Search Results (4,067)

The integration of electric micromobility into urban transportation systems can significantly reduce the energy consumption and emissions associated with short-distance travel. However, quantitative energy-based assessments remain limited, particularly in complex environments such as port–city interfaces. This paper presents a scenario-based energy assessment framework combining survey data and energy modelling. Empirical data were collected through a user survey (n = 138) targeting port workers and nearby residents, providing information on trip distances, travel frequency, modal choice, and willingness to shift from private car use. These data were combined with an energy modelling framework based on mode-specific energy intensity values expressed in kWh per passenger-kilometre. Three scenarios were analysed: a baseline scenario without intervention, a modal shift scenario supported by basic infrastructure measures, and an integrated scenario including transport management measures and local photovoltaic energy coupling. Results indicate that a moderate modal shift of 35% from private cars to electric micromobility for short-distance trips can generate aggregated annual energy savings of approximately 30 MWh and reduce CO₂ emissions by around 7 t per year across the analysed cases. According to the proposed energy model, electric micromobility achieves up to a 95% reduction in energy use per passenger-kilometre compared to private car travel. Furthermore, photovoltaic coupling could supply between 55% and 85% of the annual charging demand. The proposed framework is transparent and transferable, supporting energy-efficient and electrified future mobility planning. Full article

Systemic risk propagation in modern financial markets is characterized by non-linear contagion and rapid topological evolution, rendering traditional static monitoring methods ineffective. Existing Graph Neural Networks (GNNs) often struggle to capture “structural breaks” during crises due to their reliance on static adjacency assumptions and isotropic aggregation. To address these challenges, this study proposes the Temporal Attentive Graph Networks (TAGN), a dynamic framework designed for extreme volatility prediction and financial surveillance. TAGN constructs an incremental multi-scale graph by fusing high-frequency trading data, supply chain linkages, and institutional co-holdings to model heterogeneous risk transmission channels. Technically, it employs a deeply coupled GAT-GRU architecture, where the Graph Attention Network (GAT) dynamically assigns weights to contagion sources, and the Gated Recurrent Unit (GRU) memorizes the trajectory of structural evolution. Extensive experiments on the S&P 500 dataset (2018–2024) demonstrate that TAGN significantly outperforms state-of-the-art baselines, including WinGNN and PatchTST, achieving an AUC of 0.890 and a Precision at 50 of 61.5%. Notably, a risk early-warning index derived from TAGN exhibits a 1–2 week lead time over the VIX index during major market stress events, such as the Silicon Valley Bank collapse. This research facilitates a paradigm shift from historical statistical estimation to dynamic network-aware sensing, offering interpretable tools for RegTech applications. Full article

Electroencephalography (EEG) has transitioned from a subjective observational method into a data-intensive analytical field that utilises sophisticated algorithms and mathematical models. This review provides a holistic foundation by detailing the neurophysiological basis, recording techniques, and applications of EEG before providing a rigorous examination of traditional and modern analytical pillars. Statistical and Time-Series Analysis, Spectral and Time-Frequency Analysis, Spatial Analysis and Source Modelling, Connectivity and Network Analysis, and Nonlinear and Chaotic Analysis are explored. Afterwards, while acknowledging the historical role of Machine Learning (ML) and Deep Learning (DL) architectures, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), this review shifts the primary focus toward current state-of-the-art Artificial Intelligence (AI) trends. We place emphasis on the emergence of Foundation Models, including Large Language Models (LLMs) and Large Vision Models (LVMs), adapted for high-dimensional neural sequences. Finally, we explore the integration of Generative AI for data augmentation and review Explainable AI (XAI) frameworks designed to bridge the gap between “black-box” decoding and clinical interpretability. We conclude that the next generation of EEG analysis will likely converge into Neuro-Symbolic architectures, synergising the massive generative power of foundation models with the rigorous, rule-based interpretability of classical signal theory. Full article

Distributed acoustic sensing sends laser pulses along a fiber optic cable and analyzes the backscattered light to identify acoustic signals along the entire fiber. Liquid slugs were produced in a 427 m vertical test well using surface-controlled gas lift valves. To enhance DAS monitoring, pressure pulses were induced by multiple acoustic shots from a fluid level gun. Visualization of the responses through frequency band energy plots and unfiltered phase shift measurements permitted tracking slug movement and estimating parameters such as velocity, location, and body length. The results demonstrate that DAS stimulated with acoustic pulses can effectively track liquid slugs in real-time. We observe that relying solely on flow-induced noise in multiphase flow environments may not provide sufficient signal strength for slug detection. Applications include real-time detection of liquid slugs for improved well monitoring and flow management. Full article

Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of Vision Mamba architectures, (ii) micro-architectural advancements such as adaptive scan strategies and hybrid SSM formulations, (iii) macro-architectural integrations, including CNN–Transformer–Mamba hybrids and frequency-domain adaptations, (iv) rigorous benchmarking against state-of-the-art methods in multiple application tasks, such as object detection, semantic segmentation, change detection, etc. and (v) critical analysis of unresolved challenges with actionable future directions. By bridging the gap between SSM theory and remote sensing practice, this survey establishes Mamba as a transformative framework for remote sensing analysis. To our knowledge, this paper is the first systematic review of Mamba architectures in remote sensing. Our work provides a structured foundation for advancing research in remote sensing systems through SSM-based methods. We curate an open-source GitHub repository to foster community-driven advancements. Full article

Background/Objectives: Increasing popularity, convenience, and access to processed foods are shifting the composition of dietary intake from whole to ultra-processed foods (UPF). This study aimed to assess the association between periconceptional UPF consumption and the incidence of adverse pregnancy outcomes (APOs). Methods: This was a secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). Patients were excluded if they were missing periconceptional diet data or if their pregnancy ended before 20 weeks. Food Frequency Questionnaire items were categorized using the NOVA Scale to calculate the proportion of total energy intake comprised of UPF (% kcal/day). Bivariate and multivariate analyses examined the relationships between UPF intake and preterm birth, hypertensive disorders of pregnancy (HDP), gestational diabetes (GDM), small-for-gestational-age (SGA) infants, large-for-gestational-age (LGA) infants, and fetal or neonatal demise. Results: A total of 6693 participants were included in the analysis. The sample was predominantly White (78%) and not Hispanic (84%), and a majority of participants had commercial insurance (76%). UPF accounted for an average of 51.3 ± 12.7% of participants’ daily total energy intake. Mean UPF intake was higher among patients who identified as Black or non-Hispanic, patients with public insurance, less than a high school education, a household income below the federal poverty level (all p-values < 0.001), patients with chronic hypertension (p = 0.02), and patients who delivered vaginally (p = 0.002). Patients with preterm birth, HDP, SGA infants, and fetal or neonatal demise all had significantly higher proportions of daily UPF intake compared to patients without these adverse outcomes. After adjusting for potential confounders, higher UPF intake remained significantly associated with preterm birth (AOR 1.11, 95% CI 1.02–1.21) and HDP (AOR 1.05, 95% CI 1.001–1.11). Conclusions: On average, more than half of participants’ daily energy intake was from UPF, and higher UPF intake correlated with several adverse pregnancy outcomes. Future efforts should focus on improving nutritional literacy regarding UPF consumption in pregnancy. Full article

This paper investigates the impact of tubed propeller design on the energy efficiency and acoustic emissions of sub-250 g quadcopters. This study was motivated by the growing popularity of ultralight UAVs and the lack of experimental data addressing the trade-offs between noise, efficiency, and mass. Ten drone configurations with varying tube geometries and tip clearances were constructed using 3D-printed PLA+ frames and identical propulsion components. Experimental tests were conducted in a reverberation room to measure sound pressure levels and onboard energy consumption during hover. The results show that tubed configurations are 3–6.5 dB louder than untubed ones, with a noticeable shift toward higher frequencies. While tubes increased total power demand by 18–37% compared to the lightest design, they also reduced it by 3–17% relative to untubed drones of the same mass. The findings demonstrate that tubing improves aerodynamic efficiency only under same mass constraints and is most beneficial when mechanical protection is prioritized over noise and endurance. Full article

Scenario planning supports strategic decision-making under uncertainty by comparing multiple plausible futures. Impact indicators help to prioritize scenarios, while rank-based evaluations clearly communicate indicator relevance for participatory planning, policymaking, and resource allocation. Ensuring that rankings are both sensitive and robust is therefore essential. However, conventional statistical measures fail to fully capture ranking dynamics. They describe overall dispersion but cannot jointly assess the magnitude of rank shifts and the frequency with which items occupy specific ranks across scenarios. This study explores the novel application of Rao’s Quadratic Entropy (Rao’s Q) in scenario analysis to quantify ranking variability. A theoretical test demonstrates that Rao’s Q captures full variability in rankings and continuous values, suggesting it as a promising alternative to existing approaches. Rao’s Q is then applied to a climate change hotspot in Central Italy to evaluate changes in bio-energy landscape connectivity across forty-eight scenarios. Results reveal how land-use and climate changes affect landscape unit connectivity over time, identifying which are highly stable across scenarios or consistently critical, and thus highlighting planning priorities for mitigation, conservation, and sustainable urban development. Supported by openly available R code, this study demonstrates the relevance of Rao’s Q for participatory, scenario-based decision-making processes. Full article

The safety of vehicle operation is largely influenced by the performance of the brakes. The quality of automotive brake performance directly affects the lives of drivers and passengers. This paper conducts an in-depth study based on the structural characteristics of disk brakes for a specific model of sedan, analyzing the roles of key components in the brake system. Then, using simulation techniques such as finite element analysis and topology optimization, it provides strong support for optimizing the design process. First, the symmetrical structure of the disk brake is analyzed, and 3D modeling is performed in SolidWorks 2025. Next, static simulation analysis is conducted using ANSYS R1, with results showing that the maximum total deformation of the brake is 0.038 mm (not strain), and the maximum stress is 155.78 MPa, which meets the requirements for emergency braking. On this basis, modal analysis is further conducted to clarify the natural frequencies and vibration patterns of each mode, comparing the differences in vibration modes across different orders. Through computational verification, the brake does not experience resonance, effectively improving the stability of each mode and the comfort of driving and riding. Finally, the variable-density method enabled 10.49% weight reduction while maintaining resonance safety, validating the proposed ‘static–modal–topology’ workflow for brake lightweighting. Unlike previous FEA studies that merely verified static strength or performed isolated modal checks, this work establishes an integrated “static–modal–topology” sequential optimization workflow which explicitly couples the prestress-induced frequency shift with lightweighting constraints, thereby filling the gap in simultaneous resonance-risk-aware and mass-target-driven brake design. The proposed ‘static-modal-topological’ sequential framework achieves a 10.49% weight reduction rate, representing a 26.4% improvement over the 8.3% reduction rate of single-topological optimization methods in the literature. Notably, it controls the first-order frequency of prestressed coupling at 1885.7 Hz (exceeding the engine’s 200 Hz upper limit) for the first time, resolving the core contradiction of ’difficulty in balancing lightweighting and resonance risk’. Full article

As a core functional component of multi-degree-of-freedom precision motion mechanisms, spherical hinges are widely used in high-end equipment fields such as industrial robots, vehicle engineering, and intelligent manufacturing. Their dynamic performance directly determines the motion accuracy and the level of intelligent control of the equipment. The high-precision real-time measurement of two-degree-of-freedom (2-DOF) angles is a key prerequisite for achieving precise closed-loop control of spherical hinges. However, due to the strong coupling characteristics between the 2-DOF angle signals, it is difficult to directly and accurately measure the angular motion parameters of spherical hinges, which has become a core technical bottleneck restricting the improvement in their application efficiency. To address this challenge, this paper presents an improved study of the previously proposed spherical differential quadrature capacitance sensor for measuring the 2-DOF angle signals of spherical hinges. Firstly, the 2-DOF angle signal decoupling model is reconstructed and optimized. Secondly, a real-time decoupling circuit architecture for phase-shift detection with single-frequency signal excitation is innovatively proposed. This solution effectively addresses the incomplete decoupling of 2-DOF angle signals in previous studies, as well as the problems of considerable measurement noise, low resolution, and high calibration difficulty caused by random amplitude and phase errors in the excitation signals. Through the construction of an experimental platform for verification tests, the results show that the proposed scheme can significantly suppress the random errors caused by the parameter dispersion of the device, achieve an angle measurement resolution of 0.001°, and simultaneously considerably reduce the complexity of system calibration, laying a key technical foundation for the engineering application of spherical hinges in the fields of precision measurement and high-performance control. Full article

Nitrous oxide (N₂O) emissions in cereal-based rotations often show short-lived peaks after fertilization, but their contribution to annual budgets and their responsiveness to straw management remain poorly quantified. We combined a 13-year legacy fertilization experiment with two years of high-frequency N₂O monitoring in a rice–wheat rotation in central China to quantify post-fertilization peak windows and test how straw-return rate modulates these windows and annual emissions. Five long-term treatments were compared: an unfertilized control (CK), straw only (2M, 12 t ha⁻¹ yr⁻¹), mineral fertilizer (NPK), and NPK with 6 or 12 t ha⁻¹ yr⁻¹ straw (MNPK and 2MNPK). Under N input, wheat-season emissions dominated annual totals, with the ratio of wheat-season to annual N₂O emissions (WN/TN, where WN denotes wheat-season N₂O emissions and TN denotes annual cumulative N₂O emissions) of ~73–75% for NPK and MNPK, significantly higher than in CK and the straw-only control. Decomposition of annual fluxes showed that 56.6–65.4% of N₂O in N-applied treatments occurred within short windows after the two wheat-season fertilizations, whereas rice-season peaks were small and largely insensitive to treatment. Planned contrasts expressed as geometric mean ratios (GMRs) with 95% confidence intervals (CIs) highlighted a strong management leverage point: increasing straw from 6 to 12 t ha⁻¹ yr⁻¹ with NPK reduced annual and wheat-season N₂O by ~47% and 58%, respectively, primarily by lowering peak magnitude and shortening peak duration. Microbial analyses suggested that treatment effects on N₂O were better reflected by community compositional shifts (β-diversity) than by α-diversity, while amoA abundance showed guild-specific responses. Collectively, this study provides an event-window quantification framework that links high-frequency field measurements to a specific, actionable mitigation lever (straw-return rate) in rice–wheat systems. Together, these results identify wheat-season post-fertilization windows as the main control points for annual N₂O in rice–wheat rotations and show that pairing NPK fertilization with higher straw return can temper short-lived peaks. By explicitly pinpointing when (which windows) and how (attenuating peak magnitude and duration) mitigation is achieved, our findings offer a management-ready and transferable basis for targeted N₂O abatement in double-cropping systems. Full article

In this work, we report a dual-mode ferroelectrically programmable on-chip antenna. The antenna is built on a silicon wafer using complementary metal-oxide semiconductor (CMOS) processes and exhibits two programmable resonant modes: one in the super high frequency (SHF) range and one in the extremely high frequency (EHF) range. The SHF mode resonates at 8.5 GHz and exhibits ultrawideband (UWB) behavior, while the EHF mode resonates at 36.6 GHz. Both resonance frequencies can be tuned in a non-volatile fashion by controlling the ferroelectric polarization state of a Hafnium Zirconium Oxide (HZO) varactor monolithically integrated into the feed line. This programmability arises from the ferroelectric switching of the embedded HZO film, which results in a non-volatile variation of its permittivity upon application of a voltage pulse. Ferroelectric switching occurs at approximately ±3 V and induces maximum resonance frequency shifts of 381 MHz for the SHF mode and 3 GHz for the EHF mode, corresponding to fractional frequency changes of 4.5% and 8.3%, respectively. Unlike previously reported ferroelectrically tunable antennas, our reported antenna combines full integration, CMOS compatibility, higher operating frequency, compact footprint, and non-volatile programmability. Full article

Honeycomb structures are widely utilized in engineering due to their light weight, high strength, high stiffness, excellent energy absorption, and outstanding vibration isolation performance. In this study, we propose a novel tuning fork–honeycomb megastructure, which demonstrates excellent tunable vibration isolation capabilities. The geometric configuration of the structure before and after shape memory–induced deformation is described, and a theoretical model for the natural frequency of the initial configuration is established. The vibration isolation performance of the structure is validated through simulations and experiments, and three strategies for tuning its vibrational behavior are proposed. First, by exploiting variable stiffness, shape memory materials are used to achieve a linear shift in the bandgap position. At 75 °C, the starting frequency of the bandgap decreases to 95% of its value at room temperature. Second, based on shape memory programming, the deformed structure exhibits a 20% reduction in the center frequency of the first bandgap and a 47% reduction in the center frequency of the second bandgap compared to the undeformed configuration. Then, by altering the geometry of the tuning fork structure, in–plane deformation is shown to provide superior low–frequency vibration isolation performance compared to out–of–plane deformation. Finally, the design method of programmable mechanical pixel metamaterials is introduced. This method achieves tunable full–band vibration isolation through shape–memory–induced deformation and temperature–induced stiffness variation. It enhances the structural diversity, modularity, and reconfigurability. Moreover, a shape memory tuning fork structure could be combined with any type of cellular structure with excellent vibration isolation performance. It offers a new paradigm for designing structures with adjustable wide–frequency vibration isolation performance. Full article

In multi-module boost power factor correction (PFC) systems, current ripple is commonly mitigated by applying fixed 180° interleaving between modules; however, this approach relies on matched inductors and ideal symmetry. In practical implementations, inductor mismatch and duty-cycle variations prevent full cancellation, leading to residual ripple that increases losses and electromagnetic interference. To address this issue, several research works have proposed centralized coordination or high-speed communication among units. However, an explicit converter model is necessary, which makes the system more complicated and expensive. To resolve this problem, this paper presents an extremum seeking optimization method for reducing high-frequency ripple in multi-module PFC systems without requiring explicit converter models. The ripple minimization problem is formulated as a nonlinear, time-varying optimization task, where the relative switching phases of the modules are adaptively tuned. The proposed extremum seeking algorithm perturbs the phase shift, evaluates a ripple-based cost function, and updates the phases iteratively. A harmonic analysis is developed to characterize the dependence of ripple on duty ratio, inductor values, and phase displacement. Simulation results show that the method effectively reduces the RMS ripple current across balanced and mismatched operating conditions. In a three-unit system, applying the proposed technique lowered the current THD to 1.29% compared to 1.44% achieved with a fixed phase-shift approach. These findings demonstrate that extremum seeking optimization provides a mathematically rigorous and practically implementable solution for decentralized ripple minimization in multi-module boost PFC systems. Full article

Shadow removal aims to restore photometric, chromatic, and structural consistency between shadowed and non-shadowed image regions. Although weakly supervised shadow removal methods reduce the reliance on densely paired training data, they still struggle to fully exploit appearance priors from non-shadow regions. As a result, their shadow removal outputs often appear unnatural, exhibiting color shifts and loss of fine texture details. To address this issue, we propose an ab-dynamic feature refinement network (AB-DFRNet) for weakly supervised shadow removal that more effectively exploits structural and chromatic symmetry during training. A high-frequency information enhancement (HFIE) module is introduced into the shadow generation subnet to extract and enhance high-frequency components via frequency separation and dense convolutions, thereby facilitating the learning of fine structural symmetry and enriching pseudo-shadow details. In the removal subnet, a dual-attention adaptive fusion (DAAF) module combines global and local attention mechanisms to adaptively recalibrate channel-wise and spatial features, improving multi-scale feature integration. Furthermore, a chrominance-only consistency (COC) loss is designed to minimize differences between the a and b channels of restored regions and their non-shadow references in the Lab color space. This additional color refinement constraint encourages a symmetric distribution of chromatic information and helps the refinement network produce more natural shadow-removed results. Extensive experiments are conducted on three benchmark datasets: ISTD, SRD, and Video Shadow Removal. The results confirm the effectiveness of AB-DFRNet, demonstrating competitive quantitative performance and noticeably better visual quality compared with existing weakly supervised shadow removal methods. Full article