Search Results (7,093)

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of exchange rates without losing any informative value of the data. The basis of the model is the trajectory analysis for the ensuing changes in price quotations and dependencies between the duration of each change. The main advantage of the model is to eliminate the threshold analysis, used in existing state models. This solution allows for a more accurate identification of investor behavior patterns, which translates into a reduction of investment risk. In order to verify obtained results in practice, the paper presents a concept of creating an algorithmic trading system and an analysis of its financial effectiveness for the exchange rate most popular among investors, namely EUR/USD. Full article

Acinetobacter baumannii is one of the most challenging nosocomial pathogens associated with a variety of hospital infections, such as ventilator-associated pneumonia, wound and urinary tract infections, meningitis, and sepsis, primarily in patients treated in critical care settings. Its classification as a high-priority pathogen is due to the emergence of multidrug-resistant strains in healthcare environments and its tendency to spread clonally. A. baumannii belongs to the Acinetobacter calcoaceticus–Acinetobacter baumannii (Acb) complex, a group of genotypically and phenotypically similar species. Differentiating between the species is important because of their distinct clinical significance. However, conventional phenotypic methods, both manual and automated, often fail to provide accurate species-level identification. This review aims to summarize current phenotypic and genotypic methods for the identification of species within the Acb complex, evaluating their strengths and limitations to offer guidance for their appropriate application in diagnostic settings and epidemiological investigations. Full article

Neurodevelopmental disorders (NDDs) significantly impact children’s health and development. They pose a substantial burden to families and the healthcare system. Challenges in early identification, accurate and timely diagnosis, and effective treatment persist due to overlapping symptoms, lack of appropriate diagnostic biomarkers, significant stigma and discrimination, and systemic barriers. Generative Artificial Intelligence (GenAI) offers promising solutions to these challenges by enhancing screening, diagnosis, personalized treatment, and research. Although GenAI is already in use in some aspects of NDD management, effective and strategic leveraging of evolving AI tools and resources will enhance early identification and screening, reduce diagnostic processing by up to 90%, and improve clinical decision support. Proper use of GenAI will ensure individualized therapy regimens with demonstrated 36% improvement in at least one objective attention measure compared to baseline and 81–84% accuracy relative to clinician-generated plans, customize learning materials, and deliver better treatment monitoring. GenAI will also accelerate NDD-specific research and innovation with significant time savings, as well as provide tailored family support systems. Finally, it will significantly reduce the mortality and morbidity associated with NDDs. This article explores the potential of GenAI in transforming NDD management and calls for policy initiatives to integrate GenAI into NDD management systems. Full article

To address the data acquisition limitations of traditional flow balance methods that stem from insufficient flow rate measurements, this study establishes a pipeline flow calculation model based on the pressure data and proposes a pipeline leak identification approach for product oil pipelines. Firstly, field leak tests are designed and conducted on a product oil pipeline in East China by discharging oil in a valve chamber to simulate the leak process. Subsequently, combining the Bernoulli equation with the Leapienzon formula, a calculation model is established for flow rate prediction using the pressure data monitored at the stations and valve chambers along the pipeline. By analyzing the instantaneous flow rate changes at each pipeline section and pressure drops at each station and valve chamber, a dual-parameter collaborative threshold is set based on the flow balance principle, and leaks are identified when both parameters exceed the threshold simultaneously. Finally, the proposed flow rate calculation model and leak identification method are validated with respect to the field test data. The results show that the flow rate model yields a relative error as low as 0.48%, and the leak identification method accurately captured all six leak events in the field test, indicating very good stability and accuracy, with great potential for leak identification and alarm systems for product oil pipelines in engineering applications. Full article

The automatic detection of traffic accidents plays an increasingly vital role in advancing intelligent traffic monitoring systems and improving road safety. Leveraging computer vision techniques offers a promising solution, enabling rapid, reliable, and automated identification of accidents, thereby significantly reducing emergency response times. This study proposes an enhanced version of the YOLO11 architecture, termed YOLO11-AMF. The proposed model integrates a Mamba-Like Linear Attention (MLLA) mechanism, an Asymptotic Feature Pyramid Network (AFPN), and a novel Focaler-IoU loss function to optimize traffic accident detection performance under complex and diverse conditions. The MLLA module introduces efficient linear attention to improve contextual representation, while the AFPN adopts an asymptotic feature fusion strategy to enhance the expressiveness of the detection head. The Focaler-IoU further refines bounding box regression for improved localization accuracy. To evaluate the proposed model, a custom dataset of traffic accident images was constructed. Experimental results demonstrate that the enhanced model achieves precision, recall, mAP50, and mAP50–95 scores of 96.5%, 82.9%, 90.0%, and 66.0%, respectively, surpassing the baseline YOLO11n by 6.5%, 6.0%, 6.3%, and 6.3% on these metrics. These findings demonstrate the effectiveness of the proposed enhancements and suggest the model’s potential for robust and accurate traffic accident detection within real-world conditions. Full article

The eye, a complex organ essential for visual perception, is composed of diverse cell populations with specialized functions; however, the complex interplay between these cellular components and their underlying molecular mechanisms remains largely elusive. Traditional biotechnologies, such as bulk RNA sequencing and in vitro models, are limited in capturing cellular heterogeneity or accurately mimicking the complexity of human ophthalmic diseases. The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized ocular research by enabling high-resolution analysis at the single-cell level, uncovering cellular heterogeneity, and identifying disease-specific gene profiles. In this review, we provide a review of scRNA-seq application advancement in ocular physiology and pathology, highlighting its role in elucidating the molecular mechanisms of various ocular diseases, including myopia, ocular surface and corneal diseases, glaucoma, uveitis, retinal diseases, and ocular tumors. By providing novel insights into cellular diversity, gene expression dynamics, and cell–cell interactions, scRNA-seq has facilitated the identification of novel biomarkers and therapeutic targets, and the further integration of scRNA-seq with other omics technologies holds promise for deepening our understanding of ocular health and diseases. Full article

This paper introduces PyBEP, a Python-based tool for the automated and optimized selection of open-circuit potential (OCP) curves and calculation of stoichiometric cycling ranges for lithium-ion battery electrodes based on open-circuit voltage (OCV) measurements. Thereby, it overcomes key challenges in traditional approaches, which are often time-intensive and susceptible to errors due to manual curve digitization, data inconsistency, and coding complexities. The originality of PyBEP arises from the systematic integration of automated electrode chemistry identification, quality-controlled database usage, refinement of the results using incremental capacity methodology, and simultaneous optimization of multiple electrode parameters. The PyBEP database leverages high-quality, curated OCP data and employs differential evolution optimization for precise OCP determination. Validation against literature data and experimental results confirms the robustness and accuracy of PyBEP, consistently achieving precision of 10 mV or better. PyBEP also offers features like electrode chemical composition identification and quality enhancement of measurement data, further extending the battery modeling functionalities without the need for battery disassembly. PyBEP is open-source and accessible on GitHub, providing a streamlined, accurate resource for the battery research community, making PyBEP a unique and directly applicable toolkit for electrochemical researchers and engineers. Full article

The Automatic Identification System (AIS) has become a key data source for ship behavior monitoring and maritime traffic management, widely used in trajectory prediction and anomaly detection. However, AIS data suffer from issues such as spatial sparsity, heterogeneous features, variable message formats, and irregular sampling intervals, while vessel trajectories are characterized by strong spatial–temporal dependencies. These factors pose significant challenges for efficient and accurate modeling. To address this issue, we propose a lightweight vessel trajectory prediction framework that integrates Spatial–Temporal Identity encoding with an MLP-Mixer architecture. The framework discretizes spatial and temporal features into structured IDs and uses dual MLP modules to model temporal dependencies and feature interactions without relying on convolution or attention mechanisms. Experiments on a large-scale real-world AIS dataset demonstrate that the proposed STID-Mixer achieves superior accuracy, training efficiency, and generalization capability compared to representative baseline models. The method offers a compact and deployable solution for large-scale maritime trajectory modeling. Full article

Prostate cancer is among the most prevalent malignancies in men worldwide, underscoring the need for early and accurate diagnostic tools. This study presents a proof-of-concept and pilot clinical validation of a novel bioelectric impedance-based biosensor for the detection of prostate-specific antigen (PSA) in human serum. The system integrates Molecular Identification through Membrane Engineering (MIME) with the xCELLigence real-time cell analysis platform, employing Vero cells electroinserted with anti-PSA antibodies. Optimization experiments identified 15,000 cells/well as the optimal configuration for impedance response. The biosensor exhibited specific, concentration-dependent changes in impedance upon exposure to PSA standard solutions and demonstrated significant differentiation between PSA-positive and PSA-negative human serum samples relative to the clinical threshold of 4 ng/mL. The biosensor offered rapid results within one minute, unlike standard immunoradiometric assay (IRMA), while showing strong diagnostic agreement. The system’s specificity, sensitivity, and reproducibility support its potential for integration into point-of-care screening workflows. This bioelectric assay represents one of the fastest PSA detection approaches reported to date and offers a promising solution for reducing overdiagnosis while improving clinical decision-making and patient outcomes. Full article

Background: Breast and hematological cancer treatments, especially with anthracyclines, have been shown to be associated with an increased risk of cardiotoxicity (CTX). An accurate prediction of cardiotoxicity risk and early detection of myocardial injury may allow for effective cardioprotection to be instituted and tailored to reverse cardiac dysfunction and prevent the discontinuation of essential cancer treatments. Objectives: The PRoactive Evaluation of Function to Evade Cardio Toxicity (PREFECT) study sought to evaluate the ability of fast-strain-encoded (F-SENC) cardiac magnetic resonance imaging (CMR) and 2D echocardiography (2D Echo) to stratify patients at risk of CTX prior to initiating cancer treatment, detect early signs of cardiac dysfunction, including subclinical CTX (sub-CTX) and CTX, and monitor for recovery (REC) during cardioprotective therapy. Methods: Fifty-nine patients with breast cancer or lymphoma were prospectively monitored for CTX with F-SENC CMR and 2D Echo over at least 1 year for evidence of cardiac dysfunction during anthracycline based chemotherapy. F-SENC CMR also monitored myocardial deformation in 37 left ventricular (LV) segments to obtain a MyoHealth risk score based on both longitudinal and circumferential strain. Sub-CTX and CTX were classified based on pre-specified cardiotoxicity definitions. Results: CTX was observed in 9/59 (15%) and sub-CTX in 24/59 (41%) patients undergoing chemotherapy. F-SENC CMR parameters at baseline predicted CTX with a lower LVEF (57 ± 5% vs. 61 ± 5% for all, p = 0.05), as well as a lower MyoHealth (70 ± 9 vs. 79 ± 11 for all, p = 0.004) and a worse global circumferential strain (GCS) (−18 ± 1 vs. −20 ± 1 for all, p < 0.001). Pre-chemotherapy MyoHealth had a higher accuracy in predicting the development of CTX compared to CMR LVEF and 2D Echo LVEF (AUC = 0.85, 0.69, and 0.57, respectively). The 2D Echo parameters on baseline imaging did not stratify CTX risk. F-SENC CMR obtained good or excellent images in 320/322 (99.4%) scans. During cancer treatment, MyoHealth had a high accuracy of detecting sub-CTX or CTX (AUC = 0.950), and the highest log likelihood ratio (indicating a higher probability of detecting CTX) followed by F-SENC GLS and F-SENC GCS. CMR LVEF and CMR LV stroke volume index (LVSVI) also significantly worsened in patients developing CTX during cancer treatment. Conclusions: F-SENC CMR provided a reliable and accurate assessment of myocardial function during anthracycline-based chemotherapy, and demonstrated accurate early detection of CTX. In addition, MyoHealth allows for the robust identification of patients at risk for CTX prior to treatment with higher accuracy than LVEF. Full article

Plaque erosion (PE) is now recognized as a common and clinically significant cause of acute coronary syndromes (ACSs), accounting for up to 40% of cases. Unlike plaque rupture (PR), PE involves superficial endothelial loss over an intact fibrous cap and occurs in a low-inflammatory setting, typically affecting younger patients, women, and smokers with fewer traditional risk factors. The growing recognition of PE has been driven by high-resolution intracoronary imaging, particularly optical coherence tomography (OCT), which enables in vivo differentiation from PR. Identifying PE with OCT has opened the door to personalized treatment strategies, as explored in recent trials evaluating the safety of deferring stent implantation in selected cases in favor of intensive medical therapy. Given its unexpectedly high prevalence, PE is now recognized as a common pathophysiological mechanism in ACS, rather than a rare exception. This growing awareness underscores the importance of its accurate identification through OCT in clinical practice. Early recognition and a deeper understanding of PE are essential steps toward the implementation of precision medicine, allowing clinicians to move beyond “one-size-fits-all” models toward “mechanism-based” therapeutic strategies. This narrative review aims to offer an integrated overview of PE, tracing its epidemiology, elucidating the molecular and pathophysiological mechanisms involved, outlining its clinical presentations, and placing particular emphasis on diagnostic strategies with OCT, while also discussing emerging therapeutic approaches and future directions for personalized cardiovascular care. Full article

The identification of unmarked graves is important in archaeology, forensics, and cemetery management, but invasive methods are often restricted due to ethical or cultural concerns. This necessitates the use of non-invasive geophysical techniques. Our study demonstrates the potential of induced polarization (IP) imaging as a non-invasive remote sensing technique specifically suited for detecting and characterizing unmarked graves. IP leverages changes in the electrical properties of soil and pore water, influenced by the accumulation of organic matter from decomposition processes. Measurements were conducted at an inactive cemetery using non-invasive textile electrodes to map a documented grave from the early 1990s, with a survey design optimized for high spatial resolution. The results reveal a distinct polarizable anomaly at a 0.75–1.0 m depth with phase shifts exceeding 12 mrad, attributed to organic carbon from wooden burial boxes, and a plume-shaped conductive anomaly indicating the migration of dissolved organic matter. While electrical conductivity alone yielded diffuse grave boundaries, the polarization response sharply delineated the grave, aligning with photographic documentation. These findings underscore the value of IP imaging as a non-invasive, data-driven approach for the accurate localization and characterization of graves. The methodology presented here offers a promising new tool for archaeological prospection and forensic search operations, expanding the geophysical toolkit available for remote sensing in culturally and legally sensitive contexts. Full article

Accurately identifying coherent vortex structures (CVSs) in backward-facing step (BFS) flows remains a challenge, particularly in reconciling visual streamlines with mathematical criteria. In this study, high-resolution velocity fields were captured using particle image velocimetry (PIV) in a pressurized BFS setup. Instantaneous streamlines reveal distinct spiral patterns, vortex centers, and saddle points, consistent with physical definitions of vortices and offering intuitive guidance for CVS detection. However, conventional vortex identification methods often fail to reproduce these visual features. To address this, an improved Q-criterion method is proposed, based on the normalization of the velocity gradient tensor. This approach enhances the rotational contribution while suppressing shear effects, leading to improved agreement in vortex position and shape with those observed in streamlines. While the normalization process alters the representation of physical vortex strength, the method bridges qualitative visualization and quantitative analysis. This streamline-consistent identification framework facilitates robust CVS detection in separated flows and supports further investigations in vortex dynamics and turbulence control. Full article

With the large-scale integration of high-penetration distributed photovoltaic (DPV) into distribution networks, its output volatility and reverse power flow characteristics are prone to causing voltage violations, necessitating the accurate identification of weak nodes to enhance operational reliability. This paper investigates the definition, quantification criteria, and multi-indicator comprehensive determination methods for weak nodes in distribution networks. A multi-criteria assessment method integrating voltage deviation rate, sensitivity analysis, and power margin has been proposed. This method quantifies the node disturbance resistance and comprehensively evaluates the vulnerability of voltage stability. Simulation validation based on the IEEE 33-node system demonstrates that the proposed method can effectively identify the distribution patterns of weak nodes under different penetration levels (20~80%) and varying numbers of DPV access points (single-point to multi-point distributed access scenarios). The study reveals the impact of increased penetration and dispersed access locations on the migration characteristics of weak nodes. The research findings provide a theoretical basis for the planning of distribution networks with high-penetration DPV, offering valuable insights for optimizing the siting of volatile loads such as electric vehicle (EV) charging stations while considering both grid safety and the demand for distributed energy accommodation. Full article

Introduction: Cystic fibrosis (CF) is a genetic condition affecting several organs and systems, including the pancreas, colon, respiratory system, and reproductive system. The detection of a growing number of CFTR variants and genotypes has contributed to an increase in the CF population which, in turn, has had an impact on the overall statistics regarding the prognosis and outcome of the condition. Given the increase in life expectancy, it is critical to better predict outcomes and prognosticate in CF. Thus, each person’s choice to aggressively treat specific disease components can be more appropriate and tailored, further increasing survival. The objective of our narrative review is to summarize the most recent information concerning the value and significance of clinical parameters in predicting outcomes, such as gender, diabetes, liver and pancreatic status, lung function, radiography, bacteriology, and blood and sputum biomarkers of inflammation and disease, and how variations in these parameters affect prognosis from the prenatal stage to maturity. Materials and methods: A methodological search of the available data was performed with regard to prognostic factors in the evolution of CF in children and young adults. We evaluated articles from the PubMed academic search engine using the following search terms: prognostic factors AND children AND cystic fibrosis OR mucoviscidosis. Results: We found that it is crucial to customize CF patients’ care based on their unique clinical and biological parameters, genetics, and related comorbidities. Conclusions: The predictive significance of more dynamic clinical condition markers provides more realistic future objectives to center treatment and targets for each patient. Over the past ten years, improvements in care, diagnostics, and treatment have impacted the prognosis for CF. Although genotyping offers a way to categorize CF to direct research and treatment, it is crucial to understand that a variety of other factors, such as epigenetics, genetic modifiers, environmental factors, and socioeconomic status, can affect CF outcomes. The long-term management of this complicated multisystem condition has been made easier for patients, their families, and physicians by earlier and more accurate identification techniques, evidence-based research, and centralized expert multidisciplinary care. Full article