Search Results (929)

Objectives: To systematically evaluate the diagnostic accuracy, clinical applicability, and methodological maturity of artificial intelligence (AI)-based methods for temporomandibular disorders (TMD), temporomandibular joint (TMJ) abnormalities, and related cranio-cervico-mandibular (CCM) musculoskeletal conditions compared with conventional diagnostic methods and accepted reference standards. Materials and Methods: This systematic review and exploratory diagnostic test accuracy meta-analysis was conducted in accordance with PRISMA 2020 and PRISMA-DTA. The protocol was retrospectively registered in PROSPERO (CRD420261428138). PubMed/MEDLINE, Embase, and Scopus were searched from database inception through February 2026. Eligibility for the primary synthesis was restricted to published studies in English or Spanish involving adults aged 18 years or older. All extracted records were re-audited article by article to align the evidence with the diagnostic question. The domain-specific quantitative synthesis was restricted to TMJ osteoarthritis studies with explicit 2 × 2 diagnostic data or a unique, verifiable reconstruction from reported class totals and sensitivity/specificity. Risk of bias was assessed with QUADAS-2. Results: From 1471 records identified, 174 entered the master extraction dataset. After reclassification, 84 records were retained for primary TMD/TMJ qualitative synthesis, 8 as secondary CCM musculoskeletal evidence, 31 as conventional or reference standard supporting evidence, 33 as methodological or contextual evidence, 4 as differential orofacial pain evidence, and 14 as excluded or minimal-background records. Twenty-one studies were assessed as potential diagnostic accuracy candidates. Three TMJ osteoarthritis studies contributed to the domain-specific exploratory meta-analysis: two with explicit 2 × 2 data and one with a reproducible reconstruction. Pooled sensitivity was 0.791 (95% CI: 0.700–0.861) and pooled specificity was 0.869 (95% CI: 0.811–0.911). Heterogeneity was substantial for sensitivity (I² = 68.2%) and moderate for specificity (I² = 57.3%). Conclusions: AI demonstrates promising performance in selected image-based TMJ osteoarthritis tasks. Nevertheless, the evidence remains exploratory because only three studies were quantitatively comparable, one table was reconstructed, and modalities and validation designs differed. AI should be interpreted as an augmentative decision support tool rather than a replacement for MRI, CBCT, or validated clinical frameworks such as DC/TMD. Clinical Relevance: AI may support image-based TMD/TMJ workflows, but present evidence does not justify autonomous diagnosis or replacement of established clinical and imaging reference standards. Full article

Labeling images is expensive, but unlabeled data is abundant. Semi-supervised learning (SSL) addresses this gap, though the dominant pseudo-labeling methods can suffer from confirmation bias—reinforcing their own confident-but-wrong predictions—most severely when labels are scarcest. Under a controlled, reproducible compute-constrained protocol on CIFAR-10, SVHN, and CIFAR-100, we examine which ingredients of consistency-based SSL actually help when as few as four labels per class are available. We propose CCM (Confidence-Calibrated Consistency Matching)—a per-class curriculum threshold, a dual strong-view consistency loss, and a smooth confidence weighting that softly admits borderline pseudo-labels—together with a unified view in which FixMatch, FlexMatch, SoftMatch, and CCM instantiate a single generalized weighting function. On CIFAR-10 with 40 labels, CCM reaches 35.14%, a significant improvement over the FlexMatch design it directly extends (+3.30 percentage points (pp), paired t-test p = 0.001). SoftMatch, re-trained under the identical budget, performs better still at the two smallest budgets (37.04% at 40 labels, p = 0.049), while CCM leads numerically at 4000 labels: the two smooth-weighting designs top the extreme-scarcity board—convergent evidence that the smoothness of the weighting function, more than the placement of its threshold, is the decisive design axis. We also report a negative result: cross-view agreement helps neither as an admission gate nor as reliability reweighting, reducing accuracy by up to 4.37 pp; agreement is a positive correctness signal, but its absolute level (approximately 44% correct among agreeing pseudo-labels) is too low to filter on safely. Curriculum thresholding, by contrast, hurts on the easier SVHN dataset and fails outright on CIFAR-100 when its per-class statistics become too thin. CCM adds no inference-time cost. We do not claim universality; we characterize when each ingredient helps within a single, identical-budget protocol. Full article

Cardiac contractility modulation (CCM) is an implantable device-based therapy that delivers biphasic, non-excitatory electrical signals to the ventricular myocardium during the absolute refractory period. By enhancing contractile performance without inducing depolarization or altering ventricular activation, CCM acts as bioelectronic myocardial conditioning. Current evidence supports its use in selected patients with symptomatic heart failure, reduced or mildly reduced left ventricular ejection fraction, narrow QRS duration, persistent symptoms despite guideline-directed medical therapy, and no indication for cardiac resynchronization therapy. In this population, CCM improves functional status and quality of life, whereas evidence for reductions in mortality or recurrent heart failure hospitalization remains less definitive. Whether CCM also reduces arrhythmic burden remains uncertain. Candidates for CCM frequently exhibit atrial and ventricular remodeling, neurohormonal activation, implantable cardioverter-defibrillators, and vulnerability to atrial fibrillation, ventricular arrhythmias, and device therapies. Mechanistically, CCM may render the failing myocardium less arrhythmogenic through coordinated effects on calcium handling, electromechanical remodeling, fibrosis-related substrate, contractile efficiency, and heart-failure stability. However, pivotal trials were not designed to assess arrhythmic endpoints, leaving the relationship between CCM and arrhythmic burden insufficiently characterized. This review summarizes CCM evidence, mechanistic rationale, available arrhythmic signals, device-related considerations, and future research priorities for prospective studies in this evolving field. Full article

This study evaluates the hydrodynamic and operability characteristics of a multipurpose high-speed craft derived from the CB90 through stern-region refinement. Calm-water performance was assessed using unsteady Reynolds-averaged Navier–Stokes simulations with volume-of-fluid free-surface capture in STAR-CCM+, while wave responses were analyzed with ANSYS AQWA for several speeds, wave headings, and sea states. The assessment covered resistance, effective power, running attitude, global motions, accelerations, deck wetness, and slamming-sensitive responses. The developed hull carries a displacement volume of 20.8 m³ against 18.0 m³ for the baseline CB90, on a waterline length 7.4% shorter. In calm water, its total resistance and effective power are 3.6–10.4% higher, its running trim is reduced by 4.1–19.3%, and its total resistance coefficient, referenced to the static wetted surface area, is 4.2–10.2% lower. The free-surface wave patterns of the two hulls remain closely similar. The developed hull reduced vertical acceleration by 6.1–15.6%, lateral acceleration by 4.9–6.7%, and deck wetness by 9.1–28.7%, but increased roll by 8.8–9.8% and pitch by 7.7–12.7% in every condition investigated. The results show that stern-region refinement of this type acts primarily on running attitude and arrangement flexibility, and they support an integrated assessment of small multipurpose high-speed craft under representative coastal conditions. Full article

Turfgrass phenotyping relies heavily on visual quality (VQ) ratings and RGB indices like DGCI, but these are limited by observer subjectivity, coarse ordinal scales, or ratio formulations that do not reflect perceptual color differences. Hyperspectral and machine-learning tools overcome some limitations but remain costly and difficult to generalize, motivating the need for scalable and interpretable RGB color metrics. We introduce ΔEg, a perceptually anchored CIELAB ΔE distance from an ideal green that provides a continuous and interpretable measure of canopy color evaluated alongside a panel of RGB-derived metrics. A 3 × 3 nitrogen × irrigation greenhouse experiment using hybrid bermudagrass (TifTuf, Cynodon dactylon × C. transvaalensis) quantified canopy responses with RGB imaging, spectral reflectance, CCM-300 fluorescence, and chlorophyll assays. ΔEg correlated strongly with chlorophyll (r = 0.72), similar to DGCI (r = 0.73), and both exceeded CCM-300 (r = 0.29). HSVi showed the strongest association with VQ (r = 0.84) and was most sensitive to irrigation (η_p² = 0.63). CIELUV v* explained the greatest model variation (R²m = 0.94) and responded most to fertilizer (η_p² = 0.84). The yellow fraction was significant across all main and interaction effects and captured canopy decline (r = −0.82 with VQ). An illustrative decision-support scenario using ΔEg indicated that moderate fertilizer combined with mild deficit irrigation optimized turf color and input efficiency. Conclusions apply to controlled conditions, with field-scale validation identified as future work. These results demonstrate that interpretable RGB color metrics, anchored by ΔEg, offer a scalable alternative to VQ scoring and spectral systems. Full article

Background: Spinal cord cavernous malformations (SCCMs) are vascular malformations characterized by blood-filled cavities, often leading to neurological deficits. Familial cerebral cavernous malformation (FCCM) is a hereditary condition that predisposes individuals to develop multiple cavernous angiomas. Understanding the incidence, clinical presentation, and management strategies for spinal cavernous angiomas in the context of FCCM is crucial for improving patient outcomes. Methods: This narrative review synthesizes existing literature on SCCMs in patients with FCCM. A comprehensive search was conducted across multiple databases, including PubMed, Scopus, and Web of Science, utilizing keywords such as “spinal cavernous angiomas” and “familial cerebral cavernomatosis”. In addition, a further search was performed among papers discussing FCCM and SCCM, respectively. Discussion: In the paucity of published data about the presence of SCCMs in FCCM, the review highlights the clinical manifestations of SCCMs in both sporadic disease and FCCM, including recurrent hemorrhagic episodes and progressive neurological deficits. Although mutations in the KRIT1, CCM2, and PDCD10 genes play a significant role in the pathogenesis of FCCM, no single gene mutation has been identified as predisposing to SCCMs. Diagnosis was usually reached in patients symptomatic for spinal cord bleeding. Surgical intervention remains the primary treatment modality; however, the decision-making process is complicated by the potential for new lesion development. Conclusions: SCCMs in FCCM present distinct challenges in diagnosis and management and their prevalence is probably underestimated. This review underscores the need for heightened awareness among clinicians regarding the hereditary nature of these lesions. Future research should focus on the molecular mechanisms underlying FCCM, aiming to develop targeted therapies and improve clinical outcomes for affected individuals. Full article

The interleaved parallel Buck–Boost converter can reduce output voltage ripple and has been widely used in engineering practice. The application of fractional-order theory has a significant influence on model accuracy and power converter performance. Based on fractional calculus theory and the state space averaging method, this paper establishes a fractional-order mathematical model of the CCM interleaved parallel Buck–Boost converter. The steady-state operating point and ripple characteristics of the converter under the Caputo fractional-order definition are analyzed and compared with those under other fractional-order definitions. Fractional-order energy storage elements are constructed, and a fractional-order circuit simulation model of the converter is established for comparative simulation analysis. Finally, experiments are carried out to verify the effectiveness of the theoretical analysis. Full article

This paper proposes a lightweight vital-sign detection framework based on Frequency-Modulated Continuous Wave (FMCW) radar. By integrating time-domain complex differencing and adaptive trend reconstruction, the proposed framework mitigates distortions in chest micro-motion signals caused by multipath reflections, radar cross-section (RCS) variations, high-frequency impulsive noise, and body-motion artifacts in practical monitoring scenarios. The framework first employs a Time-Domain Complex Differencing and Sliding Accumulated Energy (TDCD-SAE) algorithm to precisely lock onto the target range-bin, followed by phase-difference extraction utilizing Complex Conjugate Multiplication (CCM). To eliminate non-physiological interference, an Adaptive Threshold-Based Bidirectional Trend Reconstruction (AT-BTR) algorithm is introduced to restore the corrupted phase profile. The optimized phase signal is converted into chest-wall displacement, followed by body motion detection to realize the joint estimation of the respiration rate and the heart rate. The experimental results demonstrate that the proposed system achieves a high precision, yielding low absolute errors of 1.24/1.71 BPM (supine) and 1.39/2.24 BPM (lateral), thereby validating its efficacy and robustness across diverse sleeping postures in resource-constrained environments. Full article

This study investigates the flow field around a multi-cage aquaculture platform under wave–current coupling. Using STAR-CCM+ with fifth-order Stokes wave theory and a porous media model, the effects of incident wave height and net solidity ratio on wave propagation and energy dissipation are analyzed. Results show the platform exerts a significant damping effect on incident waves. Inside the platform, wave height distribution follows a characteristic pattern: it is relatively high at the front, attenuated in the front cage, recovered in the central cage, and stabilized in the rear cage. Pronounced wave diffraction and energy concentration around the columns cause a marked wave height reduction at the center of the first cage, while wave heights in the middle and rear cages tend to stabilize. The overall wave attenuation pattern remains consistent under different incident wave heights. Higher waves induce greater attenuation in the central culture area, with the transmission coefficient showing a stable variation trend. A higher net solidity ratio enhances front-edge wave reflection and rear wave-height reduction, improving overall damping; a lower ratio results in weaker damping and a higher wave response at the rear. The findings provide practical references for platform design and application. Full article

Cloud and quality screening removes approximately 65% of daily TROPOMI tropospheric NO₂ pixels, creating structured data gaps that coincide with meteorological conditions driving pollution extremes. Standard gap-filling methods—kriging, Random Forests and other machine learning methods—act as statistical smoothers that systematically suppress extreme concentrations and ignore the Missing Not At Random (MNAR) character of cloud-induced missingness. Here we present a physically informed framework that treats urban NO₂ as a forced nonlinear dynamical system and reconstructs missing satellite observations through geometric navigation on a shadow manifold rather than statistical interpolation. The framework integrates five components: (i) Multivariate State-Space Reconstruction (MSSR) using multiview embeddings of continuous ground-based NO₂, O₃, and ERA5 meteorology, grounded in Stark’s forced-system embedding theorem; (ii) Short-Time Regime-Conditioned Convergent Cross Mapping (ST-RC-CCM) with a spatial-mismatch negative control for falsifiable causal validation; (iii) Inverse Probability Weighting (IPW) to correct the clear-sky sampling bias; (iv) trajectory-matrix denoising via Singular Spectrum Analysis (SSA) and Robust PCA; (v) topology-inspired fidelity metrics—Manifold Overlap Ratio (MOR) and Dynamic Trend Capture (DTC)—that penalize smoothing artefacts. The physical basis for this coupling is the shared dynamical history of surface and column NO₂: tropospheric NO₂ has a photochemical lifetime of 1–4 h near urban emission sources, comparable to the boundary layer mixing timescale, ensuring that surface and column concentrations are jointly governed by the same emission–photolysis–transport attractor. The planetary boundary layer height (PBLH), solar zenith angle (SZA), and surface O₃—all included as MSSR coordinates—are the dominant physical drivers of the instantaneous surface-to-column scaling, and their joint trajectory in state space constitutes the physically grounded basis for analogue selection. The framework is validated on a synthetic forced Lorenz-96 system, then applied to five European primary cities spanning contrasting regimes (Sofia, Milano, Stuttgart, Kraków, Hamburg) plus five N1 spatial-mismatch control stations (Plovdiv, Genova, Frankfurt, Warszawa, Berlin)—ten urban-background stations across four countries—with structured ablations (A0-A4V-A4K). Across >3600 evaluations, MOR_ext distributions for EDM and non-EDM methods are non-overlapping by a factor exceeding 5× (EDM minimum 0.59 vs. non-EDM maximum 0.10; median non-EDM MOR_ext ≤ 0.05 at every city × mask combination), while EDM achieves MOR_ext up to 0.915 (Milano Po Valley). Under a fair-comparison benchmark that withholds ground-level NO₂ from Random Forest, EDM’s RMSE advantage remains robust at a median of 3.9× (RF_FULL) and increases to 4.2× (RF_METEO), confirming that the performance gap is physical rather than an information artefact. A three-level temporal validation—within-window pseudo-cloud masking, cross-year transfer (full 2022 holdout and DJF 2023/24), and a COVID-19 out-of-distribution test—demonstrates robustness beyond standard train/test splits, with CCM library-length convergence confirmed for 60/60 ablations (p < 0.001) across all ten stations. Spatial-mismatch tests confirm local dynamical specificity at all five primary–control pairs (Δρ = 0.090–0.210), with seasonal modulation driven by orographic and synoptic mechanisms. These results establish manifold-based gap filling as a dynamically informative complement to statistical approaches, particularly in topographically confined, stagnation-prone basins where preserving extreme-event geometry is essential for exposure assessment. Full article

Upstream–downstream nutrient connectivity strongly regulates water-quality risk in reservoir cascades, yet its quantification remains difficult where discharge, reservoir release, and hydraulic residence-time records are unavailable. This study developed a causal–explainable framework to diagnose total nitrogen (TN) connectivity between the upstream Shimantan Reservoir and downstream Banqiao Reservoir in the Huai River Basin, China. Long-term water-quality records, meteorological variables, land-cover indicators, seasonal descriptors, and lagged upstream predictors were integrated within a leakage-safe analytical workflow combining nonlinear causal inference, time–frequency coupling diagnostics, predictive modeling, SHAP-based attribution, and counterfactual analysis. Convergent Cross Mapping identified statistically significant asymmetric bidirectional nonlinear coupling, with a CCM skill of ρ = 0.746 for Shimantan TN → Banqiao TN and p = 0.730 for the reverse direction (p = 0.002 for both directions). Wavelet coherence showed that upstream–downstream TN coupling was concentrated mainly at short temporal scales, with a cone-of-influence-restricted mean squared coherence of 0.7114 in the 2–6-month intra-seasonal band. The validation-selected Gradient Boosting model provided interpretable test-period predictive skill, on independent source-derived observations from 2021–2023, achieving R² = 0.567, RMSE = 0.391 mg/L, and MAE = 0.312 mg/L. The GAN-generated 2024–2025 segment was excluded from empirical model evaluation and retained only for exploratory future-scenario assessment. SHAP decomposition indicated that upstream-related predictors accounted for 77.04% of the total absolute model attribution during high-TN events, while seasonal conditioned counterfactual upstream neutralization produced mean model-predicted changes of 0.162 mg/L across all test observations and 0.807 mg/L during high-TN events. Together, these results demonstrate that hidden upstream-downstream TN connectivity can be diagnosed through convergent causal, temporal, predictive, and model-attribution evidence in data-limited reservoir cascades. The findings support asymmetric coupled dynamics and downstream inheritance of upstream information but should not be interpreted as proof of exclusively one-way physical nutrient transport. The proposed framework offers a diagnostic decision-support approach for reservoir systems with sufficiently long monitoring records where direct hydraulic observations are unavailable. Full article

Background/Objectives: Surgical management of cerebral cavernous malformations (CCMs) within eloquent brain regions presents a high risk of neurological deficits. This study describes the clinical outcomes and technical feasibility of microsurgical resection for symptomatic eloquent CCMs, detailing the integration of advanced intraoperative adjuncts aimed at optimization of seizure control and functional preservation. Methods: We conducted a retrospective multicenter analysis of nine adult patients who underwent microsurgical resection for symptomatic eloquent CCMs between January 2021 and December 2025 across two tertiary centers in Slovenia. Intraoperative modalities included 100% neuronavigation, 55.6% intraoperative ultrasound, 77.8% intraoperative neuromonitoring (IONM) with motor and somatosensory evoked potentials, and 22.2% awake craniotomies. Results: Seizures were the primary clinical presentation in 77.8% of patients (66.7% medically refractory), and the overall hemorrhage rate was 66.7%. Gross total resection of the CCM was achieved in 100% of cases, with complete hemosiderin rim removal in 80% of applicable lesions. Early postoperative complications occurred in four patients, but at the maximum 48-month follow-up, 100% of the cohort achieved complete seizure control, and 44.4% successfully discontinued antiepileptic drugs. Long-term focal neurological deficits persisted in only two patients, while 77.8% were able to work following surgery. Conclusions: Microsurgical resection remains a well-established treatment modality for symptomatic CCMs. In this small, descriptive series of patients with lesions in functionally critical regions, high rates of gross total resection and favorable long-term seizure freedom were observed. These findings suggest that a multimodal approach combining anatomical neuronavigation with functional IONM may help minimize permanent morbidity, though larger cohorts are required to establish definitive efficacy. Full article

Numerous studies have focused on Computational Fluid Dynamics (CFD) Unsteady Reynolds-Averaged Navier–Stokes (URANS) maneuvering simulations of benchmark ships such as the David Taylor Model Basin (DTMB) 5415 model. However, the availability of hydrodynamic derivatives obtained from verified and validated simulations at Froude numbers (Fr) of 0.41 remains limited. In this study, CFD-URANS oblique towing simulations are conducted for the DTMB 5415 benchmark hull at Fr = 0.41 and Fr = 0.28 using STAR-CCM+. Verification and validation are performed according to ITTC guidelines for Fr = 0.41 and a drift angle of 10 degrees. The validation criterion is satisfied for the longitudinal force, side force, and yawing moment, with validation uncertainties of 13.5%, 2%, and 4%, respectively. The resulting forces and moments are fitted to derive a partial set of hydrodynamic derivatives. The CFD results are compared with SIMMAN workshop (2008) Experimental Fluid Dynamics (EFD) data, showing higher prediction accuracy for linear derivatives. At Fr = 0.41, the relative errors of Y’_V and N’_V are approximately 3% and 4%, while at Fr = 0.28, they are approximately 2% and 5%. Nonlinear components exhibit discrepancies exceeding 15%. Hydrodynamic coefficients show different dependencies on Fr, consistent with previous studies. Full article

PM2.5 pollution control in the Guangdong–Hong Kong–Macao Greater Bay Area requires a clearer understanding of how PM2.5-related linkages are organized across closely connected cities and jurisdictions. This study develops a CCM-based directional-network framework using PM2.5 pollution data from nine cities and two special administrative regions in the Greater Bay Area. Convergent cross mapping is first applied to identify nonlinear directional associations among cities, based on which an intercity PM2.5 pollution network is constructed. Social network analysis and motif analysis are then used to reveal the network’s macro-level connectivity and micro-level interaction patterns. An exponential random graph model is further introduced to identify the natural and socioeconomic factors associated with the emergence of intercity PM2.5 pollution linkages. The results show that PM2.5 levels in the Greater Bay Area generally declined across the selected years, with high-value areas becoming more localized, while the CCM results revealed heterogeneous nonlinear directional linkages among cities. The strongest CCM linkage was observed from Foshan to Guangzhou (0.8978), whereas the weakest linkage was observed from Macao SAR to Zhaoqing (0.4993). The results derived from social network analysis indicate an east–west cross-regional linkage pattern, with most cities serving as bridging nodes in the network. Natural factors, including temperature and precipitation, as well as socioeconomic factors, including economic development and population density, were significantly associated with the formation of intercity PM2.5 pollution linkages. These findings highlight the need for an integrated governance approach that combines source-oriented control, coordinated management of intercity PM2.5-related linkages, and public participation to improve collaborative PM2.5 pollution management in the Greater Bay Area. Full article

This work reports, for the first time, the use of agave bagasse from “Tequila Weber Var” as an efficient and eco-friendly template for the microwave-assisted solvothermal synthesis of C@TiO₂ photoelectrodes. The characterization of the C@TiO₂ materials was performed using composition and elemental analysis, diffuse reflectance/UV-visible spectroscopy, N₂ adsorption/desorption isotherms, scanning and transmission electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction patterns, cyclic voltammetry, impedance spectroscopy, and variations of the open-circuit potential in a conventional electrochemical cell. Three 1:1, 4:1, and 8:1 agave:Ti volume ratios were used to explore the influence of carbon content upon the optical and photoelectric properties of TiO₂. The composite with a 1:1 ratio showed a charge transfer kinetic capacity of 0.86 C·cm⁻²·s⁻¹ with the highest current density flow of 2.2 mA·cm⁻², and the lowest optical band gap (E_bg) value of 2.92 eV, boosting the optoelectronic behavior of TiO₂. The photoanode composed of FTO/C@TiO₂ with the hybrid material with a 1:1 ratio was preliminarily evaluated in a photovoltaic solar cell, showing a light-to-electricity conversion efficiency higher than the other two composites and up to 12.5 times higher than the photoanode only composed of neat TiO₂. The present results contribute to the state-of-the-art of eco-friendly organic–inorganic thin film photoelectrodes for the sustainable synthesis of third-generation solar cells using bagasse-derived waste as an efficient carbon source for the synthesis of hybrid photoactive semiconductors. Full article