Search Results (27,407)

This study presents a comparative analysis of hyperspectral image segmentation algorithms for fruit defect detection under different illumination conditions. The research evaluates the performance of four segmentation methods (Spectral Angle Mapper, Random Forest, Support Vector Machine, and Neural Network) using three distinct illumination modes (local, simultaneous and sequential). The experimental setup employed hyperspectral imaging to assess tomato fruit samples, with data acquisition performed across the 450–850 nm spectral range. Quantitative metrics, including accuracy, error rate, precision, recall, F1-score, and Intersection over Union (IoU), were used to evaluate algorithm performance. Key findings indicate that Random Forest demonstrated superior performance across most metrics, particularly under simultaneous illumination conditions. The highest accuracy was achieved by Random Forest under sequential illumination (0.9971), while the best combination of segmentation metrics was obtained under simultaneous illumination, with an F1-score of 0.8996 and an IoU of 0.8176. The Neural Network showed competitive results. The Spectral Angle Mapper proved sensitive to illumination variations but excelled in specific scenarios requiring minimal memory usage. By demonstrating that acquisition protocol optimization can substantially improve segmentation performance, our results support the development of accurate, non-contact, high-throughput inspection systems and contribute to reducing postharvest losses and improving supply chain quality control. Full article

Objective: This study aims to investigate the protective effect of Shengmai San (SMS) against high-glucose (HG)-induced injury in neonatal rat ventricular myocytes (NRVMs) and to elucidate the underlying pharmacological molecular mechanisms. We hypothesize that SMS ameliorates HG-induced calcium homeostasis imbalance in NRVMs by improving mitochondrial energy metabolism disorder, and this protective effect is associated with the downregulation of oxidized and phosphorylated CaMKII expression to inhibit CaMKII signaling pathway overactivation. Herein, we verify this hypothesis by assessing mitochondrial function, calcium transients, sarcoplasmic reticulum (SR) calcium handling and CaMKII phosphorylation levels in NRVMs. Methods: First, ultra-high performance liquid chromatography–high resolution mass spectrometry was used to identify the chemical components of SMS to clarify its material basis. Primary NRVMs were then cultured under low-glucose (LG) or HG conditions, with 2% SMS-medicated serum (SMS-MS) as the experimental intervention, and NAC (ROS scavenger) and KN93 (CaMKII inhibitor) as positive controls. Following intervention, we sequentially detected key indicators corresponding to the proposed pathological pathway: intracellular reactive oxygen species (ROS) levels (oxidative stress), mitochondrial ROS, mitochondrial function indices including oxygen consumption rate (OCR) (energy metabolism), calcium transients and diastolic intracellular free calcium concentration (global calcium homeostasis), sarcoplasmic reticulum (SR) calcium leak (calcium handling disorder), and, finally, the phosphorylation, oxidation levels of CaMKII and RyR2 phosphorylation (Ser2814) (p-RyR2) (key regulatory pathway) via Western blot to systematically elucidate the mechanistic link between SMS intervention and HG-induced NRVM injury. Results: Quantitative analysis revealed that high-glucose (HG) induction significantly reduced calcium transient amplitude and prolonged the decay time constant (tau) in NRVMs at 72 h (p < 0.01 vs. LG), with these parameters normalizing by 120 h—an effect indicative of a compensatory adaptive response. The 2%SMS-MS markedly ameliorated HG-induced calcium transient abnormalities at 72 h (p < 0.01 vs. HG). Additionally, 2%SMS-MS significantly enhanced mitochondrial basal oxygen consumption rate, spare respiratory capacity, ATP production, and maximal respiration in HG-exposed NRVMs (p < 0.01 vs. HG). SMS also significantly reduced intracellular reactive oxygen species (ROS) levels (p < 0.01 vs. HG), mitochondrial ROS levels (p < 0.01 vs. HG), diastolic intracellular free calcium concentration (p < 0.01 vs. HG), and SR calcium leak (p < 0.05 vs. HG). Western blot analysis revealed that 2%SMS-MS intervention effectively downregulated the expression of oxidized CaMKII (Ox-CaMKII) (p < 0.01 vs. HG), phosphorylated CaMKII (p-CaMKII) (p < 0.01 vs. HG), and RyR2 phosphorylation (Ser2814) (p < 0.05 vs. HG), which may be the potential mechanism in maintaining calcium homeostasis in HG-induced NRVMs. Conclusions: This study suggests that SMS enhances mitochondrial energy metabolism and exerts a protective effect against high-glucose-induced calcium homeostasis imbalance in NRVMs, which supports our proposed hypothesis. Its potential mechanism indicates that the protective effects of SMS are associated with its ability to downregulate the expression of oxidized and phosphorylated CaMKII. These findings highlight SMS as a potential therapeutic candidate for alleviating HG-related myocardial injury and provide evidence for its application in the prevention of early diabetic cardiomyopathy. Full article

In the field of engineering, the advancement of automated scoring systems for laboratory reports has been significantly hampered by three persistent challenges: scarcity of high-quality annotated data, high domain-specific complexity, and insufficient model interpretability. To address these limitations, this study proposes an AdaBoost regression model based on multi-level feature engineering and threshold control, denoted as MFTC-ABR. This method constructs a multi-dimensional feature set using a lightweight neural network, which evaluates laboratory reports across four core dimensions: comprehension of experimental principles, completion of experimental procedures, depth of result analysis, and plagiarism detection. At the scoring algorithm level, a dynamic threshold adjustment mechanism is integrated into the AdaBoostReg ensemble learning framework. By redesigning the sample weight update rule, the prediction errors of samples are divided into three intervals: the acceptable region, the stable learning range, and the focus range. Accordingly, a differentiated weight update strategy is implemented, and a history-aware mechanism is introduced to further regulate the attention allocated to individual samples. Finally, experimental results on the power electronics laboratory report dataset show that MFTC-ABR model achieves a mean absolute error (MAE) of 3.09 and a scoring consistency rate of 82% within a five-point error tolerance. These findings validate the effectiveness and practicability of the proposed method for automatic assessment in specialized domains with limited data availability. Full article

This paper presents an onboard, markerless perching system for a quadrotor UAV, validated in outdoor flight experiments, to reduce hovering energy during long-endurance unmanned missions. Existing autonomous landing research predominantly focuses on planar surfaces, cooperative environments with visual markers, or specialized hardware, limiting scalability to scenarios requiring detection and perching on thin rod-like targets in uncooperative outdoor settings. This study proposes a markerless perching system for autonomously perching a drone on a hurdle’s horizontal bar. The system employs a single-axis gimbal camera, altitude LiDAR, and ToF sensor, integrating perception, post-processing, and control. On the perception side, we augment a YOLOv12n-based segmentation model with a high-resolution P2 pathway for small-object detection and apply module compression for real-time inference on edge devices. Robustness is improved by jointly utilizing the full hurdle and horizontal bar while constructing negative samples to suppress false positives. On the control side, a state machine controller leverages centroid coordinates, orientation, and distance measurements to achieve a stable long-range approach and precise close-range alignment. Experiments on a Jetson Orin NX-based system demonstrate successful perching in all six outdoor flight tests. Ablation studies quantitatively analyze each component’s contribution to perching success rate and completion time. This research validates perching technology’s practical applicability through outdoor markerless perching on thin 3D structures. Full article

SARS-CoV-2 continues to cause recurrent waves in the post-pandemic period, yet genomic data from Southeast Asia remain limited for several emerging Omicron lineages, including NB.1.8.1. In this study, routine acute respiratory infection (ARI) surveillance performed in Bangkok, Thailand, from January to December 2025 was integrated with real-time RT-PCR testing and complete spike-gene sequencing for lineage assignment and evolutionary analysis. Among 4756 ARI specimens, 473 (9.9%) tested positive for SARS-CoV-2. Positivity increased in late April, peaked in May (epidemiological week 21; 58.4%), and declined through late June. Lineage typing was successful for 165/473 positive samples (34.9%), identifying 16 Pango lineages. Early 2025 showed heterogeneous circulation, including XEC- and XEC.8-related lineages, whereas NB.1.8.1 predominated during the main wave, accounting for 92.4% of typed cases in May and 89.8% in June. No recombination signals meeting predefined criteria were detected in the spike dataset. The mean spike substitution rate was estimated at 1.11 × 10⁻³ substitutions/site/year (95% HPD, 9.13 × 10⁻⁴–1.31 × 10⁻³), and the major Thai-containing NB.1.8.1 clade had an estimated tMRCA of 17 July 2024. These findings show that routine ARI surveillance combined with spike-based genomics can provide timely insights into SARS-CoV-2 circulation, lineage replacement, and ongoing viral evolution in Thailand. Full article

Antivirus (AV) solutions remain a core defence mechanism against malicious software. However, many of these engines struggle to detect metamorphic malware, which continually alters its internal form in unpredictable ways. To address this limitation, we present an adversarially oriented approach that automatically generates novel malicious variants of existing malware that evade detection by a substantial proportion of AV systems, thereby providing material for strengthening defensive techniques. In this work, an Evolutionary Algorithm (EA) is used to evolve undetectable variants, guided by three fitness criteria: the evasiveness of the produced samples, and their behavioural and structural similarity to the original malware. The proposed method is assessed across three malware families to evaluate the effectiveness of the EA-generated variants. Results indicate that the EA produces diverse mutant variants capable of evading up to 94% of AV detectors for a given malware family, significantly surpassing the evasion rate of the original malware. Furthermore, we evaluated whether the mutants produced by the EA could enhance the training of machine learning models. In this context, a pretrained Natural Language Processing (NLP) transformer was employed within a transfer learning framework to improve the classification of metamorphic malware. When the evolved variants were incorporated into the training data, the approach achieved classification accuracies of up to 93%. These results highlight the value of using diverse EA-generated samples to strengthen malware classifiers, thereby improving the robustness of security systems against evolving threats. Full article

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

This study presents HGRN2-based Flexible Dynamic Encoder Personal VAD (FDE-HGRN2), a recurrent framework for personal voice activity detection (PVAD). Building on the original LSTM-based FDE-RNN backbone, we replace all recurrent modules with the recently introduced HGRN2 gated linear RNN and adopt a cosine-annealing learning rate schedule to improve both detection accuracy and efficiency. HGRN2 uses gated linear recurrence with non-parametric state expansion, enlarging the recurrent state without increasing the number of trainable parameters and enabling more expressive long-range temporal modeling than conventional LSTMs. We evaluate FDE-HGRN2 on a LibriSpeech-derived PVAD benchmark, where multi-speaker mixtures are constructed by concatenating one to three speakers per utterance and randomly designating a target speaker, following established PVAD data construction practices to ensure direct comparability with prior work. The system uses 40-dimensional Mel-filterbank features as acoustic inputs and conditions the detector on 256-dimensional d-vector embeddings extracted from a pretrained speaker verification network. Experimental results show that FDE-HGRN2 consistently outperforms the original FDE-RNN baseline and several state-of-the-art PVAD models in terms of mean Average Precision and frame-level accuracy, while reducing the parameter count of the recurrent backbone by roughly 15% and yielding substantially smaller models than many competing systems. These findings indicate that HGRN2 provides a more temporally expressive and parameter-efficient alternative to LSTM for PVAD, offering a favorable accuracy–efficiency trade-off for real-world, deployment-oriented personalized speech interfaces. Full article

Objective: The application of artificial intelligence (AI) in orthodontics has evolved rapidly in recent years, encompassing areas such as diagnosis, treatment planning, and patient management, and AlimGPT is an AI-based tool that provides treatment options based on data and algorithms. Methods: Fourteen different orthodontic questions were asked to each model, and answers were analyzed. This study aimed to compare AlimGPT with GPT-4o, Gemini, and Llama using standardized tests to evaluate the quality of information provided, including the Likert scale, modified DISCERN (mDISCERN), and modified Global Quality Score (mGQS). Results: Significant differences were detected for reliability (χ² = 15.267, p = 0.0016) and usefulness (χ² = 20.557, p = 0.0001). Post hoc tests showed AlimGPT > Gemini and Llama for reliability and AlimGPT > GPT-4o, Gemini, and Llama for usefulness. mDISCERN was significant overall (χ² = 11.047, p = 0.0115), but no pairwise contrast met adjusted significance; mGQS showed no significant differences (χ² = 7.071, p = 0.0697). Inter-rater agreement was moderate-to-good for reliability (ICC = 0.710, 95% CI 0.60–0.80) and usefulness (ICC = 0.729, 95% CI 0.63–0.82), moderate for mGQS (ICC = 0.596, 95% CI 0.47–0.71), and poor-to-moderate for mDISCERN (ICC = 0.435, 95% CI 0.30–0.58). Conclusions: In this blinded, within-subjects experiment, the domain-specific model (AlimGPT) received higher clinician ratings for usefulness and, for reliability, exceeded two general baselines. Differences in mGQS were not detected. Expanding the number of raters, increasing item diversity or integrating updated baselines would be beneficial. Full article

Background/Objectives: Enterococcus spp. are important indicators of AMR and potential opportunistic pathogens. Urban green spaces, frequented by dogs and humans, may serve as reservoirs for resistant bacteria. This study assessed the occurrence, AMR profiles, and virulence traits of Enterococcus spp. in dog feces from urban green spaces in Porto (Portugal). Methods: In December 2023 and May 2024, 240 dog fecal samples were collected from 12 urban green spaces across Porto. Enterococcus spp. were isolated using selective culture, identified to species level, and tested for antimicrobial susceptibility following CLSI guidelines. PCR screening was performed for resistance genes (vanA, vanB, erm(A/B/C), vatD/E, tet(M/O/L/K)) and virulence genes (gelE, ace). Environmental and socioeconomic features, including vegetation density (NDVI), presence of water features, and neighborhood deprivation (EDI), were recorded to explore associations with bacterial occurrence and traits. Results: Thirty-two isolates were recovered, mainly E. faecium (n = 9) and E. faecalis (n = 7). High resistance rates were observed to tetracycline (56.3%) and quinupristin/dalfopristin (37.5%), with lower rates for vancomycin, teicoplanin, and ciprofloxacin (3.1%), and imipenem (6.3%). Tet(M) was the most prevalent resistance gene (40.6%), and gelE and ace were frequently detected, often co-occurring with resistance determinants. Distribution of resistance and virulence genes varied across green spaces, with widely used parks showing more isolates. Vegetation density and water features were not directly associated with bacterial recovery. Conclusions: Dog feces in urban green spaces contribute to localized AMR hotspots, acting as potential reservoirs of resistant and potentially pathogenic Enterococcus spp. These findings highlight the importance of One Health strategies for urban sanitation and AMR surveillance. Full article

Coral reef ecosystems are declining rapidly due to climate change, disease, and anthropogenic stressors, driving the expansion of land-based coral propagation for reef restoration. A major bottleneck in these efforts is the manual measurement of coral recruit tissue area from microscopy images, which requires 2–7 min per image and limits scalability. We present a hierarchical deep learning pipeline that automates this measurement by integrating YOLO-based detection with Segment Anything Model (SAM) segmentation. YOLO localizes recruits and classifies them by developmental stage; stage-specific fine-tuned SAM models then segment live tissue using bounding box and background point prompts to suppress segmentation leakage and improve boundary precision. Surface area is computed directly from the segmented masks using pixel size extracted from image metadata. The pipeline reduces processing time to approximately 3–5 s per image—a 24–140× speedup over manual tracing. Evaluated on 3668 microscopy images from two national coral research facilities, the system achieves a mean IoU exceeding 95% and an auto-acceptance rate (AAR) of 71.51%, where predicted-to-ground-truth area ratios fall within a ±5% tolerance of expert annotation, substantially reducing manual workload while maintaining measurement reliability across species, developmental stages, and imaging conditions. This workflow addresses a critical bottleneck in restoration research and demonstrates the broader applicability of AI-based image analysis in marine ecology. Full article

Rapid urbanization necessitates high-frequency monitoring of construction-ready bare land to timely detect and prevent illegal construction. However, the utility of optical imagery is often compromised in cloud-prone regions. While Synthetic Aperture Radar (SAR) offers all-weather capabilities, it struggles to distinguish construction-ready bare land from recently harvested agricultural land, leading to severe false alarms. To address the conflict between high-frequency monitoring and semantic identification, this study proposes the SAR-based Change Screening and Optical-Scene-Informed Identification (SCS-OI) framework. The first stage performs high-recall candidate screening based on SAR backscattering changes, while the second stage incorporates historical cloud-free optical imagery as semantic guidance, enabling refined identification without requiring synchronous optical data. Experiments in Guangzhou demonstrate that the framework achieves a False Alarm Rate of 13.31%, Recall of 90.63%, Precision of 74.81%, F1-score of 81.95%, and IoU of 69.43%. Compared with the SAR-only baseline (FR = 22.4%), the two-stage design reduces false alarms while maintaining high recall. Other deep learning baselines exhibit lower F1-scores (59–73%), highlighting the effectiveness of the overall framework. These results show that the proposed two-stage framework effectively integrates high-recall candidate screening and semantic-guided refinement, providing a robust solution for high-frequency monitoring of construction-ready bare land in cloud-prone regions of Guangzhou. Full article

With the widespread application of deep neural networks across various fields, issues related to model security have become increasingly prevalent. Backdoor attacks, as a covert method of attack, can implant malicious behavior during the model training process, causing the model to perform predetermined tasks under specific trigger conditions. However, current backdoor attacks struggle to achieve a good balance between stealthiness and attack success rate, and there is an issue in which certain data transformation operations can negatively impact attack performance. To address these issues, this paper proposes a specialized backdoor attack method called Ditto. It first uses a boundary detection algorithm and a padding algorithm to determine the trigger’s insertion position. The trigger is then dynamically generated using a generative adversarial network, taking into account the texture features of the images. Subsequently, the trigger is applied to the images, and its level of stealthiness is adjusted. Compared to existing popular backdoor attack methods, the experimental results ensure a high level of stealthiness while also maintaining a high attack success rate and a high accuracy for clean data. Furthermore, our attack method exhibits considerable robustness and adaptability, demonstrating effective resistance against baseline backdoor defense techniques. Full article

Purified water from vending machines offers consumers an alternative source of clean, safe water. However, data regarding its microbiological quality are limited, particularly concerning the prevalence of Pseudomonas aeruginosa harboring virulence traits. This study aimed to evaluate the microbiological quality of 125 purified water samples collected from vending machines across six cities of Michoacan, Mexico. Additionally, it sought to assess the occurrence of Pseudomonas aeruginosa and characterize its antimicrobial resistance profiles and biofilm-forming capacity. Aerobic mesophilic bacteria (AMB) were detected in all analyzed samples. A total of 71 (56.8%), 40 (32.0%), and 31 (24.8%) samples were positive for total coliforms (TC), fecal coliforms (FC), and Escherichia coli, respectively. Among the samples, 43 (34.4%) were positive for P. aeruginosa. There were significant correlations between the presence of P. aeruginosa and AMB (rho = 0.4445; p < 0.0001), TC (rho = 0.4094; p < 0.0001), FC (rho = 0.3389; p = 0.0001), and E. coli (rho = 0.3242; p = 0.0002). Moreover, the presence of TC in purified water samples increased the risk of P. aeruginosa nearly seven-fold (odds ratio = 6.91; p < 0.001). The resistance rate among P. aeruginosa strains to the most tested antibiotics ranged from 2.3 to 16.3%, and two (4.6%) of the isolates were multidrug-resistant. All P. aeruginosa strains were strong biofilm producers. Consequently, we recommend periodic maintenance of vending machines, the establishment of P. aeruginosa control protocols, and enhanced regulatory monitoring of the water vending industry. Full article