Visual false pattern perception can increase in response to a lack of control. However, prior studies have manipulated control over relatively long timescales by altering participants' beliefs. We investigated whether short-timescale sensorimotor discrepancies, which are known to disrupt sense of agency, also lead to an increase in false perceptions. Participants were instructed either to voluntarily press a key, which triggered immediate or delayed visual feedback (i.e., white noise images with or without an embedded object), or to passively observe the feedback. We also explored metacognition in visual detection using post-trial confidence ratings. Sense of agency over visual feedback was successfully manipulated via voluntary keypress and feedback delay. Contrary to our hypothesis, we found no evidence that reduced sense of agency was associated with increased visual false perception. However, when participants experienced sense of agency over the feedback, both correct detection (i.e., hit rates) and metacognitive sensitivity were enhanced. This improvement in detection appeared to reflect a shift in response bias rather than a change in perceptual sensitivity. We found no effect of feedback delay on detection or metacognitive sensitivity. These results suggest that voluntary action, rather than sense of agency per se arising from feedback congruence, plays a greater role in shaping perception of action-related feedback.

K-mer counts are fundamental in many genomic data analysis tasks, providing valuable information for genome assembly, error correction, and variant detection. State-of-the-art k-mer counting tools employ various techniques, such as parallelism, probabilistic data structures, and disk utilization, to efficiently extract k-mer frequencies from large datasets. The distribution of k-mer counts in raw sequencing reads reveals key genomic characteristics such as genome size, heterozygosity, and basecalling quality. The number of reads containing a k-mer has also shown application in genome assembly and sequence analysis. We present ntStat, a toolkit that employs succinct Bloom filter data structures to track both k-mer count and depth information and use in downstream applications. ntStat models the k-mer count histogram using evolutionary computation, and infers valuable insights about the genome, sequencing data, and individual k-mers, de novo. ntStat consistently ran faster than DSK, BFCounter, hackgap, and Squeakr in all of our tests. Jellyfish performed faster than ntStat for human data with k = 25 but fell behind with k = 64. KMC3 was faster overall but at a high disk usage and memory cost. ntStat also used less memory than other non-disk-based k-mer counters and typically, 99.5-99.9% of the k-mers processed by ntStat are counted correctly. ntStat’s histogram analysis module detected heterozygosity percentages and k-mer coverage for long-read datasets simulated from a diploid human genome with less than 1% and 0.5-fold difference to the ground truth. The analysis of simulated long read datasets showed an average error of just 2% in k-mer robustness estimates.

The errors of human annotation and the noise of the environment such as lighting changes, occlusions and cluttered backdrop limit the correct detection of the plant diseases in the field condition. The research hypothesis is to present a robust deep learning model that can withstand noise and be interpretable in controlled and noisy environments to achieve high plant disease classification. The hybrid EfficientNet-Vision Transformer (ViT) network proposed is based on an EfficientNet-B4 branch of CNN and a branch of Vision Transformer (ViT) network, which focuses on capturing fine-grained lesion features and global contexts information. A data augmentation pipeline based on CycleGAN is used to introduce field-style distortions (e.g., (lighting shifts, shadowing, debris and partial occlusions), to be more robust to environmental noise, and an Adaptive Symmetric Cross-Entropy (ASCE) loss identifies and down-weights uncertain samples with normalized prediction entropy. The training is done in two phases, Stage 1 pretraining with clean images of PlantVillage and Stage 2 with increasingly noisy samples. The framework is tested in two different noise conditions, and these include the controlled synthetic label noise with PlantVillage and the real environmental noise with PlantDoc. The proposed model has an accuracy of 94.5% on the clean PlantVillage test set. It achieves 85.0% accuracy on the PlantVillage dataset under the 20% synthetic label noise protocol, outperforming ResNet-50V2 (76.5%), DenseNet-121 (78.9%), and Co-Teaching (79.5%). Macro-precision, macro-recall and macro-F1 of the model on the external PlantDoc field dataset are 0.718, 0.681, 0.681, respectively with a top-1 accuracy of 72.0, which is a manifestation of cross-domain generalization. The lesion-centric Grad-CAM images indicate that the model places emphasis on symptomatic areas of leaves and represses reactions of background soil, shadows, and clutters. The suggested hybrid EfficientNet-ViT architecture offers, in general, a robust and explainable solution to precision agriculture and intelligent crop tracking systems that are resistant to noise.

Anonymous networks are designed for confidentiality, while simultaneously keeping the sender’s identity hidden from all nodes except the designated receiver. The decryption process requires the actual receiver to try out all possible keys stored in memory, thereby making computation very time-consuming, especially in noisy conditions where errors and hash collisions occur. This paper proposes a probabilistic algorithm to reduce the number of keys that must be processed, without compromising accuracy. The algorithm models the decryption process as a discrete Markov chain the Bernoulli formula in all its facets to calculate the probability of correct key detection and thereby determine an optimal stopping condition. Simulation results with 15 sources and an error probability of 0.15 exhibited up to a 14% reduction in the number of key trials required compared to brute-force search, with expected higher efficiencies for larger network sizes. This research contribution introduces a lightweight, scalable model that reduces computational cost, saves energy, and extends node lifetime. These findings stress the importance of providing a practical means for enhancing the performance of anonymous communication systems, especially while working under a resource-constrained environment such as the IoT and decentralized networks.

Rapid eye movement (REM) sleep is increasingly understood as a heterogeneous state composed of two neurophysiologically distinct microstates: tonic REM and phasic REM. Phasic REM, defined by brief clusters of saccadic eye movements and transient cortical activation, has been linked to emotional memory consolidation, sensorimotor integration, and autonomic modulation. Despite its importance, automated quantification of phasic versus tonic REM remains uncommon, mainly because existing electrooculography (EOG) methods rely on fixed thresholds or generic wavelet families that do not accurately capture real saccade morphology in clinical polysomnography (PSG). This study introduces a fully automated framework for detecting phasic REM based on hybrid adaptive segmentation of a single EOG channel. The segmentation algorithm fuses median absolute deviation (MAD) amplitude-change detection with a morphology score derived from a custom saccade kernel built from manually verified EyeCon recordings. Segment boundaries are refined using local derivative extrema to improve temporal alignment. A supervised support vector machine (SVM) classifier further refines segment labels using features based on saccade morphology, including correlations with custom log-sigmoid templates and a morphology similarity measure. All segmentation and classification hyperparameters were optimized exclusively on controlled EyeCon datasets with precise ground-truth event markers. The final model was then applied without modification to 21 full-night clinical PSG recordings. Event-level analysis on EyeCon yielded 92.9% correct detections, with 5.3% fragmentation and 1.8% missed events. When aggregated into saccadic bursts, the resulting REM microstructure was physiologically consistent: phasic REM accounted for 31.8 ± 3.5% of REM duration, and tonic REM for 68.2 ± 3.5%. Additional EEG analysis confirmed increased beta and gamma power during phasic REM, supporting physiological validity. The proposed framework provides an interpretable, morphology-aware, and computationally efficient tool for large-scale REM microstructure research. Its single-channel design and external validation on clinical PSG recordings make it suitable for both retrospective analyses and future clinical applications.

Distinguishing between intake of the prohibited substance trimetazidine from administration of approved migraine medicine containing the nonprohibited substance, lomerizine, is crucial for anti-doping control laboratories. Investigations in males after lomerizine intake have been conducted leaving lomerizine M6 as the most useful metabolite. To our knowledge, the excretion profile of lomerizine among women has not been studied and leaves a potential gap to be misused by athletes in the case of appeal of a reported adverse analytical finding. We have investigated lomerizine M6 in women to address the gender-specific biology to correctly detect misusage of the prohibited substance trimetazidine within sports. To determine the importance of lomerizine M6 in trimetazidine cases, we analyzed urine samples collected from 12 individuals (eight females and four males) over 144 h, after oral intake of 5-mg lomerizine dihydrochloride. A dilute and shoot method was used, and the samples were analyzed on a LC-HRMS instrument. In all study subjects, trimetazidine was found from 2 h up to 48 h, and lomerizine M6 was detected from 2 h and onward. Lomerizine was only detected above the limit of identification of the method for a few of the subjects. The study demonstrated that by monitoring lomerizine M6, it was possible to determine the origin of trimetazidine in women. The metabolism for lomerizine dihydrochloride does not appear to differ significantly between genders. However, lomerizine M6 concentrations are not always higher than trimetazidine concentrations. Special care should be taken when interpreting trimetazidine concentrations at 1 ng/mL or below.

Abstract. This study proposes a multi-source methodology for monitoring bathymetry in continental water bodies in central Mexico by integrating in-situ and satellite-based techniques. A 3D-printed Unmanned Surface Vehicle (USV), equipped with echo sounders and GPS, was used to collect high-resolution depth data from five dams: Cointzio and Queréndaro (Michoacán) and Mata, Soledad, and Esperanza (Guanajuato). This data was correlated with Sentinel-2 imagery accessed through the Microsoft Planetary Computer, which provided multispectral, spatio-temporal data across different seasons. To assess water body delineation accuracy, several water indices were compared, including the Normalized Difference Water Index (NDWI), Automated Water Extraction Index with shadows (AWEI_sh), without shadows (AWEI_nsh), Modified NDWI (MNDWI), Sentinel Multi-Band Water Index (SMBWI), and Sentinel-2 Water Index (SWI), along with the Scene Classification Layer (SCL). SWI consistently yielded the most reliable contours. Although the SCL layer struggled in areas with dense aquatic vegetation, misclassifying water surfaces, it proved useful when combined with SWI. This integration produced the most accurate results for most dams— except in Queréndaro, where dense hyacinth cover in parts of the water body and irrigation agriculture in the surroundings impedes the correct detection of water body boundaries. A strong correlation between USV data and satellite-derived contours confirms that combining in-situ and remote sensing sources offers a robust and precise framework for bathymetric mapping in inland waters of Mexico.

ABSTRACT Cydia pomonella L. (Lepidoptera: Tortricidae) is a major pest in pome fruit production, requiring accurate monitoring and timely interventions. This study provides key insights into trap design and performance validation and supports the integration of automated technologies into sustainable pest management tactics. We present the development and 3‐year field validation of a fully automated prototype trap, equipped with a camera and a YOLOv8‐based object detection model, for remote identification and counting of C. pomonella . In a laboratory setting, the YOLOv8‐based system outperformed a previously published rule‐based system (ImageJ1 + CNN). The manually evaluated proportion of correct detections and proportion of detections found (precision = 0.77 and recall = 0.83, respectively) indicated strong model performance. A moderate decline under field conditions was observed (precision = 0.65, recall = 0.63). Over six field experiments between 2022 and 2024, the prototype trap performed comparably to or better than commercial delta traps. No significant differences were observed among C. pomonella captures in white, orange, red, or green traps (with a mean cumulative capture per trap = 13.25), but white traps captured significantly more non‐target insects. Trap entrance size also influenced capture performance under specific field conditions, and in one instance, the narrow‐opening prototype captured significantly more C. pomonella than the wide‐opening prototype (mean captures of 21.36 and 10.71, respectively). The system offers a reliable, effective, scalable, and automated solution for C. pomonella monitoring.

BackgroundExtracting temporally sensitive outcomes such as tumor progression from unstructured electronic medical records (EMRs) remains a major challenge in oncology. This study evaluates a solution with a domain-adapted natural language processing (NLP) pipeline designed to extract structured, temporally anchored clinical outcomes from narrative EMR data.Patients and methodsPatients with oncogene-addicted advanced or metastatic non-small-cell lung cancer (NSCLC) treated with oral targeted therapies between January 2020 and June 2023 at a French academic hospital were included. Extracted Facts were benchmarked against expert annotations. All outputs were mapped to Observational Medical Outcome Partnership vocabularies. F1-scores were calculated for the correct Concept detection without and with their Temporality. Real-world progression-free survival (rwPFS) was estimated based on retrieved clinical outcomes.ResultsAmong 1030 NSCLC patients treated between 2020 and 2023, 112 were confirmed to have advanced or metastatic disease with an oncogenic driver mutation, primarily EGFR (n = 66), ALK (n = 23), and KRAS (n = 16). The NLP pipeline achieved high accuracy in extracting clinical concepts, with an F1-score of 79.7% for tumor evolution concepts and 62.0% when temporality was included. Overall performance across all domains reached F1-scores of 76.5% for concept extraction and 63.7% with temporality. Median rwPFS was 21.9 months for EGFR-mutated, 52.4 months for ALK-translocated, and 5.0 months for KRAS-mutant tumors, in line with published benchmarks. Reviewing automatically collected data was 5.8 times faster compared with manual collection.ConclusionsOur solution demonstrates robust performance for extracting temporally structured tumor outcomes from EMRs and supports the reconstruction of real-world endpoints in oncology.

Driver distraction, particularly mobile phone use while driving, remains one of the leading causes of road traffic incidents worldwide. In response to this issue and leveraging recent technological advances and increased access to intelligent systems, this research presents the development of an application running on an intelligent embedded architecture for the automatic detection of mobile phone use by drivers, integrating computer vision, inertial sensing, and edge computing. The system, based on the YOLOv8n model deployed on a Jetson Xavier NX 16Gb—Nvidia, combines real-time inference with an inertial activation mechanism and cloud storage via Firebase Firestore, enabling event capture and traceability through a lightweight web-based HMI interface. The proposed solution achieved an overall accuracy of 81%, an inference rate of 12.8 FPS, and an average power consumption of 8.4 W, demonstrating a balanced trade-off between performance, energy efficiency, and thermal stability. Experimental tests under diverse driving scenarios validated the effectiveness of the system, with its best performance recorded during daytime driving—83.3% correct detections—attributed to stable illumination and enhanced edge discriminability. These results confirm the feasibility of embedded artificial intelligence systems as effective tools for preventing driver distraction and advancing intelligent road safety.

Abstract Background Passive acoustic monitoring (PAM) enables continuous, non-invasive surveys of vocal species but requires careful validation of automated classifications. Critically, intra-specific vocal variation in animals across seasons is often overlooked, even though classifier performance depends on it. Using BirdNET, we analyzed over 20,000 three-second recordings from forest and alpine grassland habitats in Central Italy to assess how temporal changes in bird vocal behavior affect classification accuracy. For 37 species, we built logistic regression models relating manual validation outcomes to BirdNET confidence scores and sampling month. Results Including month as a covariate improved model performance for 32 species, revealing strong temporal variation in detection reliability linked to phenological phases. Species-specific confidence thresholds (CT) corresponding to a 90% probability of correct detection varied widely across months (ΔCT up to 0.9). Average model performance was high (AUC = 0.875; precision = 0.91). Conclusions These results demonstrate that dynamic, time-adjusted thresholds increase the robustness of semi-automatic detection workflows, enhancing the reliability of PAM-derived biodiversity assessments.

Detection and attribution of heat waves with the Multivariate Autoencoder Flow-Analogue Method (MvAE-AM)

Detection of thermokarst landforms in the European Arctic region using Earth observation and Artificial Intelligence

As embedded processors are increasingly deployed in safety-critical systems, the storage overhead associated with conventional error correction code (ECC) schemes presents a significant challenge for area-sensitive microarchitectures. This article presents a zero-instruction-storage-overhead microarchitecture that achieves robust error protection by leveraging intrinsic architectural redundancies. The proposed methodology leverages the latent structural and alignment redundancies within the instruction set architecture (ISA) to implement an address-correlated Hsiao (AC-Hsiao) encoding scheme. By embedding parity information directly into vacant instruction slots and establishing a mathematical dependence between syndromes and physical fetch addresses, the framework facilitates both memory bit-error correction and instruction-fetch path integrity verification without requiring dedicated check-bit storage. The architecture was implemented and validated on a 28-nm dual-issue SweRV EH1 processor core. The experimental results demonstrate that the design achieves 100% single-error correction and double-error detection (SEC-DED) coverage. Compared to a conventional Hamming-based ECC implementation, the methodology yields a memory reduction of the instruction cache (I-Cache) by 23.53% and the instruction closely-coupled memory (ICCM) by 17.95%, respectively. Relative to the unprotected baseline core, the logic integration incurs a 3.70% area increase and a 2.92% frequency penalty while maintaining native execution efficiency (2.90 DMIPS/MHz and 4.67 CoreMark/MHz). These results substantiate that the proposed strategy effectively offsets minor logic overhead with substantial memory savings and enhanced path-level reliability, providing a viable solution for high-integrity embedded processors.

Immunologic and genetic biomarkers in acute pancreatitis.

Projector-based Augmented Reality (AR) provides intuitive guidance by projecting virtual content directly onto a physical workspace. However, when projected images overlap real objects, they distort the camera view, and detection accuracy drops for models like MediaPipe and YOLO. Moreover, a phenomenon known as ‘Visual Echo’, situation that system mistakes bright projected shapes as real objects, can occur. In addition, the projector-camera response is highly non-linear, which makes simple real-time correction difficult. To overcome these issues, we present a two-stage image preprocessing algorithm designed to suppress projection interference. Our method combines Color Refinement based on a Color Transformation Table and masked Lightness Compensation to effectively remove projection artifacts and enhance the visibility of physical objects. Experimental results show that the algorithm significantly reduces positional error by 70.47% and instability by 70.17% in MediaPipe hand landmark detection, while achieving 100% correct detection rate and reducing positional error by 86.32% in YOLOv8 object detection by effectively eliminating visual echoes. Furthermore, our algorithm maintains real-time performance at 27.4 FPS, making it suitable for practical applications. We successfully demonstrate the robust performance of our method through three distinct use cases: AR-based virtual ring try-on, dining etiquette education, and assembly training, highlighting its potential to enhance the reliability of projector-based AR systems across various fields.

Respiratory rate (RR) is a sensitive parameter for indicating critical physiological deteriorations, however, its measurement remains the most inaccurate within the field of vital signs monitoring. This paper proposes three main contributions. Firstly, the development of a wireless stethoscope is proposed to optimize the fidelity of recorded respiratory sounds. Secondly, a new labeled biomedical database is presented that has been created capturing respiratory sounds using the proposed stethoscope and a commercial one. This database aims to be a valuable tool to others researchers in evaluating novel approaches for measuring this vital sign. Thirdly, a new method based on Orthogonal Non-negative Matrix Factorization (ONMF) is presented for the purpose of RR estimation highlighting that ONMF has never before been applied in this biomedical task to our knowledge. Furthermore, the proposed ONMF method has been improved by minimizing the detection of individual respiratory stages, inspiration or expiration, instead the complete respiratory cycle. Preliminary results indicated that the quality of the sound captured by the proposed stethoscope is comparable to that of the commercial stethoscope used in this study. The results indicate the superior performance of the ONMF approach over several signal processing-based methods and artificial intelligence-based methods, irrespective of the acoustic environment, achieving at least a 2.5 bpm reduction in Absolute Error (AE) compared to signal processing-based methods and at least a 0.6 bpm reduction compared to artificial intelligence-based methods. Finally, the proposed fine-tuning algorithm demonstrated a significant improvement in RR estimation, underscoring the task of correct detection of periodic respiratory patterns at the cycle rather than respiratory stage level to provide an accurate measurement of human respiration rate. • Development of a prototype of wireless stethoscope to capture respiratory sounds. • A database is created using respiratory sounds labeled with respiratory rates. • The respiratory rate is estimated applying a novel ONMF approach. • The ONMF performance is improved maximizing the detection of the respiratory cycle. • Results indicate a promising detection performance in noiseless and noise scenarios.

An algorithm is given for calculating the probability of correct detection of a signal with a given error for an inertial model of a single-channel receiving optical system operating in photon counting mode with a limited number of photons. When modeling the system, the proposed algorithm also allows us to estimate the minimum possible number of photons sufficient to achieve a given error in the probability of correct detection.

Coincidental Correctness (CC) arises when a test case executes faulty entity in a program without causing a failure. This phenomenon injects noise into coverage information, as CC tests weaken the connection between faulty entities and test failures. Since many fault localization (FL) approaches relies on analyzing test execution traces to locate faulty entities, the compromised reliability of test results directly undermines FL accuracy. Furthermore, the detrimental effects of CC extend beyond fault localization to subsequent software maintenance tasks like automatic program repair. Therefore, identifying and mitigating CC tests becomes critical not only for enhancing FL but also for ensuring robust software quality assurance. Thus, we propose FusionCC: an approach that applies multiscale coverage features and handcrafted features to fuse complementary feature representations for CC test case detection. Specifically, FusionCC first refines original coverage data by filtering out noisy irrelevant elements, then extracts multiscale features from the refined matrix, and finally fuses the coverage and handcrafted features to generate highly informative feature representations for CC detection. FusionCC realizes a comprehensive fusion of complementary features across different scales and from diverse sources, which significantly enhances the accuracy of CC detection. To evaluate the effectiveness of FusionCC, we conduct large-scale experiments on 277 faulty versions of six representative benchmarks. The experimental results show that FusionCC significantly improves CC detection (e.g., average improvements of 50.93% precision and 82.03% in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F_{1}$</tex-math></inline-formula> value compared to state-of-the-art CC detection approaches) and fault localization effectiveness (e.g., 10.33, 19.33, 25.67 average faults can be found in terms of Top-1, Top-3, Top-5 metrics at relabel strategy compared with state-of-the-art FL approaches).

ABSTRACT Capsule Camera and automated image analysis technologies offer one solution to the challenges of seabird monitoring; however, their application is often not straightforward and there is currently unlikely to be a universally applicable method across sites and species. Aims Evaluate a range of time-lapse camera and automated image analysis methods to monitor seabirds in various coastal settings. Provide recommendations and guidelines on the minimum equipment needed to obtain representative information from time-lapse images. Methods To establish an efficient and effective method to generate count and/or presence/absence data from images of seabirds, we assess the application of two image analysis methods: (1) bespoke and site-specific object detection, and (2) open-source and generalised object detection. Using a worked example of time-lapse photography of three coastal seabird roosts, we compare data from each image analysis method, evaluating performance and overall success relative to manually collected data from the same images. Results When detecting seabird presence/absence, we found no consistency in image analysis performance (3.2–94.7% correct detection of true presence; 9.8–98.2% correct detection of true absence). When generating counts, the performance of the different approaches varied (6.3–37.5% of automated counts were correct, relative to manual counts). Notably, whilst the bespoke method was the most successful when generating counts, both object detection methods consistently underestimated seabird abundance. Conclusions Bespoke, site-specific analyses could be required where count data are needed, whereas open-source generalized detection methods may be suitable where presence/absence data are sufficient to meet study aims. Automating image analysis is unlikely to remove the need for manual analysis, whether to produce training data sets, validate output, and/or compliment observations. By offering lessons learnt from a worked example and resultant recommendations, this study informs future projects aiming to use inexpensive and accessible image technologies for ecological research.