Search Results (13,240)

In this research, we examined the potential sensor characteristics of branched polyethyleneimine (BPEI)-derived carbon dots (CDs) synthesized using BPEI as a nitrogen source and citric acid (CA) as a carbon source, specifically for the recognition of various metal ions. Among the BPEI CDs produced with different amounts of BPEI to CA BPEI:CA ratios of 0.5:1, 1:1, and 2:1 w/w, named as BPEI^0.5 CD, BPEI¹ CD, and BPEI² CD, respectively. The BPEI^0.5 CD, which contains the least BPEI, exhibited the highest fluorescence intensity: 50,300 a.u. in a 0.6 mg/mL solution were recorded as λ_em: 420 nm at λ_ex: 360 nm and 600 V PMT voltage with 5 nm of slit width for both excitation and emission. We investigated the fluorescence variations in BPEI CD-based CDs in 2 mL solutions containing Cd²⁺, Co²⁺, Cu²⁺, Ni²⁺, and Pb²⁺ metal ions at various concentrations. Amongst these metal ions, the most pronounced sensitivity was noted for Cu²⁺ ions with a limit of detection (LOD) value of 0.39 ppm. For BPEI CDs created with BPEI:CA ratios of 0.5:1, 1:1, and 2:1 w/w, the sensitivity to Cu²⁺ ions increased with a higher BPEI ratio, with a LOD value of 0.30 ppm recorded for BPEI² CDs. Moreover, Cu²⁺ ion solutions were prepared from various salts, including chloride, acetate, nitrate, and sulfate; aside from some fluorescence variation observed for BPEI^0.5 CDs, no significant difference in BPEI CD fluorescence change was observed with the use of the various salt solutions of Cu²⁺ ion. In quenching experiments conducted on mixtures of Cd²⁺, Co²⁺, Cu²⁺, Ni²⁺, and Pb²⁺ metal ions with Cu²⁺, it was noted that BPEI CDs displayed selectivity for Cu²⁺ ions. Furthermore, the structures of BPEI CDs have been effectively utilized in real water samples, such as tap water and seawater, demonstrating a quenching capability of over 65% in the presence of 50 ppm Cu²⁺ ions. Full article

Minimally Invasive Surgery is often limited by the lack of tactile feedback. Indeed, surgeons have traditionally relied heavily on tactile feedback to estimate tissue stiffness - a critical factor in both diagnostics and treatment. With this in mind we present in this paper a flexible sensor foil, based on polymer optical waveguide. This sensor has been applied for real-time contact force measurement, material stiffness differentiation and surface texture reconstruction. Interrogated by a commercially available optoelectronic device, the sensor foil offers precise and reproducible feedback of contact forces up to 5 N, with a minimal detectable limit of 0.1 N. It also demonstrates distinct optical attenuation responses when indenting silicone samples of varying stiffnesses under controlled displacement. When integrated onto a 3D-printed module resembling an endoscopic camera and manipulated by a robotic arm, the sensor successfully generated spatial stiffness mapsof a phantom. Moreover, by sliding over structures with varying surface textures, the sensor foil was able to reconstruct surface profiles based on the light attenuation responses. The results demonstrate that the presented sensor foil possesses great potential for surgical applications by providing additional haptic information to surgeons. Full article

Automated violence detection in video surveillance is critical for public safety; however, existing methods frequently suffer notable performance degradation across diverse real-world scenarios due to domain shift. Substantial distributional discrepancies between source training data and target environments severely hinder model generalization, limiting practical deployment. To overcome this, we propose CoMT-VD, a new contrastive Mean Teacher-based violence detection model, engineered for enhanced adaptability in unseen target domains. CoMT-VD innovatively integrates a Mean Teacher architecture to adequately leverage unlabeled target domain data, fostering stable, domain-invariant feature representations by enforcing consistency regularization between student and teacher networks, crucial for bridging the domain gap. Furthermore, to mitigate supervisory noise from pseudo-labels and refine the feature space, CoMT-VD incorporates a dual-strategy contrastive learning module. DCL systematically refines features through intra-sample consistency, minimizing latent space distances for compact representations, and inter-sample consistency, maximizing feature dissimilarity across distinct categories to sharpen decision boundaries. This dual regularization purifies the learned feature space, boosting discriminativeness while mitigating noisy pseudo-labels. Broad evaluations on five benchmark datasets unequivocally demonstrate that CoMT-VD achieves the superior generalization performance (in the four integrated scenarios from five benchmark datasets, the improvements were 5.0∼12.0%, 6.0∼12.5%, 5.0∼11.2%, 5.0∼11.2%, and 6.3∼12.3%, respectively), marking a notable advancement towards robust and reliable real-world violence detection systems. Full article

African swine fever (ASF), induced by the African swine fever virus (ASFV), is an acute hemorrhagic disease characterized by high fever, systemic hemorrhages, and elevated mortality. Current diagnostic techniques including PCR and ELISA present limitations in field applications due to requirements for specialized equipment and prolonged processing duration. Therefore, rapid and accurate detection of ASFV has become a key link in ASF prevention and control. This study established a rapid and precise visual diagnostic approach by integrating the CRISPR/AapCas12b system with lateral flow strip (LFS) technology, specifically targeting the B646L gene encoding the major capsid protein p72. The CRISPR/AapCas12b-LFS platform achieved a sensitivity threshold of 6 copies/µL for B646L gene detection, completing analysis within an hour. Validation study confirmed exceptional specificity against common porcine pathogens including PRRSV, CSFV, PRV, PPV4, and PCV3. The developed assay demonstrated complete concordance with real-time PCR results when analyzing 34 clinical specimens including three heart samples, three liver samples, three spleen samples, three lung samples, three kidney samples, three lymph node samples, five serum samples, five blood samples, and five oral swab samples for ASFV detection. Overall, this method is sensitive, specific, and practicable onsite for ASFV detection, showing a great application potential for monitoring ASFV in the field. Full article

General progressive Type-II censoring is widely applied in life-testing experiments to enhance efficiency by allowing early removal of surviving units, thereby reducing experimental time and cost. This paper develops exact inference and prediction procedures for one- and two-parameter exponential models based on multiple independent general progressively Type-II censored samples. Using the recursive algorithm repeatedly, exact confidence intervals for model parameters and exact prediction intervals for unobserved failure times are constructed. The proposed methods are illustrated with simulated and real (tire wear) data, demonstrating their practical applicability to partially censored reliability experiments. Full article

Accurate remote sensing scene classification is essential for applications such as environmental monitoring and disaster management. In real-world scenarios, particularly during emergency response and disaster relief operations, acquiring nadir-view satellite images is often infeasible due to cloud cover, satellite scheduling constraints, or dynamic scene conditions. Instead, off-nadir images are frequently captured and can provide enhanced spatial understanding through angular perspectives. However, remote sensing scene classification has primarily relied on nadir-view satellite or airborne imagery, leaving off-nadir perspectives largely unexplored. This study addresses this gap by introducing Off-nadir-Scene10, the first controlled and comprehensive benchmark dataset specifically designed for off-nadir satellite image scene classification. The Off-nadir-Scene10 dataset contains 5200 images across 10 common scene categories captured at 26 different off-nadir angles. All images were collected under controlled single-day conditions, ensuring that viewing geometry was the sole variable and effectively minimizing confounding factors such as illumination, atmospheric conditions, seasonal changes, and sensor characteristics. To effectively leverage abundant nadir imagery for advancing off-nadir scene classification, we propose an angle-aware active domain adaptation method that incorporates geometric considerations into sample selection and model adaptation processes. The method strategically selects informative off-nadir samples while transferring discriminative knowledge from nadir to off-nadir domains. The experimental results show that the method achieves consistent accuracy improvements across three different training ratios: 20%, 50%, and 80%. The comprehensive angular impact analysis reveals that models trained on larger off-nadir angles generalize better to smaller angles than vice versa, indicating that exposure to stronger geometric distortions promotes the learning of view-invariant features. This asymmetric transferability primarily stems from geometric perspective effects, as temporal, atmospheric, and sensor-related variations were rigorously minimized through controlled single-day image acquisition. Category-specific analysis demonstrates that angle-sensitive classes, such as sparse residential areas, benefit significantly from off-nadir viewing observations. This study provides a controlled foundation and practical guidance for developing robust, geometry-aware off-nadir scene classification systems. Full article

Bimodal emotion recognition based on audio and text is widely adopted in video-constrained real-world applications such as call centers and voice assistants. However, existing systems suffer from limited cross-domain generalization and monolingual bias. To address these limitations, a cross-lingual bimodal emotion recognition method is proposed, integrating Mamba-based temporal encoders for audio (Wav2Vec2.0) and text (Jina-v3) with a Transformer-based cross-modal fusion architecture (BiFormer). Three corpus-adaptive augmentation strategies are introduced: (1) Stacked Data Sampling, in which short utterances are concatenated to stabilize sequence length; (2) Label Smoothing Generation based on Large Language Model, where the Qwen3-4B model is prompted to detect subtle emotional cues missed by annotators, producing soft labels that reflect latent emotional co-occurrences; and (3) Text-to-Utterance Generation, in which emotionally labeled utterances are generated by ChatGPT-5 and synthesized into speech using the DIA-TTS model, enabling controlled creation of affective audio–text pairs without human annotation. BiFormer is trained jointly on the English Multimodal EmotionLines Dataset and the Russian Emotional Speech Dialogs corpus, enabling cross-lingual transfer without parallel data. Experimental results show that the optimal data augmentation strategy is corpus-dependent: Stacked Data Sampling achieves the best performance on short, noisy English utterances, while Label Smoothing Generation based on Large Language Model better captures nuanced emotional expressions in longer Russian utterances. Text-to-Utterance Generation does not yield a measurable gain due to current limitations in expressive speech synthesis. When combined, the two best performing strategies produce complementary improvements, establishing new state-of-the-art performance in both monolingual and cross-lingual settings. Full article

Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality globally, driven by marked molecular and cellular heterogeneity that complicates diagnosis and treatment. Despite advances in targeted therapies and immunotherapies, treatment resistance frequently emerges, and clinical benefits remain limited to specific molecular subtypes. To improve early detection and dynamic monitoring, novel diagnostic strategies—including liquid biopsy, low-dose computed tomography scans (CT) with radiomic analysis, and AI-integrated multi-modal platforms—are under active investigation. Non-invasive sampling of exhaled breath, saliva, and sputum, and high-throughput profiling of peripheral T-cell receptors and immune signatures offer promising, patient-friendly biomarker sources. In parallel, multi-omic technologies such as single-cell sequencing, spatial transcriptomics, and proteomics are providing granular insights into tumor evolution and immune interactions. The integration of these data with real-world clinical evidence and machine learning is refining predictive models and enabling more adaptive treatment strategies. Emerging therapeutic modalities—including antibody–drug conjugates, bispecific antibodies, and cancer vaccines—further expand the therapeutic landscape. This review synthesizes recent advances in NSCLC diagnostics and treatment, outlines key challenges, and highlights future directions to improve long-term outcomes. These advancements collectively improve personalized and effective management of NSCLC, offering hope for better-quality survival. Continued research and integration of cutting-edge technologies will be crucial to overcoming current challenges and achieving long-term clinical success. Full article

Text-based CAPTCHAs remain a widely deployed mechanism for mitigating automated attacks across web platforms. However, the increasing effectiveness of convolutional neural networks (CNNs) and advanced computer vision models poses significant challenges to their reliability as a security measure. This study presents a comprehensive forensic and security-oriented analysis of text-based CAPTCHA systems, focusing on how individual and combined visual distortion features affect human usability and machine solvability. A real-world dataset comprising 45,166 CAPTCHA samples was generated under controlled conditions, integrating diverse anti-recognition, anti-segmentation, and anti-classification features. Recognition performance was systematically evaluated using both a CNN-based solver and actual human interaction data collected through an online exam platform. Results reveal that while traditional features such as warping and distortion still degrade machine accuracy to some extent, newer features like the hollow scheme and multi-layer structures offer better resistance against CNN-based attacks while maintaining human readability. Correlation and SHAP-based analyses were employed to quantify feature influence and identify configurations that optimize human–machine separability. This work contributes a publicly available dataset and a feature-impact framework, enabling deeper investigations into adversarial robustness, CAPTCHA resistance modeling, and security-aware human interaction systems. The findings underscore the need for adaptive CAPTCHA mechanisms that are both human-centric and resilient against evolving AI-based attacks. Full article

The aramid honeycomb composite material plays an important role in industry. Defects of this material seriously influence its performance. However, conventional detecting tools such as X-ray or computer tomography (CT) imaging, ultrasonic testing, and visual inspection are not able to meet the requirements of fast, safe, and high resolution at the same time. In this study, we numerically use rapid terahertz time−domain spectroscopy (THz-TDS) to identify defects in the aramid paper composite structure effectively. Simulation results demonstrate that THz-TDS technology enables the non-destructive reflection imaging of layered defects in glass fiber covering and glue layers as supporting components within the composite structure, with a spatial resolution of 0.5 mm and a depth range exceeding 10 mm. During the study, the finite difference time domain (FDTD) simulation with a real pulse waveform is achieved, and the defect position can be recognized by the anomaly in the reflection profile when compared with the waveform reflected by non-defect samples. At the same time, it is found that the defect identification ability is obviously affected by the incident position. The numerical research illustrates that the detectable defect is as thick as 0.1 mm and has a diameter of 1 mm. The results will offer valuable guides to the real application of THz-TDS systems in the detection of a similar structure. Full article

To support the understanding of cardiovascular monitoring and physiological signal processing, we present a portable, open-source photoplethysmography (PPG) acquisition platform developed for educational and research applications. The system is built entirely with commercial off-the-shelf components and centers around an ESP32 microcontroller, which performs high-speed analog signal acquisition at 500 samples per second, alongside real-time control, and wireless communication. A cross-platform, Python-based graphical user interface enables real-time signal visualization, peak detection, and the computation of heart rate variability (HRV) metrics, including RMSSD and SDNN, during offline analysis. All hardware and software resources are openly available to enable replication and further development. This project emphasizes accessibility, transparency, and hands-on learning in biomedical signal acquisition. System functionality is validated offline through controlled data collection from human subjects, demonstrating results consistent with established HRV benchmarks. Full article

Macrolides are crucial for the management and treatment of bovine respiratory disease (BRD). However, antimicrobial resistance (AMR) threatens the efficacy of these and other antimicrobials. We developed real-time recombinase polymerase amplification (RPA) assays targeting three clinically relevant macrolide antimicrobial resistance genes (ARGs)—msrE-mphE and erm42—in ≤30 min using extracted DNA. A set of 199 deep nasopharyngeal swabs (DNPS) collected from feedlot calves near the time of arrival were selected based on bacterial culture (BC) results for Mannheimia haemolytica, Pasteurella multocida, and Histophilus somni and antimicrobial susceptibility testing (AST) for tulathromycin, tilmicosin, tildipirosin, or gamithromycin. Samples were also tested for the same targets using RPA and polymerase chain reaction (PCR). In samples that were culture-positive for one or more macrolide-resistant BRD-associated bacteria (n = 101), msrE-mphE and/or erm42 were detected in 95% of cases using RPA. The remaining 98 samples were either culture-negative, or the recovered bacteria were macrolide-susceptible: 43% of these were RPA-positive for at least one macrolide ARG. Together with BC-AST and PCR, Bayesian latent class modelling estimated the clinical sensitivity of RPA for macrolide ARGs to be 95% and specificity to be 58%, with moderate agreement between RPA and BC-AST (κ = 0.52) or PCR (κ = 0.55). The estimated sensitivity of the RPA multiplex assay for the targeted macrolide ARGs was very good, although estimated specificity was limited. However, Sanger sequencing confirmed RPA detection of msrE-mphE in BC-AST/PCR-negative samples (n = 23), reflecting the presence of this locus in non-target bacteria, as well as potential ARG variants among BRD bacteria. These findings support the potential of RPA for rapid ARG detection from extracted DNA. Continued assay optimization and evaluation for detection of respiratory bacteria and ARGs will further enhance its diagnostic utility. Full article

This study compared velocity-based training (VBT) with percentage-based training (PBT) on acceleration (30-m sprint) and explosive power in high school triple jump athletes. Twelve male national-level athletes were randomized (1:1, concealed allocation; blinded assessors) to VBT (n = 6) or PBT (n = 6). Both groups completed identical lower-body resistance training three times per week for eight weeks; the VBT group additionally received real-time barbell-velocity feedback with velocity-loss (VL) based set termination (15–20%). Performance was assessed using 30-m sprint, standing long jump (SLJ), standing triple jump (STJ), and vertical jump (VJ) at pre- and post-test. Statistical analysis included baseline-adjusted ANCOVA and effect sizes (Hedges’ g). VBT improved 30-m sprint (−1.08%, d = 0.89), SLJ (+2.07%, d = 1.02), STJ (+1.64%, d = 0.63), and VJ (+6.01%, d = 1.39; all p < 0.001). PBT also improved SLJ (+1.03%, d = 0.69; p < 0.001) and showed a moderate, statistically significant within-group gain in STJ (+0.56%, d = 0.72; p = 0.001), while improvements in 30-m sprint and VJ were modest. Between-group effects favored VBT across all outcomes. These preliminary findings suggest that VBT may provide more targeted neuromuscular adaptations than PBT, particularly in explosive movements relevant to triple jump performance. However, due to the modest sample size and limited precision, the results should be interpreted with caution and confirmed in larger, adequately powered randomized trials. Nevertheless, this study offers practical insight into load prescription for youth jump athletes and represents one of the first randomized trials to directly compare VBT and PBT in this population. Full article

The biogeochemical cycling of nitrogen (N) in natural waterbodies, ranging from freshwaters to estuaries and seawater, is fundamental to the health of aquatic ecosystems. Anthropogenic pressures (agricultural runoff, atmospheric deposition, and wastewater discharge) have profound effects on these cycles, leading to widespread problems, such as eutrophication, harmful algal blooms, and contamination of drinking water sources. Monitoring of different N-species—ammonium (NH₄⁺), nitrite (NO₂⁻), nitrate (NO₃⁻) ions, dissolved organic nitrogen (DON), and total nitrogen (TN)—is of crucial importance to protect and mitigate environmental harm. Traditional analytical methodologies, while providing accurate laboratory data, are hampered by logistical complexity, high cost, and the inability to capture transient environmental events in near-real time. In response to this demand, miniaturised microfluidic technologies offer the opportunity for rapid, on-site measurements with significantly reduced reagent/sample consumption and the development of portable sensors. Here, we review and critically evaluate the principles, state-of-the-art applications, inherent advantages, and ongoing challenges associated with the use of microfluidic colorimetry for N-species in a variety of environmental waterbodies. We explore adaptations of classical colorimetric chemistry to microfluidic-based formats, examine strategies to mitigate complex matrix interferences, and consider future trajectories with autonomous platforms and smart sensor networks for simultaneous multiplexed N-species determination. Full article

In this paper, the half-logistic generalized power Lindley distribution, a new two-parameter lifetime model for positive and heavy-tailed data, is proposed and studied. Several mathematical properties are derived, including closed-form expressions for the density, distribution, survival, hazard, and the Lambert W quantile function, as well as series expansions for moments, skewness, kurtosis, and Rényi entropy. Parameter estimation is performed using maximum likelihood and Bayesian methods, where Bayesian estimation is implemented via the Metropolis–Hastings algorithm. A Monte Carlo simulation study is conducted to evaluate the estimators’ performance, showing decreasing bias and mean squared error with larger samples. Finally, three real-world datasets are analyzed to demonstrate that the proposed distribution provides superior fit compared to Lindley-type competitors and the Weibull distribution, based on likelihood values, information criteria, and empirical diagnostics. Full article