Explore the vast range of titles available.

A bstract We investigate the gravitational form factors (GFFs) of the nucleon and the baryon octet, decomposed into their flavor components, utilizing a pion mean-field approach grounded in the large N c limit of Quantum Chromodynamics (QCD). Our focus is on the contributions from the twist-2 operators to the flavor-triplet and octet GFFs, and we decompose the mass, angular momentum, and D -term form factors of the nucleon into their respective flavors. The strange quark contributions are found to be relatively mild for the mass and angular momentum form factors, while providing significant corrections to the D -term form factor. In the course of examining the flavor decomposition of the GFFs, we uncover that the effects of twist-4 operators play a crucial role. While the gluonic contributions are suppressed by the packing fraction of the instanton vacuum in the twist-2 case, contributions from twist-4 operators are of order unity, necessitating its explicit consideration.

Transcranial focused ultrasound (FUS) is making progress as a new non-invasive mode of regional brain stimulation. Current evidence of FUS-mediated neurostimulation for humans has been limited to the observation of subjective sensory manifestations and electrophysiological responses, thus warranting the identification of stimulated brain regions. Here, we report FUS sonication of the primary visual cortex (V1) in humans, resulting in elicited activation not only from the sonicated brain area, but also from the network of regions involved in visual and higher-order cognitive processes (as revealed by simultaneous acquisition of blood-oxygenation-level-dependent functional magnetic resonance imaging). Accompanying phosphene perception was also reported. The electroencephalo graphic (EEG) responses showed distinct peaks associated with the stimulation. None of the participants showed any adverse effects from the sonication based on neuroimaging and neurological examinations. Retrospective numerical simulation of the acoustic profile showed the presence of individual variability in terms of the location and intensity of the acoustic focus. With exquisite spatial selectivity and capability for depth penetration, FUS may confer a unique utility in providing non-invasive stimulation of region-specific brain circuits for neuroscientific and therapeutic applications.

The prevalence of wildfires presents significant challenges for fire detection systems, particularly in differentiating fire from complex backgrounds and maintaining detection reliability under diverse environmental conditions. It is crucial to address these challenges for developing sustainable and effective fire detection systems. In this paper: (i) we introduce a channel-wise attention-based architecture, achieving 95% accuracy and demonstrating an improved focus on flame-specific features critical for distinguishing fire in complex backgrounds. Through ablation studies, we demonstrate that our channel-wise attention mechanism provides a significant 3–5% improvement in accuracy over the baseline and state-of-the-art fire detection models; (ii) evaluate the impact of augmentation on fire detection, demonstrating improved performance across varied environmental conditions; (iii) comprehensive evaluation across color spaces including RGB, Grayscale, HSV, and YCbCr to analyze detection reliability; and (iv) assessment of model vulnerabilities where Fast Gradient Sign Method (FGSM) perturbations significantly impact performance, reducing accuracy to 41%. Using Local Interpretable Model-Agnostic Explanations (LIME) visualization techniques, we provide insights into model decision-making processes across both standard and adversarial conditions, highlighting important considerations for fire detection applications.

Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area of the input sample and moves across the sample to provide a feature map for the subsequent layers. In our study, we hypothesized that a 3D-CNN model with down-sampling operations such as pooling and/or stride would have the ability to extract robust feature maps from the shifted and scaled neuronal activations in a single functional MRI (fMRI) volume for the classification of task information associated with that volume. Thus, the 3D-CNN model would be able to ameliorate the potential misalignment of neuronal activations and over-/under-activation in local brain regions caused by imperfections in spatial alignment algorithms, confounded by variability in blood-oxygenation-level-dependent (BOLD) responses across sessions and/or subjects. To this end, the fMRI volumes acquired from four sensorimotor tasks (left-hand clenching, right-hand clenching, auditory attention, and visual stimulation) were used as input for our 3D-CNN model to classify task information using a single fMRI volume. The classification performance of the 3D-CNN was systematically evaluated using fMRI volumes obtained from various minimal preprocessing scenarios applied to raw fMRI volumes that excluded spatial normalization to a template and those obtained from full preprocessing that included spatial normalization. Alternative classifier models such as the 1D fully connected DNN (1D-fcDNN) and support vector machine (SVM) were also used for comparison. The classification performance was also assessed for several k-fold cross-validation (CV) schemes, including leave-one-subject-out CV (LOOCV). Overall, the classification results of the 3D-CNN model were superior to that of the 1D-fcDNN and SVM models. When using the fully-processed fMRI volumes with LOOCV, the mean error rates (± the standard error of the mean) for the 3D-CNN, 1D-fcDNN, and SVM models were 2.1% (± 0.9), 3.1% (± 1.2), and 4.1% (± 1.5), respectively (p = 0.041 from a one-way ANOVA). The error rates for 3-fold CV were higher (2.4% ± 1.0, 4.2% ± 1.3, and 10.1% ± 2.0; p < 0.0003 from a one-way ANOVA). The mean error rates also increased considerably using the raw fMRI 3D volume data without preprocessing (26.2% for the 3D-CNN, 75.0% for the 1D-fcDNN, and 75.0% for the SVM). Furthermore, the ability of the pre-trained 3D-CNN model to handle shifted and scaled neuronal activations was demonstrated in an online scenario for five-class classification (i.e., four sensorimotor tasks and the resting state) using the real-time fMRI of three participants. The resulting classification accuracy was 78.5% (± 1.4), 26.7% (± 5.9), and 21.5% (± 3.1) for the 3D-CNN, 1D-fcDNN, and SVM models, respectively. The superior performance of the 3D-CNN compared to the 1D-fcDNN was verified by analyzing the resulting feature maps and convolution filters that handled the shifted and scaled neuronal activations and by utilizing an independent public dataset from the Human Connectome Project.

The cryogenic pump is a crucial component of the magnetic resonance imaging (MRI) system for delivering liquid helium to a magnet for superconductivity, thereby generating a mechanical vibration. Thus, the cryogenic pump for liquid helium (helium pump) contaminates the electroencephalography (EEG) simultaneously acquired with functional MRI. The recursive approach of EEG‐segment‐based principal component analysis (rsPCA) has recently demonstrated its efficacy in removing this helium pump artefact. In the rsPCA, the recursion depth and EEG‐segment size are crucial hyperparameters. rsPCA's performance and computational time across recursion depth (1, 2, 3) and segment sizes (165, 220, 265) are systematically evaluated. It is found that the recursion depth of 2 yielded significant reductions in the computational time compared to the depth of 3 across all segment sizes while maintaining the denoising performance. The binary classification performance (left‐hand versus right‐hand clenching) was also enhanced in this scenario (especially the use of EEG‐segment size of 165 and 220) by using EEG gamma‐band activity (30–50 Hz), which is predominantly contaminated by the helium‐pump artefact.

Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging companies or new startups in recent years. As crowdfunding prevails, it is also under substantial risk of the occurrence of fraud. Though a growing number of articles indicate that crowdfunding scams are a new imminent threat to investors, little is known about them primarily due to the lack of measurement data collected from real scam cases. This paper fills the gap by collecting, labeling, and analyzing publicly available data of a hundred fraudulent campaigns on a crowdfunding platform. In order to find and understand distinguishing characteristics of crowdfunding scams, we propose to use a broad range of traits including project-based traits, project creator-based ones, and content-based ones such as linguistic cues and Named Entity Recognition features, etc. We then propose to use the feature selection method called Forward Stepwise Logistic Regression, through which 17 key discriminating features (including six original and hitherto unused ones) of scam campaigns are discovered. Based on the selected 17 key features, we present and discuss our findings and insights on distinguishing characteristics of crowdfunding scams, and build our scam detection model with 87.3% accuracy. We also explore the feasibility of early scam detection, building a model with 70.2% of classification accuracy right at the time of project launch. We discuss what features from which sections are more helpful for early scam detection on day 0 and thereafter.

We investigate the electromagnetic form factors of the baryon decuplet within the framework of the \\[\\mathrm {SU(3)}\\] self-consistent chiral quark-soliton model, taking into account the \\[1/N_c\\] rotational corrections and the effects of flavor \\[\\mathrm {SU(3)}\\] symmetry breaking. We first examine the valence- and sea-quark contributions to each electromagnetic form factor of the baryon decuplet and then the effects of the flavor SU(3) symmetry breaking. We also compute the charge radii, the magnetic radii, the magnetic dipole moments and the electric quadrupole moments, comparing their results with those from other theoretical works. We also make a chiral extrapolation to compare the present results with the lattice data in a more quantitative manner. The results show in general similar tendency to the lattice results. In particular, the results of the M1 and E2 form factors are in good agreement with those of lattice QCD.

Efficient transport of solutes in the cerebrospinal fluid (CSF) plays a critical role in their clearance from the brain. Convective bulk flow of solutes in the CSF in the perivascular space (PVS) is considered one of the important mechanisms behind solute movement in the brain, before their ultimate drainage to the systemic lymphatic system. Acoustic pressure waves can impose radiation force on a medium in its path, inducing localized and directional fluidic flow, known as acoustic streaming. We transcranially applied low-intensity focused ultrasound (FUS) to rats that received an intracisternal injection of fluorescent CSF tracers (dextran and ovalbumin, having two different molecular weights–M w ). The sonication pulsing parameter was determined on the set that propelled the aqueous solution of toluidine blue O dye into a porous media (melamine foam) at the highest level of infiltration. Fluorescence imaging of the brain showed that application of FUS increased the uptake of ovalbumin at the sonicated plane, particularly around the ventricles, whereas the uptake of high-M w dextran was unaffected. Numerical simulation showed that the effects of sonication were non-thermal. Sonication did not alter the animals’ behavior or disrupt the blood-brain barrier (BBB) while yielding normal brain histology. The results suggest that FUS may serve as a new non-invasive means to promote interstitial CSF solute transport in a region-specific manner without disrupting the BBB, providing potential for enhanced clearance of waste products from the brain.

We report that regions‐of‐interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed‐effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). A real‐world dataset for the neuronal response to nicotine use was acquired using a custom‐made MRI‐compatible apparatus for the smoking of electronic cigarettes (e‐cigarettes). Nineteen participants smoked e‐cigarettes in an MRI scanner using the apparatus with two experimental conditions: e‐cigarettes with nicotine (ECIG) and sham e‐cigarettes without nicotine (SCIG) and subjective ratings were collected. The right insula was identified in the ECIG condition from the χ2‐test of the MEMA but not from the t‐test, and the corresponding activations were significantly associated with the similarity scores (r = −.52, p = .041, confidence interval [CI] = [−0.78, −0.17]) and the urge‐to‐smoke scores (r = .73, p <.001, CI = [0.52, 0.88]). From the contrast between the two conditions (i.e., ECIG > SCIG), the right orbitofrontal cortex was identified from the χ2‐tests, and the corresponding neuronal activations showed a statistically meaningful association with similarity (r = −.58, p = .01, CI = [−0.84, −0.17]) and the urge to smoke (r = .34, p = .15, CI = [0.09, 0.56]). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was further evaluated using the fMRI and behavioral data acquired from the working memory and gambling tasks available from the Human Connectome Project. We report that regions‐of‐interest (ROIs) associated with idiosyncratic individual behavior can be identified from functional magnetic resonance imaging (fMRI) data using statistical approaches that explicitly model individual variability in neuronal activations, such as mixed‐effects multilevel analysis (MEMA). We also show that the relationship between neuronal activation in fMRI and behavioral data can be modeled using canonical correlation analysis (CCA). The validity of our analysis pipeline (i.e., MEMA followed by CCA) was evaluated using the fMRI and behavioral data in a small dataset from our nicotine craving experiment and a large dataset from the Human Connectome Project.

Transport of interstitial fluid and solutes plays a critical role in clearing metabolic waste from the brain. Transcranial application of focused ultrasound (FUS) has been shown to promote localized cerebrospinal fluid solute uptake into the brain parenchyma; however, its effects on the transport and clearance of interstitial solutes remain unknown. We demonstrate that pulsed application of low-intensity FUS to the rat brain enhances the transport of intracortically injected fluorescent tracers (ovalbumin and high molecular-weight dextran), yielding greater parenchymal tracer volume distribution compared to the unsonicated control group (ovalbumin by 40.1% and dextran by 34.6%). Furthermore, FUS promoted the drainage of injected interstitial ovalbumin to both superficial and deep cervical lymph nodes (cLNs) ipsilateral to sonication, with 78.3% higher drainage observed in the superficial cLNs compared to the non-sonicated hemisphere. The application of FUS increased the level of solute transport visible from the dorsal brain surface, with ~ 43% greater area and ~ 19% higher fluorescence intensity than the unsonicated group, especially in the pial surface ipsilateral to sonication. The sonication did not elicit tissue-level neuronal excitation, measured by an electroencephalogram, nor did it alter the molecular weight of the tracers. These findings suggest that nonthermal transcranial FUS can enhance advective transport of interstitial solutes and their subsequent removal in a completely non-invasive fashion, offering its potential non-pharmacological utility in facilitating clearance of waste from the brain.