Search Results (62)

Background and Clinical Significance: Cerebral amyloid angiopathy (CAA) is a neurovascular disorder characterized by the deposition of amyloid beta (Aβ) peptides within the walls of small-to-medium-sized cerebral vessels, leading to vascular fragility and an increased risk of lobar intracerebral hemorrhage (ICH), cognitive decline, and recurrent stroke. CAA is an important cause of spontaneous ICH in elderly patients and may be underrecognized, particularly when presenting with acute neurologic symptoms that mimic ischemic stroke. Early identification has significant implications for management, prognosis, and secondary prevention. Case Presentation: An 80-year-old male presented to the emergency department with incoherent speech, rambling, and severe headache concerning for acute stroke. His medical history was notable for a prior cerebrovascular accident, hypertension, diabetes mellitus, benign prostatic hyperplasia, and recent evaluation for dementia-like symptoms. Initial neuroimaging revealed a 3.2 cm intraparenchymal hemorrhage in the left occipital lobe with surrounding edema. Subsequent MRI demonstrated a lobar hemorrhage pattern suggestive of CAA based on imaging findings and clinical context. The patient was admitted to the intensive care unit (ICU) for close neurologic monitoring. He remained hemodynamically stable with no new motor or sensory deficits. Over a three-day hospital course, his speech and visual deficits improved. Blood pressure was carefully controlled, and repeat imaging demonstrated stable hemorrhage without progression. He was diagnosed with probable CAA and discharged home with supportive services. Conclusions: This case highlights the importance of considering cerebral amyloid angiopathy in elderly patients presenting with spontaneous lobar intracerebral hemorrhage and cognitive symptoms. Prompt recognition and appropriate neuroimaging are critical for diagnosis, risk stratification, and guiding management. Full article

Large language models (LLMs) offer new possibilities for natural-language interaction with geospatial analysis systems, but their use in remote sensing instrument data analysis remains limited by weak execution control, poor reproducibility, and limited integration with domain-specific computation. The paper presents an agent for Incoherent Doppler wind LiDAR (ICDL) data analysis, named ICDL-Agent, a tool-augmented LLM framework for remote sensing instrument workflows. The system maps conversational user requests to executable analysis pipelines for wind retrieval, uncertainty estimation, visualization, and higher-level diagnostics through structured planning over a registry of domain-specific tools. To improve execution reliability, the system combines schema-constrained workflow generation, shared-state reuse of intermediate scientific products, and validation with bounded repair. In addition to supporting routine LiDAR processing, the framework can generate new tools when required and adapt to related analytical tasks through domain-aware guidance and procedural documentation. We evaluate the system on multiple atmospheric wind-observation datasets in China and show that it faithfully reproduces the refined Doppler wind-retrieval pipeline, achieving representative R²/MAE values of 0.52/3.73 m/s against ERA5 and 0.80/2.31 m/s against radiosonde observations, while supporting downstream analyses such as profile comparison, climatological interpretation, and gravity-wave diagnostics. More broadly, this study demonstrates how constrained LLM orchestration can support LiDAR researchers, remote-sensing instrument teams, and geospatial analysts seeking transparent, reproducible, and automated scientific data-processing workflows. Full article

This study investigates the seismic response of a natural draft reinforced concrete cooling tower subjected to spatially varying earthquake ground motion, with particular emphasis on nonlinear material behavior, damage evolution, and stiffness degradation. The analysis is based on a constitutive description of concrete using the Concrete Damaged Plasticity (CDP) model, enabling the representation of tensile cracking, compressive crushing, and irreversible plastic deformation under cyclic dynamic loading. Two structural configurations of the lower shell region–a locally thickened shell and a bottom ring-stiffened solution–are examined from the perspective of material performance and damage control. Spatially varying seismic excitation is defined using a real earthquake record from the Carpathian Flysch region, with wave passage and incoherence effects calibrated from in-situ measurements. Nonlinear time-history analyses, performed to capture the coupling between material degradation mechanisms and global structural response, demonstrate that the seismic performance of the cooling tower is controlled primarily by local material behavior rather than global dynamic characteristics. Spatial variability of ground motion activates complex deformation modes, leading to pronounced tensile damage, plastic strain accumulation, and stiffness degradation in the lower shell region. The structural variant with thickened lower zone of the shell exhibits extensive material deterioration, including the formation of a continuous plastic zone and irreversible deformation associated with damage localization. In contrast, the ring-stiffened configuration effectively limits damage propagation, reduces plastic strain by up to 80%, and maintains predominantly elastic material response with significantly lower stiffness degradation. The bottom ring stiffener is shown to provide superior performance by mitigating damage evolution of the concrete structure under spatially non-uniform seismic loading. The study highlights the critical role of advanced constitutive material modeling in capturing the realistic seismic behavior of reinforced concrete shell structures and demonstrates that structural strengthening strategies should be evaluated based on their ability to control material degradation mechanisms. Full article

Smooth window functions that restrict field actions to finite spacetime domains appear throughout quantum field theory, quantum optics, and open quantum systems, wherever interactions are switched on and off, detectors couple for finite durations, or systems decohere within bounded regions. When such a window function $◊\left(x\right)$ is introduced into the matter action of a covariant field theory, two structural consequences are unavoidable: the windowed Ward identities acquire boundary layer corrections confined to the window transition region, and the contracted Bianchi identity requires a compensating stress-energy contribution at the window boundary. Both consequences follow from the product rule of covariant differentiation and are independent of any specific physical motivation for the window. The present paper develops these consequences systematically for each sector of the Standard Model in curved spacetime. The windowed action prescription is applied to Dirac fermions, complex scalar fields, Maxwell theory, and the complete $SU{\left(3\right)}_c\times SU{\left(2\right)}_L\times U{\left(1\right)}_Y$ gauge Lagrangian. Each sector is shown to recover standard curved spacetime quantum field theory exactly within the localization window, with all deviations confined to a boundary layer whose thickness is set by the applicable operational localization scale—including decoherence, detector resolution, generalized uncertainty, or clock-precision bounds as appropriate. A Noether analysis yields windowed Ward identities of the form ${\nabla }_{\mu }(◊J^{\mu })=0$: gauge invariance and Lorentz symmetry are preserved exactly within the window, and apparent non-conservation is a kinematic boundary effect structurally identical to the open-system flux terms that arise when tracing over environmental degrees of freedom. The non-local boundary term $T_{\mu \nu }^{\mathrm{nl}}$ required by the Bianchi identity decomposes as $T_{\mu \nu }^{\mathrm{nl}}=T_{\mu \nu }^{\mathrm{comp}}+T_{\mu \nu }^{\mathrm{Rem}}$, where $T_{\mu \nu }^{\mathrm{comp}}$ is the boundary layer compensator and $T_{\mu \nu }^{\mathrm{Rem}}$ is its macroscopic coarse-grained remnant in the high-localization-density regime. A formal lemma establishes that, under stated regularity, phase-incoherence, finite-correlation-length, and variance-control assumptions, $T_{\mu \nu }^{\mathrm{comp}}$ vanishes upon coarse-graining for ordinary quantum fields, so standard field evolution leaves no macroscopic stress-energy remnant. The sharp-window limit recovers the Israel junction conditions exactly, and the smooth-window generalization is structurally identical to the Ashtekar–Krishnan dynamical horizon flux balance laws. The generalized uncertainty principle (GUP), extended uncertainty principle (EUP), relativistic GUP (RGUP), and Salecker–Wigner clock bounds constrain only the admissible operational thickness of the window boundary layer, $ϵ$, and do not alter the product rule origin of the windowed Ward identities or the Bianchi-required compensator. Full article

Multi-label emotion detection (MLED) suffers from long-tailed label distributions and structured inter-label correlations, which jointly suppress rare label recall and yield incoherent predictions. We present Graph Neural Network-Enhanced Transformer with Hybrid Loss Weighting (GTHL-Emo), a unified framework that addresses both challenges without heavy additional machinery. First, an adaptive imbalance-aware training scheme combines binary cross-entropy, asymmetric focal, and pairwise ranking losses under a learned batch-wise controller, emphasizing rare labels while stabilizing thresholding. Second, a lightweight correlation alignment module learns transformer-based label embeddings and aligns their predicted affinities with empirical co-occurrence via Kullback–Leibler (KL) regularization, smoothing rare label predictions through correlated frequent labels. A transformer encoder with learnable attention pooling provides semantic representations, and a dynamic GraphSAGE layer captures inter-instance structural dependencies. Comprehensive evaluation across three Arabic benchmarks—SemEval-2018-Ec-Ar, ExaAEC, and SemEval-2025 (Track A, Arq)—demonstrates competitive or leading performance. On SemEval-2018-Ec-Ar, GTHL-Emo attained a Jaccard accuracy of 58.70%, micro-F1 score of 71.02%, and macro-F1 score of 60.48%. On ExaAEC, it achieved a Jaccard accuracy of 65.99%, micro-F1 score of 70.72%, and macro-F1 score of 68.71%. On SemEval-2025-Arq, it obtained a Jaccard accuracy of 41.47%, micro-F1 score of 56.78%, and macro-F1 score of 56.69%. Ablation studies revealed that the GraphSAGE structure and ranking loss contributed most significantly (1.45% and 1.46% Jaccard accuracy drops, respectively), while label correlation alignment provided consistent improvements across the scales. These findings demonstrate that jointly optimizing imbalance-aware objectives and label dependencies yields robust Arabic MLED with minimal overhead. Full article

Mild traumatic brain injury (mTBI) frequently causes subtle brain changes that are difficult to detect with conventional diagnostic approaches. In this exploratory pilot study, we combined tri-exponential intravoxel incoherent motion (IVIM) and pseudocontinuous arterial spin labeling (pCASL) MRI with Multimodal Canonical Correlation Analysis and joint independent component analysis (mCCA+jICA) to identify imaging signatures distinguishing mTBI patients from healthy controls (HCs) and their associations with clinical function. Cerebral blood flow (CBF) and IVIM-derived metrics were extracted from 90 brain regions in 19 mTBI patients and 24 HCs, and multivariate components were identified using mCCA+jICA. Two independent components (IC2, IC15) showed group differences at the uncorrected level (p < 0.05) but did not survive false discovery rate (FDR) correction. IC2 correlated positively with CBF and perfusion fraction (F_p) and negatively with tissue diffusion fraction (F_s), consistent with reduced vascular integrity in mTBI, while IC15 showed similar trends. One component correlated with Glasgow Outcome Scale–Extended (GOS-E) scores (uncorrected p = 0.046). Although this study is preliminary and limited by a small sample size, our findings suggest that mTBI is associated with perfusion and microstructural alterations, particularly in subcortical regions, and demonstrate the potential value of combining IVIM and ASL within multivariate fusion frameworks to reveal patterns not captured by single-modality approaches. Full article

Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potential to exploit high-dimensional feature maps, entanglement-enabled correlations, and non-classical priors. Yet, practical realizations remain constrained by the Noisy Intermediate-Scale Quantum (NISQ) era, where limited qubit counts, gate errors, and coherence losses necessitate frugal, noise-aware strategies. The Data Re-Uploading (DRU) algorithm has emerged as a strong NISQ-compatible candidate, offering universal classification capabilities with minimal qubit requirements. While DRU has been experimentally demonstrated on ion-trap, photonic, and superconducting platforms, no implementations exist for spin-based quantum processing units (QPU-SBs), despite their scalability potential via CMOS-compatible fabrication and recent demonstrations of multi-qubit processors. Here, we present a pulse-level, noise-aware DRU framework for spin-based QPUs, designed to bridge the gap between gate-level models and realistic spin-qubit execution. Our approach includes (i) compiling DRU circuits into hardware-proximate, time-domain controls derived from the Loss–DiVincenzo Hamiltonian, (ii) explicitly incorporating coherent and incoherent noise sources through pulse perturbations and Lindblad channels, (iii) enabling systematic noise-sensitivity studies across one-, two-, and four-spin configurations via continuous-time simulation, and (iv) developing a noise-aware training pipeline that benchmarks gate-level baselines against spin-level dynamics using information-theoretic loss functions. Numerical experiments show that our simulations reproduce gate-level dynamics with fidelities near unity while providing a richer error characterization under realistic noise. Moreover, divergence-based losses significantly enhance classification accuracy and robustness compared to fidelity-based metrics. Together, these results establish the proposed framework as a practical route for advancing DRU on spin-based platforms and motivate future work on error-attentive training and spin–quantum-dot noise modeling. Full article

Multi-wavelength random lasers with switchable modes have advantages in the fields of novel light source and information security. Here, we propose a dual-wavelength phase transition random laser, which can modulate lasing modes arbitrarily assisted by the phase transition hydrogel. Once the phase transition occurs in hydrogel, the scattering properties of light in the random system changes, affecting the optical feedback mechanism and enabling reversible switching of the dual-wavelength random laser mode between incoherent and coherent states. More appealing, random lasing mixed incoherent mode and coherent mode have been obtained for the first time by controlling the local phase transition of the sample. Based on these properties, an information encryption system is constructed by encoding spectral fingerprints at different modes. This work provides an effective way to precisely control the output modes at different wavelengths in the multi-wavelength random laser, further expanding the application of random lasers in multifunctional light sources, color imaging, and information safety. 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

This study presents a comprehensive statistical analysis of ionospheric midnight collapse events over Arecibo, based on incoherent scatter radar (ISR) observations collected between 1971 and 2019. A total of 224 nights with valid measurements were examined to characterize the timing, intensity, and seasonal variation of these collapse events. The results showed that midnight collapses occurred on 94.6% of the nights, with the highest occurrence rate observed during spring and winter. The first collapse typically began between 22:00 and 00:00 LT, lasted for 1–4 h, initiated at altitudes between 350 and 400 km, and involved a vertical collapse of 50–100 km. A second collapse was identified on 18.8% of nights, occurring predominantly between 01:00 and 02:00 LT, with a notably higher frequency during winter. Compared to the first collapse, the second collapse tended to originate at lower altitudes and exhibited faster collapse rates. Seasonal patterns in the vertical ion drift ($V_z$) were also identified, with winter events characterized by a persistently downward $V_z$ throughout the night. Further decomposition of $V_z$ into field-aligned ($V_{ap}$) and perpendicular ($V_{pn}$) components indicated that $V_{ap}$ played a dominant role in modulating $V_z$, particularly on nights with double collapses. Analysis of meridional wind variations revealed that nighttime changes in $V_{ap}$ were largely controlled by meridional wind, suggesting a strong coupling between thermospheric wind dynamics and field-aligned ion motion. These findings suggest that variations in $V_z$, primarily driven by meridional-wind-controlled changes in $V_{ap}$, are a key driver of ionospheric midnight collapse events at Arecibo. Full article

This paper explores the application of transform-domain sparsification and compressed sensing (CS) techniques to improve the efficiency and quality of magnetic resonance imaging (MRI). We implement and evaluate three sparsifying methods—discrete wavelet transform (DWT), fast Fourier transform (FFT), and discrete cosine transform (DCT)—which are used to simulate subsampled reconstruction via inverse transforms. Additionally, one accurate CS reconstruction algorithm, basis pursuit (BP), using the L₁-MAGIC toolbox, is implemented as a benchmark based on convex optimization with L₁-norm minimization. Emphasis is placed on basis pursuit (BP), which satisfies the formal requirements of CS theory, including incoherent sampling and sparse recovery via nonlinear reconstruction. Each method is assessed in MATLAB R2024b using standardized DICOM images and varying sampling rates. The evaluation metrics include peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index measure (SSIM), execution time, memory usage, and compression efficiency. The results show that although discrete cosine transform (DCT) outperforms the others under simulation in terms of PSNR and SSIM, it is inconsistent with the physics of MRI acquisition. Conversely, basis pursuit (BP) offers a theoretically grounded reconstruction approach with acceptable accuracy and clinical relevance. Despite the limitations of a controlled experimental setup, this study establishes a reproducible benchmarking framework and highlights the trade-offs between the quality of transform-based reconstruction and computational complexity. Future work will extend this study by incorporating clinically validated CS algorithms with L₀ and nonconvex Lp (0 < p < 1) regularization to align with state-of-the-art MRI reconstruction practices. Full article

Background: Multiple sclerosis (MS) is a chronic and heterogeneous disease characterized by demyelination and axonal loss and damage. Magnetic resonance imaging (MRI) has been employed to distinguish these changes in various types of MS lesions. Objectives: We aimed to evaluate intravoxel incoherent motion (IVIM) diffusion and perfusion MRI metrics across different brain regions in healthy individuals and various types of MS lesions, including enhanced, non-enhanced, and black hole lesions. Methods: A prospective study included 237 patients with MS (65 males and 172 females) and 29 healthy control participants (25 males and 4 females). The field strength was 1.5 Tesla. The imaging sequences included three-dimensional (3D) T₁, 3D fluid-attenuated inversion recovery, two-dimensional (2D) T₁, T₂-weighted imaging, and 2D diffusion-weighted imaging (DWI) sequences. IVIM-derived parameters—apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion (D*), and perfusion fraction (f)—were quantified for commonly observed lesion types (2506 lesions from 224 patients with MS, excluding 13 patients due to MRI artifacts or not meeting the diagnostic criteria for RR-MS) and for corresponding brain regions in 29 healthy control participants. A one-way analysis of variance, followed by post-hoc analysis (Tukey’s test), was performed to compare mean values between the healthy and MS groups. Receiver operating characteristic curve analyses, including area under the curve, sensitivity, and specificity, were conducted to determine the cutoff values of IVIM parameters for distinguishing between the groups. A p-value of ≤0.05 and 95% confidence intervals were used to report statistical significance and precision, respectively. Results: All IVIM parametric maps in this study discriminated among most MS lesion types. ADC, D, and D* values for MS black hole lesions were significantly higher (p < 0.0001) than those for other MS lesions and healthy controls. ADC, D, and D* maps demonstrated high sensitivity and specificity, whereas f maps exhibited low sensitivity but high specificity. Conclusions: IVIM parameters provide valuable diagnostic and clinical insights by demonstrating high sensitivity and specificity in evaluating different categories of MS lesions. Full article

Collision models have attracted significant attention in recent years due to their versatility to simulate open quantum systems in different dynamical regimes. They have been used to study various interesting phenomena such as the dynamical emergence of non-Markovian memory effects and the spontaneous establishment of synchronization in open quantum systems. In such models, the repeated pairwise interactions between the system and the environment and also the possible coupling between different environmental units are typically modeled using the coherent partial SWAP (PSWAP) operation as it is known to be a universal homogenizer. In this study, we investigate the dynamical behavior of incoherent collision models, where the interactions between different units are modeled by the incoherent controlled SWAP (CSWAP) operation, which is also a universal homogenizer. Even though the asymptotic dynamics of the open system in cases of both coherent and incoherent swap interactions appear to be identical, its transient dynamics turns out to be significantly different. Here, we present a comparative analysis of the consequences of having coherent or incoherent couplings in collision models, namely, PSWAP or CSWAP interactions, respectively, for the emergence of memory effects for a single-qubit system and for the onset synchronization between a pair of qubits, both of which are strictly determined by the transient dynamics of the open system. Full article

Due to their inherent advantages, optical fiber sensors (OFSs) can substantially contribute to the monitoring and performance enhancement of energy infrastructure. However, optical fiber sensor systems often are standalone solutions and do not connect to the main energy infrastructure control systems. In this paper, we propose a solution for the digitalization of an optical fiber sensor system realized by the Open Platform Communications Unified Architecture (OPC UA) protocol and the Internet of Things (IoT) platform Insights Hub. The optical fiber sensor system is based on bidirectional incoherent optical frequency domain reflectometry (biOFDR) and is used for the interrogation of fiber Bragg grating (FBG) arrays. To allow for an automated sensor identification and thus measurement procedure, an optical sensor identification marker based on a unique combination of fiber Bragg gratings (FBGs) is established. To demonstrate the abilities of the digitalized sensor network, a field test was performed in a power plant test facility of Siemens Energy. Temperature measurements of a packaged FBG sensor fiber were performed with a portable demonstrator, illustrating the system’s robustness and the comprehensive data processing stream from sensor value formation to the cloud. The realized network services promote sensor data quality, fusion, and modeling, expanding opportunities using digital twin technology. Full article

The present work is motivated by the necessity of handling and controlling three-dimensional polarization states, whose appropriate preparation has increasing interest in areas like nanotechnologies, quantum computing and near-field phenomena. By virtue of the so-called characteristic decomposition, any polarization state of light can be represented as an incoherent superposition of a pure state, a fully unpolarized state and a discriminating state. The discriminating component has nonzero spin in general, in which case the state is said to be nonregular. A simple procedure to transform an arbitrary nonregular state to a regular one through its incoherent composition with a pure state is described, resulting in a state that lacks a discriminating component. In addition, a method to suppress the spin vector of any given polarization state through its incoherent combination with a circularly polarized pure state is presented. Both approaches allow for the configuration of polarization states with simple features. Full article