Search Results (1,696)

Impressionist artworks, marked by distinctive brushstrokes and light effects, pose challenges for holistic deep learning models. Existing classification methods often lack interpretability and mathematical rigor. To address this, we propose an explainable AI framework for Impressionist painting classification based on feature disentanglement. The framework uses Fourier transform to decompose images into magnitude and phase spectra and then applies frequency filtering to separate high-frequency components (brushstroke textures, edges) from mid-to-low frequencies (global composition, color transitions). A state space model captures long-range dependencies within frequency representations. Disentangled features are fused with the original image features via a dual-branch injection mechanism. Experiments show that a lightweight model with 10M parameters, using this injection strategy, outperforms conventional baselines with 25M parameters. Moreover, the approach provides interpretability by explicitly linking classification decisions to brushstroke organization, frequency energy distribution, and semantic composition. This work offers a unified, computationally efficient, and mathematically rigorous solution for feature disentanglement in artistic image analysis, laying a theoretical foundation for explainable computational aesthetics and human–AI collaborative appreciation. Full article

Mycoplasma pneumoniae (MP) is a major etiological agent of respiratory tract infections. This study sought to systematically map the current landscape, thematic progression, collaborative networks, and emerging priorities in diagnostic research on MP infections. Data were extracted from the Web of Science Core Collection from 1 January 1980, to 12 May 2025. Bibliometric and visualization analyses across countries/regions, institutions, authors, co-cited references, keywords, and disease-related terms were conducted using CiteSpace, VOSviewer, Pajek, and SCImago Graphica. Analysis of 2093 articles revealed consistent growth in research output related to MP diagnosis. Most publications originated in China (n = 623). The United States Centers for Disease Control and Prevention demonstrated the greatest total link strength in institutional collaboration networks. Key high-frequency keywords reflect the ongoing transition from traditional pathogen confirmation to integrated diagnostic approaches incorporating molecular testing, macrolide-resistance detection, co-infection evaluation, and risk stratification. Current research has increasingly targeted diagnostic optimization and the early identification of refractory or severe Mycoplasma pneumoniae pneumonia. This study provides a structured overview of the knowledge structure, thematic evolution, and technological frontiers in MP diagnostic research. The findings highlight key challenges, thereby offering guidance for interdisciplinary innovation and informing future diagnostic research directions. Full article

Background: Short-video platforms (e.g., Douyin, TikTok) have gained immense popularity among young adults, yet their addictive potential and health consequences remain understudied. Unlike gaming addiction, which has been linked to myopia and mental distress, short-video viewing may pose distinct risks due to its fragmented, high-frequency, and pre-sleep usage patterns. This study aimed to investigate the relationships between short-video viewing and multifaceted health outcomes in Chinese young adults, with a focus on dose–response relationships. Methods: A cross-sectional survey was conducted among 328 young adults aged 20–33 years. Participants were categorized into a non-addicted group (daily short-video viewing < 0.5 h, n = 164) and a short-video viewing group (daily viewing ≥ 1 h, n = 164), further divided by duration: 1–2 h (n = 96), 3–4 h (n = 29), 5–6 h (n = 22), and >6 h (n = 17). Outcome measures included the Hospital Anxiety and Depression Scale (HADS), Van Dream Anxiety Scale (VDAS), 36-Item Short-Form Health Survey (SF-36), Ocular Surface Disease Index (OSDI), Internet Addiction Test (IAT), Chinese Internet Addiction Scale—Revised (CIAS-R), and Mobile Phone Addiction Index (MAPI). Linear regression, one-way ANOVA with Tukey post hoc comparisons (or Kruskal–Wallis tests with Dunn’s test for non-normal variables), and dose–response curve fitting were applied. Segmented regression was used to explore potential dose–response patterns. Results: Compared with the non-addicted group, the short-video viewing group had significantly shorter sleep duration, higher HADS, VDAS, OSDI, IAT, CIAS-R, and MAPI scores, and lower SF-36 scores (all p < 0.0001). Clear dose–response relationships were observed: as daily viewing duration increased, sleep duration decreased linearly (r = −0.73, p < 0.001), while OSDI (r = 0.89, p < 0.001) and VDAS (r = 0.68, p < 0.001) increased. Among participants viewing >6 h/day, all (100%) had moderate-to-severe dry eye (OSDI ≥ 23), and 71% (12/17) had probable anxiety/depression (HADS ≥ 11). Notably, MAPI showed the strongest correlation with VDAS (r = 0.77, p < 0.001), indicating that mobile phone addiction was closely associated with nightmare-related anxiety. The SF-36 physical component summary (PCS) was negatively correlated with daily short-video viewing duration (r = −0.62, p < 0.001). Exploratory threshold analyses suggested that viewing > 3 h/day was associated with greater severity of sleep and ocular surface complaints. The dose–response thresholds identified in this study are exploratory, as the number of participants in the higher-duration categories was limited (3–4 h: n = 29; 5–6 h: n = 22; >6 h: n = 17). These breakpoints should be interpreted with caution and validated in future large-scale studies. Conclusions: Short-video viewing is associated in a dose-dependent manner with sleep quality, ocular surface health, and psychological well-being, with effect sizes generally comparable to, and in some domains (e.g., dry eye and dream anxiety) somewhat larger than, those reported for gaming addiction. The strong association between mobile phone addiction (MAPI) and dream anxiety (VDAS) highlights the distinctive association pattern of pre-sleep short-video use. Future interventional studies are warranted to determine the optimal duration limit. Our exploratory thresholds (approximately 3.2 h/day for sleep and 4.0 h/day for OSDI) provide preliminary reference points, but the specific target duration remains to be established through adequately powered studies designed to compare multiple cut-off levels. Full article

When extracting representative paths from large-scale vehicle trajectory data, origin–destination (OD) pairs have typically been treated as a homogeneous unit of analysis. This study used ~2.5 million OD pair–day (OD-day) navigation records collected over 21 days in Bucheon City, Republic of Korea, and evaluated representative paths and links using weighted indices and cumulative explained travel (CET). The results showed that 86.9% of OD-days exhibited a single observed path, whereas 13.1% exhibited multiple paths. Within multipath OD-days, 96.8% (12.7% of all OD-days) were classified as path-dispersed (top-path share ≤ 0.8). Although comprising only 13% of OD-days, multipath cases averaged 8.3 times more observed vehicles than single-path cases and accounted for 55.7% of observed vehicle trips. CET analysis showed that representative-path selection was primarily meaningful for dispersed OD-days. Across all OD-days, a single path covered, on average, 92.9% of an OD-day’s vehicle-kilometers traveled (VKT); on dispersed OD-days, the mean coverage was 45.2%, increasing to 84.7% and 93.5% with two and three paths, respectively. Further, weighted indices aligned substantially closer with the official arterial hierarchy than frequency-based indices: among top-50 representative links, the proportion of high-speed arterials increased from 30% to 80%, against a network average of 4.2%. These findings suggest that representative-path and link extraction should account for OD-day path-structure heterogeneity. Full article

Intersecting fissures strongly affect stress transfer and crack coalescence in rock, but their coupled effects on mechanical degradation and acoustic emission signatures remain incompletely understood. We conducted uniaxial compression tests with synchronous acoustic emission monitoring on prismatic sandstone specimens containing five intersecting-fissure configurations and on intact controls. The specimens measured 50 mm × 50 mm × 100 mm, and each prefabricated fissure was 30 mm long and 2 mm wide. The central 0–90° configuration produced the greatest degradation: its peak stress, peak strain, and elastic modulus were about 70%, 36%, and 50% lower, respectively, than those of the intact specimen. Acoustic emission ring-down counts captured the transition from early crack activation to unstable coalescence and increased sharply near macroscopic failure. Low-inclination configurations generated predominantly intermediate- and high-frequency signals, consistent with the activation of numerous small tensile cracks. Gaussian mixture model clustering of rise angle and average frequency values further showed that tensile microcracking dominated all fissured specimens, whereas the shear-crack fraction increased with fissure angle α. These findings link fissure geometry, macroscopic weakening, and acoustic emission source characteristics under uniaxial loading, providing a laboratory basis for identifying potentially hazardous intersecting-fissure configurations in underground rock engineering. Full article

Retinal responses to ultrasound depend on carrier frequency, but whether a constrained tissue-to-neuron model can predict this dependence on a common published response scale remains unclear. We developed an acoustic-radiation-force (ARF) modelling workflow that couples an effective tissue-scale stress proxy to a stochastic Hodgkin–Huxley retinal ganglion cell population. One non-negative affine observation model, shared across frequencies, linked raw simulated spike counts to the published three-frequency ex vivo RGC response scale without frequency-specific rescaling. In a nested leave-one-frequency-out model-selection assessment, the workflow achieved an overall $Q^2$ of 0.915 and an RMSE of 0.0752 across 25 held-out observations. Mean-response transfer remained strong across frequencies, although probabilistic calibration was weakest at 1.9 MHz. An in-sample fixed-map sensitivity analysis using moment-matched log-normal, Gamma, and Weibull thresholds yielded overall RMSEs of 0.0434–0.0461 and preserved positive overall K-ablation NLPD differences of 0.219–0.238, with the effect concentrated at 43 MHz. These results show that shared-scale modelling captures the main cross-frequency response structure and that a high-threshold inhibitory functional component improves the description of high-intensity rolloff. Within the published three-frequency dataset, the workflow provides a reproducible benchmark for targeted comparisons of retinal ultrasound mechanisms. Full article

Climate change poses significant challenges to agricultural production, water availability, and food and nutrition security, particularly in semi-arid regions where rural livelihoods depend heavily on rain-fed agriculture. In Eswatini, increasing temperatures, erratic rainfall, and recurrent droughts have intensified pressures on smallholder farming systems; however, limited empirical research has examined how climate variability simultaneously affects agriculture, water resources, and household food security within an integrated food–water–nutrition nexus framework. Therefore, this study assessed the impacts of climate change on agricultural production, water availability, food security, and access to nutritious food among smallholder households in the Lubombo Region of Eswatini. A concurrent triangulated mixed-methods approach was employed, combining quantitative data from 880 households with qualitative insights from open-ended responses. Descriptive statistics, Spearman’s correlation, binary logistic regression, and thematic analysis were used to analyse the data. The findings reveal that climate change significantly affects both agricultural and water systems, which in turn directly influence household food security outcomes. The majority of households reported declining crop yields (56.5%), widespread food shortages (79.5%), meal skipping (69.0%), and high levels of food-related anxiety (87.6%). Water insecurity is also prevalent, with over 83% of households experiencing water shortages and nearly all respondents indicating that climate change has affected water access. Correlation and regression analyses demonstrate that drought frequency and reduced rainfall are the strongest predictors of both water insecurity and food insecurity, highlighting the central role of climate variability. Water scarcity emerged as a critical pathway linking climate change to food insecurity, with strong associations between water shortages and reduced crop yields, food shortages, and coping strategies such as skipping meals. Qualitative findings further highlighted declining agricultural productivity, loss of traditional foods, reduced dietary diversity, and increased psychological stress associated with food insecurity. The study concludes that food insecurity in the Lubombo region is multidimensional, driven by the interconnected effects of climate variability, water scarcity, and socio-economic constraints. The findings emphasise the need for integrated, climate-resilient strategies that simultaneously address agricultural production, water resource management, and household adaptive capacity to enhance food and nutrition security. Future research should evaluate the effectiveness of nexus-based adaptation strategies and climate-smart interventions in enhancing long-term food, water, and nutrition security in vulnerable smallholder farming systems. Full article

The rapid electrification of transportation has accelerated the demand for high-power electric vehicle (EV)-charging systems with high efficiency, compact size, galvanic isolation, and flexible scalability. Conventional isolated EV chargers typically adopt cascaded AC–DC and DC–DC conversion stages, which require additional semiconductor devices, passive components, and bulky dc-link capacitors, thereby increasing system complexity and limiting power density. This paper proposes a scalable three-phase modular parallel quasi-single-stage isolated single-ended primary-inductor converter (SEPIC) for high-power EV fast-charging applications. The proposed converter integrates power factor correction, voltage regulation, and high-frequency isolation within a unified SEPIC-based conversion cell, eliminating the intermediate dc-link capacitor while reducing the number of magnetic components and power conversion stages. By employing a Δ-connected three-phase input and input/output-parallel modular configuration, the proposed architecture provides a flexible power expansion approach based on a 9 kW basic module, with the potential to extend to higher power levels, such as 54 kW, through paralleling multiple identical modules. The operating principle, steady-state characteristics, continuous conduction mode (CCM)/discontinuous conduction mode (DCM) transition mechanism, current-sharing behavior, and control strategy are systematically investigated. An 18 kW prototype consisting of two parallel modules is experimentally validated under 380 V three-phase AC input and 400 V DC output conditions. The experimental results demonstrate a peak efficiency of 97.5%, a rated efficiency of 97.3%, a power factor (PF) of 0.999, and an input current total harmonic distortion (THD) of 2.55%, confirming the effectiveness and scalability of the proposed converter for high-power EV fast-charging applications. Full article

Wide- and narrow-band composite-axis servo systems in inter-satellite laser communication face critical challenges in balancing high-precision dynamic tracking and strong robust anti-disturbance performance under the coupling effect of high-speed inter-satellite relative motion and multiple strong disturbances. To address this issue, this paper proposes a composite control method integrating adaptive non-singular terminal sliding-mode control and a nonlinear extended state observer. First, a full-link dynamic model covering electromechanical coupling and inter-axis disturbance transmission is constructed to accurately quantify the disturbance characteristics of coarse- and fine-tracking loops. Second, a third-order nonlinear extended state observer is designed to realize real-time high-precision estimation and feedforward compensation of lumped disturbances. On this basis, a self-consistent adaptive non-singular terminal sliding-mode control law is formulated. Under the explicitly stated observer-residual and reaching-phase assumptions, the ideal continuous model provides finite-time convergence of the sliding variable and tracking error. Finally, a wide- and narrow-band cooperative strategy based on error frequency division is introduced to achieve complementary performance between large-stroke coarse tracking and ultra-high-precision fine tracking. Numerical simulations yield a steady-state tracking-error point estimate of 0.30 μrad and a 20 dB disturbance-suppression bandwidth of 1200 Hz. In the semi-physical dynamic-tracking test, the proposed controller limits the peak error to 1.2 μrad; the instrument-only expanded uncertainty of the detector output is estimated as 0.12 μrad (coverage factor k = 2). At the reported evaluation points, the proposed method outperforms PID, conventional sliding-mode control, and linear active-disturbance-rejection control. Deterministic robustness simulations also show smaller tracking errors and shorter recovery times under parameter perturbation, actuator saturation, and temporary link occlusion. No Monte Carlo loss-of-lock probability is claimed. Full article

To address output-side high-frequency harmonics in a three-phase-to-single-phase matrix converter (3-1MC) with an inductive compensation unit and topological port asymmetry, two PWM switching-sequence optimization methods are proposed. Without power decoupling, the pulsating power associated with the single-phase output is coupled to the input side through the bidirectional switching network because the converter has no large energy-storage DC link. Under conventional modulation, the state sequence can produce large output-voltage steps and nonuniform commutation paths, thereby increasing switching-frequency harmonic components. The first proposed method avoids direct commutation of the line voltage with the largest instantaneous magnitude to the zero state by inserting line-voltage segments with smaller instantaneous magnitudes. The second method rearranges the switching-state sequence without changing the effective-vector durations, thereby reducing the number of switching transitions within each switching cycle. Compared with the conventional modulation method, Method 1 reduces the output-voltage THD from 35.4% to 31.4%, corresponding to a relative reduction of 11.3%. Method 2 reduces the THD from 35.4% to 27.5%, corresponding to a relative reduction of 22.3%. Full article

Although asymbiotic orchid seed germination was first achieved in vitro in 1922, the prediction of germination requirements under in vitro conditions still remains complicated. To address this, we developed a machine learning framework to classify the ex situ asymbiotic germination potential of wild orchids into four discrete groups: Low (0–30%), Mid (31–50%), High (51–80%), and Max (81–100%). Models were trained on a dataset of 203 species, utilizing seed morphometrics—specifically, the embryo-to-testa (E:S) length ratio—alongside core ecological traits (subfamily, growth habit, habitat, and climate zone), as well as chemical scarification duration as a proxy of seed permeability. Validation leveraged novel germination and trait data from 26 taxa from Greece (17) and Armenia (9), published here for the first time. To mitigate class imbalance and prevent algorithmic bias toward highly germinating species, we applied inverse frequency weighting during training. Iterative testing of six algorithms revealed that the “Step 4” feature matrix (excluding climate zone and pretreatment duration) yielded the optimal predictive balance. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) emerged as the superior models, achieving overall accuracies of 44.4% and 61.1%, respectively, with both achieving 100% accuracy for low-germinating species. Finally, we synthesized a novel database compiling new seed morphometrics from Armenia (17 taxa), Greece (52 taxa), and the data from the literature (479 taxa). After filtering previously utilized species, we generated a prediction pool of 361 orchid taxa. Applying our Step 5 KNN and SVM models to forecast their germination behavior revealed distinct variations linked to ecological profiles. This high-accuracy framework, particularly for low-germinability groups, offers a powerful screening tool for ex situ conservation planning. The final trained models are compiled in the publicly available R (v. 4.6.0) package OrchidGermClass. Full article

This paper presents a novel fractional-order hybrid observer framework for robust sensorless control of brushless DC (BLDC) motor drives in unmanned aerial vehicle (UAV) propulsion systems, addressing the fundamental limitations of conventional integer-order observers through the lens of fractional calculus. The proposed architecture synergistically integrates high-frequency square-wave signal injection for zero/low-speed operation with an adaptive fractional-order extended Kalman filter (AFEKF) augmented by online stator resistance and flux linkage estimation, capitalizing on the memory and hereditary properties inherent to fractional-order systems. A minimum-order current observer enables accurate three-phase current reconstruction using a single DC-link sensor, substantially reducing hardware complexity and cost. The complete algorithm is implemented on an STM32H7 microcontroller and experimentally validated on a 1.5 kW drone propulsion testbench and in-flight platform. Results demonstrate reliable startup under 50% rated load, stable operation from standstill to 5000 RPM on the UAV motor (and validated up to 22,000 RPM on a high-speed test motor, <4° electrical position error at 5 kRPM, and strong robustness against 35% stator resistance variation. In-flight tests confirm improved thrust smoothness and hover stability compared to conventional sensorless strategies. The proposed fractional-order architecture offers a practical, resilient, and computationally feasible solution for next-generation autonomous aerial systems, establishing a new paradigm for observer design in electric propulsion. Full article

Objective: Depressive symptoms exist on a continuum in the general population, yet the underlying neurobiological mechanisms, particularly the interplay between resting-state networks and task-evoked social cognitive responses, remain elusive. Methods: Leveraging the Human Connectome Project (HCP) dataset, we included 867 participants. With depression scores as the independent variable and age/sex as covariates, we systematically examined associations with sleep quality, negative emotions, sensory scores, gray matter volume (GMV), fractional amplitude of low-frequency fluctuations (fALFF), multi-seed resting-state functional connectivity (rsFC), as well as brain activation and behavioral performance during working memory, emotion recognition, social cognition, relational reasoning, language comprehension, and gambling tasks. The statistical threshold was set at voxel-level p < 0.001 (uncorrected) combined with cluster-level FWE correction at p < 0.05. Results: (1) Depression scores were positively correlated with sleep disturbances, negative emotions (anger/fear), and pain. (2) In resting-state, depression scores negatively correlated with ventral striatum (VS)–cerebellum/parahippocampal gyrus/fusiform rsFC, yet positively correlated with pregenual anterior cingulate cortex (preACC)–supplementary motor area (SMA) rsFC. (3) In task-fMRI, only the social task showed a positive association with task accuracy and regional activation in bilateral pre/postcentral gyri, superior temporal gyri, left middle frontal gyrus, and SMA/paracentral lobule. Conclusions: Elevated depression scores are linked to a pattern that may reflect relative decoupling between reward and perceptual systems, along with enhanced connectivity in cognitive control circuits. Socially, high scorers exhibit a pattern suggestive of compensatory hypervigilance, accompanied by enhanced behavioral performance. This study provides multidimensional evidence for the dimensional neural representation of depressive symptoms. Full article

To meet the growing demand for reliable space–air–sea-integrated communications and underwater information backhaul, this paper proposes and analyzes a three-hop heterogeneous space–air–sea communication system consisting of a satellite, a high-altitude platform (HAP), a sea-surface buoy, and an autonomous underwater vehicle (AUV). Specifically, the satellite-to-HAP link employs free-space optical (FSO) transmission, the HAP-to-sea-surface buoy link adopts mixed FSO/radio-frequency (RF) transmission, and the sea-surface buoy-to-AUV link utilizes underwater wireless optical communication (UWOC). To enhance the reliability of the HAP-to-sea-surface buoy link in complex atmospheric and maritime environments, a threshold-based adaptive combining scheme for mixed FSO/RF transmission is designed. Meanwhile, nonzero-boresight pointing error models are incorporated into the FSO and UWOC links to characterize practical link misalignment. Based on the proposed system model, analytical expressions for the end-to-end bit error rate (BER) are derived and validated through Monte Carlo simulations. The numerical results show that the proposed adaptive combining scheme achieves better BER performance than conventional dual-hop and hard-switching schemes. In addition, the effects of pointing errors, underwater turbulence, underwater transmission distance, shadowed fading, detection techniques, and modulation schemes on the system BER performance are further investigated. This work provides theoretical guidance for reliable cross-domain heterogeneous transmission in future space–air–sea integrated communication systems. Full article

Optical Camera Communication (OCC) enables data reception using common CMOS cameras and commercial webcams. However, applying multi-level modulation with rolling-shutter sensors is constrained by temporal acquisition parameters that may be undocumented or not directly accessible, making it challenging since many existing solutions rely on specialized hardware or require high processing complexity. This paper demonstrates that reliable multi-level OCC can be achieved using only unmodified commercial hardware and straightforward signal processing by experimentally characterizing and validating a 4-level pulse width modulation (4-PWM) link. Data are encoded in the duty cycle of the transmitted signal and recoveblack from the width of the captublack rolling-shutter stripes. Two internal timing parameters are estimated directly from the captublack images without access to the internal camera timing: the row readout period (34.38 $\mathsf{\mu }$s), obtained from the spatial periodicity of the stripes, and the effective integration time (490 $\mathsf{\mu }$s), inferblack from the deformation of the received constellation with carrier frequency. A single-parameter model is derived to describe this deformation and is validated at two carrier frequencies differing by a factor of four, pblackicting constellation compression, a fixed point at a duty cycle of 0.5, and constellation collapse (followed by inversion) when the exposure-to-carrier-period ratio reaches 0.5. We evaluate system performance under different exposure settings, showing that automatic camera control strongly degrades multi-level detection (BER of 0.290, with mean image level variation constrained to $0.14\%$ compablack to $61\%$ under fixed exposure). Under optimal fixed-exposure operating conditions, a prospective 15 min transmission achieved zero bit errors over 35,878 bits at 40 bps, corresponding to a $95\%$ upper confidence bound on the BER of $8.4\times {10}^{-5}$. These results reveal a practical balance between cost, complexity, and performance, demonstrating that 4-PWM rolling-shutter OCC is a viable solution for Internet of Things (IoT) signaling and low-rate data transmission using commercially available devices. Full article