Search Results (1,000)

Driven by the increasing global population and rapid urbanization, aircraft noise pollution has emerged as a significant environmental challenge, impeding the sustainable development of the aviation industry. Traditional noise prediction methods are limited by incomplete datasets, insufficient spatiotemporal consistency, and poor adaptability to complex meteorological conditions, making it difficult to achieve precise noise management. To address these limitations, this study proposes a novel noise prediction framework based on a hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory–Attention (CNN–BiLSTM–Attention) model. By integrating multi-source data, including meteorological parameters (e.g., temperature, humidity, wind speed) and aircraft trajectory data (e.g., altitude, longitude, latitude), the framework achieves high-precision prediction of aircraft noise. The Haversine formula and inverse distance weighting (IDW) interpolation are employed to effectively supplement missing data, while spatiotemporal alignment techniques ensure data consistency. The CNN–BiLSTM–Attention model leverages the spatial feature extraction capabilities of CNNs, the bidirectional temporal sequence processing capabilities of BiLSTMs, and the context-enhancing properties of the attention mechanism to capture the spatiotemporal characteristics of noise. The experimental results indicate that the model’s predicted mean value of 68.66 closely approximates the actual value of 68.16, with a minimal difference of 0.5 and a mean absolute error of 0.89%. Notably, the error remained below 2% in 91.4% of the prediction rounds. Furthermore, ablation studies revealed that the complete CNN–BiLSTM–AM model significantly outperformed single-structure models. The incorporation of the attention mechanism was found to markedly enhance both the accuracy and generalization capability of the model. These findings highlight the model’s robust performance and reliability in predicting aviation noise. This study provides a scientific basis for effective aviation noise management and offers an innovative solution for addressing noise prediction problems under data-scarce conditions. Full article

Alumina ceramic substrates are ideal materials for next-generation microelectronic systems and devices, widely used in aerospace, 5G communications, and LED lighting. High-quality hole processing is essential for system interconnection and device packaging. Millisecond lasers have emerged as a promising choice for hole processing in alumina ceramic due to their high processing efficiency. However, existing research has rarely explored the mechanisms and processing techniques of millisecond laser oblique hole formation. This study systematically investigates the dynamic evolution of oblique hole processing in alumina ceramic through theoretical simulations, online detection, and process experiments. Through the simulation model, we have established the relationship between material temperature and hole depth. By analyzing the ablation phenomena on the upper and lower surfaces of the ceramic during the transient interaction process between the millisecond laser and the ceramic, the material removal mechanism in this process is elucidated. Additionally, this study examines the millisecond laser oblique hole processing technology by analyzing the influence of various laser parameters on hole formation. It reveals that appropriately increasing the single-pulse energy of millisecond lasers can optimize the material removal rate and hole taper. Ultimately, the formation mechanism of millisecond laser oblique hole processing in alumina ceramics is comprehensively summarized. The results provide theoretical and methodological guidance for high-speed laser drilling of alumina ceramic substrates. Full article

Background: Early detection of patients at high risk for recurrence or local tumor progression (LTP) following thermal ablation of hepatocellular carcinoma (HCC) is essential for treatment selection and individualized follow-up. This systematic review aims to assess and compare the performance of prognostic models predicting recurrence or LTP in patients with HCC treated with thermal ablation. Methods: PubMed, Web of Science, Cochrane, and Embase were searched for studies developing models to predict recurrence after thermal ablation in treatment-naïve HCC patients, using imaging and clinical data with reported test set performance. Risk of bias and applicability were assessed by the Prediction model Risk of Bias Assessment Tool. Data on model performance, feature extraction and modeling technique was collected. Results: In total, 16 studies comprising 39 prognostic models were included, all developed using retrospective data from China or Korea. Outcomes included recurrence-free survival, (intrahepatic) early recurrence, LTP, late recurrence and aggressive intrasegmental recurrence. Predictive parameters varied across models addressing identical outcomes. Outcome definitions also differed. Nine models were externally validated. Most studies had a high risk of bias due to methodological limitations. Conclusions: Variability in model development methodology and type of predictors was found. Models that integrated multiple types of predictors consistently outperformed those relying on one type. To advance predictive tools toward clinical implementation, future research should prioritize standardized outcome definitions, external testing, and transparent reporting. Until these challenges are addressed, current evaluated models should be regarded as promising but preliminary tools. Full article

Numerous studies on specific cannabis compounds (cannabinoids and phenolic acids) have demonstrated their therapeutic potential, with their administration methods remaining a key research focus. Transdermal drug delivery (TDD) systems are gaining attention due to their advantages, such as painless administration, controlled release, direct absorption into the bloodstream, and its ability to bypass hepatic metabolism. The thin films obtained via pulsed laser deposition consist of micro- and nanoparticles capable of migrating through skin pores upon contact. This study investigates the interaction of phenolic compounds in hemp seeds with pulsed laser beams. The main goal is to achieve the ablation and deposition of these compounds as thin films suitable for TDD applications. The other key objective is optimizing laser energy to enhance the industrial feasibility of this method. Thin layers were deposited on glass and hemp fabric using dual pulsed laser (DPL) ablation on a compressed hemp seed target held in a stainless steel ring. The target was irradiated for 30 min with two synchronized pulsed laser beams, each with parameters of 30 mJ, 532 nm, pulse width of 10 ns, and a repetition rate of 10 Hz. Each beam had an angle of incidence with the target surface of 45°, and the angle between the two beams was also 45°. To improve laser absorption, two approaches were used: (1) HS-DPL/glass and HS-DPL/hemp fabric, in which a portion of the stainless steel ring was included in the irradiated area, and (2) HST-DPL/glass and HST-DPL/hemp fabric—hemp seeds were mixed with turmeric powder, which is known to improve laser interaction and biocompatibility. The FTIR and Micro-FTIR spectroscopy (ATR) performed on thin films compared to the target material confirmed the presence of hemp-derived phenolic compounds, including tetrahydrocannabinol (THC), cannabidiol (CBD), ferulic acid, and coumaric acid, along with other functional groups such as amides. The ATR spectra have been validated against Gaussian 6 numerical simulations. Scanning electron microscopy (SEM) and substance transfer tests revealed the microgranular structure of thin films. Through the analyzes carried out, the following were highlighted: spherical structures (0.3–2 μm) for HS-DPL/glass, HS-DPL/hemp fabric, HST-DPL/glass, and HST-DPL/hemp fabric; larger spherical structures (8–13 μm) for HS-DPL/glass and HST-DPL/glass; angular, amorphous-like structures (~3.5 μm) for HS-DPL/glass; and crystalline-like structures (0.6–1.3 μm) for HST-DPL/glass. Microparticle transfer from thin films on the hemp fabric to the filter paper at a human body temperature (37 °C) confirmed their suitability for TDD applications, aligning with the “whole plant medicine” or “entourage effect” concept. Granular, composite, thin films were successfully developed, capable of releasing microparticles upon contact with a surface whose temperature is 37 °C, specific to the human body. Each of the microparticles in the thin films obtained with the DPL technique contains phenolic compounds (cannabinoids and phenolic acids) comparable to those in hemp seeds, effectively acting as “microseeds.” The obtained films are viable for TDD applications, while the DPL technique ensures industrial scalability due to its low laser energy requirements. Full article

With accelerating urbanization, the growing density of buildings and the expansion of road networks have fundamentally reshaped the interplay between geological hazards and urban infrastructure. Traditional vulnerability assessment models for buildings (VAB) frequently overlook how human engineering activities—such as construction and city expansion—intensify disaster risk. To address this gap, we introduce VAB-HEAIC, a novel framework that integrates three dimensions of vulnerability: geological environment, building attributes, and dynamics of human engineering activity. Leveraging historical high-resolution imagery, we construct a human engineering activity intensity change indicator by quantifying variations in both road network density and building density. Nineteen evaluation factors, identified via spatial statistical analysis and field surveys, serve as model inputs. Within this framework, we evaluate four machine learning algorithms (Support Vector Regression, Random Forests, Back Propagation Neural Networks, and Light Gradient Boosting Machines), each coupled with four hyperparameter-optimization techniques (Particle Swarm Optimization, Sparrow Search Algorithm, Differential Evolution, and Bayesian Optimization), and three data augmentation strategies (feature combination, numerical perturbation, and bootstrap resampling). Applied to 5471 buildings in Dajing Town, the approach is validated using Root Mean Squared Error (RMSE). The optimal configuration—LGBM tuned with Differential Evolution and enhanced via bootstrap resampling—yields an RMSE of 0.3745. An ablation study further demonstrates that including the human engineering activity intensity change factor substantially improves prediction accuracy. These results offer a more comprehensive methodology for urban disaster risk management and planning by explicitly accounting for the role of human activity in building vulnerability. Full article

Background: Abdominal wall endometriosis (AWE) is a rare but debilitating condition, often occurring in surgical scars after Caesarean sections. It is characterized by cyclic pain and a palpable mass, significantly impacting patients’ quality of life. Traditional treatments, including hormonal therapy and surgery, have limitations, prompting interest in minimally invasive techniques such as cryoablation. This study evaluates the efficacy and safety of percutaneous image-guided single-probe cryoablation using liquid nitrogen for symptomatic AWE. Purpose: To evaluate the effectiveness and safety of percutaneous image-guided single-probe cryoablation using liquid nitrogen in treating symptomatic AWE lesions, with a primary objective to assess pain relief using the Visual Analog Scale (VAS). Materials and Methods: This retrospective, single-center study included 14 patients (23 lesions) treated with percutaneous cryoablation between September 2022 and April 2025. Clinical, imaging (MRI and ultrasound), and procedural data were analyzed. Pain scores (VAS scale) were assessed before treatment and at 3-month follow-up. Hydro- and/or carbo-dissection were used to protect adjacent structures. Response to treatment was evaluated with MRI and clinical follow-up. Statistical analysis was performed using median, range, and percentage calculations, with comparisons made using the Mann–Whitney test. Results: A total of 23 AWE lesions were treated in 14 patients (mean age: 39.6 years). The median lesion volume was 3546 mm³, with a range from 331 mm³ (8 × 4.6 × 9 mm) to 45,448 mm³ (46 × 26 × 38 mm). Most of the lesions were located in the muscle (69.6%, n = 16), while 17.4% (n = 4) involved both muscle and subcutaneous tissue, and 13.0% (n = 3) were purely subcutaneous. Among the 23 treated lesions, 8.7% (n = 2) appeared as purely hemorrhagic, 13.0% (n = 3) as fibrotic, and 78.3% (n = 18) were classified as mixed, based on imaging characteristics. Procedures were performed under general anesthesia in 65% of cases and under sedation in 35%. Hydrodissection was used in 48% of lesions, carbo-dissection in 4%, and combined hydro–carbo-dissection in 26%. A single 13G cryoprobe was used in 83% of cases, and a 10G probe in 17%. The median ablation time was 15 min (range: 6–28 min), and the median total procedure time was 93 min (range: 22–240 min). Pain scores significantly decreased from a median of 8/10 (range: 6–10) before treatment to 0/10 (range: 0–2) at follow-up (p < 0.0001). MRI follow-up confirmed complete coverage of the ablation zone and disappearance of hemorrhagic inclusions in all cases. Two patients (14%) required re-treatment, both with satisfactory outcomes. No peri- or post-procedural complications were observed, and no visible scars were noted. Conclusions: Percutaneous cryoablation using a single probe with liquid nitrogen is a safe and effective treatment for AWE, offering significant pain relief, minimal morbidity, and excellent cosmetic outcomes. It should be considered as part of multidisciplinary care. Further prospective studies with longer follow-up are warranted to confirm these findings. Full article

Ground-Penetrating Radar (GPR) is a non-destructive detection technique widely employed for identifying underground targets. Despite its utility, conventional approaches suffer from limitations, including poor adaptability to multi-scale targets and suboptimal localization accuracy. To overcome these challenges, we propose a lightweight deep learning framework, the Dual Attentive YOLOv11 (You Only Look Once, version 11) Keypoint Detector (DAYKD), designed for robust underground target detection and precise localization. Building upon the YOLOv11 architecture, our method introduces two key innovations to enhance performance: (1) a dual-task learning framework that synergizes bounding box detection with keypoint regression to refine localization precision, and (2) a novel Convolution and Attention Fusion Module (CAFM) coupled with a Feature Refinement Network (FRFN) to enhance multi-scale feature representation. Extensive ablation studies demonstrate that DAYKD achieves a precision of 93.7% and an mAP50 of 94.7% in object detection tasks, surpassing the baseline model by about 13% in F1-score, a balanced metric that combines precision and recall to evaluate overall model performance, underscoring its superior performance. These findings confirm that DAYKD delivers exceptional recognition accuracy and robustness, offering a promising solution for high-precision underground target localization. Full article

As face forgery techniques, particularly the DeepFake method, progress, the imperative for effective detection of manipulations that enable hyper-realistic facial representations to mitigate security threats is emphasized. Current spatial domain approaches commonly encounter difficulties in generalizing across various forgery methods and compression artifacts, whereas frequency-based analyses exhibit promise in identifying nuanced local cues; however, the absence of global contexts impedes the capacity of detection methods to improve generalization. This study introduces a hybrid architecture that integrates Efficient-ViT and multi-level wavelet transform to dynamically merge spatial and frequency features through a dynamic adaptive multi-branch attention (DAMA) mechanism, thereby improving the deep interaction between the two modalities. We innovatively devise a joint loss function and a training strategy to address the imbalanced data issue and improve the training process. Experimental results on the FaceForensics++ and Celeb-DF (V2) have validated the effectiveness of our approach, attaining 97.07% accuracy in intra-dataset evaluations and a 74.7% AUC score in cross-dataset assessments, surpassing our baseline Efficient-ViT by 14.1% and 7.7%, respectively. The findings indicate that our approach excels in generalization across various datasets and methodologies, while also effectively minimizing feature redundancy through an innovative orthogonal loss that regularizes the feature space, as evidenced by the ablation study and parameter analysis. Full article

Radiofrequency ablation (RFA) and pulsed radiofrequency (PRF) are non-pharmacological techniques employed in humans for chronic pain, but their veterinary application is unexplored. This pilot study evaluated clinical effects of RFA and PRF in twenty-four horses with chronic distal forelimb lameness. Ultrasound-guided RFA (N = 8; 60–90 °C, 2–8 min) or PRF (N = 16; 42 °C; 12 min) was applied to palmar digital nerves. Lameness was scored (American Association of Equine Practitioners scale) at baseline and monthly for six months (T1-T6). At T2, partial- and non-responders in both groups received PRF. Complications and return to previous work were recorded. At T2, the PRF group had significantly lower lameness scores (1, 0–3) than the RFA group (3, 2–4; p < 0.001) and significantly improved from baseline (3, 2–4; p < 0.01). RFA caused more complications (N = 6) than PRF (N = 1; p < 0.001), including increased lameness and allodynia. Sixteen horses (RFA: N = 7; PRF: N = 9) were retreated at T2. Overall, lameness significantly improved from T2 (2, 0–4) to T6 (0, 0–3; p < 0.001). At T6, 83% (19/23) of horses resumed previous work. RFA was ineffective and caused complications, whereas PRF appeared safer and more effective. Two PRF treatments yielded better outcomes with fewer side effects and may help manage lameness and associated pain for up to six months. Full article

Isotopic investigations focused on determining the mobility and provenance of ancient human civilizations and sourcing of archeological artifacts continue to gain prominence in archeology. Most studies focus on the premise that the geographic variation in isotope systems of interest (e.g., Sr, Pb, Nd, O) in the natural environment is recorded in both human hard tissues of local individuals and raw materials sourced for artifacts within the same region. The introduction of multi-collection–inductively coupled plasma mass spectrometry (MC-ICP-MS) and laser ablation systems are techniques that consume smaller sample sizes compared to previous mass spectrometric approaches due to their higher ionization efficiency and increased sensitivity. This development has facilitated the isotopic measurement of trace elements present at low abundances (e.g., Pb, Nd, <1-to-low ppm range) particularly in human tooth enamel. Accurate interpretation of any isotope ratio measurement for the proveniencing of such low-abundance samples requires the adequate evaluation of post-mortem diagenetic alteration. A synopsis of practices currently in use for identifying post-mortem alteration in human archeological samples is discussed here. Post-mortem shifts in radiogenic isotope signatures resulting from secondary alteration are distinct from those potentially related to the impact of climate change on the bioavailable budgets for these elements. This topic is of interest to the archeological community and discussed here in the context of Holocene-aged samples from burial sites within the Nile River Valley System, and preferred dust source areas from the neighboring Sahara Desert. Full article

The shrubland ecosystems in arid areas are highly sensitive to global climate change and human activities. Accurate extraction of shrubs using computer vision techniques plays an essential role in monitoring ecological balance and desertification. However, shrub extraction from high-resolution GF-2 satellite images remains challenging due to their dense distribution and small size, along with complex background. Therefore, this study introduces a Feature Enhancement and Transformer Network (FETNet) by integrating the Feature Enhancement Module (FEM) and Transformer module (EdgeViT). Correspondently, they can strengthen both global and local features and enable accurate segmentation of small shrubs in complex backgrounds. The ablation experiments demonstrated that incorporation of FEM and EdgeViT can improve the overall segmentation accuracy, with 1.19% improvement of the Mean Intersection Over Union (MIOU). Comparison experiments show that FETNet outperforms the two leading models of FCN8s and SegNet, with the MIOU improvements of 7.2% and 0.96%, respectively. The spatial details of the extracted results indicated that FETNet is able to accurately extract dense, small shrubs while effectively suppressing interference from roads and building shadows in spatial details. The proposed FETNet enables precise shrub extraction in arid areas and can support ecological assessment and land management. Full article

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities. Full article

Diabetes mellites (DM) is a chronic disease with increasing prevalence worldwide and multiple health implications. Among them, sarcopenia is a metabolic disorder characterized by loss of muscle mass and function. The two age-related diseases, DM and sarcopenia, share underlying pathophysiological pathways. This narrative literature review aims to provide an overview of the existing evidence on metabolomic studies evaluating DM associated with sarcopenia. Advancements in targeted and untargeted metabolomics techniques could provide better insight into the pathogenesis of sarcopenia in DM and describe their entangled and fluctuating interrelationship. Recent evidence showed that sarcopenia in DM induced significant changes in protein, lipid, carbohydrate, and in energy metabolisms in humans, animal models of DM, and cell cultures. Newer metabolites were reported, known metabolites were also found significantly modified, while few amino acids and lipids displayed a dual behavior. In addition, several therapeutic approaches proved to be promising interventions for slowing the progression of sarcopenia in DM, including physical activity, newer antihyperglycemic classes, D-pinitol, and genetic USP21 ablation, although none of them were yet validated for clinical use. Conversely, ceramides had a negative impact. Further research is needed to confirm the utility of these findings and to provide potential metabolomic biomarkers that might be relevant for the pathogenesis and treatment of sarcopenia in DM. Full article

Background: The influence of the initial ablation modality on pulmonary vein (PV) reconnection and substrate characteristics in redo procedures for recurrent atrial fibrillation (AF) remains unclear. We assessed how different thermal strategies—ablation index (AI)-guided radiofrequency (RF) versus cryoballoon (CB) ablation—affect remapping findings during redo pulmonary vein isolation (PVI). Methods: We included patients undergoing redo ablation between 2015 and 2024 with high-density electroanatomic mapping. Initial PVI modalities were retrospectively classified as low-power, long-duration (LPLD) RF; high-power, short-duration (HPSD) RF; or second-/third-generation CB. Reconnection sites were mapped using multielectrode catheters. Redo PVI was performed using AI-guided RF. Segments showing PV reconnection were reisolated; if all PVs remained isolated and AF persisted, posterior wall isolation was performed. Results: Among 195 patients (LPLD: 63; HPSD: 30; CB: 102), complete PVI at redo was observed in 0% (LPLD), 23.3% (HPSD), and 10.1% (CB) (p < 0.01 for LPLD vs. HPSD). Reconnection patterns varied by technique; LPLD primarily affected the right carina, while HPSD and CB showed reconnections at the LSPV ridge. Organized atrial tachycardia was least frequent after CB (12.7%, p < 0.002). Conclusion: Initial ablation strategy significantly influences PV reconnection and post-PVI arrhythmia patterns, with implications for redo procedure planning. Full article