Search Results (2,154)

Dense and complex underground structures impose stringent requirements on shield tunneling. In the close-proximity construction of super-large-diameter shield tunnels, challenges may arise, including adverse impacts on the normal operation of existing structures, as well as difficulties in ensuring the bearing capacity and deformation control of these structures during excavation. This study, based on the stratigraphic conditions of the Chengdu area, employs FLAC3D 7.0 version software to simulate the section where the Shuanghua Road Tunnel underpasses both Metro Line 10 and the Chengdu-Guiyang High-Speed Railway. The main conclusions are as follows: (1) Tunnel underpassing induces uneven settlement in the metro tunnel, with a maximum settlement reaching 47.7 mm. The settlement trough exhibits a twin-peak morphology during dual-line construction. When a single super-large-diameter tunnel line crosses the existing structural cluster, the maximum settlement is located directly above the crossing point. During dual-line crossing, the maximum settlement shifts towards the midpoint between the two new tunnel lines. (2) As the left line of the new tunnel approaches the existing structure, the cross-sectional deformation of the existing structure is “pulled” towards the direction of the excavated new tunnel. After the new left line moves away, the cross-sectional deformation gradually recovers to a bilaterally symmetrical state. (3) The tunnel cross-section undergoes dynamic “compression-tension” convergence changes during the construction process, with a maximum longitudinal tensile convergence of −1.28 mm. (4) During the underpassing of the existing structural cluster by the super-large-diameter tunnel, the maximum torsion angle is approximately −0.016°, occurring at the moment when the shield machine head first passes directly beneath, located directly above the new tunnel. The torsion angle of the existing structure is greatest during the first underpassing event, and the maximum torsion angle during the second underpassing is lower than that during the first. This study reveals the composite deformation mode of “settlement-convergence-torsion” during the underpassing of existing structural clusters by super-large-diameter shield tunnels, providing a theoretical basis for risk control in similar adjacent engineering projects. Full article

Developing flexible lifetime distributions is essential for accurately modeling reliability and lifetime data across various scientific and engineering contexts. In this work, we introduce a new three-parameter lifetime distribution, which extends the well-known two-parameter Sarhan–Tadj–Hamilton model. We derive and discuss several of its important theoretical properties, including the reliability characteristics and moments. The parameter estimation is carried out using both maximum likelihood and Bayesian approaches, providing a comprehensive comparison of inferential techniques. To further examine the efficiency and robustness of the proposed estimators, a detailed Monte Carlo simulation study is conducted under different sample sizes and parameter settings. The practical usefulness of the distribution is illustrated through its application to three real-world datasets, namely cancer and COVID-19 data, where it demonstrates superior fit and flexibility compared to existing and nested lifetime models. These findings highlight the potential of the proposed model as a valuable addition to the toolbox of applied statisticians and reliability practitioners. Full article

The representation of atmospheric phenomena and, in particular, clouds was a prominent theme for painters during the transition from the eighteenth to the nineteenth centuries. During this period, under the influence of rationalism and encyclopedism, Luke Howard’s cloud classification (1803) was proposed, gaining followers among scientists and artists of the time. Among the latter, Goethe was instrumental, as he intensely promoted this cloud classification, even dedicating his own poems and drawings to it. From then on, some painters depicted cloud studies following the academic principles recommended by Goethe. Caspar David Friedrich did not adopt these principles and depicted clouds as bodies endowed with freedom and feeling, as fragments of soul. The work of P. H. de Valenciennes played a prominent role in this approach; it was translated into German and became a reference manual for Romantic landscape painting. This paper addresses the scientific and cultural context of that historical moment, studies the importance of the landscape, and its aerial aspect, in the painting of the time and details the role of Friedrich as a singular author of German Romanticism, who did not want to participate in the academic ideas of representing clouds, since the sky was, for this painter, a symbol of the transcendent. Full article

Achieving dynamic stability in bipedal locomotion against sustained external disturbances remains a significant challenge in humanoid robotics. Traditional methods, such as zero moment point (ZMP) preview control, often lack the reactive compliance and predictive planning necessary for robust performance on uneven terrain or under continuous force. This paper proposes a novel control framework that synergistically integrates a resistance torso compliance controller with a nonlinear model predictive control (NMPC)-based walking pattern generator. The compliance controller actively modulates the torso’s dynamics via impedance control, creating a virtual mass–spring–damper system that absorbs impacts and generates counterforces to resist sustained pushes. Concurrently, the NMPC module reformulates gait generation as a real-time optimization problem, simultaneously determining optimal footstep positions and orientations while respecting nonlinear constraints derived from centroidal momentum dynamics. Simulation results demonstrate that this integrated approach reduces the maximum ZMP error by 34.1% and the RMS ZMP error by 37.3% compared to traditional ZMP preview control, with a 38.9% improvement in settling time after a disturbance. This work establishes that the tight coupling of reactive impedance control and predictive optimization provides a foundational framework for enhancing the robustness and adaptability of bipedal locomotion. Full article

The development of renewable energy has emerged as a cornerstone of sustainable economic transformation, offering a pathway to reduce carbon dependence and enhance long-term energy security. As a result, this study examines the influence of supply chain digitalization, economic growth, and environmental stringency policies on renewable energy consumption (REC) across 33 OECD countries from 2000 to 2021. Using the Method of Moments Quantile Regression (MMQR) approach, the research provides robust, distribution-sensitive insights into how these factors shape renewable energy dynamics. In addition to the main variables, financial development and economic globalization were included as control variables to capture broader macroeconomic effects. The empirical results reveal that supply chain digitalization exerts a negative and consistent influence on REC across all quantiles, suggesting that technological advancement within supply chains may still be heavily dependent on non-renewable energy inputs. Conversely, environmental stringency policies demonstrate a positive and significant impact on REC at all quantiles, indicating that stricter environmental regulations effectively drive the transition toward cleaner energy sources. However, the effect of economic growth varies across quantiles, reflecting a nonlinear relationship—fostering renewable energy use in some instances while increasing conventional energy demand in others. Among the control variables, economic globalization enhances REC, implying that greater international integration facilitates technology transfer and access to green innovations. In contrast, financial development negatively affects REC, suggesting that current financial systems may still prioritize fossil fuel investments. Overall, the study emphasizes the need to align digital transformation strategies, financial reforms, and policy frameworks to strengthen renewable energy development and ensure a sustainable, low-carbon future across OECD nations. Full article

High-resolution NMR spectroscopy is the leading method for determining nuclear magnetic moments. It is designed to measure stable nuclei, which can be investigated in macroscopic samples. In this work, we discuss the progress in research into light nuclei from the first three periods of the Periodic Table and several selected heavy nuclides. The ¹H and ³He nuclear magnetic moments, established using the new double Penning trap facility, are also considered. Both nuclei can be used as references in gaseous mixtures. Gas-phase NMR spectroscopy enables precise measurements of the frequencies and shielding constants of isolated single molecules. They can be used to determine new, accurate nuclear magnetic moments of nuclides in stable, gaseous substances. Particular attention is paid to the importance of diamagnetic corrections for obtaining accurate results. Finding precise diamagnetic corrections—shielding factors —even for light nuclei in molecules is a significant challenge. To date, nuclear moments have been obtained primarily from experimental data. The theoretical approach is mostly unable to predict these values accurately. Some remarks are also made on pure theoretical treatments of nuclear moments. Full article

Freezing and thawing affect the structural integrity and quality of meat, yet these processes remain difficult to monitor due to spatial temperature gradients and non-uniform phase transitions. This study investigates the ability of ultrasound to detect dynamic freezing and thawing events in pork tissues with different fat contents. Specimens of water, lean meat, marbled meat, layered lean–fat structures, and lard were subjected to controlled freeze–thaw cycles while ultrasonic signals and internal temperatures were continuously monitored. Consistent amplitude drops in the megahertz range at entering the freezing phase formed characteristic signal patterns that differed sharply between lean and fatty tissues. Lean meat, dominated by water content, exhibited rapid signal loss at the onset of ice crystallization and a clear recovery of amplitude once fully frozen. Fat-rich tissues demonstrated prolonged attenuation and near disappearance of high-frequency signals, with incomplete recovery even at deep-frozen states. A jump of ultrasound velocity from 1.4–1.6 km/s in a warm state to 2.6–3.7 km/s in a frozen state indicated complete freezing. Hysteresis between temperature readings and actual phase transition moments was found. Distinct ultrasonic freeze–thaw signatures reflecting tissue composition suggest a novel approach for monitoring the true onset and completion of freezing and thawing in meat. Full article

In this paper, a hierarchical adaptive control strategy is proposed to enhance the handling stability of distributed drive electric vehicles. In this strategy, the upper-level fuzzy controller calculates the additional yaw moment and rear wheel angle by utilizing the error between the actual and the target yaw velocity, as well as the error between the actual and the target sideslip angle. The quadratic programming algorithm is adopted to achieve the optimal torque distribution scheme through the lower-level controller, and the electronic stability control system (ESC) is utilized to generate the braking force required for each wheel. The four-wheel steering controller optimizes the rear wheel angle by using proportional feedforward combined with fuzzy feedback or Akerman steering based on the steering wheel angle and vehicle speed, through actuators such as active front-wheel steering (AFS) and active rear-wheel steering (ARS), which generate the steering angle of each wheel. This approach is validated through simulations under serpentine and double-lane-change conditions. Compared to uncontrolled and single-control strategies, the actuators are decoupled, the actual sideslip angle and yaw velocity of the vehicle can effectively track the target value, the actual response is highly consistent with the expected response, the goodness of fit exceeds 90%, peak-to-peak deviation with a small tracking error. Full article

Conventional High Dynamic Range (HDR) image fusion algorithms generally require two or more original images with different exposure times for synthesis, making them unsuitable for real-time processing scenarios such as video streams. Additionally, the synthesized HDR images have the same bit depth as the original images, which may lead to banding artifacts and limits their applicability in professional fields requiring high fidelity. This paper utilizes a Field Programmable Gate Array (FPGA) to support an image sensor operating in Clear HDR mode, which simultaneously outputs High Conversion Gain (HCG) and Low Conversion Gain (LCG) images. These two images share the same exposure duration and are captured at the same moment, making them well-suited for real-time HDR fusion. This approach provides a feasible solution for real-time processing of video streams. An adaptive adjustment algorithm is employed to address the requirement for high fidelity. First, the initial HCG and LCG images are fused under the initial fusion parameters to generate a preliminary HDR image. Subsequently, the gain of the high-gain images in the video stream is adaptively adjusted according to the brightness of the fused HDR image, enabling stable brightness under dynamic illumination conditions. Finally, by evaluating the read noise of the HCG and LCG images, the fusion parameters are adaptively optimized to synthesize an HDR image with higher bit depth. Experimental results demonstrate that the proposed method achieves a processing rate of 46 frames per second for 2688 × 1520 resolution video streams, enabling real-time processing. The bit depth of the image is enhanced from 12 bits to 16 bits, preserving more scene information and effectively addressing banding artifacts in HDR images. This improvement provides greater flexibility for subsequent image processing tasks. Consequently, the adaptive algorithm is particularly suitable for dynamically changing scenarios such as real-time surveillance and professional applications including industrial inspection. Full article

A first-principles investigation was conducted to characterize the novel Cr₂ZnC MAX phase and its exfoliated MXene nanosheet, Cr₂C. The study critically examines the effect of electron correlations on the bulk phase, revealing that the PBE+U framework, unlike standard PBE, yields a dramatically enhanced magnetic moment of 12.80 μ_B (vs. 1.88 μ_B), confirming the necessity of this approach for Cr-based carbides. The phase stability is confirmed through rigorous analysis of its thermodynamic, dynamic, and mechanical properties. For the derived 2D Cr₂C, results confirm a robust half-metallic state with a total magnetic moment of 8.00 μ_B, characterized by a metallic spin-majority channel and a semiconducting spin-minority channel with a 2.41 eV direct gap, leading to near-ideal spin polarization. These combined features establish Cr₂C as a highly promising candidate for next-generation spintronic applications and 2D magnetic devices requiring room-temperature stability. Full article

Large Language Models (LLMs) are increasingly used in higher education, but their pedagogical role in fields like cybersecurity remains under-investigated. This research explores integrating LLMs into a university cybersecurity course using a designed pedagogical approach based on active learning, problem-based learning (PBL), and computational thinking (CT). Instead of viewing LLMs as definitive sources of knowledge, the framework sees them as cognitive tools that support reasoning, clarify ideas, and assist technical problem-solving while maintaining human judgment and verification. The study uses a qualitative, practice-based case study over three semesters. It features four activities focusing on understanding concepts, installing and configuring tools, automating procedures, and clarifying terminology, all incorporating LLM use in individual and group work. Data collection involved classroom observations, team reflections, and iterative improvements guided by action research. Results show that LLMs can provide valuable, customized support when students actively engage in refining, validating, and solving problems through iteration. LLMs are especially helpful for clarifying concepts and explaining procedures during moments of doubt or failure. Still, common issues like incomplete instructions, mismatched context, and occasional errors highlight the importance of verifying LLM outputs with trusted sources. Interestingly, these limitations often act as teaching opportunities, encouraging critical thinking crucial in cybersecurity. Ultimately, this study offers empirical evidence of human–AI collaboration in education, demonstrating how LLMs can enrich active learning. Full article

The potential energy curves and spectroscopic constants of the ground and several low-lying excited states of the Ca^q+-Kr (q = 0, 1, 2) van der Waals complexes were investigated using one- and two-electron pseudopotential approaches. This treatment effectively reduces the number of active electrons in Ca^q+-Kr to a single valence electron for q = 1 and two valence electrons for q = 0, allowing the use of large and flexible basis sets for both Ca and Kr atoms. Within this work, potential energy curves (PECs) were calculated at the SCF level for the Ca⁺-Kr system, while both SCF and full configuration interaction (FCI) calculations were performed for the neutral Ca-Kr. Spin–orbit coupling effects were explicitly included in all calculations to accurately describe the fine-structure splitting of the asymptotic atomic states. The short-range core–core interaction for Ca²⁺-Kr was obtained using high-level CCSD(T) calculations. Spectroscopic constants were derived from the computed PECs and compared with available theoretical and experimental results, showing consistent trends. Furthermore, the transition dipole moments (TDM) were evaluated as a function of internuclear distances, including spin–orbit effects, to provide a comprehensive description of the electronic structure and radiative properties of these weakly bound systems. Full article

Pollen identification is critical for melissopalynology (honey authentication), ecological monitoring, and allergen tracking, yet manual microscopic analysis remains labor-intensive, subjective, and error-prone when multiple grains overlap in realistic samples. Existing automated approaches often fail to address multi-grain scenarios or lack systematic comparison across classical and deep learning paradigms, limiting their practical deployment. This study proposes a subinstance-based classification framework combining YOLOv12n object detection for grain isolation, independent classification via classical machine learning (ML), convolutional neural networks (CNNs), or Vision Transformers (ViTs), and majority voting aggregation. Five classical classifiers with systematic feature selection, three CNN architectures (ResNet50, EfficientNet-B0, ConvNeXt-Tiny), and three ViT variants (ViT-B/16, ViT-B/32, ViT-L/16) are evaluated on four datasets (full images vs. isolated grains; raw vs. CLAHE-preprocessed) for four berry pollen species (Ribes nigrum, Ribes uva-crispa, Lonicera caerulea, and Amelanchier alnifolia). Stratified image-level splits ensure no data leakage, and explainable AI techniques (SHAP, Grad-CAM++, and gradient saliency) validate biological interpretability across all paradigms. Results demonstrate that grain isolation substantially improves classical ML performance (F1 from 0.83 to 0.91 on full images to 0.96–0.99 on isolated grains, +8–13 percentage points), while deep learning excels on both levels (CNNs: F1 = 1.000 on full images with CLAHE; ViTs: F1 = 0.99). At the instance level, all paradigms converge to near-perfect discrimination (F1 ≥ 0.96), indicating sufficient capture of morphological information. Majority voting aggregation provides +3–5% gains for classical methods but only +0.3–4.8% for deep models already near saturation. Explainable AI analysis confirms that models rely on biologically meaningful cues: blue channel moments and texture features for classical ML (SHAP), grain boundaries and exine ornamentation for CNNs (Grad-CAM++), and distributed attention across grain structures for ViTs (gradient saliency). Qualitative validation on 211 mixed-pollen images confirms robust generalization to realistic multi-species samples. The proposed framework (YOLOv12n + SVC/ResNet50 + majority voting) is practical for deployment in honey authentication, ecological surveys, and fine-grained biological image analysis. Full article

Lighter-than-air parakites deployed at sea in the close proximity of wind turbines may offer the possibility of mitigating wake losses encountered in large offshore wind farms. Such devices, having an order of magnitude similar to wind turbine rotors, can divert the stronger winds available at high altitudes to the lower level within the atmospheric boundary layer to enhance the wind flow between turbines. Mooring the parakites directly to the offshore wind turbine support structures would avoid the need for additional offshore structures. This paper investigates a novel and simple approach for mooring a parakite to an offshore wind turbine. The proposed approach exploits the lift forces of the inflatable parakite to reduce the tower bending moment at the base of the turbine induced by the rotor thrust. An iterative numerical model coupling the parakite loads to a catenary cable piecewise model is developed in Python 3.12.7 to quantify the bending moment reduction and shear load variations at the wind turbine tower base induced by the different kite geometries, windspeeds, and mooring cable lengths. The numerical model revealed that the proposed approach for mooring parakites can substantially reduce the tower bending loads experienced during rotor operation without considerably increasing the shearing forces. It was estimated that the tower bending moment decreased by 7.7% at the rated wind speed, where the rotor thrust is at its maximum, while the corresponding shear force increased by 0.6%. At higher wind speeds, where the magnitude of the rotor thrust decreases, the percentage reduction in bending moment gradually increases to 51.7% at a wind speed of 24 m/s, with the corresponding shear force increasing by only around 4.6%. Furthermore, while upscaling the parakite augments the tower bending moment reduction, changes in cable length had little effect on bending moment reduction and shear increase. Full article

Partial demagnetization of multiple in-wheel motors changes torque distribution characteristics and can reduce vehicle stability, which poses a challenge for in-wheel motor drive electric vehicles (IWMDEVs) to maintain a balance between safety and efficiency. To address this issue, a hierarchical multi-objective adaptive fault-tolerant control (FTC) strategy based on wheel terminal torque compensation is developed. In the upper layer, a nonlinear model predictive controller (NMPC) generates the desired total driving force and corrective yaw moment according to vehicle dynamics and driving conditions. The lower layer employs a quadratic programming (QP) scheme to allocate the wheel torques under actuator and tire constraints. Two adaptive coefficients—the stability–efficiency weighting factor and the current compensation factor—are updated through a randomized ensembled double Q-learning (REDQ) algorithm, enabling the controller to adaptively balance yaw stability preservation and energy optimization under different fault scenarios. The proposed method is implemented and verified in a CarSim–Simulink–Python co-simulation environment. The simulation results show that the controller effectively improves yaw and lateral stability while reducing energy consumption, validating the feasibility and effectiveness of the proposed strategy. This approach offers a promising solution to achieve reliable and energy-efficient control of IWMDEVs. Full article