Effects Of Dimensions Research Articles

Non-compact extra dimensions and flavor dependence of cc̄ and bb̄ mesons masses in a hot QCD medium with lattice, LO and NLO parametrizations of the Debye mass

In this paper, we consider a heavy quarkonium state in a hot QCD medium, namely the ground state of charmonium and bottomonium. The quark–antiquark potential is proposed in the form of a temperature-dependent Cornell potential, and is extended to include a temperature-dependent parameter. Then, for the proposed potential with different parametrizations of the Debye mass, the N-dimensional Schrödinger equation is analytically solved using the biconfluent Heun equation. The obtained energy eigenvalues and wave functions are employed to study the flavor dependence, the effect of dimensionality and the influence of the additional term on the mass and binding energy of 1S state of charmonium and bottomonium at finite temperature. The findings of this research are compared to those from past studies. We concluded that this work provides a simple and consistent description of quarkonium states at finite temperature in a hot QCD medium, highlighting the effect of noncompact extra dimensions and the flavor dependence of the binding energies of heavy quarkonia.

Exploring Brain Imaging and Genetic Risk Factors in Different Progression States of Alzheimer's Disease Through OSnetNMF-Based Methods.

Alzheimer's disease (AD) is a neurodegenerative disease with no effective treatment, often preceded by mild cognitive impairment (MCI). Multimodal imaging genetics integrates imaging and genetic data to gain a deeper understanding of disease progression and individual variations. This study focuses on exploring the mechanisms that drive the transition from normal cognition to MCI and ultimately to AD. As an effective joint feature extraction and dimensionality reduction method, non-negative matrix factorization (NMF) and its improved variants, particularly the network-based non-negative matrix factorization (netNMF), have been widely used in multimodal analysis to mine brain imaging and genetic data by considering the interactions between different features. However, many of these methods overlook the importance of the coefficient matrix and do not address issues related to data accuracy and feature redundancy. To address these limitations, we propose an orthogonal sparse network non-negative matrix factorization (OSnetNMF) algorithm, which introduces orthogonal and sparse constraints based on netNMF. By establishing linear relationships between structural magnetic resonance imaging (sMRI) and corresponding gene expression data, OSnetNMF reduces feature redundancy and decreases correlation between data, resulting in more accurate and reliable biomarker extraction. Experiments demonstrate that the OSnetNMF algorithm can accurately identify risk regions of interest (ROIs) and key genes that characterize AD progression, revealing significant trends in ROI pairs such as l4thVen-HIF1A, rBst-MPO, and rBst-PTK2B. Comparative experiments show that the improved algorithm outperforms traditional methods, identifying more disease-related biomarkers and achieving better reconstruction performance.

Band Selection Algorithm Based on Multi-Feature and Affinity Propagation Clustering

Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification and recognition of these images present significant challenges. In this paper, we propose a band selection method (GE-AP) based on multi-feature extraction and the Affine Propagation Clustering (AP) algorithm for dimensionality reduction of hyperspectral images, aiming to improve classification accuracy and processing efficiency. In this method, texture features of the band images are extracted using the Gray-Level Co-occurrence Matrix (GLCM), and the Euclidean distance between bands is calculated. A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. Through simulation and comparison experiments evaluating the overall classification accuracy (OA) and Kappa coefficient, it was found that the GE-AP method achieves the highest OA and Kappa coefficient compared to three other methods, with maximum increases of 8.89% and 13.18%, respectively. This verifies that the proposed method outperforms traditional single-information methods in handling spatial and spectral redundancy between bands, demonstrating good adaptability and stability.

Direct Photocatalytic Oxidation of Methane to Formic Acid with High Selectivity via a Concerted Proton-Electron Transfer Process.

Light-driven direct conversion of methane to formic acid is a promising approach to convert methane to value-added chemicals and promote sustainability. However, this process remains challenging due to the complex requirements for multiple protons and electrons. Herein, we report the design of WO3-based photocatalysts modified with Pt active sites to address this challenge. We demonstrate that modulating the dimensional effect of Pt on the WO3 support is key to enhancing the catalytic performance of selective CH4-to-HCOOH conversion. The Pt nanoparticles on WO3 exhibit superior conversion rate, selectivity and durability in the production of HCOOH compared to the Pt-free sample and WO3 decorated with Pt single atoms. The optimal PtNPs-WO3 catalyst achieves a HCOOH conversion rate of 17.7 mmol g-1, with 84% selectivity and stability maintained for up to 48 h. Mechanistic studies show that the protonation of O2 to hydroxyl radicals is the limiting step for HCOOH yield. Pt nanoparticles can facilitate electron transfer and promote O2 dissociation, generating hydroxyl radicals via a proton-coupled electron transfer process. This process provides sufficient protons to lower the formation barrier for •OH radicals, thereby promoting the activation of CH4. In addition, Pt nanoparticles regulate the adsorption of oxygenated hydrocarbon intermediates, increasing the selectivity of the reaction. This work advances our understanding of catalyst design for methane conversion and the effective regulation of complex reaction pathways.

Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network

An intelligent modelling method driven by flow field images for predicting steady and unsteady flow filed around aerofoils has been developed. Signed Distance Field (SDF) images achieve dimensionality enhancement of aerofoil geometric information, and ‘synthesised images’ achieve dimensionality enhancement of the angle of attack of the aerofoil and Mach number. An intelligent aerodynamic model for steady flow field of aerofoils is constructed based on the U-Net neural network architecture, and further incorporating a long short-term memory (LSTM) module to construct a U-Net-LSTM neural network architecture to extract the temporal features. Typical NACA aerofoils results show that, the prediction error for steady flow is less than 1.98%, while the prediction error for unsteady flow is less than 2.56%. Additionally, the model demonstrates good generalization capability, with a generalization error for steady flow less than 2.45% and a generalization error for unsteady flow less than 3.34%. This research provides a new method for intelligent aerodynamic modelling based on physical representations. Compared to existing methods, this method avoids the need for extracting aerofoil geometry information and eliminates the necessity of predicting the flow field point by point, making it more concise and efficient. Highlights 1. An aerodynamic model was constructed using U-Net to rapidly predict the steady flow field around airfoils. 2. A Long Short-Term Memory (LSTM) module was incorporated to capture temporal information, enabling the rapid prediction of the unsteady flow field around airfoils. To address the problem of ‘dimension loss’ in the modelling datasets, effective data dimensionality enhancement was achieved using SDF images and ‘synthesized images’.

Effective dimensionality of space-time in f(R)-general relativity with quantum boundary terms included

I study an extended theory of General Relativity that incorporates normalized relativistic velocities, where the boundary terms in the varied f(R)-action are considered as a part of the physical system to be studied. Within this context, I investigate how the classical perturbations can be originated by quantum geometric perturbations, that alters the dynamics of the physical system without perturbations. To do it we use a modified nonlinear quantum algebra recently introduced. The quantum field perturbations of space-time also alter the geodesic equations that describe the dynamics of the relativistic velocities, the metric tensor and the Ricci tensor. But perhaps the most intriguing of this phenomena is that these space-time alterations change the effective number of space-time dimensions of the system with perturbations included N(z), in a such manner that N(z) can take an irrational value N(z=2)=8/(1+5)≃2.4721, when the nonlinear quantum perturbations of space-time are related to the relativistic velocities U¯μ. To illustrate the theory, we calculate exactly the cosmological parameter in an early inflationary universe.

Quasi-One-Dimensional Spin Dynamics in a Molecular Spin Liquid System.

The molecular triangular lattice system, β^{'}-EtMe_{3}Sb[Pd(dmit)_{2}]_{2}, is considered as a candidate material for the quantum spin liquid state, although ongoing debates arise from recent controversial results. Here, the results of electron spin resonance and muon-spin relaxation measurements on β^{'}-EtMe_{3}Sb[Pd(dmit)_{2}]_{2} are presented. Both results indicate characteristic behaviors related to quasi-one-dimensional spin dynamics, whereas the direction of anisotropy found in electron spin resonance is in contradiction with previous theories. We succeed in interpreting the experiments by combining density-functional theory calculations and analysis of the effective model taking into account the multiorbital nature of the system. While the quantum-spin-liquid-like origin of β^{'}-EtMe_{3}Sb[Pd(dmit)_{2}]_{2} was initially attributed to the magnetic frustration of the triangular lattice, it appears that the primary origin is a 1D spin liquid resulting from the dimensional reduction effect.

Methylhydrazinium Lead Halide Perovskites: Effect of Composition and Dimensionality on Their Optoelectronic and Photodetection Properties

Methylhydrazinium Lead Halide Perovskites: Effect of Composition and Dimensionality on Their Optoelectronic and Photodetection Properties

The Moderating Effect of a Fixed Mindset in Dimensional Comparison Effects

The dimensional comparison effect is a phenomenon in which achievement in one domain negatively affects self-concept in another as a consequence of comparing one’s achievements across multiple domains. The present study examined whether the dimensional comparison effects on self-concept and intrinsic value would differ according to middle school students’ fixed mindset in the domains of math and first language. Data from a sample of 6,817 Korean middle school students obtained from a nationwide database was analyzed. The results revealed that Korean achievement negatively predicted math self-concept and intrinsic value (contrast effect), whereas math achievement positively predicted Korean self-concept and intrinsic value (assimilation effect). The contrast effect revealed the moderating role of a fixed mindset in dimensional comparisons. Specifically, Korean achievement predicted math self-concept more negatively, especially among students with a stronger fixed mindset. This study discusses the need to consider cultural aspects in dimensional comparisons and the potential limitations of adopting a fixed mindset, which can strengthen the dimensional comparison effect in learning situations.

Giant piezoelectricity in fluoropolymer fiber mats achieved by corona poling in water

Polymer piezoelectrics hold great potential for energy harvesting and wearable electronics. Efforts have been dedicated to enhancing piezoelectric coefficients and thermostability for several decades, but most of these have not been successful. In this report, we demonstrate a straightforward way to achieve high piezoelectric coefficients and output voltages while maintaining high thermostability at temperatures over 110 °C. Poly(vinylidene fluoride-co-trifluoroethylene) [P(VDF-TrFE)] 80/20 mol.% nanofiber mats (made by electrospinning) with extremely high crystallinity and Curie temperatures were obtained via a two-step annealing process, from which large ferroelectric domains were formed in extended-chain crystals. After corona poling using water, which is a high dielectric constant medium, giant piezoelectricity (apparent d33 = 1045 ± 20 pC/N) and high output voltages (29.9 ± 0.5 V) were achieved. It is found that the dimensional effect induced significant polarization changes, which is the key requirement for piezoelectricity. Our finding in this work paves a way to further improve high-performance polymer piezoelectrics.

Comparison effects on self- and external ratings: Testing the generalizability of the 2I/E model to parents and teachers of academic track school students

BackgroundAccording to the 2I/E model, social, dimensional, and temporal comparisons represent key determinants of students' academic self-concepts. However, it is unclear to what extent these assumptions also apply to external ratings of students’ self-concepts and abilities provided by central socializers. AimsThis study aimed to examine the extent of generalizability of the 2I/E model to different parent and teacher ratings simultaneously for the first time. SampleParticipants were 545 ninth-grade students from academic track schools in Germany, their parents, math teachers, and German teachers. MethodsWe investigated the joint effects of social, dimensional, and temporal comparisons on student self-concepts, parent and teacher ratings of student self-concepts, and parent and teacher ratings of student abilities in math and German using structural equation modeling. ResultsOur results strongly support the assumptions of the 2I/E model for student self-concepts, as we found strong social, moderate dimensional, and small temporal comparison effects in both domains. For parent ratings of student self-concepts and abilities, we found strong social and—except for parent German ability ratings—moderate to small dimensional comparison effects. For teacher ratings of student self-concepts and abilities, results showed only strong social comparison effects in both domains. Temporal comparison effects were not found for either parents or teachers. ConclusionsGiven the lack of temporal comparison effects on parent and teacher ratings, this study's findings question the generalizability of the 2I/E model to external ratings. However, they point to interesting discrepancies regarding social and dimensional comparison effects on parent and teacher ratings.

Frequency-Dependent Covariance Reveals Critical Spatiotemporal Patterns of Synchronized Activity in the Human Brain.

Recent analyses, leveraging advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons across regions in the brain, compellingly support the hypothesis that neural dynamics operate near the edge of instability. However, these and related analyses often fail to capture the intricate temporal structure of brain activity, as they primarily rely on time-integrated measurements across neurons. Here, we present a novel framework designed to explore signatures of criticality across diverse frequency bands and construct a much more comprehensive description of brain activity. Furthermore, we introduce a method for projecting brain activity onto a basis of spatiotemporal patterns, facilitating time-dependent dimensionality reduction. Applying this framework to a magnetoencephalography dataset, we observe significant differences in criticality signatures, effective dimensionality, and spatiotemporal activity patterns between healthy subjects and individuals with Parkinson's disease, highlighting its potential impact.

Are you what you emoji? How skin tone emojis and profile pictures shape attention and social inference processing

Emojis can express emotions and some aspects of the sender’s identity; however, only limited research has explored how the choice of skin tone in emojis influences the perceptions of the users. We examined the interaction between emoji skin tones and profile pictures in instant messaging, using self-reported and eye tracking measures. White participants viewed 14 screenshots of conversations (9 target and 5 fillers) where the sender used an emoji in a Darker or Lighter skin tone, or the default Yellow; alongside profile pictures displaying a Black or White individual, or a landscape as a neutral condition. Results showed that Black senders using Darker emojis were seen as warmer and closer to the receiver, but less competent, suggesting a dimensional compensation effect. Conversely, Black senders using Lighter emojis appeared more competent, but less warm. In the Neutral condition, Lighter emojis improved warmth and relationship quality, but reduced competence inferences, unlike Yellow and Darker emojis, suggesting a black sheep effect (in-group strictness). Yellow emojis were assumed to be sent by White individuals. Eye-tracking measures revealed an implicit bias towards White senders using Darker emojis, although such an impact was not observed for Black senders using Lighter emojis. Overall, findings indicate that skin tone emojis and profile pictures influence sender perception and challenge the neutrality of Yellow emojis.

Multimodal Operation Data Mining for Grid Operation Violation Risk Prediction

With the continuous expansion of the power grid, the issue of operational safety has attracted increasing attention. In power grid operation control, unauthorized operations are one of the primary causes of personal accidents. Therefore, preventing and monitoring unauthorized actions by power grid operators is of critical importance. First, multimodal violation data are integrated through information systems, such as the power grid management platform, to construct a historical case database. Next, word vectors for three types of operation-related factors are generated using natural language processing techniques, and key vectors are selected based on generalized correlation coefficients using mutual information, enabling effective dimensionality reduction. Independent component analysis is then employed for feature extraction and further dimensionality reduction, allowing for the effective characterization of operational scenarios. For each historical case, a risk score is derived from a violation risk prediction model constructed using the Random Forests (RF) algorithm. When a high-risk score is identified, the K-Nearest Neighbor (KNN) algorithm is applied to locate similar scenarios in the historical case database where violations may have occurred. Real-time violation risk assessment is performed for each operation, providing early warnings to operators, thereby reducing the likelihood of violations, and enhancing the safety of power grid operations.

Retraction Note: Effective dimensionality reduction by using soft computing method in data mining techniques

Retraction Note: Effective dimensionality reduction by using soft computing method in data mining techniques

What can be learnt with wide convolutional neural networks?*

Abstract Understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harness the local and hierarchical structure of natural data such as images. Yet, we lack a quantitative understanding of how such structure affects performance, for example the rate of decay of the generalisation error with the number of training samples. In this paper, we study infinitely wide deep CNNs in the kernel regime. First, we show that the spectrum of the corresponding kernel inherits the hierarchical structure of the network, and we characterise its asymptotics. Then, we use this result together with generalisation bounds to prove that deep CNNs adapt to the spatial scale of the target function. In particular, we find that if the target function depends on low-dimensional subsets of adjacent input variables then the decay of the error is controlled by the effective dimensionality of these subsets. Conversely, if the target function depends on the full set of input variables then the error decay is controlled by the input dimension. We conclude by computing the generalisation error of a deep CNN trained on the output of another deep CNN with randomly initialised parameters. Interestingly, we find that, despite their hierarchical structure, the functions generated by infinitely wide deep CNNs are too rich to be efficiently learnable in high dimensions.

Electronic 1/f noise as a probe of dimensional effects on spin-glass dynamics

Over the past decade, spin-glass simulations have improved to the point that they now access time- and length-scales comparable to experiments at the mesoscale. A recent series of thin-film field-cooled/zero-field-cooled magnetization (FC/ZFC) experiments demonstrated activated spin dynamics, with a temperature-independent activation energy proportional to the logarithm of the film thickness and with coefficients in remarkable agreement with the simulation. These measurements require the application of small magnetic fields, which has been shown to affect the spin-glass energy landscape. Measurements of the 1/f noise in metallic spin-glasses have been previously shown to be a sensitive probe of the spin dynamics, and the measurements can be made without applying a magnetic field. In this mini-review, we review these techniques and discuss how transport measurements can fit into the current landscape of spin-glass measurements. We compare previous measurements to more recent measurements on similar films, made with ostensibly different cooling protocols, and compare both the previous and recent measurements to the magnetometry. The transport measurements—taken over a wider range of temperature than magnetometry—suggest that the maximum spin-glass energy barrier height is temperature-dependent, not fixed, possibly due to two-dimensional dynamics. We discuss this possibility, along with future measurements, which may be able to resolve this mystery.

Human macrophage polarisation and regulation of angiogenesis and osteogenesis is dependent on culture extracellular matrix and dimensionality

The immune system plays a crucial role in tissue repair and regeneration. Macrophages have been identified as master regulators of the early immune response and healing outcome, by orchestrating the temporal nature of the initial inflammation phase and coordinating the fate of stem/progenitor cells involved in regeneration. However, traditional in-vitro models for the study of macrophages often fail to fully replicate the complexity of the in-vivo microenvironment, therefore generating models which do not fully capture the extensive spectrum of macrophage behaviour seen in native tissues. To this end, we used a hematoma-mimetic 3D fibrin matrix characteristic of early injured tissues to generate a 3D in-vitro model mirroring the local macrophage microenvironment. Leveraging this framework, we demonstrated significant effects of extracellular matrix and dimensionality on macrophage basal signalling and polarisation, achieving more pronounced regenerative phenotypes upon stimulation with the M2a polarisation factors compared to traditional 2D tissue culture conditions. Moreover, this enhanced physiological macrophage behaviour corresponded to increased coordination of angiogenesis and osteogenesis, better mirroring the healing processes seen in-vivo. Taken together, this study demonstrates the critical importance of integrating tissue composition and 3D architecture when investigating the macrophage behaviour in-vitro, establishing a powerful tool that overcomes known limitations associated with traditional 2D culture on plastic, and can be used to identify and validate novel immunomodulation strategies to enhance tissue regeneration.

A hybrid novel SWARA-ELECTRE-I method using probabilistic uncertain linguistic information for feature selection in image recognition

In the digital age, the exponential growth of data poses significant challenges for analysts and machine learning algorithms in pattern detection due to its high dimensionality. This study addresses the dimensionality problem by leveraging Probabilistic Uncertain Linguistic Term Set (PULTS), which combine Uncertain Linguistic Term Set (ULTS) with associated probabilities to handle uncertainty in decision-making. We introduce the PUL-weighted average operator to integrate the opinions of multiple decision-makers and propose a novel ELimination and Choice Translating REality (ELECTRE-I) method for optimizing alternatives in multiple attribute group decision-making (MAGDM) scenarios. This method is enhanced by the Stepwise Weight Assessment Ratio Analysis (SWARA) method to determine the relative weight of each attribute. By integrating SWARA with the ELECTRE-I method, we develop a comprehensive approach to tackle MAGDM problems using PULTS. A numerical example involving feature selection in image recognition demonstrates the method’s effectiveness and accuracy. Comparative studies highlight the advantages of our approach in producing a small feature set with high classification accuracy. The proposed method offers a robust solution for feature selection in image recognition and other MAGDM problems, significantly improving decision-making accuracy and efficiency. The methodology’s simplicity and computational ease make it applicable across various domains requiring effective dimensionality reduction.

Streamlining latent spaces in machine learning using moment pooling

Many machine learning applications involve learning a latent representation of data, which is often high-dimensional and difficult to directly interpret. In this work, we propose “moment pooling,” a natural extension of deep sets networks which drastically decreases the latent space dimensionality of these networks while maintaining or even improving performance. Moment pooling generalizes the summation in deep sets to arbitrary multivariate moments, which enables the model to achieve a much higher effective latent dimensionality for a fixed learned latent space dimension. We demonstrate moment pooling on the collider physics task of quark/gluon jet classification by extending energy flow networks (EFNs) to moment EFNs. We find that moment EFNs with latent dimensions as small as 1 perform similarly to ordinary EFNs with higher latent dimension. This small latent dimension allows for the internal representation to be directly visualized and interpreted, which in turn enables the learned internal jet representation to be extracted in closed form. Published by the American Physical Society 2024