Search Results (14,938)

Stance detection has emerged as an essential tool in natural language processing for understanding how individuals express agreement, disagreement, or neutrality toward specific targets in social and online discourse. It plays a crucial role in bilingual and multilingual environments, including English-Arabic social media ecosystems, where differences in language structure, discourse style, and data availability pose significant challenges for reliable stance modelling. Existing approaches often struggle with target awareness, cross-lingual generalization, robustness to noisy user-generated text, and the interpretability of model decisions. This study aims to build a reliable, explainable target-aware bilingual stance-detection framework that generalizes across heterogeneous stance formats and languages without retraining on a dataset specific to the target language. Thus, a unified dual-encoder architecture based on mDeBERTa-v3 is proposed. Cross-language contrastive learning offers an auxiliary training objective to align English and Arabic stance representations in a common semantic space. Robustness-oriented regularization is used to mitigate the effects of informal language, vocabulary variation, and adversarial noise. To promote transparency and trustworthiness, the framework incorporates token-level rationale extraction, enables fine-grained interpretability, and supports analysis of hallucination. The proposed model is tested on a combined bilingual test set and two structurally distinct zero-shot benchmarks: MT-CSD and AraStance. Experimental results show consistent performance, with accuracies of 85.0% and 86.8% and F1-scores of 84.7% and 86.8% on the zero-shot benchmarks, confirming stable performance and realistic generalization. Ultimately, these findings reveal that effective bilingual stance detection can be achieved via explicit target conditioning, cross-lingual alignment, and explainability-driven design. Full article

We aim to address the insufficient robustness of navigational line detection for rapeseed seed production sires in complex field scenarios and the challenges faced by existing models in balancing precision, real-time performance, and resource consumption. Taking YOLOv8n-seg as the baseline, we first introduced the ADown module to mitigate feature subsampling information loss and enhance computational efficiency. Subsequently, the DySample module was employed to strengthen target feature representation and improve object discrimination in complex scenarios. Finally, the c2f module was replaced with c2f_FB to optimise feature fusion and reinforce multi-scale feature integration. Performance was evaluated through comparative experiments, ablation studies, and scenario testing. The model achieves an average precision of 99.2%, mAP50-95 of 84.5%, a frame rate of 90.21 frames per second, and 2.6 million parameters, demonstrating superior segmentation performance in complex scenarios. SegNav-YOLOv8n balances performance and resource requirements, validating the effectiveness of the improvements and providing reliable technical support for navigating agricultural machinery in rapeseed seed production. Full article

Nanobodies (VHHs or single-domain antibodies) are powerful affinity reagents, but their routine use is often limited by production constraints and by the lack of a conserved Fc region for secondary detection. We describe a simplified strategy in which functional GST–nanobody fusion proteins are expressed directly in the cytoplasm of Escherichia coli Origami^TM 2 (DE3), a strain that supports disulfide bond formation through trxB/gor mutations. Using well-characterized nanobodies against GFP (Lag2) and mCherry (C11), we designed N-terminal GST fusions and confirmed by AlphaFold3-based modeling that both constructs preserve the GST fold and the VHH (Variable domain of the Heavy-chain antibody of Heavy-chain-only antibodies) β-sandwich with defined CDR loops and a predicted intradomain disulfide bond. Following IPTG induction and purification by glutathione affinity and size-exclusion chromatography, we obtained soluble GST-nb-GFP and GST-nb-mCherry at ~8–12 mg/L. Isothermal titration calorimetry showed nanomolar binding to their antigens (K_d ~123 nM for GFP and ~199 nM for mCherry). Consistent with conformational epitope recognition, GST-nanobodies were reactive in native-state dot blots but not in denaturing Western blots under the conditions tested. The GST moiety enabled indirect immunofluorescence via anti-GST antibodies, yielding specific labeling of GFP- or mCherry-tagged TGN38 in HeLa and H4 cells. Finally, we demonstrate “GST-nanobody pulldown” as a robust method for affinity capture from cell lysates. Together, this platform provides a low-cost, versatile route to functional nanobody reagents without requiring tag removal, and complements other nanobody designs (e.g., VHH-Fc fusions) in an application-dependent manner. Full article

Steep slope failures adjacent to residential areas are becoming an increasingly serious hazard. However, satellite-based monitoring is often limited by revisit time and spatial resolution, which can impede the timely identification of small, precursory deformations. To support dense in situ surveillance, embedded glass fiber-reinforced polymer (GFRP) sensor rods were installed in a susceptible slope, and ground-displacement data were recorded at 5 min intervals for five months. Based on these multivariate time series, we propose PRISM-TAD, a masked Transformer-based anomaly detection approach that integrates kinematic priors computed from displacement and velocity to model normal slope dynamics and detect departures from typical behavior. The proposed method was benchmarked against six baselines: robust velocity threshold screening, PCA-based reconstruction, Isolation Forest, one-class SVM, a 1D convolutional autoencoder, and a standard Transformer reconstructor. In a field test using a documented slope failure case in Seocheon, PRISM-TAD generated an alert approximately 22 h before collapse while yielding the lowest false alarm rate. Although some baseline methods showed longer nominal lead times, they produced substantially more false positives. Overall, the results suggest that coupling high-frequency IoT displacement sensing with domain-informed deep learning can enhance the operational reliability of early warning for slope failures. Full article

Titanium-based alloys are widely used in dental implantology; however, the mechanical limitations of commercially pure titanium (cpTi) and unresolved concerns regarding stress shielding remain. This study evaluated the structure–property–function relationship of a novel β-type titanium–niobium–zirconium (Ti–Nb–Zr; TNZ) alloy for dental implant applications. Laboratory testing assessed the elemental composition, tensile properties, and fatigue resistance of the cpTi, compared with modified Grade 4 cpTi (MG4T). In parallel, a randomized, single-blind, controlled clinical trial was conducted over 12 months to compare the clinical performance of TNZ and MG4T implants under functional loading. A total of 80 participants (mean age: 54.2 years; 43 females, 37 males) were enrolled, with 77 completing the 12-month follow-up (TNZ: n = 38; MG4T: n = 39). Clinical outcomes included implant success and survival, peri-implant soft tissue parameters, marginal bone levels, fractal dimension (FD) analysis of trabecular bone, and adverse events. TNZ implants demonstrated superior fatigue resistance without an increase in the elastic modulus relative to MG4T. Clinically, both groups achieved 100% implant success and survival, with no implant-related adverse events. FD analysis revealed time-dependent bone remodeling without evidence of pathological adaptation. These findings support the functional viability of TNZ as a mechanically robust, biocompatible implant material. Further long-term, multicenter trials are warranted to confirm sustained clinical benefits and broader applicability. Full article

This study aimed to develop and validate a rapid and robust reverse-phase high-performance liquid chromatography assay for amiodarone, creating an alternative to the previously established pharmacopeial method (retention time: 26 min). For method optimization, 20 mM of triethylamine (TEA) was added to the mobile phase buffer, followed by adjusting the organic solvent ratio and using a C18 column as the stationary phase. Under the optimized analytical conditions, the retention time of amiodarone was 5.51 min, which represents a reduction of approximately 80% compared to the previous method. Following the ICH Q2 guideline, the developed method was validated for key performance characteristics, including system suitability, specificity, linearity, accuracy, precision, and robustness. The method showed high linearity with a correlation coefficient (R² > 0.999) across a concentration range of 0.06–0.14 mg/mL (representing 60–140% of the target concentration of 0.1 mg/mL). The analytical method developed in this study significantly reduces analysis time and costs while providing reproducible results. Therefore, it is expected to be highly useful as a routine quality control test for amiodarone generic drug products. Full article

Background: Obstructive Sleep Apnea (OSA) is highly prevalent yet underdiagnosed due to the scarcity of Polysomnography (PSG) resources. Audio-based screening offers a scalable solution, but often lacks the granularity to precisely localize respiratory events or accurately estimate the Apnea-Hypopnea Index (AHI). This study aims to develop a fine-grained and lightweight detection framework for OSA screening, enabling precise respiratory event localization and AHI estimation using non-contact audio signals. Methods: A Dual-Stream Convolutional Recurrent Neural Network (CRNN), integrating Log Mel-spectrograms and energy profiles with BiLSTM, was proposed. The model was trained on the PSG-Audio dataset (Sismanoglio Hospital cohort, 286 subjects) and subjected to a comprehensive three-level evaluation: (1) frame-level classification performance; (2) event-level temporal localization precision, quantified by Intersection over Union (IoU) and onset/offset boundary errors; and (3) patient-level clinical utility, assessing AHI correlation, error margins, and screening performance across different severity thresholds. Generalization was rigorously validated on an independent external cohort from Beijing Tongren Hospital (60 subjects), which was specifically curated to ensure a relatively balanced distribution of disease severity. Results: On the internal test set, the model achieved a frame level macro F1 score of 0.64 and demonstrated accurate event localization, with an IoU of 0.82. In the external validation, the audio derived AHI showed a strong correlation with PSG-AHI (r = 0.96, MAE = 6.03 events/h). For screening, the model achieved sensitivities of 98.0%, 89.5%, and 89.3%, and specificities of 88.9%, 90.9%, and 100.0% at AHI thresholds of 5, 15, and 30 events per hour, respectively. Conclusions: The Fine-Grained and Lightweight Dual-Stream CRNN provides a robust, clinically interpretable solution for non-contact OSA screening. The favorable screening performance observed in the external cohort, characterized by high sensitivity for mild cases and high specificity for severe disease, highlights its potential as a reliable tool for accessible home-based screening. Full article

Persistent fiscal stress in South African municipalities undermines service delivery, yet practical tools for early detection remain limited. This study predicts one-year-ahead municipal financial distress to support risk-based prioritisation. We develop machine learning models using a 2018/19–2022/23 municipality panel, combining 13 financial health indicators from State of Local Government (SoLG) reports with selected socio-economic variables. Penalised logistic regression is benchmarked against random forest and XGBoost under a leakage-aware, time-ordered split into training, validation, and an out-of-time test year; class imbalance is handled through class weighting. Performance is evaluated using PR-AUC, ROC-AUC, calibration, and a capacity-constrained Top-30 rule. All models outperform a naïve last-year baseline on the out-of-time test (PR-AUC 0.934–0.954; ROC-AUC 0.886–0.923), with bootstrap intervals supporting robustness. Random forest performs best overall, while penalised logistic regression remains competitive. Under the Top-30 rule (12.3% workload), precision is high (precision@30 0.967–1.000) while recall is modest (recall@30 0.186–0.192). SHAP values and logistic odds ratios identify liquidity, solvency, cash coverage, and employment deprivation as key drivers. The Top-30 rule corresponds to an annual intensive monitoring portfolio that is reasonable under constrained staffing and budget capacity in national and provincial oversight units, while probability thresholds are reported as conventional benchmarks rather than as policy triggers. Full article

Reliable field estimation of near-surface soil hydraulic parameters remains challenging, particularly in heterogeneous or stony soil environments. Conventional drip infiltrometers (DI) are widely used, but their field deployment may limit mobility and testing efficiency. This study presents a portable drip infiltrometer (PDI) methodology that enhances field applicability while reducing testing time without compromising parameter robustness. The approach enables estimation of saturated hydraulic conductivity (Ks), effective net capillary drive (G), and sorptivity (S) by integrating image-based analysis of ponded surface areas using the Portable Drip Infiltrometer Software (PDIS v1.5) with linear and non-linear infiltration formulations optimized through evolutionary algorithms. A total of 34 PDI field tests were conducted across two Mexican regions with contrasting climatic and soil conditions. In semi-arid environments, Ks ranged from 1.07 to 12.82 mm h⁻¹ and G from 89.1 to 1999.99 mm, whereas in semi-warm sub-humid settings, Ks ranged from 30.68 to 117.68 mm h⁻¹ and G from 2.65 to 121.64 mm. Results indicate that linear formulations perform adequately under relatively homogeneous conditions, while non-linear PDI formulations become necessary as surface structural complexity increases. The PDI–PDIS framework provides a rapid, repeatable, and physically grounded tool for parameterizing near-surface hydraulic processes in heterogeneous soils. Full article

This paper tackles the growing uncertainty in distribution networks caused by distributed generation, load fluctuations, and frequent topological changes. It proposes a multi-source data fusion framework using enhanced selective convolution (SKNet) and graph attention networks (GAT). First, heterogeneous measurement data, including Phasor Measurement Unit (PMU) and Supervisory Control and Data Acquisition (SCADA) data, are processed through a unified normalization and outlier elimination technique to ensure data quality. Second, SKNet is utilized to extract spatiotemporal multi-scale features, improving the detection of both rapid disturbances and long-term trends. Third, the extracted features are fed into GAT to model node electrical couplings, while power flow residual constraints are embedded in the loss function to enforce the physical validity of the estimated states. This physics-informed design overcomes a key limitation of pure data-driven models and enables an end-to-end framework that integrates data-driven learning with physical mechanism constraints. Finally, comprehensive validation is performed on the improved IEEE 33-node and IEEE 123-node test systems. The test scenarios include Gaussian measurement noise, data outliers, missing measurements, and topological changes. The results show that the proposed method outperforms baseline models such as Multi-Scale Graph Attention Network (MS-GAT), Bidirectional Long Short-Term Memory (BiLSTM), and traditional weighted least squares (WLS). It achieves Root Mean Square Error (RMSE) reductions of up to 18% and Mean Absolute Error (MAE) reductions of up to 15%. The average inference latency is only 10–18 ms. Even under unknown topological changes, the estimation error increases by only 15–25%. These results demonstrate the superior accuracy, robustness, and real-time performance of the proposed method for intelligent distribution network state estimation. Full article

Reliable multivariable control is critical for industrial sectors where processes exhibit severe nonlinearities and interactions. A Continuous Stirred Tank Reactor (CSTR) is a rigorous benchmark for testing control strategies addressing these complexities. This work first establishes a linear MIMO mathematical framework to define the specific structure of such interactive systems. Analysis via phase planes and steady-state analysis reveals low controllability, bistability, and strong coupling, leading to the collapse of traditional decoupled control schemes. To address these issues via multivariable control, we propose a centralized MIMO RST control structure synthesized via a Matrix Fraction Description (MFD) and the extended Bézout equation. Simulations for performance evaluation and comparison highlight the following key findings: (1) the centralized RST maintains stability and tracking precision in regions where decentralized RST loops fail; (2) it exhibits performance comparable to the Augmented State Pole Placement with Integral Action (ASPPIA) method and outperforms the standard Model-Based Predictive Control (MPC) baseline, particularly during critical equilibrium point transitions; and (3) it offers a robust yet computationally simple design that provides superior flexibility for pole placement, accommodating future identification-based models and adaptive tuning. These results validate our algebraic synthesis as a robust, computationally efficient solution for managing highly interactive nonlinear dynamics. Full article

Planar mirrors play a crucial role in autocollimation testing and optical beam relay systems of telescopes and other fields. However, for the next-generation large-aperture telescopes, typical monolithic planar mirrors fall short in meeting anticipated performance requirements, owing to their high costs and fabrication limitations. Here, a new integrated multimodal testing method for 3–4m-class segmented planar mirrors is proposed. The presented system utilizes an innovative keystone architecture with a central mirror and keystone-shaped segments, which is superior to the traditional hexagonal architecture. To facilitate rapid coarse alignment, a machine vision system based on edge detection is investigated. Furthermore, the dispersed fringe technique is used for robust co-phasing. By using a segmented planar mirror designed with sub-aperture stitching strategy and combining local apertures, the system cost was reduced and high-precision measurement was achieved. Eventually, the alignment, co-focus and co-phasing measurements based on the proposed concept were completed, and the transfer characteristics were determined by analyzing the Optical Transfer Function (OTF). Test data shows co-phasing accuracy of better than 30 nm RMS (root-mean-square) and alignment accuracy less than 10 arcseconds. In addition, the system uses small-aperture mirrors in autocollimation testing to facilitate flexible alignment and testing of individual segments. The test optical path is configured to match the effective focal length of the system under test, and the optical lever effect of reflectors enhances the alignment sensitivity. The method combines autocollimation and wavefront sensing which allows the approach to provide high-precision control of co-focus, co-phasing, and surface errors correction. Full article

Pneumonia is one of the main reasons for child mortality, with chest radiography (CXR) being essential for its diagnosis. However, the low radiation exposure in pediatric analysis complicates the accurate detection of pneumonia, making traditional examination ineffective. Progress in medical imaging with convolutional neural networks (CNN) has considerably improved performance, gaining widespread recognition for its effectiveness. This paper proposes an accurate pneumonia detection method based on different deep CNN architectures that combine optimal feature fusion. Enhanced VGG-19, ResNet-50, and MobileNet-V2 are trained on the most widely used pneumonia dataset, applying appropriate transfer learning and fine-tuning strategies. To create an effective feature input, the Chi-Square technique removes inappropriate features from every enhanced CNN. The resulting subsets are subsequently fused horizontally, to generate more diverse and robust feature representation for binary classification. By combining 1000 best features from VGG-19 and MobileNet-V2 models, the suggested approach records the best accuracy (97.59%), Recall (98.33%), and F1-score (98.19%) on the test set based on the supervised support vector machines (SVM) classifier. The achieved results demonstrated that our approach provides a significant enhancement in performance compared to previous studies using various ensemble fusion techniques while ensuring computational efficiency. We project this fused-feature system to significantly aid timely detection of childhood pneumonia, especially within constrained healthcare systems. Full article

As a key production factor in the era of the intelligent economy, Artificial Intelligence is profoundly reshaping the production methods and energy usage structures of the manufacturing industry. Based on the data of 55 economies from 2002 to 2020, this paper systematically examines the impact and mechanism of AI on carbon emissions embodied in manufacturing production from the perspective of the intelligent economy. The results show that AI presents an “inverted U-shaped” characteristic in relation to carbon emissions embodied in manufacturing production, that is, it has a “carbon-increasing” effect in the early stage and a “carbon-reducing” effect in the later stage. This conclusion remains valid after a series of robustness tests. Mechanism analysis indicates that AI jointly affects carbon emissions embodied in manufacturing production by improving the technical level of manufacturing production and energy utilization efficiency, but there is certain national heterogeneity in the relevant transmission paths, with green inflection points appearing earlier in developed countries. Heterogeneity analysis shows that AI first reduces and then expands the carbon emission gap between different manufacturing industries, and at the same time, the carbon reduction effect on industries varies significantly due to differences in technical gaps, production energy consumption, and the status of intelligent applications. Therefore, China should accelerate the promotion and application of AI in the manufacturing industry, enhance the transmission effect of the manufacturing industry’s production technology level and energy utilization efficiency on carbon emission reduction in the manufacturing industry, and at the same time, rationally plan the industrial layout of AI investment to fully release the carbon emission reduction capacity of AI. Full article

Background/Objectives: Accurate preoperative characterization of ovarian masses is essential for appropriate clinical management, particularly for borderline ovarian tumors (BOTs), which are less common and often difficult to distinguish from benign or malignant lesions on ultrasound. Although expert subjective ultrasound assessment achieves high diagnostic accuracy, limited availability of highly trained sonologists restricts its widespread application. Artificial intelligence-based approaches offer a potential solution; however, the low prevalence of BOTs restricts the development of robust deep learning models due to severe class imbalance. This study aimed to develop a Convolutional Neural Network (CNN)-based classifier enhanced with Generative Adversarial Networks (GANs) to improve the discrimination of ovarian masses as benign, malignant, or BOT using ultrasound images. Methods: A total of 3816 ultrasound images from 636 ovarian masses were retrospectively analyzed, including 390 benign lesions, 202 malignant tumors, and 44 BOTs. To address class imbalance, a Deep Convolutional GAN (DCGAN) was used to generate 2000 synthetic BOT images for data augmentation. A three-class ensemble CNN model integrating VGG16, ResNet50, and InceptionNetV3 architectures was developed. Performance was assessed on an independent test set and compared with a baseline model trained without DCGAN augmentation. Results: The incorporation of DCGAN-generated BOT images significantly enhanced classification performance. The BOT F1-score increased from 68.4% to 86.5%, while overall accuracy improved from 84.7% to 91.5%. For BOT identification, the final model achieved a sensitivity of 88.2% and specificity of 85.1%. Class-specific AUCs were 0.96 for benign lesions, 0.94 for malignant tumors, and 0.91 for BOTs. Conclusions: DCGAN-based augmentation effectively expands limited ultrasound datasets and improves CNN performance, particularly for BOT detection. This approach demonstrates potential as a decision support tool for preoperative assessment of ovarian masses. Full article