogenic water use) all contributed negatively to certain extents. The contribution rates of precipitation, evapotranspiration, runoff, and anthropogenic water use were 0.363, −0.265, −0.258 and −0.115, respectively. The results are helpful for the scientific planning and management of water resources in Northwest China. Full article

Background/Objectives: The growing use of artificial intelligence (AI) in musculoskeletal radiographs presents significant potential to improve diagnostic accuracy and optimize clinical workflow. However, assessing its performance in clinical environments is essential for successful implementation. We hypothesized that our AI applied to urgent bone X-rays could detect fractures, joint dislocations, and effusion with high sensitivity (Sens) and specificity (Spec). The specific objectives of our study were as follows: 1. To determine the Sens and Spec rates of AI in detecting bone fractures, dislocations, and elbow joint effusion compared to the gold standard (GS). 2. To evaluate the concordance rate between AI and radiology residents (RR). 3. To compare the proportion of doubtful results identified by AI and the RR, and the rates confirmed by GS. Methods: We conducted an observational, double-blind, retrospective study on adult bone X-rays (BXRs) referred from the emergency department at our center between October and November 2022, with a final sample of 792 BXRs, categorized into three groups: large joints, small joints, and long-flat bones. Our AI system detects fractures, dislocations, and elbow effusions, providing results as positive, negative, or doubtful. We compared the diagnostic performance of AI and the RR against a senior radiologist (GS). Results: The study population’s median age was 48 years; 48.6% were male. Statistical analysis showed Sens = 90.6% and Spec = 98% for fracture detection by the RR, and 95.8% and 97.6% by AI. The RR achieved higher Sens (77.8%) and Spec (100%) for dislocation detection compared to AI. The Kappa coefficient between RR and AI was 0.797 for fractures in large joints, and concordance was considered acceptable for all other variables. We also analyzed doubtful cases and their confirmation by GS. Additionally, we analyzed findings not detected by AI, such as chronic fractures, arthropathy, focal lesions, and anatomical variants. Conclusions: This study assessed the impact of AI in a real-world clinical setting, comparing its performance with that of radiologists (both in training and senior). AI achieved high Sens, Spec, and AUC in bone fracture detection and showed strong concordance with the RR. In conclusion, AI has the potential to be a valuable screening tool, helping reduce missed diagnoses in clinical practice. Full article

As a natural disaster, drought can endanger global ecology, socio-economic systems, and sustainable development. To address sudden droughts in the future, assess drought disasters, and propose mitigation measures, in-depth research on the spatiotemporal variations in and driving factors of meteorological drought is essential. To study drought in the Yellow River Basin, we calculated the multi-scale Standardized Precipitation Evapotranspiration Index (SPEI), derived from monthly meteorological data recorded at weather stations from 1968 to 2019. We examined the features of drought and its driving factors using the trend-free pre-whitening Mann–Kendall (TFPW-MK) test and Sen’s slope estimator, as well as a drought frequency analysis, center of gravity migration model, standard deviation ellipse model, and geographic detector. Our analysis shows that (1) from 1968 to 2019, the Yellow River Basin exhibited a shift from aridity to increased moisture on an annual basis, with the smallest SPEI of −1.47 in 2002 indicating a moderate drought; SPEI3 showed a growing tendency in all seasons, particularly in winter (0.00388/year), followed by spring (0.00214/year), summer (0.00232/year), and fall (0.00196/year). The SPEI3 exhibited higher fluctuations in frequency compared to the annual-scale SPEI12; (2) in terms of spatial variability, there was no significant change in drought conditions at any scale, with the probability of a drought event being greater in the eastern and northwestern portions of the watershed. The epicenter of the drought exhibited a tendency to migrate southwestward; (3) among the seven driving factors, land use and night lighting were the dominant factors affecting drought conditions, with driving force values of 0.75 and 0.63, respectively. Full article

This review paper focuses on the development and application of the Sustainable Livelihood Approach (SLA), especially regarding its limitations, both in terms of its formulation and link to theory as well as practice. The SLA has proved to be a popular approach, not least because it is holistic and ‘people-centered’, and forces a requirement that livelihoods, along with their vulnerability and institutional contexts, are well understood before interventions are designed and implemented to help the community. However, its theoretical underpinning has been questioned, and some have pointed to the weak representation of important dimensions such as power, including its link to globalization, and culture, with the latter including faith. This paper explores the various ways that these issues have been addressed by using faith as a lens, and makes a case for a ‘Sustainable Living Approach’ (SLivA) to provide a stronger dovetailing with the capabilities/functionings approach of Amartya Sen. However, there is a trade-off between the complexity of frameworks and their practicability, and more work is needed in this area, especially in terms of the potential contribution of technologies such as very-high-resolution Earth Observation, machine learning, and artificial intelligence. Full article

The results of the identification of sensitive areas are affected by the forecast time and verification area settings in targeted observations. Understanding this setting issue is important for improving the effectiveness of the identification of sensitive areas in real-time field campaigns. To determine this, a series of experiments were carried out based on the Ensemble Transform Sensitivity (ETS) method, and the results are as follows: (1) First, Observation System Simulation Experiments (OSSEs) were conducted to assimilate simulated dropsondes in sensitive areas (SENS) or non-sensitive areas (OTHR). The results showed that the SENS experiment improved forecasts of typhoon intensity, track, precipitation score, and RMSE of forecast elements. However, the OTHR experiment only improved the forecast in some aspects and even had negative effects on other aspects. This indicates that the sensitive areas identified by the ETS method are effective. (2) Different forecast time experiments were carried out. There were significant differences between the sensitive areas of fixed verification times and variable targeted observation times, indicating that the sensitive areas changed greatly with time. In the field campaign, it was necessary to calculate the sensitive area for multiple times in advance and to design or adjust the observation scheme according to the time. (3) Finally, comparative experiments of position deviation and size change in the verification area were carried out. It was found that for a big deviation, too large or too small a verification area will result in significant differences in the sensitive areas. Based on the study in this article, a verification area size of about 6° × 6° is recommended; this can not only accommodate the position deviation of the verification area from the typhoon center caused by forecast errors, but also does not contain too much noise unrelated to typhoons, which may affect the accuracy of identification of sensitive areas. Full article

Drought, as a natural disaster with wide-ranging impacts and long duration, has an adverse effect on the global economy and ecosystems. In this paper, four remote sensing drought indices, namely the Crop Water Stress Index (CWSI), Vegetation Supply Water Index (VSWI), Temperature Vegetation Dryness Index (TVDI), and Normalized Difference Water Index (NDWI), are selected for drought analysis. The correlation analysis is carried out with the self-calibrated Palmer Drought Severity Index (sc-PDSI), and based on the optimal index (CWSI), the spatiotemporal characteristics of drought in Shaanxi Province from 2001 to 2021 were studied by SEN trend analysis, Mann–Kendall test, and a center of gravity migration model. The results show that (1) the CWSI performs best in drought monitoring in Shaanxi Province and is suitable for drought studies in this region. (2) Drought in Shaanxi Province shows a decreasing trend from 2001 to 2021; the main manifestation of this phenomenon is the decrease in the occurrence of severe drought, with severe drought covering less than 10% of the area in 2010 and subsequent years. The most severely affected regions in the province are the northern Loess Plateau region and Guanzhong Plain region. In terms of the overall trend, only 0.21% of the area shows an increase in drought, primarily concentrated in the Guanzhong Plain region and the outskirts of the Qinling–Bashan mountainous region. (3) Drought conditions are generally improving, with the droughts’ center of gravity moving northeastward at a rate of 3.31 km per year. The results of this paper can provide a theoretical basis and a practical reference for drought control and decision-making in Shaanxi Province. Full article