bluuebunny/crossref_metadata_2025 huggingface dataset:
Attitude and influencing factors on exercise in patients with moderately to severely active inflammatory bowel disease: a qualitative study
Authors: Wang Jing , Tong Shuping , Niu Yuehong , Lu Bingqing
Published: December 2023

Objectives

To explore the attitudes towards exercise, factors influencing exercise and exercise-related needs of patients with moderately to severely active inflammatory bowel disease.

Design

A qualitative phenomenological research.

Setting

The study was conducted at a comprehensive tertiary hospital in Suzhou, China, which is a secondary care facility.

Participants

The study included 17 participants who met specific inclusion criteria: aged between 16 and 70 years, diagnosed with inflammatory bowel disease and in a state of moderate to severe disease activity. Participants were required to be capable of clear self-expression and provide voluntary consent. Exclusion criteria included the presence of cancer or severe physical illness, cognitive impairment or mental illness.

Interventions

Semistructured interviews were used to collect data.

Results

The exercise experiences of participants with moderate to severe inflammatory bowel disease yielded three themes: attitudes towards exercise, factors influencing exercise and exercise-related needs.

Conclusion

The majority of participants had negative attitudes towards exercise during periods of moderate to severe activity, largely influenced by disease activity, symptom management, inadequate knowledge of exercise and uncertainty about the value of exercise. Of particular note, professional guidance was generally recognised as stimulating a willingness to exercise positively, and participants demonstrated a strong need for professional guidance. Therefore, it is recommended that clear exercise guidelines for inflammatory bowel disease be constructed to ensure that patients receive safe and effective guidance to develop a healthy lifestyle in order to maximise the benefits of exercise.
Assessment of willingness and determinants to receive the COVID-19 vaccine among the general population of the Somali region, Eastern Ethiopia: a 2021 cross-sectional study
Authors: Wolde Maereg , Wasihun Yosef , Melesse Addisalem , Tadesse Trhas , Sharma Rachana , Mon Hnin Su , Challa Andinet , Simireta Tesfaye , Addisu Hailemariam
Published: December 2023

Objective

This research aimed to assess COVID-19 vaccine acceptance and the factors influencing it among the population of the Somali region in Ethiopia through a cross-sectional COVID-19 survey.

Design

Community-based cross-sectional study.

Setting

The survey was conducted in eight selected districts of the Somali region in Ethiopia from 20 October 2021 to 30 October 2021.

Participants

Participants were chosen using simple random sampling and data analysis used Stata V.14. Both bivariable and multivariable binary logistic regression methods were applied, with variables having a p value below 0.2 considered for inclusion in the final model, where statistically significant factors were identified at p<0.5.

Primary outcome measure

Willingness to take the vaccine.

Results

A total of 1010 respondents participated in this study. The proportion of people’s willingness to take the vaccine was 65% (95% CI 62 to 68). When you believe moderately or highly that getting a COVID-19 vaccine will protect others (AOR=6.2, 95% CI 1.43 to 26.6) and (AOR=7.2, 95% CI 1.7 to 29.7), then you will protect others as well. Whereas, a desire to get vaccinated little, moderately and highly (AOR=4.3, 95% CI 1.77 to 10.4), (AOR=20, 95% CI 8.5 to 47) and (AOR=147, 95% CI 55 to 392), respectively, was significantly associated with willingness to take the vaccine. Moreover having close family and friends who want them to get a COVID-19 vaccine (AOR=2, 95% CI 1.2 to 3.57) and religious leaders or community leaders (AOR=1.8, 95% CI 1 to 3.1) were significant factors in the multivariable logistic regression model.

Conclusion

This study found that COVID-19 vaccine acceptance was low in the Somali region. Factors positively linked to one’s willingness to get vaccinated included the belief that it protects others, personal desire for vaccination and support from family, friends, community and religious leaders.
Transparent boundaries as scenographies of trust: the COVID-19 pandemic from the view of material cultural studies and artistic works
Authors: Ankele Monika , Kaiser Céline
Published: December 2023

From the start, the profound transformations that accompanied the COVID-19 pandemic found expression in a plethora of objects and facilities that dominated our daily lives far beyond the clinical sphere. Supermarkets, hotel receptions, taxis, restaurants, doctors’ surgeries and even schools were equipped with plexiglass screens of all sizes and shapes to continue to allow face-to-face encounters. In our paper, we trace these changes and their social impact in our everyday world. Starting from the material cultures of our daily spaces that changed in the context of COVID-19 and the new patterns of movement that had to be practised, we ask how our sensory modes of perception and social spaces changed temporarily. With the methodological approaches offered to us by material cultural studies and artistic practices, we pursue these questions on the one hand with historical examples of the clinical design of space and on the other with a view to artistic interventions that deal with the pandemic present and reflect the transparent boundary markings in their meaning for sensual and social inter-relations.
anyH3: precise predictions for the trilinear Higgs coupling in the Standard Model and beyond
Authors: Bahl Henning , Braathen Johannes , Gabelmann Martin , Weiglein Georg
Published: December 2023

The trilinear Higgs coupling $$\lambda _{hhh}$$

λ

hhh

of the detected Higgs boson is an important probe for physics beyond the Standard Model. Correspondingly, improving the precision of the theoretical predictions for this coupling as well as the experimental constraints on it are among the main goals of particle physics in the near future. In this article, we present the public code , which provides precise theoretical predictions for $$\lambda _{hhh}.$$

λ

hhh

.

The program can easily be used for any renormalisable model, where for the input the format is adopted. It allows including corrections up to the full one-loop level with arbitrary values of the external squared momenta and features a semi-automatic and highly flexible renormalisation procedure. The code is validated against known results in the literature. Moreover, we present new results for $$\lambda _{hhh}$$

λ

hhh

in models which so far have not been investigated in the literature.
Machine-learned exclusion limits without binning
Authors: Arganda Ernesto , Perez Andres D. , de los Rios Martín , Sandá Seoane Rosa María
Published: December 2023

Machine-learned likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the experimental sensitivity of high-dimensional data sets. We extend the MLL method by including kernel density estimators (KDE) to avoid binning the classifier output to extract the resulting one-dimensional signal and background probability density functions. We first test our method on toy models generated with multivariate Gaussian distributions, where the true probability distribution functions are known. Later, we apply the method to two cases of interest at the LHC: a search for exotic Higgs bosons, and a $$Z'$$

Z
′

boson decaying into lepton pairs. In contrast to physical-based quantities, the typical fluctuations of the ML outputs give non-smooth probability distributions for pure-signal and pure-background samples. The non-smoothness is propagated into the density estimation due to the good performance and flexibility of the KDE method. We study its impact on the final significance computation, and we compare the results using the average of several independent ML output realizations, which allows us to obtain smoother distributions. We conclude that the significance estimation turns out to be not sensible to this issue.
Viscous effect in the late time evolution of phantom universe
Authors: Yang Jing , Lin Rui-Hui , Feng Chao-Jun , Zhai Xiang-Hua
Published: December 2023

We investigate the cosmological implications of a phantom dark energy model with bulk viscosity. We explore this model as a possible way to resolve the big rip singularity problem that plagues the phantom models. We use the latest type Ia supernova and Hubble parameter data to constrain the model parameters and find that the data favor a significant bulk viscosity over a non-constant potential term for the phantom field. We perform a dynamical analysis of the model and show that the only stable and physical attractor corresponds to a phantom-dominated era with a total equation of state that can be greater than $$-1$$

-
1

due to the viscosity. We also study the general effect of viscosity on the phantom field and the late time evolution of the universe. We apply the statefinder diagnostic to the model and find that it approaches a nearby fixed point asymptotically, indicating that the universe can escape the big rip singularity with the presence of bulk viscosity. We conclude that bulk viscosity can play an important role in affecting the late-time behavior as well as alleviating the singularity problem of the phantom universe.
Observable gravitational waves from hyperkination in Palatini gravity and beyond
Authors: Sánchez López Samuel , Dimopoulos Konstantinos , Karam Alexandros , Tomberg Eemeli
Published: December 2023

We consider cosmology with an inflaton scalar field with an additional quartic kinetic term. Such a theory can be motivated by Palatini $$R+R^2$$

R
+

R
2

modified gravity. Assuming a runaway inflaton potential, we take the Universe to become dominated by the kinetic energy density of the scalar field after inflation. Initially, the leading kinetic term is quartic and we call the corresponding period hyperkination. Subsequently, the usual quadratic kinetic term takes over and we have regular kination, until reheating. We study, both analytically and numerically, the spectrum of primordial gravitational waves generated during inflation and re-entering the horizon during the subsequent eras. We demonstrate that the spectrum is flat for modes re-entering during radiation domination and hyperkination and linear in frequency for modes re-entering during kination: kinetic domination boosts the spectrum, but hyperkination truncates its peak. As a result, the effects of the kinetic period can be extended to observable frequencies without generating excessive gravitational waves, which could otherwise destabilise the process of Big Bang Nucleosynthesis. We show that there is ample parameter space for the primordial gravitational waves to be observable in the near future. If observed, the amplitude and ‘knee’ of the spectrum will provide valuable insights into the background theory.
Constraining physical parameters of DESs via the secondary component of the GW190814 event and other self-bound NS pulsars in f(Q)-gravity theory
Authors: Bhar Piyali , Errehymy Abdelghani , Ray Saibal
Published: December 2023

The spherically symmetric dark energy (DE) stellar model is presented here within the context of the f(Q) theory of gravity. In order to develop the model, we take into account the linear functional form of f(Q) as $$f(Q)=mQ+n$$

f
(
Q
)
=
m
Q
+
n

, where m is the coupling parameter and n is a real constant. We further assume that the stellar model is composed of normal baryonic matter along with DE; however, for the sake of simplicity, we avoid the interaction between them. The impact of the coupling parameter m on different physical parameters of DE stars (DESs) has been thoroughly investigated. For various values of m specified in the figure, the numerical values of the physical parameters are shown in tabular form. It is found that as m increases, the DES candidates become gradually massive and larger in size. In order to compare the behaviour of DESs with the observational results, we use the measurement of the GW190814 event and the three NS pulsars, viz. 4U1608-52 (mass $$= 1.74_{- 0.14}^{+0.14}~M_{\odot }$$

=
1
.

74

-
0.14

+
0.14

M
⊙

), PSR J1614-2230 (mass $$=1.97_{-0.04}^{+0.04}~M_{\odot }$$

=
1
.

97

-
0.04

+
0.04

M
⊙

), and PSR J0952-0607 (mass $$=2.35_{- 0.17}^{+0.17}~M_{\odot }$$

=
2
.

35

-
0.17

+
0.17

M
⊙

). With the help of the $$M-R$$

M
-
R

plot, the maximum mass of the DES obtained from our model is $$2.57~M_{\odot }$$

2.57

M
⊙

, which is located within the lower “mass gap” range. To cover the observational constraints, this DES can be a representative for the secondary component of the GW190814 event, whose mass range is detected to be $$2.59_{-0.09}^{+0.08}~M_{\odot }$$

2
.

59

-
0.09

+
0.08

M
⊙

by LIGO/VIRGO experiments.
Untangling pair synergy in the evolution of collaborative scientific impact
Authors: Son Gangmin , Yun Jinhyuk , Jeong Hawoong
Published: December 2023

Synergy, or team chemistry, is an elusive concept that explains how collaboration is able to yield outcomes beyond expectations. Here, we reveal its presence and underlying mechanisms in pairwise scientific collaboration by reconstructing the publication histories of 560,689 individual scientists and 1,026,196 pairs of scientists. We quantify pair synergy by extracting the non-additive effects of collaboration on scientific impact, which are not confounded by prior collaboration experience or luck. We employ a network inference methodology with the stochastic block model to investigate the mechanism of pair synergy and its connection to individual attributes. The inferred block structure, derived solely from the observed types of synergy, can anticipate an undetermined type of synergy between two scientists who have never collaborated. This suggests that synergy arises from a suitable combination of certain, yet unidentified, individual characteristics. Furthermore, the most relevant to pair synergy is research interest, although its diversity does not lead to complementarity across all disciplines. Our results pave the way for understanding the dynamics of collaborative success in science and unlocking the hidden potential of collaboration by matchmaking between scientists.
Epileptic Seizure Detection with an End-to-End Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model
Authors: Dong Xingchen , Wen Yiming , Ji Dezan , Yuan Shasha , Liu Zhen , Shang Wei , Zhou Weidong
Published: December 2023

Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and the capability of bidirectional long short-term memory (BiLSTM) in mining the long-range dependency of multi-channel time-series, we propose an automatic seizure detection method with a novel end-to-end TCN-BiLSTM model in this work. First, raw EEG is filtered with a 0.5–45 Hz band-pass filter, and the filtered data are input into the proposed TCN-BiLSTM network for feature extraction and classification. Post-processing process including moving average filtering, thresholding and collar technique is then employed to further improve the detection performance. The method was evaluated on two EEG database. On the CHB-MIT scalp EEG database, our method achieved a segment-based sensitivity of 94.31%, specificity of 97.13%, and accuracy of 97.09%. Meanwhile, an event-based sensitivity of 96.48% and an average false detection rate (FDR) of 0.38/h were obtained. On the SH-SDU database we collected, the segment-based sensitivity of 94.99%, specificity of 93.25%, and accuracy of 93.27% were achieved. In addition, an event-based sensitivity of 99.35% and a false detection rate of 0.54/h were yielded. The total detection time consumed for 1[Formula: see text]h EEG data was 5.65[Formula: see text]s. These results demonstrate the superiority and promising potential of the proposed method in real-time monitoring of epileptic seizures.
Modular categories of Frobenius–Perron dimension p2q2r2 and perfect modular categories
Authors: Zhou Dewei , Dong Jingcheng
Published: December 2023

We prove that modular categories of Frobenius–Perron dimension [Formula: see text] are solvable, where [Formula: see text] are prime numbers. As applications, we get that integral modular categories of Frobenius–Perron dimension less than [Formula: see text] are solvable, and hence integral perfect modular categories have Frobenius–Perron dimension greater than or equal to [Formula: see text]. When the modular categories considered are weakly group-theoretical, we further prove that integral perfect modular categories have Frobenius–Perron dimension greater than or equal to [Formula: see text].
Research on Multiple Slack Due-Date Assignments Scheduling with Position-Dependent Weights
Authors: Wang Ji-Bo , Bao Han , Wan Congying
Published: December 2023

This paper considers a single-machine scheduling problem where jobs have multiple slack due-date assignments, i.e., some jobs have a common flow allowance and all jobs have [Formula: see text] ([Formula: see text] is a given constant) common flow allowances. The objective is to determine the multiple common flow allowances and optimal job sequence that minimizes the weighted sum of earliness-tardiness penalty and slack due-date assignment cost, where the weights only depend on their positions in a sequence, i.e., the position-dependent weights. Optimal properties are given for this problem, and then the problem is shown to be polynomially solvable when the number of multiple slack due-date assignments is a given constant. The model with multiple slack due-date assignments can also be extended to position (time)-dependent scheduling.
Topological defects on relativistic quantum oscillator in wormhole space–time background
Authors: Ahmed Faizuddin
Published: December 2023

In this research work, the relativistic quantum dynamics of oscillator field in wormhole background with a topological defect produced by a cosmic string is investigated. We consider an example of Morris–Thorne-type wormhole including a osmic string and derived the wave equation of the relativistic quantum oscillator. Through the Heun equation, we solve analytically the radial equation and obtain the ground state energy level [Formula: see text] and the radial wave function [Formula: see text] as particular cases. In fact, it is shown that the eigenvalue solution of the oscillator field is influenced by the topological defect of cosmic string and shifted the result. Furthermore, the wormhole throat radius also modifies the energy levels and the wave function of the relativistic quantum oscillator.
Thermal leptogenesis in anisotropic cosmology
Authors: Dehpour Mehran
Published: December 2023

There is no evidence that the universe must have been homogeneous and isotropic before big bang nucleosynthesis. The Bianchi type-I cosmology is the simplest homogeneous but anisotropic cosmology. In this work, we investigate thermal leptogenesis, as a baryogenesis scenario, in the Bianchi type-I cosmology. Our results show that for specific values of the anisotropy, the modified thermal leptogenesis generated more baryon asymmetry than the standard one. In this way, anisotropy can help to achieve low-scale leptogenesis.
OIPNet: Multimodal Network with Orthogonal Information Processing for Semantic Segmentation in Indoor Scenes
Authors: Ye Mengting , Yu Kaili , Chen Zhenxue , Guo Yixin , Liu Longcheng
Published: December 2023

Semantic segmentation in indoor environments is a crucial task for artificial intelligence-driven visual robotics, enabling pixel-level classification results to facilitate robot path planning. Inspired by the success of multimodal models, we propose an end-to-end multimodal semantic segmentation model for image segmentation tasks in indoor scenes, which we call OIPNet. We design the OIP module to enhance the network’s ability to extract global information and enable information interaction in different directions. We have validated on NYUv2 and Sun RGB-D datasets, and the experiments show the generality and effectiveness of the proposed model. Our code is available at https://github.com/Mantee0810/OIP.
Transformer with a Parallel Decoder for Image Captioning
Authors: Wei Peilang , Liu Xu , Luo Jun , Pu Huayan , Huang Xiaoxu , Wang Shilong , Cao Huajun , Yang Shouhong , Zhuang Xu , Wang Jason , Yue Hong , Ji Cheng , Zhou Mingliang
Published: December 2023

In this paper, a parallel decoder and a word group prediction module are proposed to speed up decoding and improve the effect of captions. The features of the image extracted by the encoder are linearly projected to different word groups, and then a unique relaxed mask matrix is designed to improve the decoding speed and the caption effect. First, since image captioning is composed of many words, sentences can also be broken down into word groups or words according to their syntactic structure, and we achieve this function through constituency parsing. Second, we make full use of the extracted features to predict the size of word groups. Then, a new embedding representing the information of the word is proposed based on word embedding. Finally, with the help of word groups, we design a mask matrix to modify the decoding process so that each step of the model can produce one or more words in parallel. Experiments on public datasets demonstrate that our method can reduce the time complexity while maintaining competitive performance.
Analysis of a Low-Power OTA-Based Neural Amplifier Design for EEG Signal Acquisition
Authors: Nath Sourav , Kundu Lokenath , Devi Swagata , Guha Koushik , Baishnab K. L.
Published: December 2023

This paper presents the design of an efficient operational transconductance amplifier (OTA) to be explicitly used for electroencephalogram (EEG) signal acquisition in neural amplifiers (NAs). The central objective of this study revolves around addressing the fundamental compromise between noise and power in the design of NAs. The overarching goal is to effectively mitigate this trade-off by introducing novel approaches. The proposed design’s novelty lies in utilizing an adaptive biasing technique instead of the traditional current mirror biasing technique. Furthermore, the conventional input differential pair of the OTA is modified to employ a self cascode flipped voltage follower (SCFVF), which eventually reduces power consumption of the NA to 0.822 [Formula: see text]W. Additionally, notable improvements in noise performance are achieved and found to be 624.9 nV/[Formula: see text] at 1 Hz. The gain and bandwidth range of the input low-noise amplifier is 54.2 dB and 2.8 Hz–207 Hz, respectively, which effectively amplifies low-amplitude noisy incoming signals, addressing the specific requirements of EEG signal acquisition.
Shale Gas Production Prediction Based on PCA-PSO-LSTM Combination Model
Authors: Chen Dengxin , Huang Cheng , Wei Mingqiang
Published: December 2023

During the shale gas extraction process, affected by the internal pressure of the geological layer and other factors, the internal pressure will gradually decrease with time, and the production will also decrease, and it is necessary to rely on artificial pressurization and other ways to keep the production stable. Accordingly, we analyzed the high-frequency data of shale gas production obtained from a block in the southwest shale gas field, and proposed a data-driven prediction model combining Principal Component Analysis (PCA), Particle Swarm Optimization (PSO) and Long Short-Term Memory (LSTM), which can determine whether artificial pressurization is needed from the predicted results. The model adopts multi-variate input and uni-variate output, firstly, the PCA algorithm for processing the characteristic parameters (casing pressure, oil pressure, pre-valve temperature) and labeling parameters (instantaneous production), secondly, the PSO algorithm is used to iteratively search for the optimal hyper-parameters of the LSTM to find the most suitable hyper-parameter configuration, and finally, established a data-driven shale gas production prediction model. The combined model is analyzed through an example study, and the accuracy is higher in shale gas production prediction.
EFSP: An Enhanced Full Scrum Process Model
Authors: Eldanasory Naglaa A. , Idrees Amira M. , Yehia Engy
Published: December 2023

Scrum has emerged as the most widely used and desired Agile approach for providing corporate strategic competency by establishing a solid foundation for project management. However, there are several issues confronted during its implementation. Some researchers tried to solve specific areas of Scrum issues except only research that covers several aspects without resolving all of them. So, this study presents the EFSP model for improving maintainability, security and reusability. Methodologically, in this study, we carry out the following tasks: (i) apply Mark or 7C model on requirements, and (ii) identify Scrum aspects (artifacts and/or activities) that should be expanded as follows: adding the concept of systematic reusability into sprint planning, classifying the requirements into four layers according to clean architecture into sprint backlog, and evolutionary model into sprint. This model offers solutions to these problems while maintaining the simplicity and flexibility of Scrum. The system evaluation results have achieved an improvement in maintainability by reducing technical debt from 1.6% to 0.9%, security from 10 to 3, timeliness from 5 to 2, and improving team productivity from 1.24 to 2.78. The EFSP model may be utilized to develop a standard in other projects.
Arc crossing change is an unknotting operation
Authors: Cericola Christopher
Published: December 2023

This paper defines a new operation through extending the idea of the 0-dimensional crossing change and Shimizu’s 2-dimensional region crossing change [A. Shimizu, Region crossing change is an unknotting operation, J. Math. Soc. Jpn. 66(3) (2014) 693–708, doi:10.2969/jmsj/06630693] to a 1-dimensional version called the arc crossing change. We will also prove that the arc crossing change is an unknotting operation with the help of Gauss diagrams.
Semiclassical corrections to the photon orbits of a nonrotating black hole
Authors: Maharana Swayamsiddha , Dasgupta Arundhati
Published: December 2023

In this paper, we discuss the corrections to the photon orbits of a nonrotating black hole due to semiclassical fluctuations of the metric. It is found that the photon orbit impact parameter differences with the critical impact parameter become of the order of the semiclassical fluctuations. We calculate the effect of the semiclassical fluctuations on the photon orbits and show that instead of circulating the black hole infinite number of times at the critical orbit, the photons bounce off the semiclassical geometry.
Deep Learning-Based Cause-Related Marketing and the Impact of the Internet on MICE Events in the Context of the Epidemic
Authors: Shi Kun , Cui Boshi , Zhao XinTong , Ma Yuwei , Yang Yang , Du Zewen
Published: December 2023

Since 2019, novel coronavirus pneumonia has been rampant around the world, and when outbreaks occur, Meetings, Incentives, Conferences and Exhibitions (MICE) events are often affected to varying degrees. In addition, in the context of the epidemic, consumers have increasingly taken the participation of MICE in charitable activities as a measure of their social responsibility and judged MICE events as good or bad accordingly. Therefore, the impact of deep learning-based good cause marketing and the Internet on MICE events in the context of the epidemic has attracted much attention. Based on the CiteSpace analysis, this study systematically reviewed the impact system of cause-related marketing on exhibition activities and fitted the neural network model with a single-factor inter-group experiment. The results show that when the complete data set is divided into 70% training set and 30% test set, the model with the training function of Train lm and seven hidden layers performs best in all models. This shows that in the process of charity marketing, the fit between consumers and charity activities determines the attitude and willingness of consumers to participate in charity marketing.
Forecasting Financial Market Trends in a Complex Business Environment
Authors: Wang Xiuyan
Published: December 2023

Applications for the stock market that can be relied upon to provide the information regular and professional investors need to make better-informed purchases are a boon to both. A well-thought-out sales approach may help buyers mitigate risk, zero in on the companies most likely to provide the highest returns, and increase their chances of making a purchase. Due to numerous interrelationships between various company values, executing stock market research utilizing batch processing methods provides an exceptionally tough task. Technology advancements, such as the licensing of global processes, usher in a new age for stock market forecasting. The present market climate has increased the significance of data applications. An interesting and new contribution made by this work is the proposal of a deep learning-based resilience time series model for forecasting future market values. By analyzing financial time series, this study aimed to build a sophisticated strategy for forecasting stock market prices. The use of artificial intelligence (AI) to forecast future market behavior is one of the most intriguing developments in this generation of technological breakthroughs. Particularly promising results have been found when applying deep learning techniques to the task of predicting the actions of market players. In this paper, we propose a method for forecasting the final prices of publicly traded firms like Tesla, Inc. and Apple, Inc. by merging a convolutional neural network with long short-term memory (LSTM). These two methods were both developed with the help of deep learning techniques. The time frame covered by statistics used in these projections is two years. To evaluate the performance of our deep learning models, we compared their market predictions using several different metrics, including mean squared error (MSE), root mean squared error (RMSE), normalized root mean squared error (NRMSE), and Pearson’s R. When compared to the options, the CNN-LSTM deep learning algorithm did the best. When comparing convolution neural networks CNN-LSTM model to the normal LSTM and a simpler deep learning variant, the latter showed to be more effective at forecasting stock market values. This was the case when examining all three variants together.
Segmented Multifractal Detrended Fluctuation Analysis in Key Economic Sectors
Authors: Saâdaoui Foued
Published: December 2023

This paper introduces an innovative method for segmented multifractal analysis, aimed at investigating the (in-) efficiency of major global economic sectors. The proposed approach, a modified version of the Multifractal Detrended Fluctuation Analysis (MF-DFA) technique, integrates change-point detection, followed by a segmentation of time series data, just before conducting multifractal measurements at various intervals. This novel method is applied to five indices representing the global financial and economic landscape, including the Standard and Poor 500 index, the Euro/USD exchange rate, Bitcoin’s price, crude oil prices, and the price of gold. The empirical findings reveal substantial structured multifractality, with particular prominence observed in the two commodity price indicators. These results prompt inquiries into the influence of significant events on the efficiency of economic and financial markets. The segmented multifractal analysis opens up new avenues for exploring the dynamics and resilience of these sectors, thereby enhancing our comprehension of their intricate behaviors and responses to diverse stimuli.
A Multi-Criteria Decision Analysis Framework for Evaluating Strategic Alternatives in Complex Business Markets
Authors: Zhang Yun , Jiang Leilei
Published: December 2023

Considering all of the criteria involved in vendor evaluation makes it a difficult Multi-Criteria Decision-Making (MCDM) problem. In this case, they factor in DMs that are knowledgeable about the suppliers and their needs. Rating, a multi-criteria assessment method based on optimum scoring is proposed in this work. To meet the requirements of the decision-making process, this method employs rating-Based Optimal-Point Sorting (RBOP). A tried-and-true MCDM method can consistently find the option that will make the DM happy by first modeling the decision-making process after how humans really behave. Additionally, they create a new subjective scoring mechanism for the MCDM that is based on human behavior; they call it the win-loss-draw (WLD) method. They apply their methods to a case study that is based on actual Iranian cheese company operations in order to find the best possible cheese supplier for a hypermarket in Iran. The WLD approach had an effect on the RBOP strategy’s findings, which differ somewhat from the other MCDM methods. The WLD method increased spread and optimum regions throughout the choosing process.
Free Ω-Rota–Baxter systems and Gröbner–Shirshov bases
Authors: Zhang Yuanyuan , Zhang Huhu , Gao Xing
Published: December 2023

In this paper, we propose the concept of an [Formula: see text]-Rota–Baxter system, which is a generalization of a Rota–Baxter system and an [Formula: see text]-Rota–Baxter algebra of weight zero. In the framework of operated algebras, we obtain a linear basis of a free [Formula: see text]-Rota–Baxter system for an extended diassociative semigroup [Formula: see text], in terms of bracketed words and the method of Gröbner–Shirshov bases. As applications, we introduce the concepts of Rota–Baxter system family algebras and matching Rota–Baxter systems as special cases of [Formula: see text]-Rota–Baxter systems, and construct their free objects. Meanwhile, free [Formula: see text]-Rota–Baxter algebras of weight zero, free Rota–Baxter systems, free Rota–Baxter family algebras and free matching Rota–Baxter algebras are reconstructed via new method.
On finite groups in which every non-nilpotent maximal invariant subgroup is normal
Authors: Shi Jiangtao , Liu Yifan
Published: December 2023

Let [Formula: see text] and [Formula: see text] be finite groups such that [Formula: see text] acts coprimely on [Formula: see text] by automorphisms, we prove that if every non-nilpotent maximal [Formula: see text]-invariant subgroup of [Formula: see text] is normal, then [Formula: see text] is [Formula: see text]-nilpotent or [Formula: see text]-closed for each prime divisor [Formula: see text] of [Formula: see text], which improves a theorem obtained by Beltr[Formula: see text]n and Shao.
Multi-Objective Optimization of Machining Parameters of 13-8 PH Steel for Die Sinking EDM Using RSM and Hybrid Human Eye Vision Algorithm
Authors: Velpula Sampath , Eswaraiah K.
Published: December 2023

The 13-8 PH steel is one of the rare and hardest metals owing to high-end applications such as Nuclear reactor components, Injection molds, Machine tools and Aerospace applications. The hardness of 13-8 PH makes it one of the most challenging materials during conventional machining processes. On the other hand, due to its application, the 13-8 PH steel requires a high degree of Surface Roughness (SR) and precision to be obtained, but most conventional machining processes fail in machining the 13-8 PH steel due to its extensive physical, mechanical properties. Hence, Die Sinking Electrical Discharge Machining (DSEDM) of 13-8 PH steel is suggested in this study. The experimentation has been designed and performed using Response Surface Methodology (RSM)-based Box Behnken design (BBD). The various machining parameters such as Peak Current (IP), Pulse On time ([Formula: see text]), pulse off time ([Formula: see text]), Tool lift time (TLT), Material Removal rate (MRR), Tool wear rate (TWR) and SR were analyzed. Besides, this investigation proposes a novel hybrid Deep Belief Network-based Human Eye Vision Algorithm (DBN-HEVA) for predicting and optimizing process parameters. The results show that the rate of required input parameters will be [Formula: see text][Formula: see text]A, [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text][Formula: see text][Formula: see text]s, [Formula: see text][Formula: see text][Formula: see text]s to obtain an optimal outcome of 142.041[Formula: see text]g/min MRR, 2.306[Formula: see text]g/min TWR and 5.89[Formula: see text][Formula: see text]m SR. In addition, during process prediction, the proposed hybrid DBN-HEVA algorithm outperformed the existing methods while predicting MRR, TWR and SR responses by 118.5%, 144.3% and 166.2%.
Error analysis for a spectral element method for solving two-parameter singularly perturbed diffusion equation
Authors: Venkatesh S. G. , Raja Balachandar S. , Jafari H. , Raja S. P.
Published: December 2023

In this paper, we study the two-parameter spectral element method based on weighted shifted orthogonal polynomials for solving singularly perturbed diffusion equation on an interval [0, 1] which are modeled with singular parameters. We continue our study to estimate the lower bound of the weighted orthogonal polynomial coefficient and the upper bound of a posteriori error estimates of the method through different weighted norms to minimize the computational cost. Numerical examples are implemented to study the applicability and efficiency of the technique. The obtained error bounds for the coefficient of orthogonal polynomials and the posteriori estimates fall within the bounds derived in the theoretical section. It is also observed that the two weighted norms decreases when the values of [Formula: see text] and [Formula: see text] increases for the three choices of [Formula: see text] and for different values of x and y. The quality and accuracy of the solution can be realized through figures and tables.
Comparing two entropy functions of a quantum channel
Authors: Cheng Junfang , Chu Yanjun , Huang Fang
Published: December 2023

Inspired by a conjecture related to numerical investigations for a quantum channel in a system of large size, we shall study relations between two different entropies of a quantum channel. One of them is defined by Gour and Wilde as the generalization of quantum relative entropy and maximally mixed state. The second one is defined as the difference between the entropy of the Choi state of the quantum channel and a constant number. At first, we show that the latter entropy is an upper boundary of the former entropy. Moreover, we also give a sufficient and necessary condition that the two entropies are equal. That is, if and only if the relative entropy reaches its supremum on the maximal entangled pure state in the former entropy. At last, to study the difference between the two entropies, we expect to find some quantum channels in which their related entropies do not reach their supremum on the maximal entangled pure states for the former entropy. By calculating the two entropies, we find that amplitude-damping channels are desired examples.
Theoretical and simulation investigation of practical QKD for both BB84 and SARG04 protocols
Authors: Djaouida Benlahcene , Ali Sellami
Published: December 2023

In this paper, we provide a simple approach for determining the rate of secure key production when single-photon and two-photon pulses are used to generate the secure key. For both BB84 and SARG04, we offer estimation and optimization of decoy-state parameters for a single decoy state protocol and two decoy state protocols. This method can be used to determine the lower bound on the fraction of single-photon counts ([Formula: see text]), the fraction of two-photon counts ([Formula: see text]), the upper bound on the QBER of single-photon pulses ([Formula: see text]), the upper bound on the QBER of two-photon pulses ([Formula: see text]), and the lower bound on the key generation rate for both BB84 and SARG04. Due to the limited data size in real-world experiments, we estimate the statistical fluctuations of several parameters, including the gain and QBER of the signal state, as well as the gain and QBER of the decoy state, which are used to estimate the lower bound of key generation rate. We propose formulae that specify the optimal decoy and signal state percentages and intensities. Then we can determine the maximum and optimal key generation rate. For both SARG04 and BB84, we simulated a free space-based QKD system using our decoy state strategy. The simulation results show that free space-based QKD systems that use the suggested approach for BB84 can achieve a higher secret key rate and a greater safe distance than SARG04-based systems.
Hamiltonian analysis in Lie–Poisson gauge theory
Authors: Bascone Francesco , Kurkov Maxim
Published: December 2023

Lie–Poisson gauge formalism provides a semiclassical description of noncommutative U(1) gauge theory with Lie algebra type noncommutativity. Using the Dirac approach to constrained Hamiltonian systems, we focus on a class of Lie–Poisson gauge models, which exhibit an admissible Lagrangian description. The underlying noncommutativity is supposed to be purely spatial. Analyzing the constraints, we demonstrate that these models have as many physical degrees of freedom as there are present in the Maxwell theory.
A universe without time produced by a disappearing wormhole
Authors: Boskoff Wladimir-Georges
Published: December 2023

Einstein’s field equations allow exotic solutions as de Sitter and anti-de Sitter universes without matter, wormholes solutions and universes without a proper time coordinate as Gödel’s one. In this paper, we are interested in highlighting a wormhole-type universe solution of Einstein’s field equations which, when the wormhole disappears, turns into a universe without a proper time coordinate. The example is done both in classical and in modified [Formula: see text] gravity.
Tolman IV perfect fluid sphere in Rastall gravity
Authors: Waseem Arfa
Published: December 2023

This paper is devoted to observe the physical attributes of isotropic relativistic spherical objects in the context of Rastall theory of gravity. In order to inspect the structural composition of spherical objects, the Tolman IV solution is taken into account. The unknown parameters associated with Tolman IV solution are evaluated through matching conditions with derived values of radii and masses of some specific star models. The consistency of the adopted ansatz is analyzed via graphical interpretation of matter variables, equation of state parameter, mass components, energy constraints, causality condition and stellar equation for several choices of Rastall parameter. It is observed that the considered solution does not admit singularities in the matter variables. Moreover, in accordance with the graphical analysis, the stellar models satisfy all physical requirements anticipated in a realistic star. It is also shown that for vanishing Rastall coupling parameter, the standard outcomes of general relativity can be retrieved.
The “Legendre scalarization” of nonlinear gravity theories
Authors: Magnano Guido
Published: December 2023

We discuss the proposal of a new method to transform an [Formula: see text] metric gravity theory into a general relativistic theory including an auxiliary scalar field, recently introduced by Cotsakis et al. We argue that (i) the fact that the fourth-order equations of [Formula: see text] metric gravity can be recast (via a Legendre transformation) into Einstein equations without any conformal rescaling has been thoroughly clarified in the previous literature, and (ii) the newly proposed method produces a set of equations that are not equivalent to the original theory. In the conclusion, a comment is added on another aspect of the Legendre transformation which seems to be generally overlooked.
A study of Morris–Thorne wormhole in Einstein–Cartan theory
Authors: Soni Sagar V. , Khunt A. C. , Hasmani A. H.
Published: December 2023

This paper focuses on the Einstein–Cartan theory, an extension of general relativity that incorporates a torsion tensor into spacetime. The differential form technique is employed to analyze the Einstein–Cartan theory, which replaces tensors with tetrads. A tetrad formalism, specifically the Newman–Penrose–Jogia–Griffiths formalism, is used to study the field equations. Also, the energy–momentum tensor is determined, considering a Weyssenhoff fluid with anisotropic matter. The spin density is derived in terms of the redshift function. Additionally, our findings demonstrate that the radial sound speed within the wormhole throat exceeds the speed of light, suggesting the existence of superluminal matter, while the tangential sound speed indicates near-light-speed propagation, particularly within the throat, emphasizing the significance of exotic matter in comprehending wormhole properties. The results also extend to examining the energy conditions at the throat of a Morris–Thorne wormhole, shedding light on the properties of wormholes within the context of the Einstein–Cartan theory, including the energy conditions at the throat.
Scalar dark matter analysis of six-dimensional effective field theory
Authors: Kuday Ayşe Elçi̇boğa , Özok Ferhat , Saka Erdinç Ulaş
Published: December 2023

We give a prescription how high energy physics tools can be used to perform scalar dark matter analysis. We also present the analysis results of scalar dark matter in the context of 6 dimensional Effective Field Theory. We present the relic density and the cross sections of s scalar DM with the current results of the actual experiments.
q-deformed Bose statistics and the Gross–Pitaevskii equation
Authors: Maleki Mahnaz , Ebadi Zahra , Mohammadzadeh Hosein
Published: December 2023

In continuation of our earlier work on the nonextensive form of the Gross–Pitaevskii equation (GPE) [M. Maleki, H. Mohammadzadeh and Z. Ebadi, Int. J. Geom. Methods Mod. Phys. 20 (2023) 2350216], we now delve into its [Formula: see text]-deformed counterpart. GPE is a type of nonlinear partial differential equation that is specifically designed to describe the behavior of a group of particles with Bose–Einstein statistics, such as atoms in a superfluid or a Bose–Einstein condensate (BEC). In some systems, the standard Bose–Einstein or Fermi–Dirac statistics may not apply, and generalized statistics may be needed to describe the behavior of the particles. Therefore in this paper, we investigate the dynamics of a system with particle obeying [Formula: see text]-deformed statistics described by the [Formula: see text]-deformed GPE. First, we use the oscillator algebra and [Formula: see text]-calculus to obtain the well-known Schrödinger equation. By selecting an appropriate Hamiltonian for the condensate phase and minimizing the free energy, we derive the [Formula: see text]-deformed time-independent GPE.