Search Results (3,238)

Gas-assisted coaxial electrospinning (GACES), a simple and versatile technique for the large-scale fabrication of coaxial nanofiber membranes, possesses significant industrial potential across advanced manufacturing sectors including semiconductors—particularly for fabricating high-precision dielectric layers, high-uniformity encapsulation materials, and flexible semiconductor substrates requiring tailored core-shell architectures. However, there is still a lack of relevant studies on the effective regulation of the core-shell structures of coaxial fibers based on GACES, which greatly limits the batch preparation and wide application of coaxial fibers. Finite element simulation analysis of the flow field and development of the coaxial jet mechanics model with a gas-driven flow field—two key methodologies in this study—successfully uncovered the influence mechanism of gas-assisted flow fields on the core-shell structures of coaxial nanofibers. By adjusting the gas-assisted flow fields parameters, we reduced the total diameter of coaxial fibers by 47.33% (average fiber diameter: 334.12 ± 16.29 nm → 175.98 ± 1.18 nm), decreased the shell thickness by 72.98%, increased the core-shell ratio by 289% (core-shell ratio: 0.49 → 1.91), and improved the uniformity of the total diameter distribution of coaxial fibers by 30.64%. This study delivers a practical conceptual framework and robust experimental underpinnings for the scalable fabrication of coaxial nanofiber membranes with controllable core-shell structures, thereby promoting their practical application in semiconductor devices such as ultra-thin dielectric layers, precisely structured encapsulation materials, and high-uniformity templates for nanoscale circuit patterning. Full article

Drivetrain systems operate under varying operating conditions (OCs), which often obscure early-stage fault signatures and hinder robust condition monitoring (CM). This work introduces an AI digital platform developed during the EEDRIVEN project, featuring a holistic CM framework that integrates statistical time series methods—using Generalized AutoRegressive (GAR) models in a multiple model fault diagnosis scheme—with deep learning approaches, including autoencoders and convolutional neural networks, enhanced through a dedicated decision fusion methodology. The platform addresses all key CM tasks, including fault detection, fault type identification, fault severity characterization, and remaining useful life (RUL) estimation, which is performed using a dynamics-informed health indicator derived from GAR parameters and a simple linear Wiener process model. Training for the platform relies on a limited set of experimental vibration signals from the physical drivetrain, augmented with high-fidelity multibody dynamics simulations and surrogate-model realizations to ensure coverage of the full space of OCs and fault scenarios. Its performance is validated on hundreds of inspection experiments using confusion matrices, ROC curves, and metric-based plots, while the decision fusion scheme significantly strengthens diagnostic reliability across the CM stages. The results demonstrate near-perfect fault detection (99.8%), 97.8% accuracy in fault type identification, and over 96% in severity characterization. Moreover, the method yields reliable early-stage RUL estimates for the outer gear of the drivetrain, with normalized errors < 20% and consistently narrow confidence bounds, which confirms the platform’s robustness and practicality for real-world drivetrain systems monitoring. Full article

Our Goal is to identify how colorectal cancer (CRC) arises in the single-layered cell epithelium (simple columnar epithelium) that lines the luminal surface of the large intestine. Background: We recently reported that the dynamic organization of cells in colonic epithelium is encoded by five biological rules and conjectured that colon tumorigenesis involves an autocatalytic tissue renewal reaction. Introduction Our objective was to define how altered crypt turnover explains tissue disorganization that leads to adenoma morphogenesis and CRC. Hypothesis: Changes in rate of tissue renewal-based cell polymerization leads to epithelial expansion and tissue disorganization during adenoma histogenesis. Methods: Accordingly, we created a computational model that considers the structure of colonic epithelium to be a polymer of cells and that tissue renewal is autocatalytic. Indeed, self-renewal of stem cells in colonic crypts continuously produces cells that act like monomers to form a polymer of cells (an interconnected, continuous cell sheet) in a polymerization-based process. Our model is a system of nonlinear differential equations that simulates changes in human crypt cell population dynamics. Results: We investigated how changes occur in the proportion of different cell types during adenoma development in FAP patients. The results show premalignant colonic crypts have a decreased rate of tissue renewal due to APC-mutation. Discussion: This slower rate of cell polymerization causes a rate-limiting step in the crypt renewal process that expands the proliferative cell population size. Conclusions: Our findings provide a mechanism that explains how a prolonged rate of crypt renewal leads to tissue disorganization with local epithelial expansion, infolding, and contortion during adenoma morphogenesis.: Full article

The boundary of a submerged floating tunnel (SFT) is flexible, and ignoring the influence of boundary and pipeline connections may reduce its structural performance. Therefore, this study uses rotating springs and linear springs to simulate the flexible boundary. Joints are simplified as shear springs and bending springs. A multi-span SFT model on discrete elastic supports is established, and its seismic response is evaluated using the transfer matrix method and the modal superposition method. The proposed method is validated by comparing it with finite element results, and the vertical mechanical response of the SFT when the cable relaxes or fractures under earthquake action is analyzed. The results indicate a significant deviation between the seismic response of flexible constraints and those modeled as simple hinged or fixed connections, and the lower boundary constraint stiffness is beneficial to the seismic response of the SFT. Introducing flexible joints can effectively reduce the internal force response of the structure, and a bending stiffness ratio of 0.01 to 0.03 for the joints is considered reasonable. In contrast, variations in the shear stiffness of the joints have a relatively small impact on the seismic response. Full article

Urban floods are becoming increasingly frequent and severe, highlighting the need for real-time information that supports safe evacuation decision-making. This study proposes and validates an unmanned aerial vehicle (UAV)-based methodology for real-time urban flood monitoring using an actual flood event caused by Typhoon Hinnamnor at the Seondeok Intersection in Gyeongju, Republic of Korea. The method comprises three simple steps: (1) collecting UAV images and data; (2) generating spatial and terrain information through photogrammetry; and (3) estimating flood extent, depth, and volume using GIS-based analysis. A total of 796 UAV images were processed, yielding a flooded area of 3847.36 m², a flood volume of 13,895.13 m³, and a maximum depth of 0.75 m. To assess performance, UAV-derived results were compared with XP-SWMM simulation outputs. Significant discrepancies were observed in flood extent, inundation volume, and flood persistence, indicating that hydrological models may not fully capture localized drainage failures or site-specific conditions in urban environments. These findings demonstrate that UAV-based monitoring provides a more accurate representation of actual flood and can supply high-resolution, rapidly obtainable information essential for real-time evacuation. This study provides empirical evidence of UAV applicability during the flood event itself and highlights its potential to enhance disaster-response capability, improve decision-making, and strengthen the resilience and sustainability of flood-prone urban areas. Full article

The present study proposes a simple and low-cost indirect method for estimating the thickness of plates by measuring the contact time (T_C) generated by the impact of a free-falling sphere. The theoretical model has been developed on Tsai approximation of Zener’s theory, which describes the dynamic interaction between the sphere and the plate taking in account the propagation of flexural waves. The methodology was validated through FEM simulations and through an extensive experimental campaign, where the contact times were measured using a simple electrical circuit. The results show excellent agreements between predicted and actual thicknesses, with relative errors below 3% for λ < 1.5 (where λ is the inelasticity parameter). For very thin plates and highly deformable materials, the above accuracy decreases due to flexibility and plastic deformation. We believe the proposed approach to be particularly promising in non-destructive testing applications within several scenarios, where speed, cost-effectiveness, and safety are essential requirements. Full article

We present an analytical model for the lethargic neutron spectrum (𝜙_u(E), i.e., per unit of u = ln(E)), which is specifically suited to nuclear fusion environments. The spectrum is represented as the sum of three components: (i) a stretched Maxwellian thermal component, (ii) a windowed power-law epithermal plateau and (iii) a log-normal high-energy peak. While being simple and concise, this model allows for accurate fitting to experimental data or transport calculation results, as well as easy extrapolation for different operating conditions. We present the physical basis of the model and provide guidelines for adjusting it. We also demonstrate how it can accurately reproduce neutron spectra from experiments or Monte Carlo simulations that are representative of various nuclear fusion environments. Finally, we use this model to estimate the soft-error rate (SER) for circuits operating in fusion environments, considering, in addition, analytical forms for the single-event neutron cross-section of the circuit in the thermal and high-energy domains to derive analytical or semi-analytical expressions of the SER. Full article

This case study evaluates the economic and energy efficiency of retrofitting a hospital heating system in Krakow, Poland, by transitioning from a district-heating-only model to a bivalent hybrid system. The analyzed configuration integrates air-to-water heat pumps (HP), a 180 kWp photovoltaic (PV) installation, and a 120 kWh battery energy storage (ES) unit, while retaining the municipal district heating network as a peak load and backup source. Utilizing high-resolution quasi-steady-state simulations in Ebsilon Professional (10 min time step) and projected 2025 market data, the study compares three modernization scenarios differing in heat pump capacity (20, 40, and 60 kW). The assessment focuses on key performance indicators, including Net Present Value (NPV), Levelized Cost of Heating (LCOH), and Simple Payback Time (SPBT). The results identify the bivalent system with 40 kW thermal capacity (Variant 2) as the economic optimum, delivering the highest NPV (EUR 121,021), the lowest LCOH (0.0908 EUR/kWh), and a payback period of 11.94 years. Furthermore, the study quantitatively demonstrates the law of diminishing returns in the oversized scenario (60 kW), confirming that optimal sizing is critical for maximizing the efficiency of bivalent systems in public healthcare facilities. This work provides a detailed methodology and data that can form a basis for making investment decisions in similar public utility buildings in Central and Eastern Europe. Full article

Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments. Full article

The rapid evolution of digital assets transforms cryptocurrencies into one of the most volatile and data-rich financial markets. Their nonlinear and unpredictable nature limits the effectiveness of traditional forecasting models, motivating the use of machine learning methods to identify hidden patterns and short-term price movements. This study compares the performance of Logistic Regression (LR), Random Forest (RF), XGBoost, Support Vector Classifier (SVC), K-Nearest Neighbors (KNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models in predicting the daily price directions of Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP). Extensive data preprocessing and feature engineering are performed, integrating a broad set of technical indicators to enhance model generalization and capture temporal market dynamics. The results show that XGBoost achieves the highest classification accuracy of 55.9% for BTC and 53.8% for XRP, while LR provides the best result for Ethereum with an accuracy of 54.4%. In trading simulations, XGBoost achieves the strongest performance, generating a cumulative return of 141.4% with a Sharpe ratio of 1.78 for Bitcoin and 246.6% with a Sharpe ratio of 1.59 for Ripple, whereas LSTM delivers the best results for Ethereum with a 138.2% return and a Sharpe ratio of 1.05. Compared to recent studies, the proposed approach attains slightly higher accuracy, while demonstrating stronger robustness and profitability in practical backtesting. Overall, the findings confirm that through rigorous preprocessing machine learning-based strategies can effectively capture short-term price movements and outperform the conventional buy-and-hold benchmark, even under a simple rule-based trading framework. Full article

Supervised ECG-based sleep apnea detection typically depends on large and fully annotated datasets, yet the rarity and cost of labeling apneic events often lead to substantial annotation scarcity in practice. This study provides a controlled evaluation of how such scarcity degrades classification performance and, as a key contribution, investigates whether a constrained, morphology-preserving ECG augmentation framework can compensate for reduced apnea-label availability. Using the PhysioNet Apnea–ECG dataset, we simulated seven levels of label retention ($r=5$–$100\%$) and trained a lightweight CNN–BiLSTM model under both subject-dependent (SD) and subject-independent (SI) five-fold protocols. Offline augmentation was applied only to apnea segments and consisted of simple, physiologically motivated time-domain perturbations designed to retain realistic cardiac and respiratory dynamics. Across both evaluation settings, augmentation substantially mitigated performance loss in the low- and mid-scarcity regimes. Under SI evaluation, the mean F1-score improved from 0.57 to 0.72 at $r=5\%$ and from 0.63 to 0.76 at $r=10\%$, with scores at $r=10$–$40\%$ (0.75–0.77) approaching the full-label baseline of 0.79. Temporal and spectral analyses confirmed preservation of P–QRS–T morphology and respiratory modulation without distortion. These results demonstrate that simple and interpretable ECG augmentations provide an effective and reproducible baseline for data-efficient apnea screening and offer a practical path toward scalable annotation and robust single-lead deployment under label scarcity. Full article

Continuous bladder volume monitoring in a wearable format can improve outcomes for patients with bladder dysfunction, heart failure, and other conditions requiring precise fluid management. Bioimpedance-based methods offer a promising, noninvasive solution; however, the influence of patient-specific anatomy, particularly waist circumference and subcutaneous fat thickness, remains poorly characterized. In this study, we use in silico finite element modeling to quantify how these anatomical factors affect two key bioimpedance metrics: voltage change ($\Delta V$) and voltage change ratio (VCR). Comprehensive simulations were performed across 15 virtual anatomies, generating a reference dataset for guiding future analog front-end and algorithm designs. We further compared generalized volume estimation models against conventional patient-specific void regression approaches. With appropriate input scaling, the generalized models achieved performance within 10% of patient-specific calibrations and, in some cases, surpassed them. Certain configurations reduced mean average error (MAE) by more than 20% relative to individualized models, potentially enabling a streamlined setup without the need for laborious ground-truth acquisition such as voided volume collection. These results demonstrate that incorporating simple anatomical scaling can yield robust, generalizable bladder volume estimation models suitable for wearable systems across diverse patient populations. Full article

This paper presents a proof-of-concept for a natural-language-based closed-loop controller that regulates the temperature of a simple single-input single-output (SISO) thermal process. The key idea is to express a relay-with-hysteresis policy in plain English and let a local large language model (LLM) interpret sensor readings and output a binary actuation command at each sampling step. Beyond interface convenience, we demonstrate that natural language can serve as a valid medium for modeling physical reality and executing deterministic reasoning in control loops. We implement a compact plant model and compare two controllers: a conventional coded relay and an LLM-driven controller prompted with the same logic and constrained to a single-token output. The workflow integrates schema validation, retries, and a safe fallback, while a stepwise evaluator checks agreement with the baseline. In a long-horizon (1000-step) simulation, the language controller reproduces the hysteresis behavior with matching switching patterns. Furthermore, sensitivity and ablation studies demonstrate the system’s robustness to measurement noise and the LLM’s ability to correctly execute the hysteresis policy, thereby preserving the theoretical robustness inherent to this control law. This work demonstrates that, for slow thermal dynamics, natural-language policies can achieve comparable performance to classical relay systems while providing a transparent, human-readable interface and facilitating rapid iteration. Full article

Landfill disposal continues to be the most economically viable municipal solid waste (MSW) management practice in many countries, including Mexico. Landfills are the third-largest source of methane emissions from human activity, a fact that has significant implications for the environment and human health. Due to the difficulty in experimentally quantifying methane emissions, mathematical models have been employed to predict gas emissions. In this work, three first-order decay models were implemented to estimate methane emissions in a landfill located in the metropolitan area of Oaxaca City, Mexico. Each model incorporated a Van’t Hoff–Arrhenius-type approach for calculating the reaction rate constant. Additionally, an uncertainty analysis of the models was presented, applying Monte Carlo simulations with triangular and log-normal distributions. The results show that the simple model exhibited the best predictive performance. For 2020, the simple model estimated 3,488,392.1 m³ of methane at a temperature of 46 °C, 3,509,625.1 m³ of methane at 47 °C, and 3,530,850.2 m³ of methane at 48 °C. The Monte Carlo simulation with a log-normal distribution exhibited more robust and natural process behavior. For the simple model, the mean was 3,486,946.03, the median was 3,487,154.73, and the standard deviation was 212,095.95. The LandGEM model exhibited more linear methane generation behavior, and the uncertainty analysis confirmed that this model had the lowest predictive capability of the three proposed models. Full article

Background: The need to improve gait emulation in people with amputation has driven the development of customized prosthetic mechanisms. This study focuses on the design and validation of a mechanism for external knee joint prostheses, based on the trajectory of the Instantaneous Center of Rotation (ICR) of a healthy knee. Objective: The objective is to design a mechanism that accurately reproduces the evolution of the ICR trajectory, thereby improving stability and reducing the user’s muscular effort. Methods: An exploratory methodology was employed, utilizing computer-aided design (CAD), kinematic simulations, and rapid prototyping through 3D printing. Multiple configurations of four- and six-bar mechanisms were evaluated to determine the ICR trajectory and compare it with a reference model obtained in the laboratory from a specific subject, using MATLAB-2023a and the Fréchet distance as an error metric. Results: The results indicated that the four-bar mechanism, with the incorporation of a simple gear train, achieved a more accurate emulation of the ICR trajectory, reaching a minimum error of 6.87 mm. Functional tests confirmed the effectiveness of the design in terms of stability and voluntary control during gait. It can be concluded that integrating the mechanism with the gear train significantly enhances its functionality, making it a viable alternative for the development of external knee prostheses for people with transfemoral amputation, based on the ICR of the contralateral leg. Full article