Search Results (840)

In recent years, wildfires have occurred with increasing frequency. Pixel-level annotation of high-resolution remote sensing wildfire imagery is costly and labor-intensive. Therefore, there is an urgent need for a weakly supervised wildfire detection method that balances detection accuracy and annotation efficiency. To address the key limitations of existing weakly supervised approaches based on class activation maps (CAMs), including imprecise delineation of fire boundaries, insufficient utilization of cross-modal information, and limited capability in modeling temporal characteristics, this paper proposes a dual-branch multi-scale feature fusion framework for weakly supervised wildfire detection. The proposed framework consists of a multispectral branch and a shortwave infrared (SWIR) temporal branch, which are designed to capture the spatial structural information of fire regions and the temporal variation of thermal anomalies, respectively. Attention-guided feature fusion modules are introduced at each network stage to enable complementary integration of cross-modal information. In addition, a multi-scale CAM-weighted fusion strategy is designed to jointly enhance region localization accuracy and semantic discrimination capability. Experimental evaluations are conducted on a high-resolution wildfire dataset covering 29 regions and consisting of 2206 images. The results demonstrate that the proposed method achieves an IoU of 58.7% and an F1-score of 73.5%, outperforming the state-of-the-art methods by 4.6% and 3.2%, respectively. Ablation and comparative experiments further verify that the dual-branch architecture and feature fusion strategy significantly improve fire localization accuracy and effectively reduce the missed detection rate. Full article

Monitoring cropland dynamics in arid regions is critical for balancing food security with water scarcity constraints. However, distinguishing fragmented agricultural oases from spectrally similar desert vegetation remains a persistent challenge due to spectral confusion and landscape heterogeneity. To address these challenges, this study developed the STGP-OCE feature cube on the Google Earth Engine platform (GEE) by integrating the Oasis Cooling Effect (OCE) into the commonly used STGP (Spectral, Textural, Geomorphic, and Phenological) feature space, coupled with the XGBoost ensemble model. Through ablation experiments and feature importance analysis, we quantified the feature construction mechanism for arid regions. Oasis Cooling Intensity emerged as the most influential variable (Gain score: 0.315), demonstrating that the thermal signature of continuous anthropogenic irrigation serves as a robust thermodynamic proxy to resolve the spectral ambiguity between crops and drought-tolerant desert vegetation. By hierarchically coupling this thermal indicator with textural features to suppress fragmentation noise, topographic constraints to filter non-arable terrain, and phenological trajectories, the STGP-OCE feature cube achieved an Overall Accuracy of 95.12% and a Precision of 94.95%, significantly outperforming models built on lower-dimensional cubes as well as existing global land cover products. We generated a 10 m annual cropland dataset for Xinjiang, China, revealing a substantial 32.9% expansion (19,360 km²) from 2015 to 2024, mainly occurring in vulnerable oasis–desert transition zones and coinciding with reported reclamation activities. These highlight the continuous agricultural encroachment into desert margins, while the proposed STGP-OCE cube provides a reliable methodology for high-precision cropland monitoring in arid regions. Full article

Lithium-ion battery thermal runaway has become a key safety hazard restricting the development of electric vehicles. Early precursor signals of thermal runaway are characterized by multi-scale features, weak signal strength and spatial coupling, posing significant challenges for traditional methods in achieving accurate early warning. To solve this problem, a multi-scale spatiotemporal attention network (MSTA-Net) is proposed for battery thermal runaway early warning. First, a systematic feature engineering process is designed, including signal denoising, normalization processing and multi-level feature construction, to fully extract discriminative information from voltage and temperature signals. Then, the MSTA-Net architecture is constructed, which includes three parallel feature extraction branches: local fine perception branch based on 1D depthwise separable convolution to capture transient anomalies, a temporal evolution modeling branch based on bidirectional gated recurrent units to learn long-term trends, and a global spatial dependence branch based on a graph attention network to model the spatial propagation of thermal runaway. Finally, an adaptive fusion gate is designed to dynamically fuse the features of each branch according to the input context. The experimental results on the self-built battery thermal runaway dataset show that the proposed MSTA-Net achieves a recall rate of 98.7%, an average early warning time of 115 s and a false alarm rate of 0 times/h. Compared with traditional machine learning and deep learning models such as Random Forest, LSTM and Transformer, the model has significant advantages in early warning accuracy, timeliness and robustness. Ablation experiments verify the effectiveness of each component of the MSTA-Net. The proposed method can provide reliable early warning of thermal runaway only by using the existing voltage and temperature sensors of the battery management system, which has important engineering application value. Full article

Background/Objectives: Image-guided percutaneous thermal ablation is an established local treatment for selected patients with liver metastases, provided that accurate tumor targeting and adequate ablation margins can be achieved. However, lesion detection, target delineation, and intraprocedural margin verification remain challenging in post-chemotherapy or previously treated lesions that may become morphologically inconspicuous or radiologically occult. Catheter-assisted C-arm (cone-beam) CT hepatic arteriography (CBCT-HA) improves intraprocedural visualization of tumor vascularity and supports streamlined workflows within the angiography suite, yet it may underestimate tumor extent in lesions with limited or absent angiographic conspicuity. This pictorial essay illustrates the feasibility and added value of integrating preprocedural PET/CT with intraprocedural CBCT-HA for liver tumor ablation. Methods: Representative clinical cases of percutaneous liver tumor ablation guided by PET–CBCT-HA fusion are presented. Preprocedural PET/CT datasets were rigidly registered and fused with intraprocedural CBCT-HA to support tumor detection, target delineation, ablation planning, and real-time intraprocedural margin assessment. The complementary roles of metabolic and angiographic imaging were evaluated qualitatively across different clinical scenarios. Results: PET–CBCT-HA fusion improved detection and delineation of viable tumor components that were occult or insufficiently defined on CBCT-HA alone, particularly in post-chemotherapy or previously treated lesions. Conversely, CBCT-HA identified angiographically evident lesions not apparent on PET/CT. The combined approach enabled confident target definition, biologically informed ablation planning, and immediate post-ablation verification of metabolic and angiographic coverage, supporting margin-oriented intraprocedural decision-making. Conclusions: By integrating complementary metabolic and vascular information into a single-session workflow, PET–CBCT-HA fusion represents a multimodal guidance strategy that enhances lesion visualization and intraprocedural margin assessment. This approach may improve local tumor control in complex post-treatment and oligometastatic liver disease. Full article

Background: The management of localized prostate cancer (PCa) suffers from the dilemma between the overtreatment associated with radical surgery and the uncertainty of active surveillance, highlighting a significant therapeutic gap specifically for intermediate-risk patients and selected low-risk patients. Focal therapy (FT) emerges as an advanced technological solution to balance rigorous oncological control with anatomical and functional preservation. Methods: A narrative review of the literature was conducted to analyze the physical principles underlying various ablative energies (thermal, cryogenic, and non-thermal) as well as radiation-based focal approaches. The review examines the oncological rationale of targeted ablation, recent innovations in imaging, and the expanding clinical scenarios for FT application. Results: Evidence supports the oncological rationale of “Index Lesion” ablation as a targeted curative strategy for clinically significant disease, rather than merely a palliative one. The review highlights the emerging concept of “pushing the disease” and demonstrates the valuable role of salvage focal therapy in the setting of radio-recurrent carcinoma. Furthermore, recent innovations in multiparametric magnetic resonance imaging (mpMRI) and fusion systems have significantly refined patient selection, rendering this minimally invasive approach highly targeted. Conclusions: The current barrier to the universal adoption of focal therapy is the lack of a standardized consensus on the definitions of therapeutic failure and the inadequacy of traditional PSA-based criteria. However, evidence suggests that FT represents a promising, organ-sparing alternative for carefully selected patients with localized PCa, though long-term comparative data are still required. Full article

The demand for materials that can operate reliably in extreme environments, including rocket nozzles, re-entry heat shields, sharp leading edges, high-velocity impact, and high-temperature energy systems, continue to drive advances in thermal–structural materials. Carbon/Carbon composites remain a leading baseline because of their low density, high-temperature mechanical retention in inert atmospheres, and excellent thermal-shock tolerance. However, long-term durability is constrained by rapid oxidation in air at elevated temperatures, limited fracture toughness and elastic modulus in many architectures, and high manufacturing cost driven by multi-cycle densification and stringent quality assurance. Consequently, contemporary strategies increasingly rely on modifying Carbon/Carbon composites with ultra-high-temperature ceramics and adopting accelerated or simplified manufacturing routes. This review synthesizes recent progress in the design, manufacture, and application of high-performance modified Carbon/Carbon composite systems for extreme aerospace environments, emphasizing composition/architecture selection, oxidation, and ablation protection, toughening concepts, and cost-aware densification. Because extreme environments performance is governed by coupled aerothermal loading, gas–surface chemistry, internal transport, recession, and thermomechanical response, the review also consolidates the multiscale modeling and software toolchains increasingly used to size thermal-protection systems, interpret experiments, and guide down-selection. Key challenges and future directions are further discussed for reusable materials and validated performances beyond ~2000 °C. Full article

Background: The parathyroid hormone (PTH) measurements in washouts from the fine-needle aspiration biopsy (FNAB) of parathyroid adenoma could be considered in preoperative diagnostics of primary hyperparathyroidism (PHPT). Aims: Preoperative remission of PHPT following FNAB is presented, with discussion of the possible pathophysiological mechanisms and clinical implications. Case presentation: We describe a case of a female patient with confirmed PHPT and a suspected parathyroid adenoma who underwent FNAB with PTH washout measurement as part of the diagnostics. Following FNAB, the patient experienced normalization of biochemical parameters, accompanied by a reduction in tumor size. This outcome is presumed to be associated with autoinfarction or hemorrhage within the adenoma triggered by the biopsy procedure. Conclusions: This case highlights a rare but clinically significant phenomenon of FNAB-induced remission of PHPT and explains why alternative treatments such as thermal ablation may be considered to avoid surgery. Full article

Expandable graphite is recognised as an effective flame retardant because of its ability to absorb thermal energy by thermal liquid–gas conversion of the intercalant between the layers in its lamellar structure. Kevlar is widely used in protective clothing due to its excellent mechanical strength and thermal resistance; however, like many materials, it is vulnerable to degradation when exposed to high-energy laser systems, which causes carbonisation and material disintegration. This study demonstrates that coatings of expandable graphite can significantly enhance the thermal protection of Kevlar against 100 W laser radiation, up to 290 J/m², with no detectable thermal damage on the side facing the wearer, using 25 g/m² of expandable graphite. At the same loading (25 g/m²), the material containing expandable graphite provides adequate protection even at higher intensities, with degradation only starting at the highest intensity tested. Coating durability tests showed that the coating, especially when expandable graphite was included, protected the Kevlar substrate from abrasion for at least 10,000 cycles, making it suitable for applications such as laser-protective gloves. Full article

Glacial lake dynamics in high-mountain regions serve as a sensitive proxy for cryospheric responses to climate warming. This study utilizes multi-temporal Sentinel-2 imagery and digital elevation model (DEM) data to quantify glacial lake evolution in the Donglin Tsangpo Watershed, a strategically important section of the China–Nepal Economic Corridor, from 2016 to 2024. The results show a significant expansion in both the number (from 43 to 56) and total area (from 3.97 km² to 4.94 km², +24.43%) of glacial lakes, primarily driven by the rapid emergence of very small lakes (0.02–0.05 km²) and a clear upward shift in elevation distribution, with new lakes forming above 5300 m and extending to elevations exceeding 5500 m. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) reveals that this expansion coincided with pronounced positive thermal anomalies, particularly the 2020 extreme warm event (daytime +3.88 °C, nighttime +1.61 °C). Mechanistic analysis using the ERA5-Land reanalysis dataset further demonstrates that persistent positive downward longwave radiation (LW) anomalies (peaking at +10.71 W/m² in 2021) effectively compensated for reduced shortwave input, inhibiting nocturnal refreezing and extending the effective ablation period. Furthermore, a rising liquid-to-solid precipitation ratio and extreme melt-day anomalies (up to +39.36 days) provided intensified hydrothermal inputs, driving the pronounced expansion of glacier-contact lakes despite non-linear interannual responses. This study also estimates individual lake volumes, identifying a transition toward rapid lake development that elevates potential downstream hazard exposure. These findings provide a high-resolution dataset and a robust physical framework for transboundary environmental monitoring and risk assessment in this climate-sensitive region. Full article

Thermal ablation is an effective treatment for primary liver cancer, but intraoperative assessment of ablation efficacy remains a clinical challenge. Microwave-induced thermoacoustic imaging (TAI) offers high tissue contrast based on dielectric properties, whereas conventional delay-and-sum reconstruction often yields limited contrast between ablated and normal tissue. To improve the contrast, we present a post-processing parametric imaging method that applies Nakagami statistics to thermoacoustic signal envelopes. The Nakagami shape parameter m is sensitive to thermal-ablation-induced alterations in tissue microstructural features. This work represents a new attempt to extract parametric images from thermoacoustic signal envelopes for intraoperative ablation assessment. In vitro and in vivo experiments were conducted to evaluate this Nakagami-based approach. Compared with conventional TAI, Nakagami images exhibited markedly improved contrast between the ablation zone and normal tissue. Quantitative analysis using pathological images as the gold standard demonstrated higher accuracy for Nakagami-based TAI across all measurements: 91.08% vs. 85.22% (in vitro diameter), 86.76% vs. 74.50% (in vitro area), 85.44% vs. 76.52% (in vivo diameter), and 79.22% vs. 72.72% (in vivo area). These findings suggest that Nakagami statistics-based TAI improves ablation zone characterization by capturing tissue microstructural information, showing potential as a tool for intraoperative assessment of liver ablation efficacy. Full article

Femtosecond laser ablation (FLA) is efficient for the machining of micro-groove arrays on the surface of ultrahard cutting tools. The depth of the groove determines the precision and efficiency of ablation. In this study, an “Attention-based Monotonic Physics-Guided Neural Network” (AM-PGNN) algorithm is proposed to accurately predict groove depth in the FLA of tungsten carbide (WC). The new algorithm incorporates machining parameters directly governing the energy deposition and thermal accumulation, thereby determining the prediction of the micro-groove depth generation. By embedding the physics-guided monotonic relationships of parameter depth into the learning process, a dedicated physical loss coupled with an attention mechanism to enable adaptive feature weighting is constructed, which strengthens the representation of causal dependencies. Experimental data for training and testing are obtained from the FLA of WC with different machining parameters. Comparison between AM-PGNN and typical algorithms, including a Support Vector Machine (SVM), Deep Neural Network (DNN), Convolutional Neural Network (CNN), Gradient Boosting Decision Tree (GBDT), and a conventional PGNN, demonstrates that the proposed AM-PGNN achieves superior prediction accuracy. Moreover, AM-PGNN attains a physical consistency degree (PCD) of 100%, indicating strict adherence to monotonicity consistent with the actual situation. AM-PGNN also exhibits enhanced robustness to input perturbations, as reflected by reduced standard deviation (Std) and normalized absolute deviation (NAD). Finally, AM-PGNN is shown to be applicable in the FLA of different materials through additional experiments on Cu and SiC, achieving R² values above 0.93 while maintaining a PCD of 100%. Full article

Background/Objectives: Pulsed Electric Field (PEF) therapy is a non-thermal ablation technique that induces immunogenic cell death through high-voltage, short-duration electrical pulses. This may enhance antitumor immunity by releasing intact tumor antigens and potentially generating abscopal effects. We report early outcomes in 32 patients with primary lung cancer or lung oligometastases treated with PEF at a community hospital, with a median (IQR) follow-up of 180.5 (158–207) days. Methods: This retrospective study collected demographics, cancer type, treatment response, and outcomes for patients undergoing PEF ablation. Tumor response was assessed using Sum of Longest Dimensions per RECIST 1.1 to classify progressive disease, stable disease, partial response, or complete response. Volumetric changes were additionally analyzed using RECIST 1.1 percentage thresholds applied to change in volume. Results: At initial 3-month follow-up, 26 of 32 patients demonstrated stable disease, partial response, or complete response, suggesting an 81.25% disease control rate/clinical benefit rate among this cohort. Among patients with Stage III–IV disease, 27.6% (8/29) showed radiographic evidence of a possible abscopal response. At 6 months, 24 of 32 patients remained alive and evaluable, with 62.5% (20/32) maintaining stable disease, partial response, or complete response. Conclusions: Despite patients having progressive disease on systemic therapy before PEF, early outcomes post-ablation suggest favorable local control and potential immunologic benefit. Patients with early-stage disease not receiving systemic therapy also showed excellent local response. Patients tolerated therapy very well. Clinical benefit was observed in 81.25% of patients at 3 months and 62.5% at 6 months, with radiographic evidence of possible abscopal responses in 27.6% of advanced-stage patients, supporting further exploration of the immunogenic potential of PEF demonstrated in preclinical and emerging clinical studies. Full article

Yellow and pink calcite samples from the Huanggangliang and Xilingol mining areas in Inner Mongolia were investigated to elucidate the relationships among chemical composition, unit-cell parameters, coloration, and luminescence. Electron probe micro-analysis, laser ablation inductively coupled plasma mass spectrometry, X-ray diffraction, infrared spectroscopy, Raman spectroscopy, UV-Vis absorption spectroscopy, and photoluminescence measurements show that samples of yellow and pink calcite differ significantly in impurity incorporation and optical behavior. Yellow calcite is relatively enriched in Mg and rare earth elements, especially Y and Ce, whereas pink calcite contains markedly higher Mn and Fe contents. The pink calcite has smaller lattice parameters and unit-cell volume, consistent with greater substitution of Ca²⁺ by smaller-radius cations. Spectra reveal that the pink coloration is mainly related to Mn-associated absorption bands at 402 and 527 nm, whereas the yellow color is attributed to weak impurity- and defect-related absorption. Under ultraviolet excitation, yellow calcite exhibits a broad blue–white emission centered at ~470 nm, whereas pink calcite shows an intense orange–red emission near 625 nm characteristic of Mn²⁺. Variable-temperature photoluminescence further demonstrates that the pink calcite has higher thermal stability, with a thermal-quenching activation energy of 0.218 eV, compared with 0.074 eV for the yellow calcite. These results demonstrate that trace element incorporation plays a key role in regulating the coloration and luminescence of calcite and provide useful insight into the optical behavior of carbonate minerals. Full article

Quicker identifying abilities are required due to the rising frequency and severity of wildfires. Although polar-orbiting satellites with medium and high resolution can accurately identify wildfires, the majority of available fire detection images originate from such platforms. However, their low temporal revisit rates restrict the potential for early warning. Geostationary satellites provide minute-level, continuous monitoring that corresponds with the quick onset of wildfires; however, their dependence on conventional threshold methods and coarse spatial resolution result in notable detection errors. This study developed an integrated deep learning framework for accurate wildfire detection in low-resolution geostationary imagery in order to get over these restrictions. A novel dynamic index, the Dynamic Normalized Burn Ratio—Thermal (DNBRT), was proposed to characterize wildfire progression by integrating instantaneous thermal anomalies with dynamic vegetation signals. Based on this, a Fire Spatiotemporal Network (FST-Net) was designed, with an efficient residual backbone, a Convolutional Block Attention Module (CBAM) for feature refinement, and a Bidirectional Long Short-Term Memory (BiLSTM) network to capture temporal evolution. Trained and evaluated on an FY-4B-based fire/non-fire dataset, the proposed framework demonstrated superior performance. FST-Net outperformed benchmark models, improving accuracy and recall by averages of 10.30% and 9.32% respectively while achieving faster inference speed. An ablation experiment confirmed the critical role of fusing thermal and vegetation features in DNBRT, with 92.7% accuracy and 94.9% recall. Compared to the FY-4B fire product, the proposed framework enables earlier detection, maintains more complete tracking of fire progression, and exhibits greater robustness under complex burning conditions while achieving sub-hectare (0.36 ha) detection sensitivity at the 2 km resolution. By synergizing a discriminative dynamic index with an efficient spatiotemporal architecture, this work provides an effective solution for operational, real-time monitoring of small and early-stage wildfires from geostationary satellites. Full article

Gem-quality garnets exhibit significant potential for U-Pb geochronological applications due to their advantageous characteristics, including high closure temperatures (750–850 °C), optical transparency, chemical homogeneity, and low inclusion content. This study focuses on the gem-quality yellow-green grossular garnet variety (commonly termed Mali garnet), a unique gemstone exclusively occurring in contact metamorphic deposits of Western Africa’s Republic of Mali. Despite its mineralogical significance, fundamental aspects, including precise age determination and chromophore mechanisms of Mali garnet, remain poorly constrained. Here, we conducted standard gemological characterization, spectroscopic analyses (UV–Vis, FTIR, and Raman), electron probe microanalysis (EPMA), micro-X-ray fluorescence (μ-XRF) elemental mapping, and in situ trace element and laser ablation U-Pb geochronological analysis on Mali garnets. The spectral data and chemical composition studies reveal that the coloration of Malian garnets is primarily attributed to the presence of iron and chromium. Our U-Pb geochronological results yield a crystallization age of 197 ± 3 Ma for the Mali garnet samples. The robustness of garnet U-Pb systems in preserving crystallization ages through multiple thermal events supports their application to Precambrian polymetamorphic terranes, where zircon systems are frequently reset. Full article