Search Results (2,464)

In this work, we applied Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) to predict key performance characteristics of quantum cascade lasers (QCLs), including material gain, current density, and emission frequency. By developing a machine learning-based surrogate modeling framework that replaces computationally expensive simulations of QCLs, we enable orders-of-magnitude-faster evaluation and optimization of a high-dimensional configuration space. The training dataset was generated using a numerical simulator based on the density-matrix transport model. By combining physics simulations with machine learning, we achieved reliable predictions of device characteristics, with standardized RMSE values ranging from 0.21 to 0.55 for RF, 0.16 to 0.51 for XGBoost, and 0.04 to 0.22 for the ANN model, demonstrating the superior predictive performance of the ANN across all investigated performance characteristics. The ANN was subsequently used to analyze the full configuration space defined by possible layer thicknesses and electric fields. Approximately 44 million configurations were evaluated in about five minutes, achieving a speedup of approximately 90,000 times over the numerical simulator for a single configuration. This approach allowed the identification of designs with improved material gain and facilitated the efficient optimization of key parameters while maintaining high prediction reliability. Full article

In environments where GNSS signals are unavailable—such as indoor spaces, underground facilities, and forested areas—autonomous UAV navigation faces challenges related to localization uncertainty and limited onboard sensing capability. This study proposes a lightweight navigation framework using a single Intel RealSense D435i depth camera, integrating RTAB-Map SLAM, DWA-based local planning, and a direction-oriented frontier exploration strategy. The proposed exploration strategy introduces heading consistency into frontier target selection to support navigation in directionally constrained environments. The system is implemented within the ROS framework and evaluated in Gazebo/ArduPilot SITL simulation environments under low-, medium-, and high-density obstacle configurations. The results show that the system successfully completed autonomous traversal and return-to-home missions across all scenarios, with traversal RMSE values of 0.195 m, 0.197 m, and 0.420 m and return RMSE values of 0.295 m, 0.474 m, and 1.084 m, respectively. Qualitative dynamic-obstacle tests further demonstrate the system’s capability for local map updating and replanning. It should be noted that the current evaluation is primarily simulation-based and conducted in simplified environments. Therefore, the results are interpreted as initial system-level validation rather than full real-world deployment verification. The proposed system should not be directly interpreted as a ready-to-deploy real-world UAV navigation solution. Future work will focus on physical UAV experiments and more realistic GNSS-denied environments. Full article

Modulation and signal classification are two highly correlated core tasks in wireless communications and are the core foundation of intelligent spectrum management in Future Internet and 6G networks. Although their objectives differ, the two tasks often share a substantial amount of underlying information in the feature space. However, focusing solely on their commonalities while neglecting their intrinsic differences may lead to suboptimal model performance. Therefore, by taking into account both the correlation and inherent differences between the two tasks, we propose TAMTNet, a task-adaptive multi-task network for edge deployment in Future Internet. TAMTNet introduces Extremely Efficient Spatial Pyramid (EESP) into the shared layer to efficiently extract multi-scale temporal information. In addition, a multi-gate mixture-of-experts (MMoE) mechanism is employed after the shared layer to enhance the modeling capability of task-specific features. Furthermore, to address the difficulty of deploying deep models on resource-constrained edge devices, a joint lightweight framework combining quantization-aware training and knowledge distillation is proposed, which significantly reduces model complexity while maintaining performance. Extensive experiments conducted on the simulation and real-world over-the-air transmission datasets demonstrate that the TAMTNet model achieves excellent performance on both modulation and signal classification tasks across a wide range of signal-to-noise ratios and radio transmit gain conditions. Meanwhile, the low-bitwidth lightweight models are able to maintain classification performance comparable to the full-precision model while significantly reducing model storage and computational complexity. Full article

We present a novel framework for epidemiological disease spread modeling that combines agent-based simulation with Boolean state-space representations and optimal filtering for state estimation under noisy observations. Our approach models individual agents in discrete Susceptible-Exposed-Infected-Recovered (SEIR) states using a compact 2-bit Boolean representation, with agent interactions governed by scheduled contact patterns. To address the challenge of inferring latent infection states from limited and noisy testing data, we develop two complementary inference approaches: (1) a Boolean Kalman particle filter for small populations that tracks the full joint distribution over agent states, and (2) a mean-field approximation for large populations that factorizes the posterior into independent marginal distributions, enabling scalability to realistic population sizes. Unlike continuous-state Kalman filters, our methods naturally handle the discrete nature of epidemiological states while accommodating realistic observation models where only a subset of agents are tested at each time step, with test results subject to false positive and false negative errors. We demonstrate that this framework enables accurate reconstruction of population-level infection dynamics and individual agent states from sparse, noisy observations across populations from 100 to 50,000 agents, providing a computationally tractable approach for real-time epidemic monitoring. Full article

Large-aperture steerable reflector antennas are pivotal for deep-space exploration and satellite communication, but their high-frequency performance is often compromised by wind-induced structural deformations. This study employs a high-fidelity fluid–structure interaction (FSI) framework, coupling Computational Fluid Dynamics (CFD) and the Finite Element Method (FEM), to investigate the dynamic response of an 18 m Square Kilometre Array (SKA) antenna under transient wind loads. The structural FEM is validated against experimental modal data, ensuring the capture of essential vibration characteristics. We evaluate steady-state wind pressure coefficients ($C_p$) and transient responses under a simulated Davenport wind spectrum across the antenna’s full operational elevation range. Surface accuracy degradation is rigorously quantified using the Root Mean Square Error (RMSE) of the best-fit paraboloid. The results demonstrate a significant correlation between peak deformation and surface error, pinpointing 15° and 90° pitch angles as the most critical configurations for profile degradation due to the “air pocket effect” and asymmetric pressure distributions, respectively. These insights establish a robust theoretical basis for structural optimization and the development of active surface control strategies for next-generation aerospace signal acquisition infrastructure. Full article

Acute respiratory failure (ARF) often leads to ICU admission, ventilatory support, illness, and death. The usual classification into hypoxemic and hypercapnic types does not capture its full complexity. Precision medicine uses the concept of “treatable traits” to guide care based on traits that are clinically relevant, identifiable, measurable, and possibly changeable. Arterial carbon dioxide pressure (PaCO₂) reflects factors like alveolar ventilation, dead space, respiratory mechanics, and how patients respond to ventilatory support. This makes it clinically relevant in selected situations. We carried out a scoping review using PRISMA-ScR and JBI guidelines to summarize evidence on hypocapnia and hypercapnia as prognostic, stratification, or clinically relevant variables during respiratory support. We searched PubMed/MEDLINE, ScienceDirect, and Web of Science (1994–2025), and checked references by hand. Thirty-four studies met our criteria and were grouped into four areas: pre-intubation or early acute presentation, non-invasive support (NIV/HFNC), invasive mechanical ventilation (IMV), and weaning or post-extubation. In summary, hypocapnia was linked to worse outcomes or failure of support in hypoxemic or cardiogenic cases. Hypercapnia helped identify patients who benefited from NIV, such as those with chronic obstructive pulmonary disease or obesity hypoventilation. For IMV, the effects depended on the presence and severity of acidosis and on its duration. Overall, PaCO₂ showed context-dependent clinical relevance, acting mainly as a prognostic or stratification marker and, in narrower settings, as a variable that may inform monitoring or support decisions. This review provides a pragmatic framework for interpreting PaCO₂ across respiratory support contexts and highlights the need for safe and clinically meaningful targets. Full article

Most artistic expressions are conveyed through images (e.g., painting) and audio (e.g., music), and deep learning has been successfully applied to neural style transfer within each of these modalities. However, there is still a lack of deep models that explicitly learn to transfer style between images and audio. Motivated by synesthesia, which reflects intrinsic connections between vision and hearing in the human brain, we propose a deep hybrid synesthesia model for audio–image style transfer. Our framework consists of two main components: (1) a component conversion module that learns cross-modal mappings between audio rhythm/spectrum and image color/shape in a continuous valence–arousal (VA) emotion space; and (2) a style conversion module that transfers high-level artistic styles between Eastern (ink-wash, shui-mo) and Western painting and their corresponding musical counterparts. We first learn emotion-aware feature networks that align low-level audio and visual components based on shared affective representations, and then model long-term stylistic structures for cross-modal style transfer. Experiments include “seeing the sound” (audio-to-image generation with controllable components) and full audio–image style transformations. Both objective analyses and subjective evaluations suggest that our model can produce cross-modal artworks whose perceived style and emotional content are consistent with human synesthetic impressions. Full article

This study investigates the traction response and operational risk of a compact ball-frame and tensioned-steel-cable drag-reduction system for submarine cable pulling inside HDD steel casings, based on local full-scale experiments. Thirteen test cases were designed by considering pipe curvature, device spacing, terminal reaction-force loading mode, and dry or sand–slurry in-casing conditions. In addition to the equivalent friction coefficient, three response descriptors, namely, the average traction force, peak coefficient, and fluctuation coefficient, were introduced to evaluate mean resistance, peak amplification, and process stability. The results show that pipe curvature significantly amplifies both traction peaks and response fluctuations, and should therefore be regarded as a key factor governing operational risk. The effect of device spacing is environment-dependent: under dry conditions, a moderate reduction in spacing improves rolling continuity, whereas under sand–slurry conditions, excessively dense deployment may aggravate local obstruction and response fluctuation. Stronger terminal reaction-force loading also increases peak amplification and instability. Based on these findings, a case-specific and experiment-oriented framework for operational-risk classification is proposed. The present results are intended to support traction-response characterization, device arrangement, and construction control under representative local conditions, rather than to replace full-scale field validation. Full article

The Model Context Protocol (MCP) has become the de facto standard for connecting large language models (LLMs) to external tools, and its remote deployment mode lets users add third-party servers with a single URL—shifting a substantial portion of the host’s attack surface to infrastructure operated by anonymous parties. Existing MCP security work has concentrated on tool-description poisoning and studied individual techniques in isolation, leaving it unclear what a malicious remote server can accomplish across its full surface. In this paper, we explore the malicious-server threat space along the axis of whether the host LLM participates in producing the harmful outcome, yielding two categories: LLM-passive attacks, which complete inside the server, and LLM-active attacks, which require the LLM to deliver the malicious content. We implement five scenarios spanning both categories—realizing each LLM-active scenario with both description-based and response-based variants against the same goal—and evaluate all configurations on ChatGPT, Claude Desktop, and Gemini CLI. We find that host-side filtering of MCP-bound data varies sharply across platforms (95% vs. 50% ASR on the same email request), that the description and response channels succeed on disjoint scenarios, and that successful attacks are almost never disclosed to the user. These findings suggest that defending remote MCP deployment requires a multi-layer approach combining host-side filtering, LLM-level response auditing, and user-visible output transparency. Full article

Orbit determination in Earth–Moon space is challenged by dynamic-model mismatch and unstable GNSS observation conditions, especially under weak and intermittent signals. To address this issue, this paper proposes a GNSS single-point positioning (SPP)-aided orbit determination method based on adaptive sliding-window filtering. A tightly coupled framework is constructed by integrating orbital dynamics propagation with SPP pseudo-range observations, allowing propagation errors to be corrected in real time through measurement updates. To enhance adaptability under time-varying observation conditions, a dynamic sliding-window strategy is introduced, in which the observation-noise covariance is adjusted according to carrier-to-noise ratio ($C/N_0$) variations. Simulations for three representative Earth–Moon trajectories, including a near-rectilinear halo orbit (NRHO), a distant retrograde orbit (DRO), and a Halo orbit, show that the proposed method significantly outperforms the conventional tightly coupled solution. The three-dimensional RMS position error is reduced from 6.65 m to 1.27 m for NRHO, from 6.57 m to 1.27 m for DRO, and from 5.91 m to 1.44 m for Halo, corresponding to improvements of 80.9%, 80.4%, and 75.4%, respectively. Under a simulated 200-epoch GNSS interruption in the Halo case, the method also improves outage robustness and post-recovery performance, reducing the three-dimensional RMS error by 23.2% in the interruption-centered interval and by 26.1% over the full arc. Full article

The present study aimed to establish a robustness modeling framework for the determination of cannabidiol (CBD) and Δ⁹-tetrahydrocannabinol (THC) in cannabis extract using a multivariate approach. A two-level full factorial design was implemented to examine four critical analytical factors, including methanol concentration (80–85% v/v), flow rate (0.8–1.2 mL/min), column temperature (23–27 °C), and detection wavelength (208–212 nm). Seven analytical responses for each compound were assessed, including peak area, retention time, resolution, asymmetry factor, number of theoretical plates, capacity factor, and peak area difference relative to the reference method. Statistical analysis demonstrated that both main effects and interaction effects significantly influenced the measured responses. Design space construction was performed based on predefined acceptance criteria to ensure method robustness: resolution > 1.5, asymmetry < 1.5, number of theoretical plates > 2000, capacity factor > 2, and peak area difference within −5% to 5%. Predictive performance of the developed models was verified by comparing predicted and experimental results. Good agreement was observed under most conditions, whereas deviation was noted for THC quantification at a detection wavelength of 212 nm. Furthermore, CBD and THC contents determined under three selected operating conditions within the established design space were statistically comparable to those obtained using the reference method, except for the condition employing 212 nm detection. The Analytical GREEnness Metric Approach (AGREE) assessment indicated moderate greenness performance of the analytical procedure. Overall, the multivariate two-level full factorial design proved to be an effective tool for robustness modeling of the HPLC method for simultaneous quantification of CBD and THC. Full article

Background/Objectives: Accurate estimation of intraoperative blood loss in endoscopic spine surgery remains challenging because of continuous saline irrigation and blood infiltration into surrounding soft tissues and potential dead spaces. Hidden blood loss (HBL), resulting from extravasation into tissue compartments or hemolysis, may substantially increase total blood loss (TBL) and contribute to postoperative bleeding-related complications. This study aimed to compare HBL in full-endoscopic unilateral laminotomy with bilateral decompression (FE-ULBD) with that in biportal endoscopic ULBD (BE-ULBD) and open microscopic ULBD (OM-ULBD). Methods: A retrospective analysis was conducted of patients who underwent single-level FE-ULBD, BE-ULBD, or OM-ULBD for lumbar spinal stenosis (LSS) at a single institution. Data on perioperative characteristics, laboratory parameters, perioperative blood loss (TBL, HBL, and visible blood loss), and clinical outcomes were collected and compared. Univariate linear regression analyses were performed to identify factors associated with HBL in the FE-ULBD group. Results: A total of 93 patients were included, comprising 34 in the FE-ULBD group, 32 in the BE-ULBD group, and 27 in the OM-ULBD group. The FE-ULBD group demonstrated significantly lower TBL than both the BE-ULBD and OM-ULBD groups (493.20 ± 183.46 vs. 675.97 ± 192.02 vs. 822.94 ± 424.11 mL, p = 0.001 and p = 0.002, respectively). HBL in the FE-ULBD group was significantly lower than in the BE-ULBD group (390.48 [268.32–506.91] vs. 513.29 [437.96–633.36] mL, p = 0.012) and was numerically lower than in the OM-ULBD group without statistical significance (390.48 [268.32–506.91] vs. 516.38 [316.41–710.68] mL, p = 0.081). Male sex was the only variable significantly associated with increased HBL in the FE-ULBD group. Conclusions: FE-ULBD showed significantly lower TBL than BE-ULBD and OM-ULBD, and lower HBL than BE-ULBD. FE-ULBD may represent a feasible surgical option for single-level LSS, with the potential advantage of reduced perioperative blood loss while maintaining comparable clinical outcomes. Full article

The emergence of Artificial Neural Networks (ANNs) and their deep learning form called Artificial Intelligence (AI) opened a new path to improve energy efficiency and the indoor environment. A small collaborating network team is now extending the passive house approach, in a book entitled Retrofitting, the Energy and Environment of Buildings (Gruyter Publishers), and presenting generalized AI modeling in the following paper. This concept uses a long-term neural network with a short-term memory (LSTM) and three stages (training, validation, and test) for optimalization to hourly data collected for one full year. The non-residential buildings are less affected by the space occupants. This paper examines the feasibility of a uniform, climate modified technology, as our objective is to create a universal and affordable approach to buildings assisting in slowing the rate of climate change. Hence, the idea of creating a generalized neural network for predicting electricity consumption linked with weather conditions was born. This network is to forecast the electricity consumption for buildings linked to the local weather conditions, but different categories of buildings are put together in one set. While this will lower the large set precision, still our question is if such a network would work. If so, in the future we will create multi-variant, local residential systems with the capability of predicting energy use. Full article

This paper probes into the propagation characteristics of Rayleigh waves in a partially saturated, porous, thermo-viscoelastic half-space, with full consideration of the fractional viscoelastic effect and thermal coupling effect. A fractional Zener model is introduced to depict the thermo-viscoelastic mechanical behavior of the solid skeleton by constructing a complete set of governing equations that include mass balance, generalized Darcy’s law, momentum balance, and generalized heat conduction. Field equations are derived by means of Helmholtz vector decomposition, and the dispersion equation, and the phase velocity expression of Rayleigh waves are obtained by combining the traction-free and adiabatic boundary conditions of the medium. The impacts of key material properties, such as medium saturation, intrinsic permeability, medium viscoelasticity, and thermal expansion coefficient, on the dispersion feature of Rayleigh waves are discussed in detail. Numerical analysis results show that an increase in the thermal expansion coefficient will lead to a rise in Rayleigh wave phase velocity, in which the increase in P1 compressional wave velocity plays a dominant role among the velocities of various types of waves. Meanwhile, the attenuation coefficient of Rayleigh waves presents a decreasing trend and gradually tends to be stable with the growth of the thermal expansion coefficient. Similarly, the phase velocity of Rayleigh waves also increases with the rise in fractional order index, which is jointly dominated by the velocity enhancement of P1 waves and S waves. In addition, the attenuation coefficient of Rayleigh waves increases first and then decreases with the increase in fractional order index and reaches the peak value when the fractional order index is about 0.4. The research results reveal the influence of laws of thermal expansion characteristics and viscoelasticity on Rayleigh wave propagation and provide theoretical support for the analysis of wave propagation characteristics in porous media in relevant engineering applications. Full article

The high concentration of CO generated in confined spaces following a gas explosion constitutes the primary lethal factor, and its rapid elimination represents a critical technical bottleneck in emergency rescue operations. This study systematically investigates the confined thermodynamic characteristics of CO catalytic oxidation over hopcalite across a wide temperature range of 15–65 °C. Based on the ideal gas assumption and constant-volume boundary conditions, the thermodynamic processes were classified into two categories: constant-volume variable-temperature and constant-temperature constant-volume. The influence of temperature on enthalpy change, heat release, entropy change, and the chemical equilibrium constant was quantitatively examined. The results demonstrate that the total enthalpy change and heat release remained negative throughout the entire temperature range, exhibiting a trend of “initial increase, subsequent decrease, followed by a slight rise”, with the maximum exothermic value observed at 25 °C. The total entropy change was persistently negative across the full temperature range; the positive offset contribution of the physical entropy change induced by temperature elevation was negligible, resulting in a consistently high absolute value of the total entropy change. The logarithm of the standard equilibrium constant decreased linearly with increasing temperature yet remained as high as 180.48 at 65 °C, indicating that the reaction maintains an extremely strong thermodynamic spontaneity and a nearly complete conversion limit under all tested conditions. Full article