Search Results (247)

A tracking method based on adaptive morphological correlation and neural predictive models is presented. The morphological correlation filters are optimized according to the aggregated binary dissimilarity-to-matching ratio criterion and are adapted online to appearance variations of the target across frames. Morphological correlation filtering enables reliable detection and accurate localization of the target in the scene. Furthermore, trained neural models predict the target’s expected location in subsequent frames and estimate its bounding box from the correlation response. Effective stages for drift correction and tracker reinitialization are also proposed. Performance evaluation results for the proposed tracking method on four image datasets are presented and discussed using objective measures of detection rate (DR), location accuracy in terms of normalized location error (NLE), and region-of-support estimation in terms of intersection over union (IoU). The results indicate a maximum average performance of 90.1% in DR, 0.754 in IoU, and 0.004 in NLE on a single dataset, and 83.9%, 0.694, and 0.015, respectively, across all four datasets. In addition, the results obtained with the proposed tracking method are compared with those of five widely used correlation filter-based trackers. The results show that the suggested morphological-correlation filtering, combined with trained neural models, generalizes well across diverse tracking conditions. Full article

Slope instability may cause severe casualties, property losses, and ecological damage. To accurately evaluate slope stability grades and mitigate geological hazards, a dynamic stability assessment method based on variable weight theory and trapezoidal cloud model is proposed. First, an evaluation index system for slope stability is established following the principles of uniqueness, purposefulness, and scientific validity. Then, to improve the accuracy of subjective constant weights, the intuitionistic fuzzy analytic hierarchy process (IFAHP) is employed to calculate subjective constant weights. Considering the contrast intensity and conflict among indicators, an improved CRITIC method is applied to determine objective constant weights. To balance subjective and objective factors and avoid constant weight imbalance, the optimal comprehensive constant weights are computed based on game theory, effectively reducing bias caused by single weighting methods. Furthermore, to fully account for the influence of indicator state values on their weights, variable weight theory is introduced to dynamically adjust the comprehensive constant weights. Finally, based on the variable weights of evaluation indicators, a trapezoidal cloud model is utilized to construct the slope stability evaluation model, which is validated through an engineering case study. The results indicate that the stability grade of Stage 1 is assessed as basically stable, while Stages 2 and 3 are evaluated as stable. Numerical simulations show the safety factors of the three stages are 1.36, 1.83, and 2.36, respectively, verifying the correctness of the proposed model. The proposed model demonstrates practical engineering value in slope stability assessment and can be referenced for slope reinforcement and hazard prevention in later stages. Full article

Modular process plants represent a promising strategy to address the increasing need for flexibility and accelerated market deployment in the production of fine and specialty chemicals. However, these modular systems are inherently susceptible to wear and fault development, while condition monitoring methods tailored to such systems remain scarce. This study presents a proof of concept for a targeted fault diagnosis approach of the modular hydraulic systems of such modular process plants and reports on its experimental validation. The methodology comprises two stages: First, model-based symptoms are calculated independently for each module and subsequently utilized within a centralized diagnostic system. This rule-based diagnosis incorporates generalized module interactions, quantified fault degrees, and the plant topology. Importantly, uncertainties arising from measurement equipment, model fidelity, and parameter variability are incorporated and systematically propagated throughout the diagnosis. The validation was conducted on a modular test rig specifically designed to simulate a range of single-fault scenarios across more than 1200 stationary operating points. The results underscore the robustness of the proposed approach: the correct fault was consistently identified, with the estimated fault magnitudes closely aligning with the actual values, exhibiting an average discrepancy of 0.029 for internal leakage of a positive displacement pump. The overall discrepancy for the experimental validation of all fault types was 0.12. Notably, no false alarms were observed, and the displayed uncertainty was considered plausible, though there remains potential for refinement. In summary, this study demonstrates the successful application of model-based symptoms for a rule-based diagnosis, representing a significant advancement toward reliable fault detection in modular hydraulic systems. Full article

A mathematical model of high-frequency geoacoustic emission for a single dislocation radiation source is suggested in the papper. The mathematical model is a linear Berlage oscillator with non-constant coefficients whose solution is the Berlage function momentum. Further, the values of the parameters of the Berlage pulse are specified using experimental data. For this purpose, the problem of multidimensional optimization is solved, which consists of two stages: global optimization using the differential evolution method and local optimization according to the Nelder-Mead method. Statistics are given to confirm the correctness of the obtained results: standard error and coefficient of determination. It is shown that two-stage multivariate optimization makes it possible to refine the parameters of the Berlage pulse with a sufficiently high accuracy to describe high-frequency geoacoustic emission. Full article

The increasing prevalence of common cold viruses and bacteria in daily life has heightened interest in sterilization lamp technologies. Compared with traditional mercury-based ultraviolet (UV) lamps, modern UV lamps offer advantages including extended operational lifespan, high energy efficiency, compact form factor, and the absence of hazardous materials, rendering them both safer and environmentally sustainable. In particular, UV-C LED lamps, which emit at short wavelengths, are capable of disrupting the molecular structure of DNA or RNA in microbial cells, thereby inhibiting cellular replication and achieving effective disinfection and sterilization. Conventional UV-C LED sterilization lamp driver circuits frequently employ a two-stage architecture, which requires a large number of components, occupies substantial physical space, and exhibits reduced efficiency due to multiple stages of power conversion. To address these limitations, this paper proposes a UV-C LED sterilization lamp driver circuit for an AC voltage supply, employing boundary conduction mode (BCM) control with integrated power factor correction (PFC). The proposed single-stage, single-switch topology combines a buck PFC converter and a flyback converter while recovering transformer leakage energy to further improve efficiency. Compared with conventional two-stage designs, the proposed circuit reduces the number of power switches and components, thereby lowering manufacturing cost and enhancing overall energy conversion efficiency. The operating principles of the proposed driver circuit are analyzed, and a prototype is developed for a 110 V AC input with an output specification of 10.8 W (90 V/0.12 A). Experimental results demonstrate that the prototype achieves an efficiency exceeding 92%, a power factor of 0.91, an output voltage ripple of 1.298%, and an output current ripple of 4.44%. Full article

Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well in a single cohort fail during external validation. We created an open, harmonized meta-database linking gene expression with curated ACT exposure and survival to enable fair benchmarking and modeling. Methods: A PRISMA-guided search of 999 GEO studies (through January 2025) used LLM-assisted triage of titles, clinical tables, and free text to identify datasets with explicit ACT status and patient-level survival. Eight Affymetrix microarray cohorts (GPL570/GPL96) met eligibility. Raw CEL files underwent robust multi-array average; probes were re-annotated to Entrez IDs and collapsed by median. Covariate-preserving ComBat adjusted platform/study while retaining several clinical factors. Batch structure was quantified by principal-component analysis (PCA) variance, silhouette width, and UMAP. Two quality-control (QC) filters, median M-score deviation and PCA leverage, flagged and removed technical outliers. Results: The final meta-database comprises 1340 patients (223 (16.6%) ACT; 1117 (83.4%) observation), 13,039 intersecting genes, and 594 overall-survival events. Batch-associated variance (PC1 + PC2) decreased from 63.1% to 20.1%, and mean silhouette width shifted from 0.82 to −0.19 post-correction. Seven arrays (0.5%) were excluded by QC. Event depth supports high-dimensional survival and heterogeneity-of-treatment modeling, and the multi-cohort design enables internal–external validation. Conclusions: This first open, rigorously harmonized NSCLC transcriptomic database provides the sample size, demographic diversity, and technical consistency required to benchmark ACT-benefit markers. By making these data openly available, it will accelerate equitable precision-oncology research and enable data-driven treatment decisions in early-stage NSCLC. Full article

High-precision angular positioning mechanisms are essential across diverse scientific and industrial applications, from optical instrumentation to automated mechanical systems. Conventional bronze–steel gear reduction units, while reliable, are often heavy, costly, and unsuitable for chemically aggressive or vacuum environments, limiting their use in advanced research setups. This work introduces a novel 1:360 gear reduction system manufactured by resin-based additive manufacturing, designed to overcome these limitations. The compact worm–gear assembly translates a single crank rotation into a precise one-degree indicator displacement, enabling fine and repeatable angular control. A primary application is the alignment of parabolic mirrors in schlieren systems, where accurate tilt adjustment is critical to correct optical alignment; however, the design is broadly adaptable to other precision positioning tasks in laboratory and industrial contexts. Compared with conventional assemblies, the resin-based reducer offers reduced weight, chemical and vacuum compatibility, and lower production cost. Its three-stage reduction design further enhances load-bearing capacity, achieving approximately double the theoretical torque transfer of equivalent commercial systems. These features establish the device as a robust, scalable, and automation-ready solution for high-accuracy angular adjustment, contributing both to specialized optical research and general-purpose precision engineering. Full article

The On-Load Tap Changer (OLTC), as a critical component of transformers, undergoes frequent switching operations that can lead to faults such as contact wear and arc discharge, which are often difficult to detect at an early stage using traditional monitoring methods. In particular, dissolved gas analysis (DGA) in OLTC oil is challenged by the unique oil gas decomposition mechanisms and the presence of background noise, making conventional DGA criteria less effective. Moreover, OLTC oil monitoring data are typically obtained through intermittent sampling, resulting in sparse time series with low resolution that complicate fault prediction. To address these challenges, this paper proposes an integrated framework combining LGOD-based anomaly detection, Locally Weighted Regression (LWR) for data repair, and the ETSformer temporal prediction model. This approach effectively identifies and corrects anomalies, restores the dynamic variation trends of gas concentrations, and enhances prediction accuracy through deep temporal modeling, thereby providing more reliable data support for OLTC state assessment and fault diagnosis. Experimental results demonstrate that the proposed method significantly improves prediction accuracy, enhances sensitivity to gas concentration evolution, and exhibits robust adaptability under both normal and fault scenarios. Furthermore, ablation experiments confirm that the observed performance gains are attributable to the complementary contributions of LGOD, LWR, and ETSformer, rather than any single component alone, highlighting the effectiveness of the integrated approach. Full article

Remote sensing visual question answering (RSVQA) involves interpreting complex geospatial information captured by satellite imagery to answer natural language questions, making it a vital tool for observing and analyzing Earth’s surface without direct contact. Although numerous studies have addressed RSVQA, most have focused primarily on answer accuracy, often overlooking the underlying reasoning capabilities required to interpret spatial and contextual cues in satellite imagery. To address this gap, this study presents a comprehensive evaluation of four large multimodal models (LMMs) as follows: GPT-4o, Grok 3, Gemini 2.5 Pro, and Claude 3.7 Sonnet. We used a curated subset of the EarthVQA dataset consisting of 100 rural images with 29 question–answer pairs each and 100 urban images with 42 pairs each. We developed the following three task-specific frameworks: (1) Zero-GeoVision, which employs zero-shot prompting with problem-specific prompts that elicit direct answers from the pretrained knowledge base without fine-tuning; (2) CoT-GeoReason, which enhances the knowledge base with chain-of-thought prompting, guiding it through explicit steps of feature detection, spatial analysis, and answer synthesis; and (3) Self-GeoSense, which extends this approach by stochastically decoding five independent reasoning chains for each remote sensing question. Rather than merging these chains, it counts the final answers, selects the majority choice, and returns a single complete reasoning chain whose conclusion aligns with that majority. Additionally, we designed the Geo-Judge framework to employ a two-stage evaluation process. In Stage 1, a GPT-4o-mini-based LMM judge assesses reasoning coherence and answer correctness using the input image, task type, reasoning steps, generated model answer, and ground truth. In Stage 2, blinded human experts independently review the LMM’s reasoning and answer, providing unbiased validation through careful reassessment. Focusing on Self-GeoSense with Grok 3, this framework achieves superior performance with 94.69% accuracy in Basic Judging, 93.18% in Basic Counting, 89.42% in Reasoning-Based Judging, 83.29% in Reasoning-Based Counting, 77.64% in Object Situation Analysis, and 65.29% in Comprehensive Analysis, alongside RMSE values of 0.9102 in Basic Counting and 1.0551 in Reasoning-Based Counting. Full article

Background and Objectives: This retrospective single-center study aimed to evaluate the epidemiological, clinical, and laboratory characteristics of syphilis cases diagnosed at our hospital between 2005 and 2024, with a focus on the performance of serological tests used for diagnosis. The study also sought to characterize changing epidemiological trends of syphilis over this 20-year period. Materials and Methods: Data from 671 patients with confirmed syphilis diagnoses were retrospectively analyzed. Demographic information, transmission routes, co-infection status, and serological test results—including Rapid Plasma Reagin (RPR) and Indirect Hemagglutination Assay (IHA)—were evaluated. Statistical analyses were performed using chi-square and Fisher-based tests, with Bonferroni correction applied for multiple comparisons Results: Of the 671 cases, 74.6% were male and 25.4% female, with the highest incidence in the 22–41 age group. The number of diagnosed cases increased approximately 6-fold after 2016 compared to the preceding years. Unprotected sexual contact was the most common transmission route. HIV co-infection was present in 32.6% of cases, predominantly in males. Significant differences in RPR and IHA titers were observed across clinical stages of syphilis, with notably higher titers in late latent and neurosyphilis cases. Conclusions: The 6-fold increase in syphilis diagnoses since 2016, alongside a high rate of HIV co-infection, underscores the need for targeted prevention and screening programs for high-risk populations. Serological testing remains essential for diagnosis and disease monitoring. Full article

Four-dimensional (4D) flow MRI has shown promise for the assessment of aortic hemodynamics. However, data analysis traditionally requires manual and time-consuming human input at several stages. This limits reproducibility and affects analysis workflows, such that large-cohort 4D flow studies are lacking. Here, a fully automated artificial intelligence (AI) 4D flow analysis pipeline was developed and evaluated in a cohort of over 350 subjects. The 4D flow MRI analysis pipeline integrated a series of previously developed and validated deep learning networks, which replaced traditionally manual processing tasks (background-phase correction, noise masking, velocity anti-aliasing, aorta 3D segmentation). Hemodynamic parameters (global aortic pulse wave velocity (PWV), peak velocity, flow energetics) were automatically quantified. The pipeline was evaluated in a heterogeneous single-center cohort of 379 subjects (age = 43.5 ± 18.6 years, 118 female) who underwent 4D flow MRI of the thoracic aorta (n = 147 healthy controls, n = 147 patients with a bicuspid aortic valve [BAV], n = 10 with mechanical valve prostheses, n = 75 pediatric patients with hereditary aortic disease). Pipeline performance with BAV and control data was evaluated by comparing to manual analysis performed by two human observers. A fully automated 4D flow pipeline analysis was successfully performed in 365 of 379 patients (96%). Pipeline-based quantification of aortic hemodynamics was closely correlated with manual analysis results (peak velocity: r = 1.00, p < 0.001; PWV: r = 0.99, p < 0.001; flow energetics: r = 0.99, p < 0.001; overall r ≥ 0.99, p < 0.001). Bland–Altman analysis showed close agreement for all hemodynamic parameters (bias 1–3%, limits of agreement 6–22%). Notably, limits of agreement between different human observers’ quantifications were moderate (4–20%). In addition, the pipeline 4D flow analysis closely reproduced hemodynamic differences between age-matched adult BAV patients and controls (median peak velocity: 1.74 m/s [automated] or 1.76 m/s [manual] BAV vs. 1.31 [auto.] vs. 1.29 [manu.] controls, p < 0.005; PWV: 6.4–6.6 m/s all groups, any processing [no significant differences]; kinetic energy: 4.9 μJ [auto.] or 5.0 μJ [manu.] BAV vs. 3.1 μJ [both] control, p < 0.005). This study presents a framework for the complete automation of quantitative 4D flow MRI data processing with a failure rate of less than 5%, offering improved measurement reliability in quantitative 4D flow MRI. Future studies are warranted to reduced failure rates and evaluate pipeline performance across multiple centers. Full article

Background and Objectives: The worldwide prevalence of chronic kidney disease (CKD) continues to increase, representing a major concern for public health systems. CKD is associated with gut microbiota dysbiosis, which may exacerbate disease progression by increasing the levels of uremic toxins, systemic inflammation, and oxidative stress. Modulation of the gut microbiota through biotic supplementation has been proposed as a potential therapeutic strategy to slow CKD progression and mitigate its complications. This study aimed to evaluate the effect of 10-month synbiotic supplementation on estimated glomerular filtration rate (eGFR), circulating concentrations of indoxyl sulfate (IS), p-cresyl sulfate (p-CS), interleukin-6 (IL-6), and malondialdehyde (MDA) in patients with stage IV–V CKD not receiving dialysis, in comparison to placebo. Materials and Methods: Fifty non-dialysis CKD IV–V patients were assigned (n = 25 each) via matched, non-randomized allocation (age, sex, and primary disease) to synbiotic or placebo. This single-blind, placebo-controlled trial blinded participants and laboratory personnel. The synbiotic group received daily capsules containing Lactobacillus acidophilus La-14 (2 × 10¹¹ CFU/g) + fructooligosaccharides; controls received identical placebo. Adherence was monitored monthly (pill counts, diaries), with < 80% over two visits resulting in withdrawal. The eGFR, IS, p-CS, IL-6, and MDA were measured at baseline and month 10. Results: Forty-two patients (21/arm) completed the study; eight withdrew (4 per arm). At 10 months, the change in eGFR was −1.2 ± 2.5 mL/min/1.73 m² (synbiotic) vs. −3.5 ± 3.0 mL/min/1.73 m² (placebo); between-group difference in change was 2.3 mL/min/1.73 m² (95% CI: 0.5–4.1; p = 0.014; adjusted p = 0.07). IS decreased by −15.4 ± 8.2 ng/L vs. −3.1 ± 6.5 ng/L; between-group difference in change was −12.3 ng/L (95% CI: −17.8 to −6.8; p < 0.001; adjusted p = 0.005). No significant differences were observed for p-CS, IL-6, or MDA after correction. Conclusions: Synbiotic supplementation over a 10-month period resulted in a trend toward decreased serum IS levels in patients with advanced CKD, suggesting potential benefits of microbiota-targeted therapies. However, no significant effects were observed on renal function, inflammatory, or oxidative stress markers. Further large-scale studies are warranted to confirm these findings and explore the long-term impact of synbiotics in CKD management. Full article

Background: Class II Division 1 malocclusion is often characterized by an increased overjet, which has traditionally been considered a negative predictor of aesthetic outcomes, treatment efficacy, and long-term stability. Although early two-phase treatment is generally perceived as less effective than a single-stage pubertal peak intervention, it may be beneficial in cases with concerns such as trauma risk or bullying. This study aimed to assess the relationship between initial overjet and sagittal correction (as measured by the ANB and WITS indices) to identify a threshold beyond which two-phase treatment might be more effective. Methods: A retrospective analysis was conducted on 58 patients (mean age: 9.01 years), all of whom were treated consecutively with the Manni Telescopic Herbst Appliance. Lateral cephalograms taken at the start (T0) and end (T1) of Herbst treatment were analyzed to evaluate changes in skeletal and dental parameters. Results: A significant positive correlation was found between higher initial overjet and increased skeletal sagittal correction. Specifically, for every 1 mm increase in overjet, there was a 0.65 mm reduction in the WITS index and a 0.30° decrease in the ANB angle (p < 0.01). These effects were more pronounced when the initial overjet exceeded 8.0 mm. Conclusions: The Manni Telescopic Herbst Appliance demonstrated enhanced skeletal correction in patients with larger initial overjet values, particularly when the overjet exceeded 8.0 mm. This suggests that early two-phase treatment may be especially beneficial in such cases. Full article

The error correction capability of the RS(255,223) code has been significantly enhanced compared to that of the RS(256,252) code, making it the preferred choice for the next generation of onboard solid-state recorders (O-SSRs). With the application of non-volatile double data rate (NV-DDR) interface technology in O-SSRs, instantaneous transmission rates of up to 1 Gbps per data I/O interface can be achieved. This development imposes higher requirements on the encoding throughput of RS encoders. For RS(255,223) encoders, throughput improvement is limited by the structures of serial architectures. The algorithm’s inherent characteristics restrict the depth of pipelining. In contrast, parallel solutions face bottlenecks in resource efficiency. To address these challenges, an interleaved pipelined architecture is proposed. By integrating interleaving technology within the pipeline, the structure overcomes the limitations of serial architectures. Using this architecture, a 36-stage pipelined RS(255,223) encoder is implemented. The throughput is greatly enhanced, and the radiation tolerance is also improved due to the application of interleaving techniques. The RS(255,223) encoder performance was evaluated on the Xilinx XC7K325T platform. The results confirm that the proposed architecture can support high data rates and provide effective error correction. With an 8-bit symbol size, a single encoder achieved throughput of 3.043 Gbps, making it highly suitable for deployment in future space exploration missions. Full article

Electrolysis desaturation has emerged as an innovative technique to mitigate liquefaction risk by reducing soil saturation in liquefiable foundations. This study evaluated the effectiveness of horizontal electrolysis on calcareous sandy foundations in marine environments by employing 35‰ NaCl solution as pore fluid under different current intensities (1A, 2A, and 4A). Experimental results demonstrated that hydrogen gas was generated at the cathode, while chlorine gas was produced at the anode, with peak gas retention rates of 100%, 90.83%, and 63.26% for 1A; 97.61%, 79.04%, and 60.94% for 2A; and 95.37%, 48.49%, and 42.81% for 4A over three electrolysis cycles. Three key findings emerged from our investigation: First, the resistivity of calcareous sand displayed a three-stage variation pattern, primarily governed by temperature and gas content evolution. Second, the temperature-corrected resistivity model provided reliable saturation data, revealing that electrode-adjacent soil layers exhibited significantly greater saturation reduction compared to intermediate layers. The average saturation variation during a single electrolysis cycle reached 3.2%, 2.6%, and 4.4% for 1A, 2A, and 4A, respectively, in the soil layers near the electrodes, compared to 2.1%, 1.7%, and 3.3% in the middle soil layers under the same current intensities. Third, upon stopping electrolysis, gas redistribution led to decreased saturation in upper soil layers, with lower current intensities more effective in retaining gases within the soil matrix. Based on these findings, an electrolytic influence coefficient for calcareous sand applicable to Archie’s formulation is proposed. This study enhances the understanding of the mechanism of electrolysis desaturation and provides a theoretical basis for the effectiveness of electrolysis desaturation on calcareous sand foundations. Full article