bluuebunny/crossref_metadata_2025 huggingface dataset:
Cosmological constraints on the $$R^2$$-corrected Appleby–Battye model
Authors: Ribeiro Bruno , Bernui Armando , Campista Marcela
Published: February 2024

Nowadays, efforts are being devoted to the study of alternative cosmological scenarios, in which, modifications of the General Relativity theory have been proposed to explain the late cosmic acceleration without assuming the existence of the dark energy component. In this scenario, we investigate the $$R^2$$

R
2

-corrected Appleby–Battye model, or $$R^2$$

R
2

-AB model, which consists of an f(R) model with only one extra free parameter b, besides the cosmological parameters of the flat-$$\varLambda $$
Λ
CDM model: $$H_0$$

H
0

and $$\varOmega _{m,0}$$

Ω

m
,
0

. Regarding this model, it was already shown that a positive value for b is required for the model to be consistent with Solar System tests, moreover, the condition for the existence of a de Sitter state requires $$b \ge 1.6$$

b
≥
1.6

. To impose observational constraints on the $$R^2$$

R
2

-AB model we consider three datasets: 31 H(z) measurements from Cosmic Chronometers (CC), 20 $$[{f\sigma }_{8}](z)$$

[

f
σ

8

]

(
z
)

measurements from Redshift-Space Distortion (RSD), and the most recent type Ia Supernovae (SNe Ia) sample from Pantheon+. Next, we perform two diferent analyses: we have cosidered only SNe Ia data and the combined likelihood SNe$$+$$
+
CC$$+$$
+
RSD. The first one has provided $$b=2.28^{+6.52}_{-0.55}$$

b
=
2
.

28

-
0.55

+
6.52

, while the second one $$b=2.18^{+5.41}_{-0.55}$$

b
=
2
.

18

-
0.55

+
5.41

. In the first case it was necessary to set the absolute magnitude $$M_B = -19.253$$

M
B

=
-
19.253

from SH0ES collaboration, while in the second we did a marginalization over the Hubble constant $$H_0$$

H
0

in the normalized growth function. We have also observed that the $$H_0-M_B$$

H
0

-

M
B

degenerecency was broken by adding CC data to the SNe data. Additionally, we perform illustrative analyses that compare this f(R) model with the flat-$$\varLambda $$
Λ
CDM model, considering several values of the parameter b, for diverse cosmological functions like the Hubble function H(z), the equation of state $$w_\textrm{eff}(z)$$

w
eff

(
z
)

, the parametrized growth rate of cosmic structures $$[f \sigma _8](z)$$

[
f

σ
8

]

(
z
)

, and $$\sigma _8(z)$$

σ
8

(
z
)

. From our results, we conclude that the $$R^2$$

R
2

-AB model fits well current observational data, although the model parameter b was not unambiguously constrained in the analyses.
Study of quasinormal modes, greybody factors, and thermodynamics within a regular MOG black hole surrounded by quintessence
Authors: Al-Badawi Ahmad , Jawad Abdul
Published: February 2024

In this article, we consider a regular black hole (BH) surrounded by quintessence in the scalar–vector–tensor (S–V–T) version of modified gravity (MOG). We examine the implications of the presence of a quintessence scalar field on astrophysical observable such as quasinormal modes (QNMs), greybody factors (GFs), and thermodynamics. In the vicinity of the MOG BH with quintessence, we calculate the effective potential generated by scalar and electromagnetic field perturbation, and then use the sixth order WKB method to compute the frequencies of the QNMs under these perturbations. We also study the impact of the MOG parameter $$\alpha $$
α
and the quintessence parameter c on QNM frequencies. Our investigation reveals that the combined effects of $$\alpha $$
α
and c parameters lead to significant decrease in oscillation frequencies, while the imaginary part generally rises. We then examine the GFs associated with the BH and found that as the model parameters $$\alpha $$
α
and c increase, GF also increases, and thus less scattering. Additionally, we investigate the thermodynamic quantities and geometries for asymptotically expanded MOG BH. Through heat capacities, Helmholtz and Gibbs free energies, it is observed that this BH shows the stable behavior for various choices of the state parameter of the quintessence $$\omega $$
ω
. It is also interesting to mentioned here that Weinhold geometry exhibits the repulsive nature and Ruppiener geometry provides the attractive nature of MOG on the particles for most of the choices of $$\omega $$
ω
.
Uncertainty principle from the noise of gravitons
Authors: Sen Soham , Gangopadhyay Sunandan
Published: February 2024

The effect of the noise induced by gravitons in the case of a freely falling particle from the viewpoint of an external observer has been recently calculated in Phys. Rev. D 107, 066024 (2023). There the authors have calculated the quantum gravity modified Newton’s law of free fall where the spacetime has been considered to be weakly curved. In our work, we extend this work by calculating the variance in the velocity and eventually the momentum of the freely falling massive particle. From this simple calculation, we observe that the product of the standard deviation in the position with that of the standard deviation in momentum picks up a higher order correction which is proportional to the square of the standard deviation in momentum. We also find out that in the Planck limit (both Planck length and Planck mass), this uncertainty product gives the well-known form of the generalized uncertainty principle. We then calculate a similar uncertainty product when the graviton is in a squeezed state, and eventually, we get back the same uncertainty product. Finally, we extend our analysis for the gravitons being in a thermal state and obtain a temperature-dependent uncertainty product. If one replaces this temperature with the Planck temperature and the mass of the particle by the Planck mass, the usual uncertainty product appears once again. We also obtain an upper bound of the uncertainty product thereby giving a range of the product of the variances in position and momentum.
A characterization of quasipositive two-bridge knots
Authors: Ozbagci Burak , Orevkov Stepan
Published: February 2024

We prove a simple necessary and sufficient condition for a two-bridge knot [Formula: see text] to be quasipositive, based on the continued fraction expansion of [Formula: see text]. As an application, coupled with some classification results in contact and symplectic topology, we give a new proof of the fact that smoothly slice two-bridge knots are non-quasipositive. Another proof of this fact using methods within the scope of knot theory is presented in Appendix A, by Stepan Orevkov.
MACRO AND MICRO: TRADE FRICTION AND WEALTH INEQUALITY IN TWO-COUNTRY HANK
Authors: ZHANG CHENXING , YANG YUJIE , HOU WENWEN
Published: February 2024

This paper develops the Two-Country HANK model to analyze the impact of tariffs on economic variables and wealth distribution from both macro and micro perspectives. In Macro analysis, trade wars are likely to only exist in situations of asymmetry between the two countries. In Micro analysis, differences in consumption, investment, and labor decisions between the poor and the rich, resulting from different capital stocks, reshape the wealth structure. In a symmetric economy, on the investment side, the rigid constraint imposed by the level of capital stock leads to inconsistent investment behavior in illiquid assets between the poor and the rich. On the consumption side, tariff increases primarily suppress the consumption of imported goods by the poor, while their impact on the consumption of the rich is limited. In an asymmetric economy, the widening gaps in consumption and labor resulting from differential tariff sensitivity between the rich and the poor exacerbate wealth distribution. This paper suggests the implementation of differentiated policies to navigate the economy out of recession.
Order-Fulfillment Coalitions in a Hybrid E-Commerce Platform: Myopic versus Farsighted Stability
Authors: Li Qingying , Zhu Shuo , Chen Hongqiao
Published: February 2024

This paper considers the order-fulfillment coalitions among an e-tailer and two third-party sellers in a hybrid e-commerce platform. The e-tailer uses a self-supported logistics system to fulfill the orders, and the seller can either use the former or turn to third-party logistics systems. By investigating the equilibrium order quantities, the strong preference relations, and the possible deviations for all the possible coalition structures, we discuss both myopic stability and farsighted stability for all the coalition structures. We find that if economies of scale are absent, fulfilling the orders independently is unique Nash stable and farsightedly stable; however, if alliances generate economies of scale, there may not exist Nash stable, but the farsightedly stable outcome always exists, which further depends on the interaction between the e-tailer’s order-fulfillment cost advantage and economies of scale. Further discussions on extremely high economies of scale and the proportional commission fee are also conducted.
Semi-Additive Integer-Valued Production Technology for Analyzing Public Hospitals in Mashhad
Authors: Ghiyasi Mojtaba , Cook Wade D.
Published: February 2024

Conventional Data Envelopment Analysis (DEA) models assume real-valued input-output data and ignore the collaboration among decision-making units (DMUs) in the analysis of efficiency. This paper proposes a novel DEA production technology that is capable of dealing with the collaboration concept and resource sharing for both integer and real-valued data in the efficiency measurement and target setting. This is accomplished by way of a mixed integer linear programming (MILP) efficiency analysis model. We also deal with the computational aspect of the proposed model and invent a MILP with the computational complexity of [Formula: see text] rather than [Formula: see text]. We explain the proposed models by numerical examples and graphical illustrations. We apply our models for efficiency analysis of 15 governmental (public) hospitals in Mashhad City in Iran and demonstrate the practical relevance and advanced future of the proposed methods.
Correlation functions and stochastic Feynman rules for self-interacting scalar fields
Authors: Byun Moongul
Published: February 2024

It is well known that perturbative solutions of the Langevin equation can be used to calculate correlation functions in stochastic quantization. However, this work is challenging due to the absence of generalized rules. In this paper, we address this difficulty by studying correlation functions up to certain orders for self-interacting scalar fields. Through the perturbative approach, we establish stochastic Feynman rules applicable to both finite and large fictitious times. Within this process, we introduce a fictitious-time ordering diagram, which serves as a keystone for finding all possible fictitious-time orderings and directly writing down an exact contribution for a given stochastic diagram with its fixed fictitious-time ordering.
Magnetization and relativistic Landau-type quantization from Lorentz symmetry violation by fixed vector background
Authors: Bakke K. , Belich H.
Published: February 2024

From a background of the Lorentz symmetry violation determined by a fixed time-like four-vector, a nonuniform electric field and a uniform magnetic field, we show that a relativistic Landau-type quantization can be achieved. We also analyze this relativistic Landau-type system under the influence of an infinity wall. Then, from the energy eigenvalues, we discuss the magnetization and the magnetic susceptibility at zero temperature.
The specific heat of superconducting metallic grains in magnetic field
Authors: Yao Yong , Liu Yu-Xiao , Luo Qiang , Tang Bin , Qu Hao , Ma Lihua
Published: February 2024

More and more studies indicate that the effects of quantum size and energy level statistics play a crucial role in the thermodynamic properties of ultrasmall metallic grains. This paper aims to investigate how they affect the specific heat of ultrasmall metallic grains in magnetic field. As the particle size decreases, fluctuation effects and the impact of energy level separation are becoming more and more important. The method of static path approximation (SPA) is adopted to handle the fluctuation effect. Random matrix theory (RMT) is adopted due to its successful description of the energy level of metal nanoparticles. The normalized specific heat of several typical temperatures and electron spins were taken in the calculation, and the results were analyzed. It was found that spin and the spin-orbit coupling affect the specific heat very obviously, and the suppressed high spin weakens the contribution of electrons to the heat capacity.
An Efficient Double Deep Q Learning Network-Based Soft Faults Detection and Localization in Analog Circuits
Authors: Puvaneswari G.
Published: February 2024

To identify whether the circuit under test is fault-free or faulty, fault diagnosis is conducted in an analog circuit. However, in the case of fault detection (FD) techniques, the size of FD is the main challenge in testing, especially for complex analog circuits. Hence to reduce the size of FD, specific test nodes are chosen to perform fault diagnosis from the available accessible test nodes. Specific test nodes are selected based on the faults classification efficiency of fault diagnosis. Therefore, this paper proposes an approach for single and multiple soft fault diagnosis using minimum test nodes in analog electronic circuits. The proposed double deep Q-learning-based hybrid Simulated annealing–Tabu search (DDQN-Hybrid SATS) technique determines minimum test nodes with the utilization of distance metric for fault classification. The DDQN-Hybrid SATS technique is used to identify minimum nodes for testing. In this, the double deep Q learning network (DDQN) ensures better reliability and faster convergence in learning but suffers from catastrophic forgetting issues. To prevent such issues, the DDQN approach is optimized using a hybrid SATS algorithm. The hybrid optimization of simulated annealing (SA) and Tabu search (TS) coalesce the advantages of individual optimization procedures to provide the optimum solution in a fast and effective way. The results for a fourth-order low pass filter are presented (i.e., first CUT) and an eight-bit digital to analog converter (i.e., second CUT). Moreover, the simulation experiment reveals that the proposed DDQN-Hybrid SATS technique achieves greater overall fault classification accuracy of about 96.25% than other compared techniques with minimum computation time.
A Novel Entity and Relation Joint Interaction Learning Approach for Entity Alignment
Authors: Wu Di , Li Tong , Zhao Yiran , Liu Junrui , Tang Zifang , Yang Zhen
Published: February 2024

Entity alignment (EA) aims to find equivalent entities in knowledge graphs (KGs) from multiple data sources and is a crucial step in integrating KGs. Recent studies learn the similarity of entity embeddings by aggregating neighboring entities. However, these methods solely compare neighboring entities and do not incorporate the connected relation between an entity and its neighbors. In this paper, we propose a novel Entity and Relation joint Interaction Learning (ERIL) approach, which effectively captures the interaction between entities and relations, enhancing the precision of alignment across different KGs. Specifically, the ERIL model jointly learns the neighborhood features of entities and the spatial structure of relations to train a shared permutation matrix, capturing comprehensive associative relations within KGs. Moreover, a semi-supervised iterative framework is designed to leverage the positive interactions between entities and relations to identify more aligned entities. Extensive experiments are conducted on five benchmark datasets to demonstrate the effectiveness of ERIL compared with existing state-of-the-art EA methods. On DBP15K, our model ERIL outperforms currently available EA methods by 1.9% on Hits@10.
Null geodesic structure for the Barriola–Vilenkin spacetime via k-essence
Authors: Majumder Bivash , Ray Saibal , Manna Goutam
Published: February 2024

Based on the work of Chandrasekhar [The Mathematical Theory of Black Holes, Chap. 3, Sec. 20 (Oxford University Press, 1992)], we investigate the null geodesic structure of the emergent Barriola–Vilenkin (BV) spacetime in the context of k-essence theory. For k-essence, the emergent gravity metric is a one-to-one correspondence with the BV metric connected to the Schwarzschild background, where the global monopole charge is replaced by the dark energy density. This equivalence holds specifically for a certain class of k-essence scalar fields that have been constructed by Gangopadhyay and Manna [Eur. Phys. Lett. 100, 49001 (2012)]. We have traced out different trajectories for null geodesic in the presence of dark energy for the k-essence emergent BV spacetime. It is demonstrated that the outcomes deviate from the typical Schwarzschild spacetime owing to the fundamental configuration with a constant dark energy density.
Influence of the Seabed Site and Island Structure on Seismic Response of Marine Artificial Islands
Authors: Chen Baokui , Bu Kailin , Wang Bowei , Shi Yan
Published: February 2024

Compared with the marine structures such as sea-crossing bridges and offshore wind turbines, the marine artificial islands are bottom sitting structures and particularly sensitive to site conditions. Therefore, the study focuses on determining the effect of seabed site conditions and construction methods on the seismic performance of marine artificial islands. A seismic wave analysis model of seabed-seawater layer–artificial island coupling is developed by combining the self-programmed fluctuation analysis program and the dynamic analysis software. Firstly, the seismic response of marine artificial islands with different site conditions are analyzed by inputting P and SV waves. On that basis, the effects of incidence angles on the seismic response are explored. Then the study further explores the effects of different backfill materials and protective structures on the seismic response of the artificial island. Numerical results show that the silty soft soil layer can amplify the seismic response of seabed sites, and this amplification effect increases with the increase of the depth of silty soft soil layers. Moreover, the coral sand as backfill material can reduce the seismic response of marine artificial islands. The seismic response of marine artificial islands can also be reduced in the model with a protection structure. These results will provide some theoretical references for the site selection and construction method of marine artificial islands.
Crashworthiness Analysis and Optimization of Thin-Walled Structures Based on the TSSA-LSSVM Surrogate Model
Authors: Wang Weiwei , He Yi , Zhang Tianci , Zhang Wenhao , Ju Fei , Xu Xiaomei , Ahmed Haris Waqar
Published: February 2024

Thin-Walled Structures (TWS) play a crucial role in enhancing both the collision safety and lightweight characteristics of vehicles. Structural design and optimization have demonstrated significant potential in improving their crashworthiness and overall weight reduction. Several interconnected factors, including the cross-sectional shape and parameters of distinctive structural characteristics (known as induction grooves), impact the TWS’ resistance to collisions. Another critical aspect in the optimization of TWS design is striking the delicate balance between the surrogate model’s effectiveness and precision. Taking all these factors into account, this paper conducts an in-depth investigation into the cross-sectional shape, grooves, and other geometric attributes of various TWSs through a comparative analysis. It reveals that the thin-walled structure with a Double-Ribbed Rectangle cross-section (DR-TWS) proves to be the optimal choice, particularly in low-speed axial impact scenarios. Furthermore, we present an enhanced Least Squares Support Vector Machine (LSSVM) method, augmented with an adaptive [Formula: see text]-distribution Sparrow Search Algorithm (TSSA), referred to as the TSSA-LSSVM surrogate model. The optimized design of the DR-TWS is realized through the integration of the TSSA-LSSVM surrogate model with the adaptive hybrid multi-objective particle swarm optimization (AHMOPSO) algorithm. This paper not only provides substantial theoretical support but also offers valuable guidelines for the application of TWSs in the automotive industry.
Model Reduction of a Continuous System with Friction-Induced Vibration Considering Separation and Re-Contact
Authors: Liu Ningyu , Ouyang Huajiang
Published: February 2024

The friction-induced vibration of a continuous system consisting of two flexible beams in sliding contact to represent a brake system is studied in this paper. Besides the motion of relative sliding when the two beams are in contact, the separation of beams may also happen in the system dynamics. The complex eigenvalue analysis for the stability of the steady sliding state and the transient dynamic analysis for the characteristics (intensity and periodicity) of the steady-state responses of the system are carried out. Moreover, the results obtained using different numbers of beam modes are acquired and compared. It is found that only a few low-order beam modes need to be incorporated to get accurate results of the stability of the steady sliding state and the steady-state responses of the continuous frictional system, which therefore theoretically justify the omission of high-order modes of continuous structures when investigating the friction-induced vibration of continuous systems and thereby greatly reduce the computational cost. Additionally, the inclusion of the separation and re-contact behavior has a significant effect on the number of required modes for accurate steady-state responses compared with that when no separation is considered, which also verifies the important role of the separation and re-contact behavior in the system dynamics. Besides, a reduced dynamic model that can produce identical dynamic behaviors to those of the continuous frictional system is constructed, with the structural parameters of the reduced model derived from the several key low-order modes of beams.
Dynamic Response Recovery of Damaged Structures Using Residual Learning Enhanced Fully Convolutional Network
Authors: Tang Qizhi , Xin Jingzhou , Jiang Yan , Zhang Hong , Zhou Jianting
Published: February 2024

Structural dynamic response corrupts frequently due to the sensor malfunction. The loss of dynamic response will hinder the structural condition assessment. In recent years, significant efforts have been devoted to recovering the dynamic response during the linear elastic stage of the structure. However, relevant researches on the response recovery of damaged structures are rarely reported due to its strong nonlinearity. With the growing significance of post-disaster structural maintenance, it is critical to develop effective methods for recovering missing data in damaged structures. To this end, this paper proposes a dynamic response recovery method for damaged structures using residual learning enhanced fully convolutional networks (FCN), which can provide a baseline for the recovery of monitoring data in operational civil infrastructure. Specifically, a FCN incorporating residual learning and skip connections is designed to capture high-dimensional nonlinear relationships between input and output channels, thereby achieving the data recovery for any concerned channel. Then, a time–frequency domain evaluation mode is constructed, in which L2 norm is used to measure the difference of recovery results in the time domain, while instantaneous frequency is employed to evaluate the integrity of the spectral information of recovery results. Finally, a destruction test of an experimental arch was conducted, and the acceleration data under different damaged state were collected to investigate the feasibility of the proposed method. Besides, the recovery effects concerning input channel location and quantity, multi-channel response and cross-state recovery are examined. The results show that even in a severely damaged state, the proposed method effectively recovers the missing data. In addition, improving the correlation between input and output channels and increasing the number of input channels can further enhance the recovery accuracy.
Structural Health Monitoring of Long-Span Continuous Girder Bridge: System Implementation and Data Analysis
Authors: Yang Xiao-Mei , Wang Zhi-Wen , Zheng Xu , Guan Ze-Xin , Yang Dong-Hui , Yi Ting-Hua
Published: February 2024

Long-span bridges are the key component of the human transportation system, linking communities over vast obstacles. To ensure the safe operation of long-span bridges, structural health monitoring (SHM) is perhaps the most effective solution. This study takes a 430[Formula: see text]m four-span continuous girder bridge as an example and systematically presents an implementation example of the SHM system on the bridge regarding monitoring items, sensor placement, and sensor parameters. The monitoring data including operational load and bridge response are analyzed and the statistical rules of these monitoring data are presented. Besides, the modal parameters of the girder are tracked from long-term vibration monitoring data. Furthermore, the correlations between structural temperature and bridge response are analyzed, and the correlation formulas between bridge modal frequency, static responses, and structural temperature are finally given. The analysis results reveal significant seasonal variations in the responses of the continuously monitored girder bridge, offering valuable insights for data-driven assessment and early warning systems for bridges.
An Econophysics Perspective on Green Bonds and Stock Market Nexus: Can Green Finance be an Investment Option for Emerging Stock Markets?
Authors: Acikgoz Turker
Published: February 2024

Green bonds are one of the most fascinating financial innovations that offer a solution to climate change and sustainable development from the financial point of view. In this study, we employ methods from statistical physics to examine the multifractal features of cross-correlations between green bonds and emerging stock markets. Utilizing multifractal detrended cross-correlation analysis (MFDCCA) on return and volatility series, our findings reveal significant cross-correlations between green bonds and emerging stock markets. The MFDCCA results uncover multifractal features and long-range cross-correlations between the two assets. Notably, volatility cross-correlations exhibit persistent behavior, while return cross-correlations vary across small and large fluctuation periods. These findings hold practical implications for policymakers and investors involved in green bonds and emerging stock markets.
The equation characterizations of generalized inverses in a ring with involution
Authors: Li Anqi , Wei Junchao
Published: February 2024

In this paper, we study the relation between the generalized inverse properties of an element in a ring with involution and related equations. Mainly, by exploring the existence of the solution in a given set and the expressions of the general solution to these constructed related equation, we obtain a lot of new characterizations of EP elements, partial isometries, SEP elements, Hermitian elements and normal elements.
APPLICATION OF NEW RETRIEVAL METHODS IN MICROSCOPIC MEDICAL IMAGES AND CT IMAGES
Authors: LIU SHUANG , ZHENG DEQUAN
Published: February 2024

Medical image retrieval is of great significance for forming correct medical judgments during the diagnosis and treatment process. In response to the fact that there are rich types of features in medical images, this paper constructs a multi-feature fusion model and applies it to medical image retrieval. This multi-feature fusion model utilizes three features. Color information is characterized using three different moments, texture information is obtained using the LBP (Local Binary Pattern) operator, and shape information is described using Hu moments. After collecting three features of medical images, they are fused into a similarity measure model through a self-set weight structure for comparison in medical image retrieval. Experiments show that our method has satisfactory image retrieval result for medical images such as endoscopic images and computed tomography (CT) images. Despite the continuous improvement of recall indicators, the proposed method still has high precision.
SSDNet: A SEMISUPERVISED DEEP GENERATIVE ADVERSARIAL NETWORK FOR ELECTROENCEPHALOGRAM-BASED EMOTION RECOGNITION
Authors: XU JUAN , XU LIJUN , LIU KAI , YANG QING , ZHENG YAXIN
Published: February 2024

This research introduces a novel method for emotion recognition using Electroencephalography (EEG) signals, leveraging advancements in emotion computing and EEG signal processing. The proposed method utilizes a semisupervised deep convolutional generative adversarial network (SSDNet) as the central model. The model fully integrates the feature extraction methods of the generative adversarial network (GAN), deep convolutional GAN (DCGAN), spectrally normalized GAN (SSGAN) and the encoder, maximizing the advantages of each method to construct a more accurate emotion classification model. In our study, we introduce a flow-form-consistent merging pattern, which successfully addresses mismatches between the data by fusing the EEG data with the features. Implementing this merging pattern not only enhances the uniformity of the input but also decreases the computational load on the network, resulting in a more efficient model. By conducting experiments on the DEAP and SEED datasets, we evaluate the SSDNet model proposed in this paper in detail. The experimental results show that the accuracy of the proposed algorithm is improved by 6.4% and 8.3% on the DEAP and SEED datasets, respectively, compared to the traditional GAN. This significant improvement in performance validates the effectiveness and feasibility of the SSDNet model. The research contributions of this paper are threefold. First, we propose and implement an SSDNet model that integrates multiple feature extraction methods, providing a more accurate and comprehensive solution for emotion recognition tasks. Second, by introducing a flow-form-consistent merging pattern, we successfully address the problem of interdata mismatches and improve the generalization performance of the model. Finally, we experimentally demonstrate that the method in this paper achieves a significant improvement in accuracy over the traditional GANs on the DEAP and SEED datasets, providing an innovative solution in the field of EEG-based emotion recognition.
LOCALIZATION METHOD OF HUMAN SKELETAL SEQUENCE MOVEMENTS IN ICE SPORTS
Authors: QIAN PENG , CAO LIANZHONG
Published: February 2024

A method based on kernel extreme learning machine is proposed for accurate localization and recognition of human bones. In this method, the human skeleton is divided into 16 key nodes based on physiology, and the human skeleton model is formed by connecting lines between the nodes. The sequence images of human actions are taken as input and fed into the input and hidden layers of the extreme learning machine. During the learning and training process, a kernel function is used to construct a kernel matrix to complete weight allocation. The experimental study was conducted on the action sequences of figure skaters, and the results showed that our method achieved an accuracy rate of over 90% in recognizing human bones, with individual sequences achieving an accuracy rate of over 95%, which is superior to convolutional neural network (CNN) and long short-term memory (LSTM) methods.
HBPGEO: AN APPLICATION SYSTEM FOR ANALYZING AND VISUALIZING GENE EXPRESSION DATA OF HYPERTENSION
Authors: LI ZONGJIN , TIAN LIQIN , YANG JIYU , JIA ZEYU , YAN CHENGYING , QU WENJING , MA XINYUE , SONG CHANGXIN , WU XIAOMING
Published: February 2024

The analysis of gene expression data using existing methods and tools has become a fundamental approach for researchers to explore the mechanisms of hypertension. However, many researchers lack programming skills or statistical knowledge. Although gene expression data is widely available, they originate from various experiments with different standards and quality levels, causing inconvenience to researchers. While tools and databases with interactive interfaces exist for data analysis, they may have limited applicability. This study aimed to construct High Blood Pressure Gene Expression Omnibus (HBPGEO), a program based on the R Shiny platform. It integrated the discovery of differentially expressed genes, hub gene identification, gene function analysis, and more. It allowed users to select suitable methods and set different parameters during analysis, visualize results interactively, and download the analyzed results. The study successfully utilized HBPGEO to identify hypertension-related biomarkers.
EFFECT OF QUANTITATIVE ACUPUNCTURE LIFTING AND INSERTING MANIPULATION ON THE EXPRESSION OF FFAR4 RECEPTOR IN OBESE RATS
Authors: ZHENG PING , WANG WEIGANG , JI FANG , LIU CHENGYONG , PAN MUZHI , HUO YAN , YUAN MENGQIAN , SI PENGLI , AI BINGWEI
Published: February 2024

This study is based on the theory of “acupuncture technique metrology” and explores the effect of acupuncture technique on lipid reduction in obese rats. After applying a new and improved acupuncture method — quantitative pulling and inserting acupuncture techniques — to rats, we found that it can alter the expression of FFAR4 receptors and inflammatory factors in obese rats. The results showed that after intervention, the FFA, IL-6, and TNF-[Formula: see text] of the quantitative lifting and inserting group and the acupuncture lifting and inserting group had significant decrease, significant increase in HDL-C and FFAR4 concentrations, and significant changes in various indicators in the quantitative lifting group compared to the acupuncture lifting group. Therefore, we believe that the quantitative lifting and insertion techniques used in this study can provide a reference for the clinical treatment of obesity and provide certain data for quantifiable acupuncture and moxibustion equipment.
A MENTAL ILLNESS EARLY WARNING ANALYSIS MODEL USING AN IMPROVED SVM ALGORITHM AND EEG SIGNALS
Authors: DONG DANHUI , SHEN XIAOYING , YAN BAO-PING
Published: February 2024

The fast-paced lifestyle, intense competition, and constantly changing environment of modern society are important factors contributing to the emergence of psychological disorders. Factors such as work pressure, family conflicts, and interpersonal relationship difficulties pose challenges to individual mental health. Prolonged use of electronic devices and the widespread use of social media further exacerbate mental health issues, as the constant influx of information makes it difficult for people to relax and focus. The harm of psychological disorders to individuals and society cannot be ignored. It not only diminishes the quality of life for patients but also affects work efficiency and social participation. In extreme cases, psychological disorders can lead to serious consequences like suicide, causing immense pain to families and society. Therefore, prioritizing mental health, raising awareness of mental well-being, and timely intervention and treatment of psychological disorders are crucial for individual happiness and social stability. Based on this, this study employs an improved Support Vector Machine (SVM) and Electroencephalogram (EEG) signals for the diagnosis of psychological disorders, aiming for more effective treatment of mental health conditions. Specifically, the EEG signals of the experimental subjects are first collected as training data for the model. The dataset undergoes preprocessing operations such as cleaning and normalization. Due to the limited availability of published information about individuals with psychological disorders, an ensemble SVM algorithm is used as a warning model to improve the accuracy of psychological disorder diagnosis. This algorithm utilizes a differential measurement method to adjust the selection of SVM parameters. The training sample size remains constant, with several iterations of training performed. By combining the differential measurement method, a specific parameter combination is obtained, maximizing the differentiation between base classifiers. This ultimately addresses issues such as small sample size and insufficient sample information in the dataset. Experimental results demonstrate that the model used in this study exhibits good performance in diagnosing psychological disorders.
ANALYSIS OF EFFECTS OF MIND-BODY ACTIVATION STRATEGIES IN PROMOTING REHABILITATION OF ELDERLY STROKE PATIENTS — REINTEGRATION BASED ON THE ICF COMPREHENSIVE FUNCTIONAL FRAMEWORK
Authors: XIE PING , GAO BUGANG , LIU YUAN , SHEN DAN
Published: February 2024

The objective of this study was to fully implement the comprehensive rehabilitation concept based on the International Classification of Functioning, Disability and Health (ICF) and integrate Mind-body Activation strategies into the holistic rehabilitation and supportive treatment of elderly patients with post-stroke hemiparesis. The aim was to assess the potential of promoting overall recovery in elderly stroke patients and explore a novel approach to stroke rehabilitation that emphasizes the integration of mind and body. A total of 103 elderly patients with post-stroke hemiplegia receiving rehabilitation treatment were selected and randomly assigned to the control group and experimental group. In the control group, 52 patients received intervention with routine rehabilitation exercise training, while in the experimental group, 51 patients adopted the Mind-body Activation strategy combined with routine rehabilitation exercise training to achieve physical and mental integration through techniques such as warm-up exercises and music therapy. Both groups were evaluated before and after the intervention in terms of walking function, balance function, daily living ability, and cognitive ability. The results showed that after the intervention for 3 months, the Holden walking functional grading, Berg scale score, Barthel scale score and MMSE score of patients in both groups were significantly higher than those before the intervention ([Formula: see text]). Additionally, the above four indicators of patients in the experimental group were significantly higher than those in the control group ([Formula: see text]). The Mind-body Activation strategy incorporated into routine rehabilitation exercise training can effectively enhance the walking function, balance function, daily living ability and cognitive ability of elderly stroke patients with hemiplegia. The Mind-body Activation strategy is a feasible and effective new rehabilitation method of hemiplegia after stroke, which is conducive to promoting the comprehensive rehabilitation of elderly stroke patients.
BIOMEDICAL IMAGE SEGMENTATION BASED ON DEEP LEARNING ALGORITHMS
Authors: NIU MIAOHE , WANG XUELI
Published: February 2024

A new biomedical image segmentation method based on deep learning networks has been constructed. Under the framework of deep learning, a dual-branch deep learning network structure was designed to perform average pooling and maximum pooling on input image data, respectively. The results of pooling processing are sent to multi-layer convolution for further processing, where 2D convolution and dilated convolution are used, respectively, and the more flexible SoftMax function is selected for activation processing. Multiple fully connected processes were used between each link and between two branches to form better fusion criteria. The experimental results show that our method has a more ideal segmentation effect in both homologous and heterogeneous training segmentation experiments, with AC, SE, and SP indicators reaching over 95%, 84%, and 99%, respectively.
MOTION-STIMULATED EEG RECOGNITION BY YOLO DEEP NETWORK
Authors: ZHANG XU
Published: February 2024

In order to elucidate the relationship between sportsman motion awareness and brain waves from a physiological perspective, research was conducted on the recognition of motion-stimulated brain waves. A motion-stimulated electroencephalogram (EEG) recognition method based on the You Only Look Once (YOLO) deep network is proposed to address the high complexity, high interference, strong randomness, and strong time-varying characteristics of EEG signals. Under the YOLO deep network framework, replacing the Efficient Layer Aggregation Networks-A (ELAN-A) module with the Convolution-Next (CvNX) module achieves the goal of lightweight processing of the YOLO network. Furthermore, the Simple Attention Mechanism (SimAM) attention mechanism is introduced, and a special loss function is set. During the experiment, the short-time Fourier transform was used to convert the EEG signals of the test subjects into time-frequency EEG images with rich features, which can be used as inputs for the YOLO network. The experimental results show that our method achieves recognition accuracy of over 90% and the highest of over 95% in the recognition of five types of motion-induced EEG waves. Compared to CNN and traditional YOLO methods, our method has 3–5% improved with recognition accuracy.
HUMAN MECHANICS MODELING AND EXPERIMENTAL RESEARCH IN SPEED SKATING SPORTS
Authors: GUAN CHENGXUE , REN HUIYING , ZHANG HUANYI , ZHANG CHUNHUI
Published: February 2024

To better assist speed skaters in scientific training, a human skeleton model was constructed from the perspectives of human mechanics and biomechanics. Based on the correspondence between the human skeletal model and the multi-body rigid body theory, the variation laws of angle and angular acceleration between human skeletal joints were deduced, and a dynamic model was constructed. An experimental study was conducted on the angle and angular acceleration changes of the lower limb joints of athletes during the speed skating process, with a period of 1 second. The experimental results showed that the left hip joint angle reached its maximum peak first, the left knee joint and left ankle joint lagged relatively, and the angle changes of the right knee joint were more severe; the angular acceleration of the left and right knee joints varies dramatically, causing significant impact on the athlete’s knee joints. The research results of this article have important value for the scientific training of speed skaters.
ANKLE JOINT MOTION RECOGNITION SYSTEM AND ALGORITHM OPTIMIZATION BASED ON PLANTAR PRESSURE
Authors: LU ZONGXING , XU ZHIWEI , ZHAO DONGZHE , YANG TIANXUE
Published: February 2024

Due to the current focus of research on ankle rehabilitation robots on structural design, there is still limited research on ankle human–machine interaction technology. In order to enable rehabilitation robots to conduct personalized rehabilitation training based on patients’ ankle movement intentions, we propose a new ankle motion recognition method based on plantar pressure. First, we designed a plantar pressure collection system based on array sensors. Then, we collected nine types of ankle joint motion pressure data from five volunteers and conducted algorithm selection, data processing, and algorithm optimization. Finally, we proposed a small sample optimization algorithm based on support vector machine, with an average recognition rate of 93.16%. The recognition method proposed in this paper can be combined with ankle rehabilitation robots to achieve active rehabilitation functions, laying the foundation for the clinical application of active rehabilitation technology.
RATIONALE FOR DESIGN CRITERIA FOR THE SECOND GENERATION OF INTERVERTEBRAL DISC PROSTHESIS
Authors: VARGAS-ALCARAZ EPIFANIO , ESPINOSA-BAUTISTA ADRIAN
Published: February 2024

First-generation intervertebral disc prostheses (IDPs) have well-established design fea-tures, but an inconsistent functional performance and they are not entirely accepted for clinical use. Currently, second-generation IDPs aim to overcome first-generation problems. Design features and performance of IDPs are related from first- to second-generation, but the relationship is unclear. In 1990, Hedman et al. determined design criteria for the first generation, but the lit-erature has not reported any further criteria for the second generation. This paper is focused on design criteria for second-generation prostheses, clarifying the relationship between the design features and performance of IDPs. We analyzed biomechanical prop-erties of natural intervertebral discs and technological evolution of IDPs to characterize functional performance. A group of functional parameters were identified. A comprehensive review of the literature regarding design criteria showed inconsistent and ambiguous criteria. So, a structure of criteria based on information content is proposed to avoid these conflicts. The criteria are structured into three parts including the functional parameters. In a straight relation to functionality, three design criteria are sufficient to evaluate holistically the features of the second-generation IDPs. The criteria avoid the absence or incongruence of information of the design features and relate the performance of IDP to its features in the route to overcome functional problems.
Corporate Social Responsibility: Analysing Its Impact on Customer Loyalty in the Textile Industry of Maharashtra
Authors: Sahu Tripti , Tripathy L. K.
Published: February 2024

Corporate social responsibility (CSR) is gaining importance for corporations and is attracting a lot of attention. A company can be held socially responsible to its consumers, stockholders, and the general population by implementing a corporate policy of self-policing called social responsibility in business (CSR). By fulfilling their social obligations, businesses can become aware of how their actions affect society’s financial, social, and environmental conditions. Efficient corporate social responsibility practices aid businesses in improving their reputations among consumers, who are then motivated to support and show loyalty to the business. The company’s social responsibility will increase trust, which will boost customer loyalty. Huge corporations participate in corporate social responsibility mostly because it is viewed as a strategic edge and offers advantages to businesses, including enhancing their image, lowering marketing and operating expenses, and attracting investors and talents. Therefore, corporate social responsibility offers businesses several benefits, including boosting brand loyalty, trust, and positive sentiments about a company as well as fighting off the unfavourable press. By the support of managers and supervisors of the textile industry in Maharashtra, the recent study’s aspiration is to check and assess numerous aspects of corporate sustainability’s in customer loyalty. The major elements of corporate social responsibility enclosed beneath the study take in environmental responsibility, ethical responsibility, philanthropic responsibility, economic responsibility, and legal responsibility. Since customer loyalty is a significant determinant of corporate success, elements that foster loyalty are of considerable interest to businesses. All of this demonstrates the worth and necessitates for thorough study in the area of corporate social responsibilities impacting customer loyalty. This study intends to analyse the control of business social responsibilities on consumer loyalty in the textile industry of Maharashtra.
Comparison of distances and entropic distinguishability quantifiers for the detection of memory effects
Authors: Vacchini Bassano
Published: February 2024

We consider a recently introduced framework for the description of memory effects based on quantum state distinguishability quantifiers, in which entropic quantifiers can be included. After briefly presenting the approach, we validate it considering the performance of different quantifiers in the characterization of the reduced dynamics of a two-level system undergoing decoherence. We investigate the different behaviors of these quantifiers in the dependence on physical features of the model, such as environmental temperature and coupling strength. It appears that the performance of the different quantifiers conveys the same physical information, though with different sensitivities, thus supporting robustness of the approach.