Search Results (56,096)

The Koldar Massif in southeastern Kazakhstan is a geologically complex area with potential for porphyry copper and rare-metal mineralization. This study applies a multi-scale remote sensing approach to delineate hydrothermal alteration zones using medium-resolution ASTER imagery and very high-resolution WorldView-3 data. Image processing techniques—including false color composites (FCCs), band ratios (BRs), and the Spectral Angle Mapper (SAM)—were employed across the VNIR and SWIR bands to detect alteration minerals such as kaolinite, illite, montmorillonite, chlorite, epidote, calcite, quartz, and muscovite. These minerals correspond to argillic, propylitic, and phyllic alteration zones. While ASTER supported regional-scale mapping, WorldView-3 enabled detailed analysis at the Aidarly deposit. Validation was performed using copper occurrences, lithogeochemical anomaly contours, and ore body boundaries. The results show a strong spatial correlation between the mapped alteration zones and known mineralization patterns. Importantly, this study reports the identification of a previously undocumented hydrothermal zone north of the Aidarly deposit, detected using WorldView-3 data. This zone exhibits concentric phyllic and argillic alterations, similar to those at Aidarly, and may represent an extension of the mineralized system. Unlike earlier studies on the Aktogay deposit based on ASTER and Landsat-8, this work focuses on the Aidarly deposit and introduces higher-resolution analysis and SAM-based classification, offering improved spatial accuracy and target delineation. The proposed methodology provides a reproducible and scalable workflow for early-stage mineral exploration in underexplored regions, especially where field access is limited. These results highlight the value of high-resolution remote sensing in detecting concealed porphyry copper systems in structurally complex terrains. Full article

The permanent magnetic synchronous motor (PMSM) is of significant use for the centrifugal hydrogen compressor (CHC) in the hydrogen fuel cell system. In order to satisfy the demand for improving the CHC’s performance, including higher accuracy, higher response speed, and wider speed range, this paper proposes a novel adaptive super-twisting sliding mode observer (ASTSMO)-based position sensorless control strategy for the highspeed PMSM. Firstly, the super-twisting algorithm (STA) is introduced to the sliding mode observer (SMO) to reduce chattering and improve the accuracy of position estimation. Secondly, to increase the convergence speed, the ASTSMO is extended with a linear correction term, where an extra proportionality coefficient is used to adjust the stator current error under dynamic operation. Finally, a novel adaptive law is designed to solve the PMSM’s problems of wide speed change, wide current variation, and inevitable parameters fluctuation, which are caused by the CHC’s complex working environment like frequent load changes and significant temperature variations. In the experimental verification, the position accuracy and dynamic performance of the PMSM are both improved. It is also proved that the proposed strategy can guarantee the stable operation and fast response of the CHC, so as to maintain the reliability and the hydrogen utilization of the hydrogen fuel cell system. Full article

In flank milling of thin-walled workpieces, machining deformation is a key issue affecting workpiece accuracy and process stability. Although the traditional finite element method (FEM) offers high accuracy, its low computational efficiency makes it difficult to meet the requirements for rapid prediction in engineering practice. For this purpose, this paper proposes an efficient method for predicting workpiece deformation based on the voxel octree model. First, based on the analysis of the contact position between the cutting tool and the workpiece, the thin-walled workpiece is divided into six levels of voxel units, using a voxel octree model. Then, the stiffness matrix and update model of the voxel units are established. Finally, the deformation prediction is completed by calculating the micro-milling force and the voxel stiffness matrix. The experimental results show that the workpiece deformation predicted by the proposed method is highly consistent with the actual machining measurement. At the same time, compared with traditional FEM and voxel model methods, the calculation time is reduced by 90% and 13.2%, respectively. This method can provide rapid decision support for the optimization of thin-walled workpiece machining processes and effectively improve the efficiency of preliminary research in actual machining. Full article

Imaging techniques such as positron emission tomography/computed tomography (PET/CT) and positron emission tomography/magnetic resonance imaging (PET/MR) have emerged as powerful and versatile tools for the comprehensive assessment of coronary artery disease (CAD). By combining anatomical and functional information in a single examination, these modalities offer complementary insights that significantly enhance diagnostic accuracy and support clinical decision-making. This is particularly relevant in complex clinical scenarios, such as multivessel disease, balanced ischemia, or suspected microvascular dysfunction, where conventional imaging may be inconclusive. This review aims to provide clinicians with an up-to-date summary of the principles, technical considerations, and clinical applications of hybrid PET/CT and PET/MR in CAD. Here, we describe how these techniques can improve the evaluation of myocardial perfusion, coronary plaque characteristics, and ischemic burden. Advantages such as improved sensitivity, spatial resolution, and quantification capabilities are discussed alongside limitations including cost, radiation exposure, availability, and workflow challenges. A dedicated focus is given to the emerging role of artificial intelligence (AI), which is increasingly being integrated to optimize image acquisition, fusion processes, and interpretation. AI has the potential to streamline hybrid imaging and promote a more personalized and efficient management of CAD. Finally, we outline future directions in the field, including novel radiotracers, automated quantitative tools, and the expanding use of hybrid imaging to guide patient selection and therapeutic decisions, particularly in revascularization strategies. Full article

Emotion recognition from speech is essential for human–computer interaction (HCI) and affective computing, with applications in virtual assistants, healthcare, and education. Although deep learning has made significant advancements in Automatic Speech Emotion Recognition (ASER), the challenge still exists in the task given variation in speakers, subtle emotional expressions, and environmental noise. Practical deployment in this context depends on a strong, fast, scalable recognition system. This work introduces a new framework combining DenseNet121, especially fine-tuned for the crowd-sourced emotional multimodal actors dataset (CREMA-D), with the convolutional block attention module (CBAM). While DenseNet121’s effective feature propagation captures rich, hierarchical patterns in the speech data, CBAM improves the focus of the model on emotionally significant elements by applying both spatial and channel-wise attention. Furthermore, enhancing the input spectrograms and strengthening resistance against environmental noise is an advanced preprocessing pipeline including log-Mel spectrogram transformation and normalization. The proposed model demonstrates superior performance. To make sure the evaluation is strong even if there is a class imbalance, we point out important metrics like an Unweighted Average Recall (UAR) of 71.01% and an F1 score of 71.25%. The model also gets a test accuracy of 71.26% and a precision of 71.30%. These results establish the model as a promising solution for real-world speech emotion detection, highlighting its strong generalization capabilities, computational efficiency, and focus on emotion-specific features compared to recent work. The improvements demonstrate practical flexibility, enabling the integration of established image recognition techniques and allowing for substantial adaptability in various application contexts. Full article

Understanding spatial variability in crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing negative environmental impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological conditions. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. A high-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation, with a relative root mean square error (RRMSE) of 23.7% (r² = 0.38). Adding hydrological variables reduced the error to 15.3% (r² = 0.73), and further combining with remote sensing data improved the explanatory power to an RRMSE of 10.0% (r² = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r² = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables, enabling the identification of long-term persistent yield regions (LTRs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency. Full article

Traditional X-ray fluorescence computed tomography (XFCT) suffers from issues such as low photon collection efficiency, slow data acquisition, severe noise interference, and poor imaging quality due to the limitations of mechanical collimation. This study proposes to design an X-ray fluorescence imaging system based on bilateral Compton cameras and to develop an optimized reconstruction algorithm to achieve high-quality 2D/3D imaging of low-concentration samples (0.2% gold nanoparticles). A system equipped with bilateral Compton cameras was designed, replacing mechanical collimation with “electronic collimation”. The traditional LM-MLEM algorithm was optimized through improvements in data preprocessing, system matrix construction, iterative processes, and post-processing, integrating methods such as Total Variation (TV) regularization (anisotropic TV included), filtering, wavelet-domain constraints, and isosurface rendering. Successful 2D and 3D reconstruction of 0.2% gold nanoparticles was achieved. Compared with traditional algorithms, improvements were observed in convergence, stability, speed, quality, and accuracy. The system exhibited high detection efficiency, angular resolution, and energy resolution. The Compton camera-based XFCT overcomes the limitations of traditional methods; the optimized algorithm enables low-noise imaging at ultra-low concentrations and has potential applications in early cancer diagnosis and material analysis. Full article

Ensuring food quality and safety is a growing challenge in the food industry, where early detection of contamination or spoilage is crucial. Using gas sensors combined with Artificial Intelligence (AI) offers an innovative and effective approach to food identification, improving quality control and minimizing health risks. This study aims to evaluate food identification strategies using supervised learning techniques applied to data collected by the BME Development Kit, equipped with the BME688 sensor. The dataset includes measurements of temperature, pressure, humidity, and, particularly, gas composition, ensuring a comprehensive analysis of food characteristics. The methodology explores two strategies: a neural network model trained using Bosch BME AI-Studio software, and a more flexible, customizable approach that applies multiple predictive algorithms, including DT, LR, kNN, NB, and SVM. The experiments were conducted to analyze the effectiveness of both approaches in classifying different food samples based on gas emissions and environmental conditions. The results demonstrate that combining electronic noses (E-Noses) with machine learning (ML) provides high accuracy in food identification. While the neural network model from Bosch follows a structured and optimized learning approach, the second methodology enables a more adaptable exploration of various algorithms, offering greater interpretability and customization. Both approaches yielded high predictive performance, with strong classification accuracy across multiple food samples. However, performance variations depend on the characteristics of the dataset and the algorithm selection. A critical analysis suggests that optimizing sensor calibration, feature selection, and consideration of environmental parameters can further enhance accuracy. This study confirms the relevance of AI-driven gas analysis as a promising tool for food quality assessment. Full article

Background: Postoperative wound complications following total joint arthroplasty (TJA) significantly impact patient outcomes and healthcare costs. Reliable preoperative biomarkers for identifying patients at increased risk are critical for optimizing patient management and reducing complication rates. This study evaluated the predictive utility of the C-reactive protein to albumin ratio (CAR) and the prognostic nutritional index (PNI) for periprosthetic joint infection (PJI) and postoperative wound complications in patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA). Methods: We retrospectively studied patients who underwent primary THA and TKA in our department from March 2019 to April 2024. The study included a total of 842 patients (568 knees and 274 hips). Preoperative blood samples were assessed for serum CRP, albumin, and total lymphocyte count, facilitating the calculation of CAR and PNI values. Patient outcomes were monitored, identifying PJI and aseptic wound complications such as persistent wound drainage, hematoma, seroma, skin erosion, and wound dehiscence within 2 weeks post-surgery. Results: The average follow-up time for patients was 39.2 months (range 13–73 months). PJI was significantly linked with elevated admission CAR and diminished PNI ratio (p < 0.001 and p < 0.001). ROC analysis demonstrated optimal predictive cut-off values for CAR at 3.1 (Area under curve [AUC]: 0.92, specificity 97.4%, sensitivity 92.3%) and PNI at 49.4 (AUC: 0.93, specificity 94.7%, sensitivity 91.7%). Furthermore, both CAR (Odds ratio [OR]: 3.84, 95% confidence interval [CI]: 1.6–9.1, p = 0.002) and PNI (OR: 21.8, 95% CI: 9–48.6, p < 0.001) were identified as two independent risk factors associated with the development of PJI following THA or TKA. Further subgroup analysis revealed distinct predictive thresholds for CAR and PNI according to surgical procedure type (TKA and THA), enhancing diagnostic accuracy. Conclusions: Preoperative admission elevated CAR and decreased PNI effectively predict PJI and postoperative wound complications in THA and TKA, supporting their utility as simple, cost-effective biomarkers in clinical practice. Incorporating CAR and PNI evaluations into preoperative assessments can enhance patient stratification and preventive strategies, thus mitigating risks and improving surgical outcomes. Full article

Deepfake audio refers to speech that has been synthetically generated or altered through advanced neural network techniques, often with a degree of realism sufficient to convincingly imitate genuine human voices. As these manipulations become increasingly indistinguishable from authentic recordings, they present significant threats to security, undermine media integrity, and challenge the reliability of digital authentication systems. In this study, a robust detection framework is proposed, which leverages the power of self-supervised learning (SSL) and attention-based modeling to identify deepfake audio samples. Specifically, audio features are extracted from input speech using two powerful pretrained SSL models: HuBERT-Large and WavLM-Large. These distinctive features are then integrated through an Attentional Multi-Feature Fusion (AMFF) mechanism. The fused features are subsequently classified using a NeXt-Time Delay Neural Network (NeXt-TDNN) model enhanced with Efficient Channel Attention (ECA), enabling improved temporal and channel-wise feature discrimination. Experimental results show that the proposed method achieves a 0.42% EER and 0.01 min-tDCF on ASVspoof 2019 LA, a 1.01% EER on ASVspoof 2019 PA, and a pooled 6.56% EER on the cross-channel ASVspoof 2021 LA evaluation, thus highlighting its effectiveness for real-world deepfake detection scenarios. Furthermore, on the ASVspoof 5 dataset, the method achieved a 7.23% EER, outperforming strong baselines and demonstrating strong generalization ability. Moreover, the macro-averaged F1-score of 96.01% and balanced accuracy of 99.06% were obtained on the ASVspoof 2019 LA dataset, while the proposed method achieved a macro-averaged F1-score of 98.70% and balanced accuracy of 98.90% on the ASVspoof 2019 PA dataset. On the highly challenging ASVspoof 5 dataset, which includes crowdsourced, non-studio-quality audio, and novel adversarial attacks, the proposed method achieves macro-averaged metrics exceeding 92%, with a precision of 92.07%, a recall of 92.63%, an F1-measure of 92.35%, and a balanced accuracy of 92.63%. Full article

Characters on liquid crystal display (LCD) interfaces often appear densely arranged, with complex image backgrounds and significant variations in target appearance, posing considerable challenges for visual detection. To improve the accuracy and robustness of character detection, this paper proposes an enhanced character detection algorithm based on the DBNet framework, named EBiDNet (EfficientNetV2 and BiFPN Enhanced DBNet). This algorithm integrates machine vision with deep learning techniques and introduces the following architectural optimizations. It employs EfficientNetV2-S, a lightweight, high-performance backbone network, to enhance feature extraction capability. Meanwhile, a bidirectional feature pyramid network (BiFPN) is introduced. Its distinctive symmetric design ensures balanced feature propagation in both top-down and bottom-up directions, thereby enabling more efficient multiscale contextual information fusion. Experimental results demonstrate that, compared with the original DBNet, the proposed EBiDNet achieves a 9.13% increase in precision and a 14.17% improvement in F1-score, while reducing the number of parameters by 17.96%. In summary, the proposed framework maintains lightweight design while achieving high accuracy and strong robustness under complex conditions. Full article

Long non-coding RNAs (lncRNAs) have emerged as pivotal regulators in plant immune responses, yet their roles in rice resistance against Magnaporthe oryzae (M. oryzae) remain inadequately explored. In this study, we integrated translatome data with conventional genome annotations to construct an optimized protein-coding dataset. Subsequently, we developed a robust pipeline (“RiceLncRNA”) for the accurate identification of rice lncRNAs. Using strand-specific RNA-sequencing (ssRNA-seq) data from the resistant (IR25), susceptible (LTH), and Nipponbare (NPB) varieties under M. oryzae infection, we identified 9003 high-confidence lncRNAs, significantly improving identification accuracy over traditional methods. Among the differentially expressed lncRNAs (DELs), those unique to IR25 were enriched in the biosynthetic pathways of phenylalanine, tyrosine, and tryptophan, which suggests that they are associated with the production of salicylic acid (SA) and auxin (IAA) precursors, which may be involved in defense responses. Conversely, DELs specific to LTH primarily clustered within carbon metabolism pathways, indicating a metabolic reprogramming mechanism. Notably, 21 DELs responded concurrently in both IR25 and LTH at 12 h and 24 h post-inoculation, indicating a synergistic regulation of jasmonic acid (JA) and ethylene (ET) signaling while partially suppressing IAA pathways. Weighted gene co-expression network analysis (WGCNA) and competing endogenous RNA (ceRNA) network analysis revealed that key lncRNAs (e.g., LncRNA.9497.1) may function as miRNA “sponges”, potentially influencing the expression of receptor-like kinases (RLKs), resistance (R) proteins, and hormone signaling pathways. The reliability of these findings was confirmed through qRT-PCR and cloning experiments. In summary, our study provides an optimized rice lncRNA annotation framework and reveals the mechanism by which lncRNAs enhance rice blast resistance through the regulation of hormone signaling pathways. These findings offer an important molecular basis for rice disease-resistant breeding. Full article

Background/Objectives: Accurate urinary bladder (UB) volume measurement is essential for diagnosing urinary retention, evaluating post-void residuals, and guiding catheterization decisions. Conventional 2D ultrasound and automated non-visual bladder scanners can be limited by operator variability and systematic errors. Three-dimensional (3D) ultrasound may improve accuracy and reproducibility, but data on handheld, semi-automated devices remain scarce. This study aimed to compare the accuracy, reproducibility, and feasibility of a handheld 3D ultrasound device versus conventional 2D ultrasound for UB volume estimation, using measured voided volume as the reference standard. Methods: Fifty-three healthy male volunteers (mean age 19.6 ± 2.0 years) underwent bladder volume assessment by two novice operators using both methods: 2D ultrasound (manual caliper-based) and handheld 3D ultrasound device (Butterfly iQ). Each operator performed two repeated measurements per method. True voided volume was recorded immediately after scanning. Accuracy was assessed using median differences, percentage error, and Bland–Altman analysis. Intra- and inter-operator reproducibility were evaluated with intraclass correlation coefficients (ICC). Results: Both methods significantly underestimated bladder volume (p < 0.001). The 3D method demonstrated higher accuracy, with a median percentage error of −11.2% to −12.0%, versus −27.6% to −36.7% for 2D. The mean bias ranged from −64.9 mL to −72.3 mL for 3D, compared to −137.4 mL to −191.6 mL for 2D. Intra-operator reproducibility was excellent for all methods (ICC > 0.96). Inter-operator agreement was higher for 3D (ICC = 0.977; bias 7.3 mL) than for 2D (ICC = 0.927; bias −54.2 mL). All scans were completed successfully; however, the 3D device occasionally faced technical errors in large bladder volumes. Conclusions: Handheld 3D ultrasound yielded greater accuracy and inter-operator consistency than conventional 2D ultrasound in healthy adults, with minimal operator input. Both methods underestimated true volume, indicating the need for clinical consideration when interpreting measurements. These findings support broader clinical adoption of handheld 3D ultrasound, particularly in settings with variable sonographic expertise, while highlighting the need for validation in elderly and pathological populations. Full article

Background: Radon, a naturally occurring radioactive gas, is increasingly recognized as a major risk factor for lung cancer (LC), especially among non-smokers. The objective of this study was to identify serum biomarkers for the early detection of LC in individuals at high risk due to prolonged residential radon exposure in Chiang Mai, Thailand, and to assess whether the use of single or combined biomarkers improves the sensitivity and specificity of detection. Methods: A total of 15 LC patients and 30 healthy controls (HC) were enrolled. The HC group was further stratified into two subgroups: low radon (LR, n = 15) and high radon (HR, n = 15) exposure. All participants were non-smokers or former smokers. Serum levels of cytokeratin 19 fragment (CYFRA 21-1), carcinoembryonic antigen (CEA), interleukin-6 (IL-6), interleukin-8 (IL-8), transforming growth factor-alpha (TGF-alpha), and indoleamine 2,3-dioxygenase-1 (IDO-1) were measured using the Milliplex^® Kit on a Luminex^® Multiplexing Instrument (MAGPIX^® System). Results: Serum CEA, IL-6 and IL-8 levels were significantly higher in LC patients compared to the HC group (p < 0.05). Among analyzed biomarkers, only IL-8 was significantly elevated in LC patients compared to the HR group (p = 0.04). Notably, CYFRA 21-1 was the only biomarker that significantly differed between LR and HR groups (p = 0.004). The diagnostic potential of these biomarkers was evaluated using receiver operating characteristic (ROC) analysis. Individually, IL-6 showed the highest discriminative ability for differentiating LC patients from both HC and HR groups, with high specificity but moderate sensitivity. Combining IL-6 and IL-8 improved specificity and increased the area under the ROC curve (AUC), though it did not enhance sensitivity for distinguishing LC from HC. For distinguishing LC from HR individuals, IL-6 and CYFRA 21-1 exhibited strong diagnostic performance. Their combination significantly improved diagnostic accuracy, yielding the highest AUC, sensitivity, and specificity. In contrast, CEA, IL-8, TGF-alpha, and IDO-1 demonstrated limited diagnostic utility. Conclusions: Based on the available literature, this is the first study to evaluate the combined use of IL-6 and CYFRA 21-1 as potential biomarkers for LC screening in individuals with high residential radon exposure. Our findings highlight their utility, particularly in combination, for improving diagnostic accuracy in this high-risk population. Full article

X-ray security screening is a well-established technology used in public spaces. The traditional method for detecting prohibited items in X-ray images relies on manual inspection, necessitating security personnel with extensive experience and focused attention to achieve satisfactory detection accuracy. However, the high-intensity and long-duration nature of the work leads to security personnel fatigue, which in turn reduces the accuracy of prohibited items detection and results in false alarms or missed detections. In response to the challenges posed by the coexistence of multiple prohibited items, incomplete identification information due to overlapping items, variable distribution positions in typical scenarios, and the need for portable detection equipment, this study proposes a lightweight automatic detection method for prohibited items. Based on establishment the sample database for prohibited items, a new backbone network with a residual structure and attention mechanism is introduced to form a deep learning algorithm. Additionally, a dilated convolutional spatial pyramid module and a depthwise separable convolution algorithm are added to fuse multi-scale features, to improve the accuracy of prohibited items detection. This study developed a lightweight automatic detection method for prohibited items, and its highest detection rate is 95.59%, which demonstrates a 1.86% mAP improvement over the YOLOv4-tiny baseline with 122 FPS. The study achieved high accurate detection of typical prohibited items, providing support for the assurance of public safety. Full article