Search Results (362)

In this study, a nonlinear dynamic model is developed to examine the stability and vibration behavior of a self-balancing electric Segway operating over irregular stochastic terrains. The Segway is treated as a three-degrees-of-freedom cart–inverted pendulum system, incorporating elastic and damping effects at the wheel–ground interface. Road irregularities are generated in accordance with international standard using high-order filtered noise, allowing for representation of surface classes from smooth to highly degraded. The governing equations, formulated via Lagrange’s method, are transformed into a Lorenz-like state-space form for nonlinear analysis. Numerical simulations employ the fourth-order Runge–Kutta scheme to compute translational and angular responses under varying speeds and terrain conditions. Frequency-domain analysis using Fast Fourier Transform (FFT) identifies resonant excitation bands linked to road spectral content, while Kernel Density Estimation (KDE) maps the probability distribution of displacement states to distinguish stable from variable regimes. The Lyapunov stability assessment and bifurcation analysis reveal critical velocity thresholds and parameter regions marking transitions from stable operation to chaotic motion. The study quantifies the influence of the gravity–damping ratio, mass–damping coupling, control torque ratio, and vertical excitation on dynamic stability. The results provide a methodology for designing stability control systems that ensure safe and comfortable Segway operation across diverse terrains. Full article

Information Systems (ISs) research frequently relies on digital trace data, often using simple activity counts as proxies for complex latent constructs like ‘experience’. However, the validity of such proxies is often assumed rather than critically scrutinized. This study problematizes this practice by treating a common proxy—a creator’s prior project count on Kickstarter—not as a measure of experience, but as a focal signal whose meaning is inherently ambiguous and context-dependent. By analyzing large-scale data (N ≈ 16,407 projects), we uncover a nuanced ‘experience paradox.’ The proxy exhibits a significant inverted-U association with backer mobilization and non-linearly moderates the value of other positive signals. Strikingly, it also maintains a persistent negative direct association with total funding, with its meaning varying significantly across project categories. These findings reveal the profound ambiguity of seemingly objective digital traces. Our primary contribution is methodological and theoretical: we provide a robust empirical critique of naive proxy use and refine signaling theory for digital contexts by integrating it with cognitive limitations and contextual factors. We urge IS scholars to develop more sophisticated measurement models and offer specific, evidence-based cautions for platform managers against the simplistic use of activity metrics in the digital economy. Full article

Underactuated systems, such as rotary and double inverted pendulums, challenge traditional control due to nonlinear dynamics and limited actuation. Classical methods like state-feedback and Linear Quadratic Regulators (LQRs) are commonly used but often require high gains, leading to excessive control effort, poor energy efficiency, and reduced robustness. This article proposes a generalized state-feedback controller with its own internal dynamics, offering greater design flexibility. To automate tuning and avoid manual calibration, we apply Bayesian Optimization (BO), a data-efficient strategy for optimizing closed-loop performance. The proposed method is evaluated on two benchmark underactuated systems, including one in simulation and one in a physical setup. Compared with standard LQR designs, the BO-tuned state-feedback controller achieves a reduction of approximately 20% in control signal amplitude while maintaining comparable settling times. These results highlight the advantages of combining model-based control with automatic hyperparameter optimization, achieving efficient regulation of underactuated systems without increasing design complexity. Full article

This research explores a phase-shift-based discontinuous PWM method used for 24 V battery-powered onboard micro inverters, which are critical for thermally limited applications like micromachines, where efficient heat dissipation and compact size are paramount. Discontinuous pulse width modulation (DPWM) reduces switching losses by clamping the phase voltage to the DC bus in order to improve inverter efficiency. Due to the change in power factor at different operating points from motors or the inductor load, the use of only one DPWM method cannot achieve the optimal efficiency of a three-phase voltage source inverter (3ph-VSI). This paper proposes a generalized DPWM method with a continuously adjustable phase shift angle, which extends the six traditional DPWM methods to any type. According to different power factors, the proposed DPWM method is divided into five power factor angle intervals, namely [−90°, −60°], [−60°, −30°], [−30°, 30°], [30°, 60°], and [60°, 90°], and automatically adjusts the phase shift angle to the optimal-efficiency DPWM mode. The power factor is calculated by means of the Synchronous Reference Frame Phase-Locked Loop (SRF-PLL) method. The switching losses and harmonic characteristics of the proposed DPWM are analyzed, and finally, a 24 V onboard 3ph-VSI experimental platform is built. The experimental results show that the efficiency of DPWM methods can be improved by 3–6% and the switching loss can be reduced by 40–50% under different power factors. At the same time, the dynamic performance of the proposed algorithm with a transition state is verified. This method is particularly suitable for miniaturized inverters where efficiency and thermal management are critical. Full article

Generational approaches to climate engagement among older adults remain limited. This study examines the evolution of eco-anxiety, environmental activism, and pro-environmental behavior from a life course perspective, focusing on older adults in Spain. A nationwide CATI survey of 3000 residents aged 18 and older was conducted, employing validated multidimensional scales for eco-anxiety, environmental activism, and pro-environmental behavior, each rescaled to a 0–10 range. Data were analyzed using descriptive statistics, analyses of variance, and hierarchical regression models to estimate linear and quadratic age effects beyond sex, education, and subjective social class. Results show that (1) eco-anxiety follows an inverted-U pattern, peaking at ages 45–49 and declining significantly after 60; (2) environmental activism remains high until the late sixties, while everyday pro-environmental behaviors sharply decline after retirement; and (3) eco-anxiety and environmental action in older adults are partially decoupled, reflecting the role of supportive personal and contextual factors beyond emotional concern. The findings challenge prevailing stereotypes of passive older adults by demonstrating that older age can constitute a significant period of climate engagement. Despite a slight decline in climate concern following retirement, the willingness to take action remains notably resilient. Older adults maintain consistent involvement in environmental volunteering and activism, often motivated by a desire to leave a lasting legacy and shaped by personal experiences of past crises and collective struggles. However, pro-environmental behaviors show a marked decrease in older adults, not due to diminished interest but likely as a result of structural constraints such as declining health, limited income, and inadequate housing conditions. This study suggests that, in the context of liquid modernity marked by rapid change and uncertainty, older adults may serve as societal anchors—preserving narratives, emotional bonds, and civic networks. Through policies that address structural barriers, this anchor role can be supported, empowering older adults to improve their well-being and strengthening community resilience in the face of climate change. Full article

Sequences of tRNAs are highly patterned in easily identifiable RNA repeats and RNA inverted repeats (stem–loop–stems). Because of patterning, the multi-step evolution of tRNA can be described in remarkable detail. To evolve life on Earth or another planet or the moon requires the evolution of tRNA or a tRNA-like molecule to act as a genetic adapter. To replace tRNA with an alternate or improved genetic adapter is a remarkably challenging problem, indicating strong chemical selection of tRNA precursors in pre-life. The genetic code, translation systems, and first proteins coevolved with tRNAomes (all of the tRNAs of an organism). Because the tRNA sequence can be separated into component parts, a simple pathway for chemical evolution of life and genetic coding can be described in sufficient detail to allow the assembly of a living entity in laboratories. Full article

In order to study the damage evolution and fracture characteristics of rock with different composite modes in three layers under dynamic loading, rock specimens with different composite modes were made by using three materials: sandstone, marble and granite. The dynamic fracture impact test was carried out by using the Hopkinson pressure bar impact loading system, the voltage signal on the Hopkinson pressure bar was calculated and processed, and the crack propagation mode of the specimen was captured by using a high-speed camera, and the stress wave characteristics, stress time–history relationship and energy change characteristics of rocks with different composite modes were studied. At the same time, combined with Distinct Lattice Spring Model numerical simulation, the fracture process of the specimen was inverted, and the changes in stress intensity factor, stress change and load–displacement change in monitoring point were analyzed to compare the dynamic fracture behavior differences between different composite rocks. The results show that the dynamic fracture process captured by the high-speed camera has a good fit with the crack propagation process simulated by numerical simulation. When marble is used as the upper material, the energy transmittance is larger, and the transmission energy ratio between sandstone and granite is basically the same due to the large difference in hardness. When the comprehensive hardness of the specimen is the same, the smaller the hardness of the material at the cracking position, the faster the cracking will be, and the smaller the hardness of the second layer of the specimen at the cracking position, the faster the cracking speed of the specimen. In terms of dynamic fracture toughness, for specimens with little difference in hardness, when the impact end material is sandstone, the dynamic fracture extreme value of the specimen is lower, and when the sandstone material is used as the impact end material, it is more likely to crack. When the first layer of material is the same, the dynamic fracture toughness of the specimen with less hardness of the second layer of material is smaller, and the easier the crack development is. Full article

Climate change threatens grain production in East Asia. This study assesses the impacts of climate variables and climate change on rice, wheat, and maize total production using an improved economy–climate model (C-D-C model). The innovation is to model a roughly inverted U-shaped relationship between dry-wet conditions (measured by Standardized Precipitation Evapotranspiration Index, SPEI) and production. Building on this, this study introduces a new metric reflecting extent of future climate change impact, the Impact Ratio of Climate Change (IRCC), to project the impact on production under three climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) for 2021–2050. Key findings include: The dry–wet conditions exhibit a significant roughly inverted U-shaped relationship with grain production in some crop areas, with optimal production levels observed near an SPEI of zero. Effective accumulated temperature positively affects wheat production in most regions, while higher effective accumulative temperatures reduce production in warm southern areas. Future climate change in 2021–2050 will likely increase rice production in northern China but decrease it in the south (IRCC > −30%). Overall impacts on wheat will be modestly negative, accounting for about 10% of future total production. Impacts in Japan and Korea will be minimal, with absolute values of IRCC not exceeding 2.5% across all scenarios. Full article

This review explores recent advancements in morphing structures for Unmanned Ariel Vehicles (UAVs), focusing on mechanical designs and control strategies of quadrotors that enable real-time geometric reconfiguration. Morphing mechanisms, ranging from closed-loop linkages to bioinspired and compliant structures, are evaluated in terms of adaptability, actuation simplicity, and flight stability. Control approaches, including model predictive control, reinforcement learning, and sliding mode control, are analyzed for their effectiveness in handling dynamic morphology. The review also highlights key morphing wing concepts such as GNATSpar and Zigzag Wingbox, which enhance aerodynamic efficiency and structural flexibility. A novel concept featuring an inverted slider-crank mechanism (ISCM) is introduced, enabling dual-mode UAV operation for both aerial and terrestrial missions, which is particularly useful in scenarios like wildfire suppression where stability and operation longevity are crucial. This study emphasizes the importance of integrated design approaches that align mechanical transformation with adaptive control. Critical gaps in real-world testing, swarm coordination, and scalable morphing architectures are identified, suggesting future research directions for developing robust, mission-adaptive UAV systems. Full article

The integration of photovoltaic (PV) systems into power grids has surged due to the global shift towards renewable energy, but this rapid adoption presents challenges like voltage regulation and inverter degradation. High PV penetration can lead to overvoltage conditions and transient voltage fluctuations, which stress inverters and accelerate their degradation. To address these issues, advanced modeling techniques, particularly Bayesian modeling, are employed to predict and mitigate inverter failures by incorporating prior knowledge and real-world data. This approach enables probabilistic predictions of failure, improving maintenance scheduling and inverter design. By leveraging datasets from inverter operations with induction motors, this study aims to enhance the reliability of PV systems and optimize voltage regulation strategies for sustainable renewable energy growth. Full article

Entity resolution (ER) is a fundamental data integration process hindered by its quadratic computational complexity, making naive comparisons infeasible for large datasets. Blocking (or partitioning) is the foundational strategy to overcome this, traditionally using methods like K-Means clustering to group similar records. However, with the rise of deep learning and high-dimensional vector embeddings, the ER task has evolved into a vector similarity search problem. This review traces the evolution of K-Means from a direct, standalone blocking algorithm into a core partitioning engine within modern Approximate Nearest Neighbor (ANN) indexes. We analyze how its role has been adapted and optimized in partition-based systems like the Inverted File (IVF) system and Google’s SCANN, which are now central to scalable, embedding-based ER. By examining the architectural principles and trade-offs of these methods and contrasting them with non-partitioning alternatives like HNSW, this paper provides a coherent narrative on the journey of K-Means from a simple clustering tool to a critical component for scaling modern ER workflows. Full article

This study investigates how new ventures on global e-commerce platforms use textual marketing signals to attract foreign consumers, a critical challenge characterized by information asymmetry. Integrating Signaling Theory and Optimal Distinctiveness Theory (ODT), we examine how two key creator-controlled textual signals—International Orientation Expression (IOE) intensity (a signal of legitimacy) and Project Genre Atypicality (GA) (a signal of differentiation)—non-linearly and interactively influence foreign customer engagement. Analyzing a large-scale dataset of 17,084 Kickstarter projects using computer-aided text analysis and fixed-effects regression models, we yield several key insights. First, we find a robust inverted U-shaped relationship between IOE intensity and foreign backer engagement, suggesting that while moderate international emphasis enhances legitimacy, excessive claims can undermine credibility. Second, GA exhibits a positive linear relationship with foreign engagement, indicating that novelty-seeking foreign consumers consistently value textual differentiation. Third, and most critically, we uncover a significant negative interaction, termed the “cost of dual extremes”, where simultaneously signaling extreme international ambition and extreme product novelty deters foreign consumers, likely due to perceived strategic incoherence and heightened execution risk. Finally, we confirm that attracting a diverse foreign audience is a strong predictor of overall project funding success. This research extends ODT by identifying a novel interactive boundary condition for distinctiveness in digital markets and advances signaling theory by demonstrating the complex, non-linear effectiveness of textual signals, offering actionable insights for optimizing communication strategy in global e-commerce. Full article

Inverting seawater sound speed profiles (SSPs) using Bayesian methods enables optimal parameter estimation and provides a quantitative assessment of uncertainty by analyzing the posterior distribution of target parameters. However, in nonlinear geophysical inversion problems like acoustic tomography, calculating the posterior distribution remains challenging. In this study, a Bayesian framework is used to construct the posterior distribution of target parameters based on acoustic travel-time data and prior information. A Hamiltonian Monte Carlo (HMC) approach is developed for SSP inversion, offering an effective solution to the computational issues associated with complex posterior distributions. The HMC algorithm has a strong physical basis in exploring distributions, allowing for accurate characterization of physical correlations among target parameters. It also achieves sufficient sampling of heavy-tailed probabilities, enabling a thorough analysis of the target distribution characteristics and overcoming the low efficiency often seen in traditional methods. The SSP dataset was created using temperature–salinity profile data from the Hybrid Coordinate Ocean Model (HYCOM) and empirical formulas for SSP. Experiments with acoustic propagation time data from the Kuroshio Extension System Study (KESS) confirmed the feasibility of the HMC method in SSP inversion. Full article

This study introduces YOLOv13-Cone-Lite, an enhanced algorithm based on YOLOv13s, designed to meet the stringent accuracy and real-time performance demands for traffic cone detection in autonomous formula racing cars on enclosed tracks. We improved detection accuracy by refining the network architecture. Specifically, the DS-C3k2_UIB module, an advanced iteration of the Universal Inverted Bottleneck (UIB), was integrated into the backbone to boost small object feature extraction. Additionally, the Non-Maximum Suppression (NMS)-free ConeDetect head was engineered to eliminate post-processing delays. To accommodate resource-limited onboard terminals, we minimized superfluous parameters through structural reparameterization pruning and performed 8-bit integer (INT8) quantization using the TensorRT toolkit, resulting in a lightweight model. Experimental findings show that YOLOv13-Cone-Lite achieves a mAP₅₀ of 92.9% (a 4.5% enhancement over the original YOLOv13s), a frame rate of 68 Hz (double the original model’s speed), and a parameter size of 8.7 MB (a 52.5% reduction). The proposed algorithm effectively addresses challenges like intricate lighting and long-range detection of small objects and offers the automotive industry a framework to develop more efficient onboard perception systems, while informing object detection in other closed autonomous environments like factory campuses. Notably, the model is optimized for enclosed tracks, with open traffic generalization needing further validation. Full article

Ultrafast lasers operating at 740 nm and 820 nm have attracted widespread attention as two-photon light sources for the detection of biological metabolism. Here, we report on a solid-like quartz-encapsulated femtosecond laser with a repetition rate of 80 MHz, delivering 740 nm and 820 nm femtosecond laser pulses. This home-built laser system was realized by employing an erbium-doped 1560 nm fiber laser as the fundamental laser source. A quartz-encapsulated nonlinear frequency conversion stage, consisting of a second-harmonic generation (SHG) stage and self-phase modulation (SPM)-based nonlinear spectral broadening stage, was utilized to deliver 30 mW, 53.7 fs, 740 nm laser pulses and the 15 mW, 60.8 fs, 820 nm laser pulses. Further imaging capabilities of both wavelengths were validated using a custom-built inverted two-photon microscope. Clear imaging results were obtained from mouse kidney sections and pollen samples by collecting the corresponding fluorescence signals. The achieved results demonstrate the great potential of this laser source for advanced two-photon microscopy in metabolic detection. Full article