Search Results (115)

Introduction: Semaglutide (SEMA) has shown potential benefits in metabolic dysfunction-associated steatotic liver disease (MASLD). This large real-world study aimed to evaluate the effects of SEMA on MASLD patients’ clinical outcomes and liver-related complications. Results: Following propensity score matching based on 34 variables (demographics, comorbidities, laboratory tests, and medication history), SEMA-treated (n = 19,112) patients were compared with non-SEMA (n = 19,112) cases. Both cohorts were well-balanced, except for higher BMI in the SEMA group (36.60 ± 6.25 vs. 34.89 ± 6.84 kg/m²). After one year, the SEMA group demonstrated ~one BMI point reduction but maintained significantly higher BMI (35.51 ± 6.34 vs. 34.11 ± 6.64, p < 0.001). LDL, triglycerides, and HbA1c levels significantly improved with SEMA, as evidenced by decreased rates of poor metabolic markers (31.13% vs. 34.32%, p < 0.001). The SEMA-treated patients demonstrated significantly higher survival, lower cardiovascular risk, and reduced progression to advanced liver disease compared to controls. Discussion: In this large real-world cohort, SEMA use in MASLD patients was associated with significantly improved 1-year survival, cardiovascular, and liver-related outcomes. These benefits appear to result primarily from metabolic improvements and anti-inflammatory effects. Materials and Methods: Data were sourced from TriNetX, a global health research platform with de-identified electronic medical records spanning 135 million patients across 112 healthcare organizations worldwide. We included MASLD adults diagnosed according to ICD9 criteria. Assessed outcomes included survival, biochemical, hematologic, AFP, metabolic and cardiovascular parameters, advanced liver disease (ALD), synthetic function, and metabolic markers. Conclusions: Semaglutide may serve as an effective therapeutic strategy to improve outcomes in MASLD. Full article

Hydroxychloroquine (HCQ), a cornerstone therapeutic agent for autoimmune diseases, requires precise serum concentration monitoring due to its narrow therapeutic window. Current HCQ monitoring methods such as HPLC and LC-MS/MS are sensitive but costly and complex. While electrochemical sensors offer rapid, cost-effective detection, their large chambers and high sample consumption hinder point-of-care use. To address these challenges, we developed a microfluidic electrochemical sensing platform based on a screen-printed carbon electrode (SPCE) modified with a hierarchical nanocomposite of gold nanoparticles (AuNPs), copper-based metal–organic frameworks (Cu-MOFs), and multi-walled carbon nanotubes (MWCNTs). The Cu-MOF provided high porosity and analyte enrichment, MWCNTs established a 3D conductive network to enhance electron transfer, and AuNPs further optimized catalytic activity through localized plasmonic effects. Structural characterization (SEM, XRD, FT-IR) confirmed the successful integration of these components via π-π stacking and metal–carboxylate coordination. Electrochemical analyses (CV, EIS, DPV) revealed exceptional performance, with a wide linear range (0.05–50 μM), a low detection limit (19 nM, S/N = 3), and a rapid response time (<5 min). The sensor exhibited outstanding selectivity against common interferents, high reproducibility (RSD = 3.15%), and long-term stability (98% signal retention after 15 days). By integrating the nanocomposite-modified SPCE into a microfluidic chip, we achieved accurate HCQ detection in 50 μL of serum, with recovery rates of 95.0–103.0%, meeting FDA validation criteria. This portable platform combines the synergistic advantages of nanomaterials with microfluidic miniaturization, offering a robust and practical tool for real-time therapeutic drug monitoring in clinical settings. Full article

Guided-wave structural health monitoring offers exceptional sensitivity to localized defects but relies on high-fidelity simulations that are prohibitively expensive for real-time use. Reduced-order models can alleviate this cost but hinge on affine parameterization of system operators. This assumption breaks down for complex, non-affine damage behavior. To overcome these limitations, we introduce a novel, non-intrusive space–time empirical interpolation method that is applied directly to the full wavefield. By greedily selecting key spatial, temporal, and parametric points, our approach builds an affine-like reduced model without modifying the underlying operators. We then train a Gaussian-process surrogate to map damage parameters straight to interpolation coefficients, enabling true real-time digital-twin predictions. Validation on both analytic and finite-element benchmarks confirms the method’s accuracy and speed-ups. All MATLAB 2024b. scripts for EIM, DEIM, Kriging, and wave propagation are available in the GitHub (version 3.4.20) repository referenced in the Data Availability statement, ensuring full reproducibility. Full article

Ultrafast laser systems, implemented at the ELI-NP, require exceptional beam quality and spatial stability due to their femtosecond pulse durations and extremely high peak powers. This work presents a diagnostic and computational framework for analyzing the ELI-NP Front-End beam characteristics, where spatial coherence and precise pulse shaping are essential for reliable amplification and experimental consistency. The methodology integrates classical beam diagnostics with image processing and machine learning tools to evaluate anomalies based on high-resolution beam profile images. We use centroid tracking to monitor pointing fluctuations, statistical intensity analysis to detect energy instabilities, and Sobel-based edge detection to evaluate beam sharpness and extract structural features from the beam image. Geometric parameters such as ellipticity, roundness, and symmetry indicators are extracted and examined over time. The system applies an unsupervised Isolation Forest algorithm to detect subtle or short-lived anomalies, identifying irregularities without relying on predefined thresholds. These diagnostics are supported by visual plots and statistical summaries, offering a clear picture of the beam’s behavior under real operating conditions. Results confirm that this integrated approach effectively captures major and minor beam instabilities, making it a practical tool for continuous monitoring and performance optimization in ultrafast laser systems. Full article

Background/Objective: Major depressive disorder (MDD) is a common mental disorder, with a significant portion of patients developing treatment-resistant depression (TRD). Esketamine is an antagonist of the N-methyl-D-aspartate receptor indicated as a nasal spray in combination with other antidepressants for adults with TRD. Signals of suspected adverse reactions (SARs) to esketamine from the EudraVigilance database in European countries were analyzed for a more defined safety profile of this drug in the real world. Methods: SARs to esketamine reported in the data system EudraVigilance were analyzed, and disproportionality analysis for adverse reactions indicating suicidality for esketamine, in comparison to the antidepressants fluoxetine and venlafaxine, was performed. Results: Increases in blood pressure (15.4%) and dissociation (15.0%) were the most frequently reported SARs. The sex distribution indicates the prevalence of women, except for increased blood pressure and completed suicide, which were signaled in men, while adults (18–64 years) and elders (65–85 years) were the ages with the largest number of reported adverse reactions to esketamine. The results indicate the existence of a potential increase in the risk of suicide in depressed patients taking esketamine when compared with fluoxetine and venlafaxine. Conclusions: Apart from carefulness due to the known limitations of pharmacovigilance research conducted by using data systems of spontaneous signals for SARs, the analysis of data points toward the need for greater attention being paid to the potential risk of suicide following the prescription of esketamine in depressed subjects. In this regard, as regulatory agencies also recommend, patients with a history of suicide-related events or those exhibiting a significant degree of suicidal ideation prior to beginning treatment should receive more careful monitoring during treatment. Full article

Background: Accurate ground segmentation in 3D point clouds is critical for robotic perception, enabling robust navigation, object detection, and environmental mapping. However, existing methods struggle with over-segmentation, under-segmentation, and computational inefficiency, particularly in dynamic or complex environments. Methods: This study proposes FASTSeg3D, a novel two-stage algorithm for real-time ground filtering. First, Range Elevation Estimation (REE) organizes point clouds efficiently while filtering outliers. Second, adaptive Window-Based Model Fitting (WBMF) addresses over-segmentation by dynamically adjusting to local geometric features. The method was rigorously evaluated in four challenging scenarios: large objects (vehicles), pedestrians, small debris/vegetation, and rainy conditions across day/night cycles. Results: FASTSeg3D achieved state-of-the-art performance, with a mean error of <7%, error sensitivity < 10%, and IoU scores > 90% in all scenarios except extreme cases (rainy/night small-object conditions). It maintained a processing speed 10× faster than comparable methods, enabling real-time operation. The algorithm also outperformed benchmarks in F1 score (avg. 94.2%) and kappa coefficient (avg. 0.91), demonstrating superior robustness. Conclusions: FASTSeg3D addresses critical limitations in ground segmentation by balancing speed and accuracy, making it ideal for real-time robotic applications in diverse environments. Its computational efficiency and adaptability to edge cases represent a significant advancement for autonomous systems. Full article

Conventional visual SLAM systems often struggle with degraded pose estimation accuracy in dynamic environments due to the interference of moving objects and unstable feature tracking. To address this critical challenge, we present a groundbreaking enhancement to visual SLAM by introducing an innovative architecture that integrates advanced feature extraction and dynamic object filtering mechanisms. At the core of our approach lies a novel Multi-Scale Feature Consolidation (MSFConv) module, which we have developed to significantly boost the feature extraction capabilities of the YOLOv8 network. This module enables superior multi-scale feature representation, leading to significant improvements in object detection accuracy and robustness. Furthermore, we have developed a Dynamic Object Filtering Framework (DOFF) that seamlessly integrates with the ORB-SLAM3 architecture. By leveraging the Lucas-Kanade (LK) optical flow method, DOFF effectively distinguishes and removes dynamic feature points while preserving the integrity of static features. This ensures high-precision pose estimation in highly dynamic environments. Comprehensive experiments on the TUM RGB-D dataset validate the exceptional performance of our proposed method, demonstrating 93.34% and 94.43% improvements in pose estimation accuracy over the baseline ORB-SLAM3 in challenging dynamic sequences. These substantial improvements are achieved through the synergistic combination of enhanced feature extraction and precise dynamic object filtering. Our work represents a significant leap forward in visual SLAM technology, offering a robust solution to the long-standing problem of dynamic environment handling. The proposed innovations not only advance the state-of-the-art in SLAM research but also pave the way for more reliable real-world applications in robotics and autonomous systems. Full article

Hydrogel-based wastewater treatment technologies show certain outstanding features, which include exceptional efficiency, sustainability, reusability, and the precise targeting of specific contaminants. Moreover, it becomes possible to minimize the environmental impact when using these materials. Their flexibility, low energy consumption, and adaptability to meet specific requirements for different purposes offer significant advantages over traditional methods like activated carbon filtration, membrane filtration, and chemical treatments. Recent advancements in hydrogel technology, including new production methods and hybrid materials, enhance their ability to efficiently adsorb contaminants without altering their biocompatibility and biodegradability. Therefore, innovative materials that are ideal for sustainable water purification were developed. However, these materials also suffer from several limitations, mostly regarding the scalability, long-term stability in real-world systems, and the need for precise functionalization. Therefore, overcoming these issues remains a challenge. Additionally, improving the efficiency and cost-effectiveness of regeneration methods is essential for their practical use. Finally, assessing the environmental impact of hydrogel production, use, and disposal is crucial to ensure these technologies are beneficial in the long run. This review summarizes recent advancements in developing polymer-based hydrogels for wastewater treatment by adsorption processes to help us understand the progress made during recent years. In particular, the studies presented within this work are compared from the point of view of the synthesis method, raw materials used such as synthetic/natural or hybrid networks, and the targeted class of pollutants—dyes or heavy metal ions. In several sections of this paper, discussions regarding the most important properties of the newly emerged adsorbents, e.g., kinetics, the adsorption capacity, and reusability, are also discussed. Full article

It is widely acknowledged in communication theory that polar codes have been proven to achieve channel capacity across a range of communication channels. However, their exceptional performance is usually evaluated through simulations or analyses conducted under the assumption of infinite precision, i.e., floating-point arithmetic, which represents an ideal numerical computation. To address this implementation challenge, this work proposes a min-sum successive cancellation (MS-SC) polar decoder employing all-integer quantization to improve practicality in real-world scenarios. To balance the trade-off between practicality and decoding performance, we investigate whether 5-bit all-integer quantization is the optimal choice for the MS-SC polar decoder. Moreover, the simulation results over fading channels show that the proposed decoder achieves a performance almost equivalent to the high-precision successive cancellation (SC) decoder. The integer-based calculation for the MS-SC polar decoder reduces computational complexity by $75\%$ compared to the conventional SC decoding algorithm with infinite-precision computation. Full article

This article considers a class of entire functions of several complex variables that are bounded in the Cartesian product of some half-planes. Each such hyperplane is defined on the condition that the real part of the corresponding variable is less than some r. For this class of functions, there are established analogs of the Wiman theorems. The first result describes the behavior of an entire function from the given class at the neighborhood of the point of the supremum of its modulus. The second result shows asymptotic equality for supremums of the modulus of the function and its real part outside some exceptional set. In addition, the analogs of Wiman’s theorem are obtained for entire multiple Dirichlet series with arbitrary non-negative exponents. These results are obtained as consequences of a new statement describing the behavior of an entire function $F\left(z\right)$ of several complex variables $z=(z_1,\dots ,z_p)$ at the neighborhood of a point w, where the value $F\left(w\right)$ is close to the supremum of its modulus on the boundary of polylinear domains. The paper has two moments of novelty: the results use a more general geometric exhaustion of p-dimensional complex space by polylinear domains than previously known; another aspect of novelty concerns the results obtained for entire multiple Dirichlet series. There is no restriction that every component of exponents is strictly increasing. These statements are valid for any non-negative exponents. Full article

MXenes, a class of two-dimensional materials, have emerged as promising candidates for developing advanced electrochemical biosensors due to their exceptional electrical conductivity, large surface area, and rich surface chemistry. These unique properties enable high sensitivity, rapid response, and versatile functionalization, making MXene-based biosensors highly suitable for detecting biomolecules and pathogens in biomedical applications. This review explores recent advancements in MXene-based electrochemical biosensors from 2020 to 2024, focusing on their design principles, fabrication strategies, and integration with microfluidic platforms for enhanced performance. The potential of MXene sensors to achieve real-time and multiplexed detection is highlighted, alongside the associated challenges. Emphasis is placed on the role of MXenes in addressing critical needs in disease diagnostics, personalized medicine, and point-of-care testing, providing insights into future trends and transformative possibilities in the field of biomedical sensing technologies. Full article

The fast development of digital images and the improvement required for security measures have recently increased the demand for innovative image analysis methods. Image analysis identifies, classifies, and monitors people, events, or objects in images or videos. Image analysis significantly improves security by identifying and preventing attacks on security applications through digital images. It is crucial in diverse security fields, comprising video analysis, anomaly detection, biometrics, object recognition, surveillance, and forensic investigations. By integrating advanced software engineering models with IoT capabilities, this technique revolutionizes copy–move image forgery detection. IoT devices collect and transmit real-world data, improving software solutions to detect and analyze image tampering with exceptional accuracy and efficiency. This combination enhances detection abilities and provides scalable and adaptive solutions to reduce cutting-edge forgery models. Copy–move forgery detection (CMFD) has become possibly a major active research domain in the blind image forensics area. Between existing approaches, most of them are dependent upon block and key-point methods or integration of them. A few deep convolutional neural networks (DCNN) techniques have been implemented in image hashing, image forensics, image retrieval, image classification, etc., that have performed better than the conventional methods. To accomplish robust CMFD, this study develops a fusion of soft computing with a deep learning-based CMFD approach (FSCDL-CMFDA) to secure digital images. The FSCDL-CMFDA approach aims to integrate the benefits of metaheuristics with the DL model for an enhanced CMFD process. In the FSCDL-CMFDA method, histogram equalization is initially performed to improve the image quality. Furthermore, the Siamese convolutional neural network (SCNN) model is used to learn complex features from pre-processed images. Its hyperparameters are chosen by the golden jackal optimization (GJO) model. For the CMFD process, the FSCDL-CMFDA technique employs the regularized extreme learning machine (RELM) classifier. Finally, the detection performance of the RELM method is improved by the beluga whale optimization (BWO) technique. To demonstrate the enhanced performance of the FSCDL-CMFDA method, a comprehensive outcome analysis is conducted using the MNIST and CIFAR datasets. The experimental validation of the FSCDL-CMFDA method portrayed a superior accuracy value of 98.12% over existing models. Full article

This paper presents a time-series point-to-point generative adversarial network (TS-p2pGAN) for synthesizing realistic electric vehicle (EV) driving data. The model accurately generates four critical operational parameters—battery state of charge (SOC), battery voltage, mechanical acceleration, and vehicle torque—as multivariate time-series data. Evaluation on 70 real-world driving trips from an open battery dataset reveals the model’s exceptional accuracy in estimating SOC values, particularly under complex stop-and-restart scenarios and across diverse initial SOC levels. The model delivers high accuracy, with root mean square error (RMSE), mean absolute error (MAE), and dynamic time warping (DTW) consistently below 3%, 1.5%, and 2.0%, respectively. Qualitative analysis using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) demonstrates the model’s ability to preserve both feature distributions and temporal dynamics of the original data. This data augmentation framework offers significant potential for advancing EV technology, digital energy management of lithium-ion batteries (LIBs), and autonomous vehicle comfort system development. Full article

We study two modifications of the trapezoidal product cubature formulae, approximating double integrals over the square domain ${[a,b]}^2=[a,b]\times [a,b]$. Our modified cubature formulae use mixed type data: except evaluations of the integrand on the points forming a uniform grid on ${[a,b]}^2$, they involve two or four univariate integrals. A useful property of these cubature formulae is that they are definite of order $(2,2)$, that is, they provide one-sided approximation to the double integral for real-valued integrands from the class ${\mathcal{C}}^{2,2}[a,b]=\{f(x,y):{\displaystyle \frac{{\partial }^{4}f}{\partial x^2\partial y^2}}\mathrm{continuous}\mathrm{and}\mathrm{does}\mathrm{not}\mathrm{change}\mathrm{sign}\mathrm{in}{(a,b)}^2\}$. For integrands from ${\mathcal{C}}^{2,2}[a,b]$ we prove monotonicity of the remainders and derive a posteriori error estimates. Full article

Three-dimensional imaging plays a crucial role at the micro-scale in fields such as precision manufacturing and materials science. However, image noise significantly impacts the accuracy of point cloud reconstruction, making image denoising techniques a widely discussed topic. Statistical analysis of laser imaging noise has led to the conclusion that logarithmically transformed noise follows a Gumbel distribution. A corresponding neural network training set was developed to address the challenges of difficult data collection and the scarcity of phase-unwrapped image datasets. Building on this foundation, a phase-unwrapped image denoising method based on the Denoising Convolutional Neural Network (DnCNN) is proposed. This method aims to achieve three-dimensional filtering by performing two-dimensional image denoising. Experimental results show a significant reduction in the Cloud-to-Mesh Distance (C2M) statistics of the corresponding point clouds before and after planar filtering. Specifically, the statistic at 97.5% of the 2σ principle decreases from 0.8782 mm to 0.3384 mm, highlighting the effectiveness of the filtering algorithm in improving the planar fit. Moreover, the DnCNN method exhibits exceptional denoising performance when applied to real-world target data, such as plaster statues with complex depth variations and PCBs made from different materials, thereby enhancing accuracy and reliability in point cloud reconstruction. This study provides valuable insights into phase-unwrapped image noise suppression in laser imaging, particularly in micro-scale applications where precision is critical. Full article