Search Results (105)

Soil erosion is an increasingly severe problem and a global focus. As one of the countries facing relatively serious soil erosion, China encounters significant ecological challenges. This study focuses on the carbon sink benefits of comprehensive soil and water conservation management in the red soil erosion area of southern China, conducting an in-depth analysis using the Ziyang small watershed in Shangyou County, Jiangxi Province, as a typical case. Research methods involved constructing an integrated monitoring approach combining basic data, measured data, and remote sensing data. Changes in soil and vegetation carbon storage in the Ziyang small watershed across different years were determined by establishing a baseline scenario and applying inverse distance spatial interpolation, quadrat calculation, feature extraction, and screening. The results indicate that from 2002 to 2023, after 21 years of continuous implementation of various soil and water conservation measures under comprehensive watershed management, the carbon storage of the Ziyang small watershed increased significantly, yielding a net carbon sink of 54,537.28 tC. Tending and Management of Coniferous and Broad-leaved Mixed Forest, Low-efficiency Forest Improvement, and Thinning and Tending contributed substantially to the carbon sink, accounting for 72.72% collectively. Furthermore, the carbon sink capacity of the small watershed exhibited spatial variation influenced by management measures: areas with high carbon density were primarily concentrated within zones of Tending and Management of Coniferous and Broad-leaved Mixed Forest, while areas with low carbon density were mainly found within zones of Bamboo Forest Tending and Reclamation. The increase in watershed carbon storage was attributed to contributions from both vegetation and soil carbon pools. Comprehensive management of soil erosion demonstrates a significant carbon accumulation effect. The annual growth rate of vegetation carbon storage was higher than that of soil carbon storage, yet the proportion of soil carbon storage increased yearly. This study provides a theoretical basis and data foundation for the comprehensive management of soil and water conservation in small watersheds in the southern red soil erosion region of China and can offer technical and methodological support for other soil and water conservation carbon sink projects in this area. Full article

In recent years, segmentation for medical applications using Magnetic Resonance Imaging (MRI) has received increasing attention. Working in this field has emerged as an ambitious task and a major challenge for researchers; particularly, brain tumor segmentation from MRI is a crucial task for accurate diagnosis, treatment planning, and patient monitoring. With the rapid development of deep learning methods, significant improvements have been made in medical image segmentation. Convolutional Neural Networks (CNNs), such as U-Net, have shown excellent performance in capturing local spatial features. However, these models cannot explicitly capture long-range dependencies. Therefore, Vision Transformers have emerged as an alternative segmentation method recently, as they can exploit long-range correlations through the self-attention mechanism (MSA). Despite their effectiveness, ViTs require large annotated datasets and may compromise fine-grained spatial details. To address these problems, we propose a novel hybrid approach for brain tumor segmentation that combines a 3D U-Net with a 3D Vision Transformer (ViT3D), aiming to jointly exploit local feature extraction and global context modeling. Additionally, we developed an effective fusion method that uses upsampling and convolutional refinement to improve multi-scale feature integration. Unlike traditional fusion approaches, our method explicitly refines spatial details while maintaining global dependencies, improving the quality of tumor border delineation. We evaluated our approach on the BraTS 2020 dataset, achieving a global accuracy score of 99.56%, an average Dice similarity coefficient (DSC) of 77.43% (corresponding to the mean across the three tumor subregions), with individual Dice scores of 84.35% for WT, 80.97% for TC, and 66.97% for ET, and an average Intersection over Union (IoU) of 71.69%. These extensive experimental results demonstrate that our model not only localizes tumors with high accuracy and robustness but also outperforms a selection of current state-of-the-art methods, including U-Net, SwinUnet, M-Unet, and others. Full article

Pulmonary carcinoids (PCs) are rare tumors, with an incidence ranging from 0.2 to 2 cases per 100,000 population per year. They account for 1–2% of all invasive pulmonary malignancies and represent approximately one-fourth to one-third of all well-differentiated neuroendocrine tumors (NETs) in the body. PCs are generally classified as low- to intermediate-grade malignant tumors, further subdivided into typical carcinoid (TC) and atypical carcinoid (AC), respectively. These tumors exhibit neuroendocrine morphology and differentiation, originating from mature cells of the pulmonary diffuse neuroendocrine system. Traditionally, they are categorized as central or peripheral based on their location relative to the bronchial tree; however, they can arise anywhere within the lung parenchyma. Over 40% of cases may be detected incidentally on a standard chest X-ray, although contrast-enhanced computed tomography (CT) remains the diagnostic gold standard. Surgical resection is the treatment of choice for PCs, with the goal of complete tumor removal while preserving as much healthy lung tissue as possible. In contrast, advanced cases are typically not amenable to surgery, and medical management is focused on controlling hormone-related symptoms and limiting tumor progression. This review aims to provide an overview of the current diagnostic and therapeutic approaches to pulmonary carcinoids. Full article

The transport properties of biased type II superconductors are strongly influenced by external magnetic fields, which play a crucial role in optimizing the stability and performance of low-noise superconducting electronic devices. A major challenge is the stochastic behavior of Abrikosov vortices, which emerge in the mixed state and lead to energy dissipation through their nucleation, motion, and annihilation. Uncontrolled vortex dynamics can introduce electronic noise in low-power systems and trigger thermal breakdown in high-power applications. This study examines the effect of a perpendicular external magnetic field on vortex pinning in biased YBa₂Cu₃O_7-δ devices containing laser-written, rectangular-shaped, partially deoxygenated regions (δ ≈ 0.2). The results show that increasing the magnetic field amplitude induces an asymmetry in the concentration of vortices and antivortices, shifting the annihilation line toward a region of lower flux density and altering the flux pinning characteristics. Oxygen-deficient segments aligned parallel to the current flow act as barriers to vortex motion, enhancing the net pinning force by preventing vortex–antivortex pairs from reaching their annihilation zone. The current–voltage characteristics reveal periodic voltage steps corresponding to the onset and suppression of thermally activated flux flow and flux creep. These features indicate magnetic field–tunable transport behavior within a narrow range of temperatures from 0.94·T_c to 0.98·T_c, where T_c is the critical temperature of the superconductor. These findings offer new insights into the design of vortex-motion-controlled superconducting electronics that utilize engineered pinning structures. Full article

Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article

Cancer metabolism is a hallmark of cancer. However, the impact of systemic metabolism and diet on tumour evolution is less understood. This study delves into the role of glucagon, as a component of the pancreatic microenvironment, in regulating features of pancreatic neuroendocrine tumour (pNET) cells and the metabolic remodelling occurring in the presence and absence of glucose. pNET cell lines (BON-1 and QGP-1) and the non-malignant pancreatic α-TC1 cell line were used as models. Results showed that pNET cells responded differently to glucose deprivation than α-TC1 cells. Specifically, pNET cells upregulated the GCGR in the absence of glucose, while α-TC1 cells did so in high-glucose conditions, allowing the glucagon-related pERK1/2 activation under these conditions in pNET cells. Glucagon enhanced cancerous features in pNET BON-1 cells under glucose-deprived and hyperglucagonemia-compatible concentrations. In the α-TC1 cell line, glucagon modulated cell features under high-glucose and physiological glucagon levels. NMR exometabolome analysis revealed differences in metabolic processes based on glucose availability and glucagon stimulation across cell lines, highlighting amino acid metabolism, glycolysis, and gluconeogenesis. The expression of metabolic genes was consistent with these findings. Interestingly, QGP-1 and α-TC1 cells produced glucose in no-glucose conditions, and glucagon upregulated glucose production in α-TC1 cells. This suggests that gluconeogenesis may be beneficial for some pNET subsets, pointing out novel metabolism-based strategies to manage pNETs, as well as a step forward in endocrinology and systemic metabolism. The association between GCGR expression and malignancy and a negative correlation between glucagon receptor (GCGR) and glucagon-like peptide-1 receptor (GLP-1R) expression was observed, indicating a biological role of glucagon in pNETs that deserves to be explored. Full article

Understanding the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its drivers is critical to sustainable land -carbon management, carbon-neutral development, and ecological restoration in fragile karst landscapes. This study proposes a Pearson Correlation—Deep Transformer (PCADT) model that integrates attention mechanisms and geospatial covariates to enhance NPP estimation accuracy in Guangxi, China—a global karst hotspot. Leveraging multisource remote sensing data (2015–2020), PCADT achieves 10.7% higher predictive accuracy (R² = 0.83 vs. conventional models) at 500 m resolution, thereby capturing the fine-scale heterogeneity essential for sustainability planning. The results reveal a significant annual NPP increase (4.14 gC·m⁻²·a⁻¹, p < 0.05), with eastern areas exhibiting higher baseline productivity (1129 gC·m⁻² in Wuzhou) but western regions showing steeper growth rates (5.2% vs. 2.1%). Vegetation carbon sequestration capacity, validated against MOD17A3HGF data (R² = 0.998), demonstrates spatial consistency (east > west), with forest-dominated Wuzhou contributing 6.5 TgC annually. Mechanistic analyses identify precipitation as the dominant climatic driver (partial r = 0.62, p < 0.01), surpassing temperature sensitivity, while bimodal NPP-altitude peaks (300 m and 900 m) and land -use transitions (e.g., forest-to-cropland conversions) explain 18.5% of NPP variability and reduce regional carbon stocks by 4593 tC. The PCADT framework offers a scalable solution for precision carbon management by emphasizing the role of anthropogenic interventions—such as afforestation—in alleviating climatic constraints. It advocates for spatially adaptive strategies to optimize water resource utilization, enhance forest conservation, and promote sustainable land -use transitions. By identifying areas where water -scarcity relief and targeted afforestation would yield the highest carbon returns, the PCADT framework directly supports SDG 13 (Climate Action) and SDG 15 (Life on Land), providing a strategic blueprint for sustainable development in water-limited karst regions globally. Full article

Background: Pancreatic neuroendocrine tumors (PanNETs) are relatively rare neoplasms with heterogeneous behavior, ranging from indolent to aggressive disease. The evolution of nuclear medicine has allowed the development of an efficient and advanced toolkit for the diagnosis and treatment of PanNETs. Case: A 45-year-old woman was diagnosed with a grade 1 PanNET and multiple liver metastases. She underwent distal pancreatectomy with splenectomy, extended liver resection, and radiofrequency ablation (RFA). Surgical planning was guided by [^99mTc]Tc-EDDA/HYNIC-TOC SPECT/CT (single-photon emission computed tomography/computed tomography) and preoperative [^99mTc]Tc-mebrofenin-based functional liver volumetry. Functional liver volumetry based on dynamic [^99mTc]Tc-mebrofenin SPECT/CT facilitated precise surgical planning and reliable assessment of the efficacy of parenchymal modulation, thereby aiding in the prevention of post-hepatectomy liver failure. Liver fibrosis was non-invasively evaluated using two-dimensional shear wave elastography (2D-SWE). Tumor progression was monitored using somatostatin receptor scintigraphy, chromogranin A, and contrast-enhanced CT. Recurrent disease was treated with somatostatin analogues (SSAs) and [¹⁷⁷Lu]Lu-DOTA-TATE peptide receptor radionuclide therapy (PRRT). Despite progression to grade 3 disease (Ki-67 from 1% to 30%), the patient remains alive 53 months post-diagnosis, in complete remission, with an ECOG (Eastern Cooperative Oncology Group) status of 0. Conclusions: Functional imaging played a pivotal role in guiding therapeutic decisions throughout the disease course. This case not only underscores the clinical utility of advanced nuclear imaging but also illustrates the dynamic nature of pancreatic neuroendocrine tumors. The transition from low-grade to high-grade disease highlights the need for further studies on tumor progression mechanisms and the potential role of adjuvant therapies in managing PanNETs. Full article

Although deep learning has significantly advanced brain tumor MRI segmentation and preoperative planning, existing methods like U-Net and Transformer, which are widely used Encoder–Decoder architectures in medical image segmentation, still have limitations. Specifically, these methods fail to fully leverage the unique characteristics of different MRI modalities during the feature extraction stage, thereby hindering further improvements in segmentation accuracy. Currently, MRI modalities are typically treated as independent entities or as uncorrelated features during feature extraction, neglecting their potential interdependencies. To address this gap, we introduce the GMD architecture (Grouped Encoder-Mixer-Decoder), which is designed to enhance information capture during feature extraction by considering the intercorrelation and complementary nature of different modalities. In the proposed GMD architecture, input images are first grouped by modality in the grouped encoder based on a modality-specific strategy. The extracted features are then fused and optimized in the mixer module, and the final segmentation is achieved through the decoder. We implement this architecture in GMDNet to validate its effectiveness. Experiments demonstrate that GMDNet not only achieves outstanding performance under complete modality conditions but also maintains robust performance even when certain modalities are missing. To further enhance performance in incomplete modality, we propose an innovative reuse modality strategy that significantly improves segmentation accuracy compared to conventional approaches. We evaluated the performance of GMDNet on the BraTS 2018 and BraTS 2021 datasets. Under complete modality conditions, GMDNet achieved Dice scores of 91.21%, 87.11%, 80.97%, and 86.43% for WT (Whole Tumor), TC (Tumor Core), ET (Enhancing Tumor) and Average on the BraTS 2018, and 91.87%, 87.25%, 83.16%, and 87.42% on the BraTS 2021. Under incomplete modality conditions, when T1, T1ce, T2, and FLAIR were missing, the Dice scores on the BraTS 2021 dataset were 86.47%, 73.29%, 86.46%, and 82.54%, respectively. After applying the reuse modality strategy, the scores improved to 87.17%, 75.07%, 86.91%, and 86.22%. Overall, extensive experiments demonstrate that proposed GMDNet architecture achieves state-of-the-art performance, outperforming the compared models of this paper in complete or incomplete modality. Full article

Brain tumors are a type of disease that affects people’s health and have received extensive attention. Accurate segmentation of Magnetic Resonance Imaging (MRI) images for brain tumors is essential for effective treatment strategies. However, there is scope for enhancing the segmentation accuracy of established deep learning approaches, such as 3D U-Net. In pursuit of improved segmentation precision for brain tumor MRI images, we propose the MEASegNet, which incorporates multiple efficient attention mechanisms into the 3D U-Net architecture. The encoder employs Parallel Channel and Spatial Attention Block (PCSAB), the bottleneck layer leverages Channel Reduce Residual Atrous Spatial Pyramid Pooling (CRRASPP) attention, and the decoder layer incorporates Selective Large Receptive Field Block (SLRFB). Through the integration of various attention mechanisms, we enhance the capacity for detailed feature extraction, facilitate the interplay among distinct features, and ensure the retention of more comprehensive feature information. Consequently, this leads to an enhancement in the segmentation precision of 3D U-Net for brain tumor MRI images. In conclusion, our extensive experimentation on the BraTS2021 dataset yields Dice scores of 92.50%, 87.49%, and 84.16% for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET), respectively. These results indicate a marked improvement in segmentation accuracy over the conventional 3D U-Net. Full article

Road extraction from UAV-based remote sensing images in rural areas presents significant challenges due to the diverse and complex characteristics of rural roads. Additionally, acquiring UAV remote sensing data for rural areas is challenging due to the high cost of equipment, the lack of clear road boundaries requiring extensive manual annotation, and limited regional policy support for UAV operations. To address these challenges, we propose a transferable contextual network (TCNet), designed to enhance the transferability and accuracy of rural road extraction. We employ a Stable Diffusion model for data augmentation, generating diverse training samples and providing a new method for acquiring remote sensing images. TCNet integrates the clustered contextual Transformer (CCT) module, clustered cross-attention (CCA) module, and CBAM attention mechanism to ensure efficient model transferability across different geographical and climatic conditions. Moreover, we design a new loss function, the Dice-BCE-Lovasz loss (DBL loss), to accelerate convergence and improve segmentation performance in handling imbalanced data. Experimental results demonstrate that TCNet, with only 23.67 M parameters, performs excellently on the DeepGlobe and road datasets and shows outstanding transferability in zero-shot testing on rural remote sensing data. TCNet performs well on segmentation tasks without any fine-tuning for regions such as Burgundy, France, and Yunnan, China. Full article

The Mongolian Plateau grassland (MPG) is critical for ecological conservation and sustainability of regional pastoral economies. Aboveground net primary productivity (ANPP) is a key indicator of grassland health and function, which is highly sensitive to variabilities in large-scale atmospheric circulations, commonly referred to as teleconnections (TCs). In this study, we analyzed the spatial and temporal variations of ANPP and their response to local meteorological and large-scale climatic variabilities across the MPG from 1982 to 2015. Our analysis indicated the following: (1) Throughout the entire study period, ANPP displayed an overall upward trend across nine ecoregions. In the Sayan montane steppe and Sayan alpine meadow ecoregions, ANPP displayed a distinct inflection point in the mid-1990s. In the Ordos Plateau arid steppe ecoregion, ANPP continuously increased without any inflection points. In the six other ecoregions, trends in ANPP exhibited two inflection points, one in the mid-1990s and one in the late-2000s. (2) Precipitation was the principal determinant of ANPP across the entire MPG. Temperature was a secondary yet important factor influencing ANPP variations in the Ordos Plateau arid steppe. Cloud cover affected ANPP in Sukhbaatar and central Dornod, Mongolia. (3) The Atlantic Multidecadal Oscillation affected ANPP by regulating temperature in the Ordos Plateau arid steppe ecoregion, whereas precipitation occurred in the other ecoregions. The Pacific/North America, North Atlantic Oscillation, East Atlantic/Western Russia, and Pacific Decadal Oscillation predominantly affected precipitation patterns in various ecoregions, indicating regional heterogeneities of the effects of TCs on ANPP fluctuations. When considering seasonal variances, winter TCs dominated ANPP variations in the Selenge–Orkhon forest steppe, Daurian forest steppe, and Khangai Mountains alpine meadow ecoregions. Autumn TCs, particularly the Pacific/North America and North Atlantic Oscillation, had a greater impact in arid regions like the Gobi Desert steppe and the Great Lakes Basin desert steppe ecoregions. This study’s findings will enhance the theoretical framework for examining the effects of TCs on grassland ecosystems. Full article

This study aims to explore the effect of microstructural parameters on the notch fatigue damage behavior of the TC21 alloy. Different levels of lamellar microstructures were achieved through distinct aging temperatures of 550 °C, 600 °C, and 650 °C. The findings reveal that increasing aging temperature primarily contributes to the augmentation of α colony (α_c) thickness, grain boundaries α phase (GBα) thickness, and α fine (α_fine) size alongside a reduction in α lath (α_lath) thickness and α_fine content. The notch alters stress distribution and relaxation effects at the root, enhancing notched tensile strength while weakening plasticity. Moreover, the increased thickness of GBα emerges as a critical factor leading to the increase area of intergranular cleavage fracture. It is noteworthy that more thickness α_lath and smaller α_fine facilitate deformation coordination and enhance dislocation accumulation at the interface, leading to a higher propensity for micro-voids and micro-cracks to propagate along the interface. Conversely, at elevated aging temperatures, thinner α_lath and larger α_fine are more susceptible to fracture, resulting in the liberation of dislocations at the interface. The reduction in α_lath thickness is crucial for triggering the initiation of multi-system dislocations at the interface, which promotes the development of persistent slip bands (PSBs) and dislocation nets within α_lath. This phenomenon induces inhomogeneous plastic deformation and localized hardening, fostering the formation of micro-voids and micro-cracks. Full article

Background and Objectives: Brain tumors are complex diseases that require careful diagnosis and treatment. A minor error in the diagnosis may easily lead to significant consequences. Thus, one must place a premium on accurately identifying brain tumors. However, deep learning (DL) models often face challenges in obtaining sufficient medical imaging data due to legal, privacy, and technical barriers hindering data sharing between institutions. This study aims to implement a federated learning (FL) approach with privacy-preserving techniques (PPTs) directed toward segmenting brain tumor lesions in a distributed and privacy-aware manner.Methods: The suggested approach employs a model of 3D U-Net, which is trained using federated learning on the BraTS 2020 dataset. PPTs, such as differential privacy, are included to ensure data confidentiality while managing privacy and heterogeneity challenges with minimal communication overhead. The efficiency of the model is measured in terms of Dice similarity coefficients (DSCs) and 95% Hausdorff distances (HD95) concerning the target areas concerned by tumors, which include the whole tumor (WT), tumor core (TC), and enhancing tumor core (ET). Results: In the validation phase, the partial federated model achieved DSCs of 86.1%, 83.3%, and 79.8%, corresponding to 95% values of 25.3 mm, 8.61 mm, and 9.16 mm for WT, TC, and ET, respectively. On the final test set, the model demonstrated improved performance, achieving DSCs of 89.85%, 87.55%, and 86.6%, with HD95 values of 22.95 mm, 8.68 mm, and 8.32 mm for WT, TC, and ET, respectively, which indicates the effectiveness of the segmentation approach, and its privacy preservation.Conclusion: This study presents a highly competitive, collaborative federated learning model with PPTs that can successfully segment brain tumor lesions without compromising patient data confidentiality. Future work will improve model generalizability and extend the framework to other medical imaging tasks. Full article

This research includes modeling and studying the performance improvement of a hybrid renewable energy power plant using the modeling software Greenius in Idlib, Syria. The system consists of solar parabolic trough collectors and an anaerobic digester for generating biogas. This study included a practical experiment for generating biogas using five identical digesters operating at five different temperatures. The raw material was a mixture of 81% food waste and 19% human waste, and average temperatures were as follows: 49.6, 45.9, 43.5, 37.5, and 33.2 °C. Modeling operations were conducted for each case, as well as for the case corresponding to the highest growth rate of methanogenic bacteria theoretically. The modeling processes were conducted at 11 different values for the storage capacity from Full Load Hours (FLHs) 0 to 10 and by varying the solar multiple factor (SM) from 1 to 8. This study showed that when operating as a net solar plant, the lowest value for the cost of produced electricity (LCOE) was 0.1785 EUR/kWh at FLHs = 5 h and SM = 2, while the annual electricity production was 25.21 GWh. The maximum annual electricity production was 48.66 GWh, achieved at FLHs = 10 h, SM = 8, and the LCOE = 0.2896 EUR/kWh. It is possible to obtain annual electrical energy of 39.7 GWh, which was about 82% of the maximum possible annual production, at a cost of LCOE = 0.1864 EUR/kWh, which is less than 5% higher than the lowest possible cost. When operating as a hybrid plant with an annual capacity factor of 1 (full load), it is discovered that the lowest value of energy produced is in the third scenario at t_AD = 43.52 °C and t_c = 63.5 °C, with FLHs = 0 h and SM = 1, with the LCOE = 0.1283 EUR/kWh. Full article