Search Results (5,523)

Producing high-quality segmentation masks for medical images is a fundamental challenge in biomedical image analysis. Recent research has investigated the use of supervised learning with large volumes of labeled data to improve segmentation across medical imaging modalities and unsupervised learning with unlabeled data to segment without detailed annotations. However, a significant hurdle remains in constructing a model that can segment diverse medical images in a zero-shot manner without any annotations. In this work, we introduce the attention diffusion zero-shot unsupervised system (ADZUS), a new method that uses self-attention diffusion models to segment biomedical images without needing any prior labels. This method combines self-attention mechanisms to enable context-aware and detail-sensitive segmentations, with the strengths of the pre-trained diffusion model. The experimental results show that ADZUS outperformed state-of-the-art models on various medical imaging datasets, such as skin lesions, chest X-ray infections, and white blood cell segmentations. The model demonstrated significant improvements by achieving Dice scores ranging from 88.7% to 92.9% and IoU scores from 66.3% to 93.3%. The success of the ADZUS model in zero-shot settings could lower the costs of labeling data and help it adapt to new medical imaging tasks, improving the diagnostic capabilities of AI-based medical imaging technologies. Full article

Prenatal maternal stress (PNMS) predicts risk for autism spectrum disorders (ASD), although the mechanisms are unknown. Because ASD and autistic-like traits have been associated with both prenatal stress and DNA methylation differences, it is important to examine whether epigenetic mechanisms mediate the pathway from PNMS to later autistic-like outcomes. This study aimed to determine the extent to which DNA methylation mediates the association between PNMS from a natural disaster and autistic-like traits in offspring assessed during adolescence. Five months following the 1998 ice storm in Quebec, we recruited women who had been pregnant during the crisis and assessed their PNMS: objective hardship, subjective distress, and cognitive appraisal. At age 13, their children provided blood samples for DNA. At ages 15, 16 and 19, the youth self-reported their own autistic-like traits using the Broad Autism Phenotype Questionnaire. This longitudinal design allowed us to track the developmental pathway from prenatal exposure, through adolescent DNA methylation, to later behavioral outcomes. Analyses included youth with data on PNMS, DNA methylation, and the BAPQ (n = 27 at age 15; 22 at age 16; and 13 at age 19). Results showed that mothers’ disaster-related objective hardship and their negative cognitive appraisal of the disaster were associated with DNA methylation at age 13, which then were associated with the severity of their children’s Aloof Personality and Pragmatic Language Deficits, but not Rigid Personality, at ages 15, 16 and 19. Mediation was significant particularly through genes within the PI3K/AKT/mTOR pathway, which has been implicated in various neurodevelopmental disorders, including ASD. Interestingly, while greater PNMS predicted more severe ASD traits, the epigenetics effects were for less severe traits. Although other interpretations are possible, these results could suggest that DNA methylation, assessed in early adolescence, may protect against ASD traits at later ages, particularly when there is a mismatch between the prenatal environment (disaster) and the postnatal environment (absence of disaster). The interpretation of these findings benefits from the longitudinal design and is discussed in the context of fetal programming and the predictive adaptive response. Full article

The growth plate is a specialized cartilage structure near the ends of long bones that orchestrates longitudinal bone growth during fetal and postnatal stages. Within this region reside a dynamic population of growth plate skeletal stem cells (gpSSCs), primarily located in the resting zone, which possess self-renewal and multilineage differentiation capacity. Recent advances in cell-lineage tracing, single-cell transcriptomics, and in vivo functional studies have revealed distinct subpopulations of gpSSCs, which are defined by markers such as parathyroid hormone-related protein (PTHrP), CD73, axis inhibition protein 2 (Axin2), forkhead box protein A2 (FoxA2), and apolipoprotein E (ApoE). These stem cells interact intricately with their niche, particularly after the formation of the secondary ossification center, through stage-specific regulatory mechanisms involving several key signaling pathways. This review summarizes the current understanding of gpSSC identity, behavior, and regulation, focusing on how these cells sustain growth plate function through adapting to biomechanical and molecular cues. Full article

To address the challenges of multi-scale missed detections, false positives, and incomplete boundary segmentation in cotton field residual plastic film detection, this study proposes the RSE-YOLO-Seg model. First, a PKI module (adaptive receptive field) is integrated into the C3K2 block and combined with the SegNext attention mechanism (multi-scale convolutional kernels) to capture multi-scale residual film features. Second, RFCAConv replaces standard convolutional layers to differentially process regions and receptive fields of different sizes, and an Efficient-Head is designed to reduce parameters. Finally, an NM-IoU loss function is proposed to enhance small residual film detection and boundary segmentation. Experiments on a self-constructed dataset show that RSE-YOLO-Seg improves the object detection average precision (mAP50(B)) by 3% and mask segmentation average precision (mAP50(M)) by 2.7% compared with the baseline, with all module improvements being statistically significant (p < 0.05). Across four complex scenarios, it exhibits stronger robustness than mainstream models (YOLOv5n-seg, YOLOv8n-seg, YOLOv10n-seg, YOLO11n-seg), and achieves 17/38 FPS on Jetson Nano B01/Orin. Additionally, when combined with DeepSORT, compared with random image sampling, the mean error between predicted and actual residual film area decreases from 232.30 cm² to 142.00 cm², and the root mean square error (RMSE) drops from 251.53 cm² to 130.25 cm². This effectively mitigates pose-induced random errors in static images and significantly improves area estimation accuracy. Full article

Open Educational Resources (OER) are increasingly recognized as key tools for promoting quality, inclusive, and equitable education. Their ease of access and the possibility of free adaptation to different contexts contribute to continuous improvement in teaching and learning. Drawing on data collected from teachers and students, this study looks at teachers’ perceptions of OER, how they influence collaboration and educational practices, and the impact of OER on students’ learning and motivation. The findings reveal both enabling and constraining factors and highlight how OER foster teacher collaboration and self-reflection on pedagogical practices. Moreover, the use of OER is associated with active and constructive teaching approaches, positively influencing student engagement. These results are triangulated with data from Likert-scale responses, indicating that students who engage with OER demonstrate significantly higher levels of motivation and deep learning compared to those who do not. Based on these findings, the study recommends implementing strategies to encourage broader integration of OER in classroom settings, alongside ongoing professional development to address existing barriers. In this context, institutional support and community-building initiatives emerge as critical levers to scale the adoption of OER. Finally, the importance of further investigation is emphasized to explore long-term impacts on teaching practices and student outcomes across diverse educational settings Full article

Inshore ship detection in synthetic aperture radar (SAR) imagery poses significant challenges due to the high density and diversity of ships. However, low inter-object backscatter contrast and blurred boundaries of docked ships often result in performance degradation for traditional object detection methods, especially under complex backgrounds and low signal-to-noise ratio (SNR) conditions. To address these issues, this paper proposes a novel detection framework, the Dynamic Weighted Joint Time–Frequency Feature Fusion DEtection TRansformer (DETR) Model (DWTF-DETR), specifically designed for SAR-based ship detection in inshore areas. The proposed model integrates a Dual-Domain Feature Fusion Module (DDFM) to extract and fuse features from both SAR images and their frequency-domain representations, enhancing sensitivity to both high- and low-frequency target features. Subsequently, a Dual-Path Attention Fusion Module (DPAFM) is introduced to dynamically weight and fuse shallow detail features with deep semantic representations. By leveraging an attention mechanism, the module adaptively adjusts the importance of different feature paths, thereby enhancing the model’s ability to perceive targets with ambiguous structural characteristics. Experiments conducted on a self-constructed inshore SAR ship detection dataset and the public HRSID dataset demonstrate that DWTF-DETR achieves superior performance compared to the baseline RT-DETR. Specifically, the proposed method improves mAP@50 by 1.60% and 0.72%, and F1-score by 0.58% and 1.40%, respectively. Moreover, comparative experiments show that the proposed approach outperforms several state-of-the-art SAR ship detection methods. The results confirm that DWTF-DETR is capable of achieving accurate and robust detection in diverse and complex maritime environments. Full article

Indirect detection phase control algorithms, such as the dithering algorithm and the stochastic parallel gradient descent algorithm (SPGD), have simple system structures and are applicable to filled-aperture coherent combinations with high efficiency. The performances of these algorithms are limited when applied to a coherent combination of pulsed fiber lasers with a low repetition rate (≤5 kHz). Firstly, due to the overlap of the phase noise frequency and repetition rate, conventional algorithms cannot effectively distinguish the phase noise from the pulse fluctuation, and directly applying filtering will result in the phase information being filtered out. Secondly, if the pulse fluctuation is ignored and only the continuous part of the phase information is utilized, it relies on the presetting of conditions to separate the pulse from the continuous part and loses the phase information of the pulse part. In this article, we propose a waveform self-referencing algorithm (WSRA) based on a two-channel near-infrared laser coherent combination system to overcome the above challenges. Firstly, by modelling a self-referencing waveform, a nonlinear scaling operation is performed on the combined signal to generate a pseudo-continuous signal, which removes the intrinsic pulse fluctuation while preserving the phase noise information. Secondly, the phase control signal is calculated based on the pseudo-continuous signals after parallel perturbation. Finally, the phase noise is corrected by optimization. The results show that our method effectively suppresses the waveform fluctuation at a 5 kHz repetition rate, the light intensity reaches an ideal value (0.9939 $I_{\max }$), and the root-mean-square (RMS) phase error is only 0.0130 $\lambda$. This method does not require the presetting of pulse detection thresholds or conditions, and solves the challenge of coherent combination at a low repetition rate, with adaptability to different pulse waveforms. Full article

Particle Swarm Optimization (PSO) is a widely used heuristic algorithm valued for its simplicity and robustness in solving diverse optimization problems. However, its high computational cost often limits large-scale applications. With the rapid development of parallel computing and Graphics Processing Units (GPUs), researchers have increasingly leveraged these technologies to enhance PSO efficiency. This paper introduces a High-Efficiency PSO (HEPSO) algorithm designed for GPU-based architectures. HEPSO improves computational performance through two key strategies: (1) transferring data initialization from the CPU to the GPU to reduce I/O overhead caused by repeated data migration, and (2) incorporating a self-adaptive thread management mechanism to enhance execution efficiency. Experiments conducted on nine benchmark optimization functions demonstrate that HEPSO achieves more than a sixfold speedup compared to conventional GPU-PSO. Moreover, in terms of convergence time, HEPSO requires only about one-third of the runtime in most cases. Full article

Background: The Gender Variance Scale (GVS) was developed to assess self-perceived masculinity and femininity across diverse gender identities, including binary and non-binary experiences. To date, no validated Italian version was available. Methods: A total of 356 participants (192 transgender and gender-diverse [TGD], 164 cisgender) completed the Italian GVS and the SF-12 Health Survey. Translation and cultural adaptation followed international guidelines. Psychometric evaluation included confirmatory factor analysis (CFA), internal consistency, test–retest reliability (n = 63), convergent validity with health-related quality of life, and group comparisons across gender identity categories. Results: CFA supported the original two-factor model (CFI = 0.916, TLI = 0.905, RMSEA = 0.076, SRMR = 0.053). Internal consistency was high (α = 0.89). The GVS distinguished between gender identity groups: TGD participants scored higher than cisgender peers, and non-binary individuals reported significantly lower scores than both binary groups. Test–retest reliability was strong (r = 0.87–0.99; ICC = 0.992–0.996). Conclusions: The Italian GVS is a valid and reliable measure of gender variance. It provides clinicians, researchers, and educators with a culturally appropriate tool to assess gender expression and support inclusive practices in both community and clinical contexts. Full article

The electrification of agricultural equipment is a critical pathway to address the dual challenges of increasing global food production and ensuring sustainable agricultural development. As the core power unit, the permanent magnet synchronous motor (PMSM) drive system faces severe challenges in achieving high performance, robustness, and reliable control in complex farmland environments characterized by sudden load changes, extreme operating conditions, and strong interference. This paper provides a comprehensive review of key technological advancements in PMSM drive systems for agricultural electrification. First, it analyzes solutions to enhance the reliability of power converters, including high-frequency silicon carbide (SiC)/gallium nitride (GaN) power device packaging, thermal management, and electromagnetic compatibility (EMC) design. Second, it systematically elaborates on high-performance motor control algorithms such as Direct Torque Control (DTC) and Model Predictive Control (MPC) for improving dynamic response; robust control strategies like Sliding Mode Control (SMC) and Active Disturbance Rejection Control (ADRC) for enhancing resilience; and the latest progress in fault-tolerant control architectures incorporating sensorless technology. Furthermore, the paper identifies core challenges in large-scale applications, including environmental adaptability, real-time multi-machine coordination, and high reliability requirements. Innovatively, this review proposes a closed-loop intelligent control paradigm encompassing environmental disturbance prediction, control parameter self-tuning, and actuator dynamic response. This paradigm provides theoretical support for enhancing the autonomous adaptability and operational quality of agricultural machinery in unstructured environments. Finally, future trends involving deep AI integration, collaborative hardware innovation, and agricultural ecosystem construction are outlined. Full article

Water treatment systems in swimming ponds support the natural self-cleaning capabilities of water based on the functions of repository macrophytes in their regeneration zone and the regulation of the internal metabolism of the reservoirs. As part of the project, a functional modular filtration chamber with system multiplication capabilities was designed and created. This element is dedicated to water treatment systems in natural swimming ponds. The prototype system consisted of modular filtration chambers and pump sections, as well as equipment adapted to the conditions prevailing in the eco-pool. An innovative solution for selective shutdown of the filtration chamber without closing the circulation circuit was also used, which forms the basis of a patent application. A verified high-performance adsorbent, Rockfos^® modified limestone, was used in the filtration chamber. In order to determine the effective filtration rate for three small test ponds with different flow rates (5 m/h, 10 m/h and 15 m/h), the selected physicochemical parameters of water (temperature, pH, electrolytical conductivity, oxygen saturation, total hardness, nitrites, nitrates, and total phosphorus, including adsorption efficiency and bed absorption capacity) were researched before and after filtration. Tests were also carried out on the composition of fecal bacteria and phyto- and zooplankton. Based on high effective phosphorus filtration efficiency of 32.65% during the operation of the bed, the following were determined: no exceedances of the standards for the tested parameters in relation to the German standards for eco-pools (FLL—Forschungsgesellschaft Landschaftsentwicklung Landschaftsbau e. V., 2011); lower number of fecal pathogens (on average 393—coliform bacteria; 74—Escherichia coli; 34—fecal enterococci, most probably number/100 mL); the lowest share of problematic cyanobacteria in phytoplankton (<250,000 individuals/dm³ in number and <0.05 µg/dm³—biomass); low chlorophyll a content (2.2 µg/dm³—oligotrophy) and the presence of more favorable smaller forms of zooplankton, an effective filtration speed of 5 m/h. This velocity was recommended in the FLL standards for swimming ponds, which were adopted in this study as a reference for rapid filters. In testing the functional efficiency of a dedicated filtration system for a Type II test pond (50 m²—area and 33 m³—capacity), at a filtration rate of 5 m/h, an average effective phosphorus adsorption efficiency of 18.28–53.98% was observed under the bed work-in-progress conditions. Analyses of other physicochemical water parameters, with appropriate calculations and statistical tests, indicated progressive functional efficiency of the system under bathing conditions. Full article

The safety, genetic distinctiveness, and functional capabilities of Lactococcus sp. KTH0-1S, a strain isolated from Thai fermented shrimp (Kung-Som), were investigated to assess its potential as a next-generation probiotic and microbial cell factory. Whole-genome sequencing and multilocus sequence typing (MLST) analysis revealed that Lactococcus sp. KTH0-1S is a novel, phylogenetically distinct strain within the Lactococcus genus. Comprehensive in silico safety evaluation confirmed the absence of antimicrobial resistance genes and major virulence factors, supporting its suitability for food-grade applications. The genome encodes multiple probiotic-relevant traits, including stress tolerance (e.g., dnaK, clpP), adhesion and biofilm formation (e.g., gapA, luxS, glf2), and nutrient acquisition genes, enabling adaptation to gastrointestinal and fermentation environments. Notably, Lactococcus sp. KTH0-1S harbors a chromosomally encoded nisin Z biosynthesis gene cluster with auto-induction capability, providing a self-regulated and stable alternative to conventional plasmid-based NICE systems in Lactococcus lactis. The strain also exhibits nisin immunity, allowing tolerance to high nisin concentrations, thus supporting robust protein production. Genomic evidence and phenotypic assays confirmed a functional respiration metabolism activated by heme supplementation, enhancing biomass yield and culture stability. Furthermore, the presence of diverse CAZyme families (GHs, GTs, CEs) enables utilization of various carbohydrate substrates, including lignocellulosic and starchy agro-industrial residues. These properties collectively underscore Lactococcus sp. KTH0-1S as a safe, stable, and metabolically versatile candidate for probiotic applications and as a cost-effective, food-grade expression host for biotechnological production. Full article

In this paper, an efficient hybrid photovoltaic (PV) power forecasting model is proposed to enhance the stability and accuracy of PV power prediction under typical weather conditions. First, the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) is employed to decompose both meteorological features affecting PV power and the power output itself into intrinsic mode functions. This process enhances the stationarity and noise robustness of input data while reducing the computational complexity of subsequent model processing. To enhance the detail-capturing capability of the Bidirectional Long Short-Term Memory (BiLSTM) model and improve its dynamic response speed and prediction accuracy under abrupt irradiance fluctuations, we integrate a Temporal Convolutional Network (TCN) into the BiLSTM architecture. Finally, a Multi-head Self-Attention (MHA) mechanism is employed to dynamically weight multivariate meteorological features, enhancing the model’s adaptive focus on key meteorological factors while suppressing noise interference. The results show that the ICEEMDAN-TCN-BiLSTM-MHA combined model reduces the Mean Absolute Percentage Error (MAPE) by 78.46% and 78.59% compared to the BiLSTM model in sunny and cloudy scenarios, respectively, and by 58.44% in rainy scenarios. This validates the accuracy and stability of the ICEEMDAN-TCN-BiLSTM-MHA combined model, demonstrating its application potential and promotional value in the field of PV power forecasting. Full article

To tackle the difficulties in cross-domain fault diagnosis for rolling bearings, researchers have devised numerous domain adaptation strategies to align feature distributions across varied domains. Nevertheless, current approaches tend to be vulnerable to noise disruptions and often neglect the distinctions between marginal and conditional distributions during feature transfer. To resolve these shortcomings, this study presents an innovative fault diagnosis technique for cross-domain applications, leveraging the Attention-Enhanced Residual Network (AttenResNet18). This approach utilizes a one-dimensional attention mechanism to dynamically assign importance to each position within the input sequence, thereby capturing long-range dependencies and essential features, which reduces vulnerability to noise and enhances feature representation. Furthermore, we propose a Dynamic Balance Distribution Adaptation (DBDA) mechanism, which develops an MMD-CORAL Fusion Metric (MCFM) by combining CORrelation ALignment (CORAL) with Maximum Mean Discrepancy (MMD). Moreover, an adaptive factor is employed to dynamically regulate the balance between marginal and conditional distributions, improving adaptability to new and untested tasks. Experimental validation demonstrates that AttenResNet18 achieves an average accuracy of 99.89% on two rolling bearing datasets, representing a significant improvement in fault detection precision over existing methods. Full article

In recent years, anomaly detection in digital environments has become a critical research area due to issues such as spam messages and fake news, which can lead to privacy breaches, social disruption, and undermined information reliability. Traditional anomaly detection models often require specific training for each task, resulting in significant time and resource consumption and limited flexibility. This study explores the use of Prompt Engineering with Transformer-based Large Language Models (LLMs) to address these challenges more efficiently. By comparing techniques such as Zero-shot, Few-shot, Chain-of-Thought (CoT), Self-Consistency (SC), and Tree-of-Thought (ToT) prompting, the study identifies CoT and SC as particularly effective, achieving up to 0.96 accuracy in spam detection without the need for task-specific training. However, ToT exhibited limitations due to biases and misinterpretation. The findings emphasize the importance of selecting appropriate prompting strategies to optimize LLM performance across various tasks, highlighting the potential of Prompt Engineering to reduce costs and improve the adaptability of anomaly detection systems. Future research is needed to explore the broader applicability and scalability of these methods. Additionally, this study includes a survey of Prompt Engineering techniques applicable to anomaly detection, examining strategies such as Self-Refine and Retrieval-Augmented Generation to further enhance detection accuracy and adaptability. Full article