bluuebunny/crossref_metadata_2025 huggingface dataset:
Genetic findings in people with schwannomas who do not meet clinical diagnostic criteria for
NF2
-related schwannomatosis
Authors: Smith Miriam J , Perez-Becerril Cristina , van der Meer Mwee , Burghel George J , Waller Sarah J , Carney Megan , Bunstone Sancha , Fryer Katherine , Bowers Naomi L , Hartley Claire L , Smith Philip T , Rutherford Scott A , Freeman Simon R , Lloyd Simon K W , Pathmanaban Omar N , King Andrew Thomas , Halliday Dorothy , Duff Chris , Evans D Gareth
Published: August 2024

Background

Most schwannomas are isolated tumours occurring in otherwise healthy people. However, bilateral vestibular schwannomas (BVS) or multiple non-vestibular schwannomas indicate an underlying genetic predisposition. This is most commonly
NF2
-related schwannomatosis (SWN), but when BVS are absent, this can also indicate
SMARCB1
-related or
LZTR1
-related SWN.

Methods

We assessed the variant detection rates for the three major SWN genes (
NF2
,
LZTR1
and
SMARCB1
) in 154 people, from 150 families, who had at least one non-vestibular schwannoma, but who did not meet clinical criteria for
NF2
-related SWN at the time of genetic testing.

Results

We found that 17 (11%) people from 13 families had a germline
SMARCB1
variant and 19 (12%) unrelated individuals had a germline
LZTR1
variant. 19 people had an
NF2
variant, but 18 of these were mosaic and 17 were only detected when 2 tumours were available for testing. The overall detection rate was 25% using blood alone, but increased to 36% when tumour analysis was included. Another 12 people had a germline variant of uncertain significance (VUS).

Conclusions

There were similar proportions of
LZTR1
,
SMARCB1
or mosaic
NF2
. However, since an
NF2
variant was detected in tumours from 103 people, it is likely that further cases of mosaicism would be detected if more people had additional tumours available for analysis. In addition, if further evidence becomes available to show that the VUSs are pathogenic, this would significantly increase the proportion of people with a genetic diagnosis. Our results indicate the importance of comprehensive genetic testing and improved variant classification.
Impact of COVID-19 on recorded blood pressure screening and hypertension management in England: an analysis of monthly changes in the quality and outcomes framework indicators in OpenSAFELY
Authors: Wiedemann Milan , Speed Victoria , Cunningham Christine , Higgins Rose , Curtis Helen J , Andrews Colm , Fisher Louis , Hopcroft Lisa , Rentsch Christopher T , Mahalingasivam Viyaasan , Tomlinson Laurie , Morton Caroline , Samuel Miriam , Green Amelia , Wood Christopher , Brown Andrew D , Massey Jon , Walters Caroline , Smith Rebecca M , Inglesby Peter , Evans David , Maude Steven , Dillingham Iain , Walker Alex J , Morley Jessica , Mehrkar Amir , Bacon Seb , Bates Chris , Cockburn Jonathan , Parry John , Hester Frank , McManus Richard J , Goldacre Ben , MacKenna Brian
Published: August 2024

Background

The COVID-19 pandemic disrupted cardiovascular disease management in primary care in England.

Objective

To describe the impact of the pandemic on blood pressure screening and hypertension management based on a national quality of care scheme (Quality and Outcomes Framework, QOF) across key demographic, regional and clinical subgroups.

Methods

With NHS England approval, a population-based cohort study was conducted using OpenSAFELY-TPP on 25.2 million NHS patients registered at general practices (March 2019 to March 2023). We examined monthly changes in recorded blood pressure screening in the preceding 5 years in patients aged ≥45 years and recorded the hypertension prevalence and the percentage of patients treated to target (≤140/90 mmHg for patients aged ≤79 years and ≤150/90 mmHg for patients aged ≥80 years) in the preceding 12 months.

Results

The percentage of patients aged ≥45 years who had blood pressure screening recorded in the preceding 5 years decreased from 90% (March 2019) to 85% (March 2023). Recorded hypertension prevalence was relatively stable at 15% throughout the study period. The percentage of patients with a record of hypertension treated to target in the preceding 12 months reduced from a maximum of 71% (March 2020) to a minimum of 47% (February 2021) in patients aged ≤79 years and from 85% (March 2020) to a minimum of 58% (February 2021) in patients aged ≥80 years before recovery. Blood pressure screening rates in the preceding 5 years remained stable in older people, patients with recorded learning disability or care home status.

Conclusions

The pandemic substantially disrupted hypertension management QOF indicators, which is likely attributable to general reductions of blood pressure measurement including screening. OpenSAFELY can be used to continuously monitor changes in national quality-of-care schemes to identify changes in key clinical subgroups early and support prioritisation of recovery from care disrupted by COVID-19.
Use of downstream stress imaging tests for risk stratification of patients presenting to the emergency department with chest pain and low HEART score
Authors: Abazid Rami M , Patil Nilkanth , Elrayes Maged , Awadallah Sameh , Ibrahim Mohamed M , Alaref Amer , Bureau Yves , Akincioglu Cigdem , Bagur Rodrigo , Tzemos Nikolaos
Published: August 2024

Background

Patients with low HEART (History, Electrocardiogram, Age, Risk factors, and Troponin level) risk scores who are discharged from the emergency department (ED) may present clinical challenges and diagnostic dilemmas. The use of downstream non-invasive stress imaging (NISI) tests in this population remains uncertain. Therefore, this study aims to investigate the value of NISI in risk stratification and predicting cardiac events in patients with low-risk HEART scores (LRHSs).

Methods

We prospectively included 1384 patients with LRHSs between March 2019 and March 2021. All the patients underwent NISI (involving myocardial perfusion imaging/stress echocardiography). The primary endpoints included cardiac death, non-fatal myocardial infarction and unplanned coronary revascularisation. Secondary endpoints encompassed cardiovascular-related admissions or ED visits.

Results

The mean patient age was 64±14 years, with 670 (48.4%) being women. During the 634±104 days of follow-up, 58 (4.2%) patients experienced 62 types of primary endpoints, while 60 (4.3%) developed secondary endpoints. Multivariable Cox models, adjusted for clinical and imaging variables, showed that diabetes (HR: 2.38; p=0.008), HEART score of 3 (HR: 1.32; p=0.01), history of coronary artery disease (HR: 2.75; p=0.003), ECG changes (HR: 5.11; p<0.0001) and abnormal NISI (HR: 16.4; p<0.0001) were primary endpoint predictors, while abnormal NISI was a predictor of secondary endpoints (HR: 3.05; p<0.0001).

Conclusions

NISI significantly predicted primary cardiac events and cardiovascular-related readmissions/ED visits in patients with LRHSs.
Comprehensive assessment of vaginal infections using a single swab
Authors: Van Der Pol Barbara , Aycock Cheri , Dixon Paula , Kodsi Salma , Paradis Sonia , Torres-Chavolla Edith , Parvu Valentin
Published: August 2024

Background

The decision to use a particular test to diagnose patients presenting with symptoms of vaginitis and/or STI is based primarily on the prevailing standards of care in the clinic at which the patient evaluation takes place. As a result, laboratory testing of vaginal samples for these patients often involves either an STI or a vaginitis test, but rarely both options simultaneously, which complicates the diagnosis and management of concurrent infections.

Methods

Using de-identified remnant vaginal specimens from symptomatic patients previously tested for STI (Chlamydia trachomatis(CT),Neisseria gonorrhoeae(GC) andTrichomonas vaginalis(TV)) with the Becton Dickinson (BD) CTGCTV2 assay for BD MAX System, positivity for bacterial vaginosis (BV) andCandidaspp (associated with vulvovaginal candidiasis (VVC)) were evaluated using the molecular-based BD MAX Vaginal Panel.

Findings

The rate of STI/BV co-infection was 79.4% (227/286) in this symptomatic population, while that of STI/VVC was 27.0% (77/285). Women diagnosed with any one of the three STIs tested had an OR 2.86 (95% CI, 1.99, 4.11; p<0.0001) for a concurrent BV infection and OR 0.96 (95% CI, 0.67, 1.37; p=0.8085) for infection withCandidaspecies.

Conclusion

Our results suggest that women being tested for STI have a high prevalence of co-infection with BV and a lower, although appreciable, prevalence of co-infection with VVC. The detection of co-occurring vaginal infections can be facilitated by molecular testing using a single sample.
Smoke-free legislation impact on the hospitality sector in the Republic of Georgia
Authors: Bakhturidze George D , Popova Lucy , Kirpich Alexander , Berg Carla J , Eriksen Michael P
Published: August 2024

Introduction

Comprehensive smoke-free (SF) policies reduce secondhand smoke exposure and improve population-level health outcomes. However, some decision-makers heed the tobacco industry’s argument that SF policies negatively impact the hospitality sector. This study examines the intermediate economic impact of the Republic of Georgia’s SF legislation (effective since early 2018) on the hospitality sector in Georgia.

Methods

Analyses used 2015–2019 hospitality sector data from Georgia’s National Statistics Office. Simple linear regression models were conducted to examine the impact of Georgia’s SF policy on economic indicators (ie, number of employees, food service facilities, hotels and international visitor trips; employee remuneration; production value; turnover; hospitality sector value added tax (VAT)).

Results

Analyses indicated no negative impact on any of the economic indicators. Instead, from 2018 to 2019, the number of food service facilities, hotels and international visitor trips increased by 20%, 17% and 7%, respectively. Additionally, there were increases in the number of employees (9%), average employee remuneration (3%), production values (13%), turnover/total revenue (13%) and VAT (26%). Moreover, the economic indicator values during the studied period were strongly correlated with each other (p>0.95) and indicated a consistent and uniform improvement.

Conclusions

After the SF legislation went into effect, the hospitality sector demonstrated significant growth and no adverse effects in the economic indicators studied. The findings underscore the importance of maintaining and enforcing SF policies in Georgia and expanding the evidence base for SF policies globally.
Field theory with the Maxima computer algebra system
Authors: Toth Viktor T.
Published: August 2024

The Maxima computer algebra system, the open-source successor to MACSYMA, the first general-purpose computer algebra system that was initially developed at the Massachusetts Institute of Technology in the late 1960s and later distributed by the United States Department of Energy, has some remarkable capabilities, some of which are implemented in the form of add-on, “share” packages that are distributed along with the core Maxima system. One such share package is itensor, for indicial tensor manipulation. One of the more remarkable features of itensor is functional differentiation. Through this, it is possible to use itensor to develop a Lagrangian field theory and derive the corresponding field equations. In this paper, we demonstrate this capability by deriving Maxwell’s equations from the Maxwell Lagrangian, and exploring the properties of the system, including current conservation.
Acupuncture: An Overview on Its Functions, Meridian Pathways and Molecular Mechanisms
Authors: Han Rong , Hu Jinlian
Published: August 2024

Recent research has extensively explored the intricate mechanisms that underlie the effectiveness of acupuncture, highlighting the importance of stimulating acupoints, the role of acupuncture techniques in managing diseases, and the interaction between meridian pathways and molecular processes. Studies have underscored the crucial role of acupuncture in activating neurons, modulating the immune system, and influencing vascular activity, all of which contribute significantly to its therapeutic benefits across a wide range of symptoms and conditions. Utilization of imaging modalities enables the identification of changes in cerebral blood flow, brain function, and regional glucose metabolism following acupuncture sessions. The interstitial fluid circulation network within meridians adheres to specific laws that facilitate the transportation of materials. Acupuncture initiates the release of neurotransmitters, neuropeptides, and immune factors, impacting pain perception, inflammation, and physiological functions. It influences the complex neuro–endocrine–immune network by activating pathways involving the nervous system, the hypothalamic-pituitary-adrenal axis, and immune responses. Moreover, acupuncture induces molecular modifications such as phosphorylation, methylation, and histone modification, leading to key molecular changes that ultimately result in anti-inflammatory effects and the regulation of immune responses.
Study on the Chemical Constituents, Pharmacological Activities, and Clinical Application of Taxus
Authors: Gao Xinyu , Guo Yifan , Chen Kuilin , Wang Hanyi , Xie Weidong
Published: August 2024

Taxus, a rare and protected genus predominantly distributed across the Northern Hemisphere’s temperate regions, has garnered global attention due to its significant potential in medical research and pharmaceutical development, bolstered by advancements in cultivation techniques and medical technology. This review primarily focuses on the chemical constituents and pharmacological activities of Taxus, underscoring the progress and potential of these components in clinical applications. Recent studies have revealed that Taxus contains not only taxane active components but also flavonoids and polysaccharides with distinct activities. These compounds from Taxus exhibit potent antitumor, anti-inflammatory, immunomodulatory, antibacterial, and antidiabetic properties with evident mechanisms of action. Notably, the representative compound, paclitaxel, has demonstrated significant efficacy in treating various cancers, such as ovarian, breast, and lung cancer. This paper also reviews the basic situation of Taxus drug formulations, with extracts primarily administered orally and monomeric taxanes typically via injection, reflecting a mature development stage with ongoing research into oral formulations. Finally, this review summarizes the pharmacokinetic characteristics of crucial compounds in Taxus, including their absorption, distribution, metabolism, and excretion patterns in the human body. These pharmacokinetic profiles provide crucial guidance for evaluating the overall dosing regimen of Taxus and its components. The paper concludes with a forward-looking analysis of the potential applications of these compounds in disease treatment, envisioning their role in the future of medical and pharmaceutical advancements.
Schwarzschild spacetime in fractal dimensions: Deflection of light, supermassive black holes and temperature effects
Authors: El-Nabulsi Rami Ahmad , Anukool Waranont
Published: August 2024

In this paper, we construct a Schwarzschild metric in the fractal dimension based on fractal calculus, and we study the deflection of light near the sun. It was observed that the new fractal spherically symmetric spacetime metric may describe supermassive black holes, which have been detected through various astronomical observations such as SDSS. We have discussed the deflection of light by the gravitational field of the sun. It was observed that the total deflection of light is affected by the fractal dimension. To estimate the numerical value of the fractal dimension, we have used the 2004[Formula: see text]analysis of almost 2 million VLBI observations of 541 radio sources made by 87 VLBI sites, which estimates the factor [Formula: see text] based on PNN formalism. It was observed that the fractal dimension is roughly less than unity. However, our analysis showed that large bending gravity gives fractal dimensions lower than unity to some extent. We also gave a naïve idea about the emission thermal Unruh process in fractal dimension. It was observed that both the temperature and the entropy of black holes are affected by the fractal dimension, and that for low numerical values, the entropy of the black hole is enhanced.
Probing 4 × 4 quark mixing matrix
Authors: Kaur Gurjit , Ahuja Gulsheen , Shukla Dheeraj , Gupta Manmohan
Published: August 2024

Without adhering to any specific model, we have presented 4 [Formula: see text] 4 quark mixing matrix as an extension of the 3 [Formula: see text] 3 Particle Data Group (PDG) parametrization of the Cabibbo–Kobayashi–Maskawa (CKM) matrix Using unitarity constraints as well as the hierarchy among the elements of the 3 [Formula: see text] 3 CKM matrix, we have found the hierarchy among the fourth row and fourth column elements of the 4 [Formula: see text] 4 quark mixing matrix. Further, for the fourth generation case, we have explicitly found the nine independent rephasing invariant parameters [Formula: see text]. Also, using phenomenological estimates of the fourth row and fourth column elements, we have numerically evaluated these nine parameters.
IoMT-Based Smart Intelligent Healthcare System Using Optimization-Driven Deep Residual Network for Brain Tumor Detection
Authors: Sivakumaran AR. , Shanthakumar P. , Joel M. Robinson
Published: August 2024

Medical information system, like the Internet of Medical Things (IoMT), has gained more attention in recent decades. Disease diagnosis is an important facility of the medical healthcare system. Wearable devices become popular in a wide range of applications in the health monitoring system and this has stimulated the increasing growth of IoMT. Recently, a smart healthcare system has been more effective, and various methods have been developed to classify the disease at the beginning stage. To capture the patient’s information and detect the disease, a new framework is designed using the developed Conditional Auto regressive Mayfly Algorithm (CAMA)-based Deep Residual Network (DRN). Initially, pre-processing is done by the T2FCS filtering technique to increase the image quality by eliminating noises. The second step is segmentation. Here, the segmentation of brain tumor is done using U-Net. After that, data augmentation is performed to enhance image dimensions using the techniques, such as flipping, shearing, and translation to solve the issues of data samples. After processing the data augmentation mechanism, the next step is brain tumor detection, which is done using DRN. Here, DRN is trained by the proposed CAMA, which is the integration of conditional auto regressive value at risk (CAViaR) with the mayfly algorithm (MA). The developed model reduces computational complexity and increases effectiveness and robustness. The proposed CAMA-based DRN outperformed with an utmost testing accuracy of 0.921, sensitivity of 0.931, specificity of 0.928, distance of 52.842 and trust of 0.697.
Image Denoising Using Deblur Generative Adversarial Network Denoising U-Net
Authors: Usha Rani B. , Aruna R. , Velrajkumar P. , Amuthan N. , Sivakumar N.
Published: August 2024

Convolutional neural networks (CNNs) are becoming increasingly popular for image denoising. U-Nets, a type of CNN architecture, have been shown to be effective for this task. However, the impact of shallow layers on deeper layers decreases as the depth of the network increases. To address this issue, the authors propose a new image denoising method called DGANDU-Net. DGANDU-Net combines the DeblurGAN design with a U-Net architecture. This combination allows DGANDU-Net to effectively remove noise from images while preserving fine details. The authors also propose the use of two loss functions, mean square error (MSE) and perceptual loss, to improve the performance of DGANDU-Net. MSE is used to learn and improve the extracted features, while perceptual loss is used to produce the final denoised image. The authors evaluate the performance of DGANDU-Net on a variety of noise levels and find that it outperforms other state-of-the-art denoising algorithms in terms of both visual quality and two evaluation indices, including peak signal-to-noise ratio (PSNR) and Structural Similarity Index Measure (SSIM). Specifically, for extremely noisy environments with a noise standard deviation of 75, DGANDU-Net achieves an average PSNR of 37.39[Formula: see text]dB in the test dataset. The authors conclude that DGANDU-Net is a promising new method for image denoising that has the potential to significantly improve the quality of medical images used for diagnosis and treatment.
A Multi-Agent Financial Investment Decision Method Based on Evolutionary Game
Authors: Guan Yuhao , Zang Ran , Chen Yu
Published: August 2024

Data-driven smart investment decisions are important for financial development, which has not received much attention from academia. As a result, this paper resorts to the evolutionary game theory, and proposes a novel multi-agent financial investment decision method. Specifically, an evolutionary game theory-based decision-making approach is formulated as the main model for the research purpose. By considering the strategic choices and adaptability among various entities, a comprehensive analysis of the behavior and decision-making process of entities in the financial market is achieved. This paper combines stock exchanges and financial data providers (Bloomberg and Thomson Reuters) to conduct case studies on this method, verifying its effectiveness and feasibility in practical applications. By comparing traditional financial investment decision-making methods, it can be seen that the proposal has significant advantages in improving investment efficiency, reducing risks, and responding to market volatility. This paper delves into the multi-agent financial investment decision-making method based on the evolutionary game, providing new ideas and methods for academic research and practical applications in the financial field.
Decentralized CP-ABE Scheme for Enhanced University Data Security Using Blockchain
Authors: Zhao Zhao , Guan Han , Li Zixuan
Published: August 2024

In studying ample data security in universities, it is essential to protect the privacy and integrity of data. Current schemes utilize blockchain technology for data sharing, where cloud servers are responsible for storing ciphertext and performing partial encryption and decryption calculations. Data encryption technology can effectively prevent hacker attacks and ensure the integrity of data transmission. However, cloud servers that are only partially trusted may tamper with the ciphertext or return incorrect results. To solve these problems, this paper improves the privacy of access policy, attribute revocation and data authenticity of traditional CP-ABE algorithm. A protection scheme for ample public data security in universities is proposed, and a decentralized network structure based on multiple nodes is designed to improve the efficiency of data queries. The simulation results show that the scheme performs well in the scenario where the number of system attributes is moderate and the complexity of the access policy is not high.
Hypertension Detection Through Speech Analysis Using Machine Learning-Based Approaches with the Identification of BP Sensitive Phonemes and Features
Authors: Malakar Mousumi , Keskar Ravindra B. , Zadgaonkar Ajit
Published: August 2024

There are certain difficulties and unpleasant issues related to conventional diagnostic tools. These factors tilted the researchers toward finding an alternative non-invasive way of diagnosis. This alternate approach usually involves physiological and lifestyle-related data. The non-invasive tools are more convenient for common people as they are user-friendly and have no side effects. At the same time, they are cost-effective as well. The non-invasive diagnosis is also preferred by the people who live in places where medical facilities are not abundant. This study concentrates on detecting a person as hypertensive by analyzing certain parameters in speech using machine learning approaches. We identify some phonemes and features of speech that are more sensitive to capture the distortions in speech due to hypertension. Four different machine learning methods involving both classical and state-of-the-art methods in our study show the effectiveness of both types of machine learning methods in different dimensions. The study shows inspiring results in terms of prediction accuracy ([Formula: see text]95%) as well as identifying a minimal set of hypertension-sensitive features. It is also found that when we combine the predictions of both classical and state-of-the-art methods, the result gives more reliable predictions.
Research on Intelligent Digital Manufacturing Platform Based on Artificial Intelligence Deep Neural Network
Authors: Wu Ming , Ye Long , Han Huijie , Tong Jun , Wang Zhenguo
Published: August 2024

In today’s information age, intelligent manufacturing has become a significant trend of future industrial development. The neural network plays a vital role as the basis of smart manufacturing. This is a kind of intelligent algorithm that can simulate human brain organization, with strong learning ability and computing power, and has achieved remarkable results in the application field; the most typical representative is deep neural network, especially in recent years, an artificial intelligence developed based on deep neural network has entered the application stage, providing a new idea and model for industrial production. Based on this, taking the clothing production process as an example, this paper combined transfer learning and network fine-tuning technology to build a Faster RCNN model based on the ResNet-101 network to realize the positioning and recognition of clothing style map features and create an intelligent generation platform for clothing process flow on this basis.
The total and the partial charge-changing cross-sections of 28Si fragmentation on C, CH2 and H targets at 500–800 A MeV
Authors: Dang Ying-Hua , Kang Jing-Xiao , Wang Ying , Li Yao , Zhang Ru , Li Jun-Sheng , Zhang Dong-Hai , Kodaira Satoshi , Yasuda Nakahiro
Published: August 2024

The total and the partial charge-changing cross-sections of [Formula: see text]Si fragmentation on C, CH2 and H targets at the intermediate and high energies of 500–800[Formula: see text]A[Formula: see text]MeV are investigated using CR-39 plastic nuclear track detector. The total charge-changing cross-sections for H target are calculated based on the results of C and CH2 targets. It is found that the total charge-changing cross-sections for fragmentation of [Formula: see text]Si on C, CH2 and H targets are independent on the beam energy but increase with the mass of the targets in our studied energies, and are consistent with the predictions of Bradt–Peters semi-empirical formula and NUCFRG2 simulation codes. The partial cross-sections for projectile fragment production are independent on the beam energy in our present studied energies and show a significant odd–even staggering effect. The partial cross-sections for projectile fragment production are also compared with different predicted models, such as Cummings, Nilsen, EPAX2, EPAX3 and FRACS. Only the FRACS model reproduces a significant odd–even staggering effect, while its strength is weaker than the experimental data. Generally, these models can reproduce the experimental data of [Formula: see text]Si on the CH2 target well compared with the C target.
MACHINING OF INCONEL-825 SUPERALLOY WITH MAGNETIC FIELD ASSISTED ELECTRICAL DISCHARGE TURNING: A OPTIMIZATION APPROACH USING RESPONSE SURFACE METHODOLOGY AND MULTI-OBJECTIVE GENETIC ALGORITHM
Authors: VARSHNEY ROOPAK , SINGH PARAM
Published: August 2024

The aim of this study is to highlight the importance of optimizing machining parameters to improve the performance and surface integrity of Inconel-825 superalloy using the Electrical Discharge Turning (EDT) process, an important configuration of Electrical Discharge Machining (EDM). The study uses a Face-Centered Central Composite Design (FCCCD) to conduct experiments and applies the Response Surface Methodology (RSM) and multi-objective genetic algorithm (MOGA) to optimize input parameters. Various factors like Gap Current (Ig), pulse on time (Ton), rotational speed (N), and Magnetic field assistance (B) are adjusted at different levels, while outcomes such as Material Removal Rate (MRR), Tool Wear Rate (TWR), Overcut (OC), and Surface Roughness (Ra) are measured. Analysis of Variance (ANOVA) is used to understand the impact of each input factor on the outcomes. The results demonstrate that both RSM and MOGA provide accurate predictions closely aligned with experimental results, with MOGA showing a slight advantage in predicting tool wear and surface roughness. Specifically, the RSM solution achieved a desirability of 0.693 with parameters Ig at 8 A, Ton at 48.082[Formula: see text][Formula: see text]s, speed at 1399.988[Formula: see text]RPM, and magnetic field at 0.3[Formula: see text]T, achieving MRR of 5.182[Formula: see text]mg/min, TWR of 3.138[Formula: see text]mg/min, OC of 166.716[Formula: see text][Formula: see text]m, and Ra of 2.047[Formula: see text][Formula: see text]m. The MOGA solution featured parameters Ig at 8.045 A, Ton at 48.557[Formula: see text][Formula: see text]s, speed at 1360.3 RPM, and magnetic field at 0.3[Formula: see text]T, yielding an MRR of 5.169[Formula: see text]mg/min, TWR of 2.983[Formula: see text]mg/min, OC of 170.037[Formula: see text][Formula: see text]m, and Ra of 2.060[Formula: see text][Formula: see text]m. SEM analysis confirmed improved surface quality under optimized conditions, while XRD analysis showed significant grain refinement and increased dislocation density.
Horizontal Dynamic Response of an Offshore Pile in Saturated Soil Considering Radial Inhomogeneity
Authors: Fu Peng
Published: August 2024

An analytical approach is proposed for analyzing the horizontal dynamic response of an offshore pile, considering the soil as a saturated porous medium with radial heterogeneity, and accounting for the dynamic pile–water interaction. The soil is divided radially into two zones: a homogeneous outer zone and a heterogeneous inner zone consisting of finite concentric annular sub-zones. The horizontal resistance of the radially inhomogeneous soil layer is determined using the transfer matrix method. Subsequently, by utilizing the calculated soil resistance and the hydrodynamic force acting on the pile, the solution for the horizontal vibration of the pile is derived. The accuracy of the proposed approach is validated through comparisons with results from previous studies. Furthermore, a parametric study is conducted to explore the horizontal dynamic response of offshore piles within radially inhomogeneous saturated soil. The investigation demonstrates that the horizontal dynamic response of offshore piles is significantly influenced by the level of soil disturbance, range of disturbance zone, soil permeability, and water depth.
The Effect of CNTs and GPLs Nanoparticles on the Low-Velocity Impact Analysis of Curved Beam as Bumper of Car
Authors: Guo Honghe , Zhou Xiaoguang , Liu Zhenyu , Ouni Mohamed Hechmi El
Published: August 2024

This study investigates the impact of Carbon Nanotubes (CNTs) and Graphene Platelets (GPLs) on the low-velocity impact behavior of curved beams. Nanofillers (CNT or GPL) are evenly distributed within each layer, but their volume fraction varies from layer to layer, creating a functionally graded pattern. The Halpin–Tsai micromechanical model is employed to predict the effective properties of the nanocomposites. The contact force between the plate and projectile is estimated using Hertzian contact law. The First-order Shear Deformation Theory (FSDT), Hamilton’s principle, finite element and Newmark integration methods are employed to model the problem. The effects of different initial velocity and mass of the impactor on the impact behavior are investigated. The analysis also takes into account the impact of different weight fractions of nanoparticles and investigates four unique reinforcement patterns: X, O, U, and V. Curved beams reinforced with GPL showed less center deflection compared to those reinforced with CNT at the same weight fraction. As the weight fraction increased, the difference in center deflection between GPL and CNT reinforcements became less significant, ultimately leading to their deflection responses converging at a weight fraction of 0.75.
Preparation of three-qubit states
Authors: Perdomo Oscar , Castaneda Nelson , Vogeler Roger
Published: August 2024

In this paper, we present a new and algebraically simple algorithm that prepares any pure three-qubit state using only local gates and at most three controlled-[Formula: see text] gates. In 2008, Znidaric et al. already showed that three is the optimal number of controlled-[Formula: see text] gates required for the preparation of three-qubit states if in addition to those gates only local gates are used. If we restrict the local gates to [Formula: see text] gates, our algorithm provides a way to prepare any pure three-qubit state [Formula: see text] with real amplitudes using at most four controlled-[Formula: see text] gates when its hyperdeterminant is negative and three or fewer controlled-[Formula: see text] gates otherwise. We conjecture the existence of three-qubit states with real amplitudes that cannot be prepared using only [Formula: see text] gates and less than four controlled-[Formula: see text] gates.
Mixture of fluids in f(R,T) theory of gravity with cosmological constant Λ
Authors: Solanke Y. S. , Mhaske Sandhya , Dagwal V. J. , Pawar D. D.
Published: August 2024

We have examined a [Formula: see text] cosmological model with mixture of two fluids by assuming a special law of variation of Hubble parameter proposed by Berman [Nuovo Cimento B 74 (1983) 182–186] that yields constant deceleration parameter in LRS-Bianchi type-I space-time in [Formula: see text] theory of gravity, where [Formula: see text] is the Ricci scalar and [Formula: see text] is the trace of the energy–momentum tensor. In this paper, one of the fluids represents the matter content of the universe and the other fluid is the cosmic microwave background. We have used the form [Formula: see text] with [Formula: see text] and [Formula: see text], where [Formula: see text] is any arbitrary coupling constant of [Formula: see text] gravity. Such a selection of functional [Formula: see text] leads to general relativistic field equations with trace [Formula: see text]-dependent term of cosmological constant [Formula: see text]. To obtain the exact solutions of field equations we have considered the equation of state [Formula: see text]. The different anisotropic physical models such as dust, radiation, hard and Zeldovich models are explained, Statefinder and om diagnostic parameter are also discussed.
A new Hilbert-type inequalities obtained via Cantor-type spherical coordinates
Authors: Vuković Predrag
Published: August 2024

In this paper, we introduce a class of Hilbert-type inequalities on a fractal set [Formula: see text]. Establishing higher-dimensional Cantor-type spherical coordinates is an essential step in our results. We also impose the corresponding requirements that lead to the best constants obtained in the fractal Hilbert-type inequalities. Our results are applied by comparing them with previous results that are well known from the literature.
Charged analog of anisotropic dark energy star in Rastall gravity
Authors: Rej Pramit
Published: August 2024

Dark energy is one of the potential strategies for preventing compact objects from gravitationally collapsing into singularities. Because it is the cause of the accelerated expansion of our universe, it has the greatest impact on the cosmos. Thus, it is plausible that dark energy will interact with any stellar object that is compact in the universe [Phys. Rev. D 103 (2021) 084042]. Our main goal in this work is to create a simplified model, in the Rastall gravitational framework, of a charged strange star coupled to anisotropic dark energy in Krori–Barua spacetime [J. Phys. A, Math. Gen. 8 (1975) 508]. Here, we consider a specific strange star object, Her [Formula: see text], with observed values of mass [Formula: see text] and radius [Formula: see text] km, so that we can develop our model. In this context, we began by modeling dark energy using the equation of state (EoS), in which the dark energy density is proportional to the isotropic perfect fluid matter-energy density. The Darmois–Israel condition has been used to calculate the unknown constants that are present in the metric. We perform a detailed analysis of the model’s physical properties, including the mass–radius relation, pressure, density, metric function, and dark energy parameters for different Rastall coupling parameter values. We also examine the stability and force equilibrium of our proposed stellar configuration. Following a comprehensive theoretical analysis, we discovered that our suggested model is both singularity-free and meets all stability requirements needed to be a stable, physically reasonable stellar model.
Comprehensive discussion of Krori–Barua Bardeen compact stars on modified f(R) theories of gravity with scalar potential
Authors: Malik Adnan , Almas Ayesha , Shafaq Attiya , Mofarreh Fatemah
Published: August 2024

The objective of this paper is to deal with physical features of charged anisotropic compact objects in the framework of [Formula: see text] modified theory of gravity. To accomplish the desired objective, we consider a spherically symmetric spacetime with charged anisotropic fluid distribution. In addition, we utilize the Krori–Barua metric, i.e. [Formula: see text] and [Formula: see text] for exploring the field equations. Moreover, we compare the interior boundary to the bardeen model as an exterior geometry to calculate the unknown constrains. Further, to check the existence of bardeen stellar structure, we discuss the behavior of physical properties such as energy density, pressure components, anisotropy, equation of state parameters, stability analysis, energy bounds, equilibrium condition, adiabatic index, compactness factor and surface redshift. Conclusively, all the obtained results show that the system under consideration is physically stable, free from singularity and viable.
Comprehensive study of exponential Krori–Barua stellar structures in modified f(R) gravity
Authors: Malik Adnan , Almas Ayesha , Farasat Shamir M. , Mofarreh Fatemah
Published: August 2024

The main purpose of this study is to explore various essential features of compact star configurations in the context of modified [Formula: see text] gravity using Krori–Barua (KB) spacetime. It is notable to say that we employ the exponential type [Formula: see text] gravity model to illustrate the entire set of a modified system of field equations. Further, we form the field equations for a spherically symmetric line element with an anisotropic matter source via KB metric potentials. We postulate the distinct stellar objects to obtain the values of the unknown parameters. Moreover, we accomplish various physical properties, such as the behavior of energy density and pressure evolution, mass and compactness functions, anisotropy, equilibrium conditions, and energy bonds, to appraise the consistency and reliability of our chosen model. It is shown that investigated stellar structure solutions confirm a steady consistency for anisotropic exponential spheres with KB metric potentials in Modified [Formula: see text] Gravity.
Generative Adversarial Network Optimization Algorithm Based on Adaptive Data Augmentation
Authors: Yu Yanan , Shi Dunhuang , Pan Qi
Published: August 2024

In the research of deep learning, an Unsupervised deep convolution-generated Generated Adversarial Network (UGAN) usually needs a large number of data samples to train. However, when faced with some small samples, the performance of the algorithm is often degraded due to over-fitting. Combined with specially designed data enhancement methods, a generated adversarial network optimization algorithm based on adaptive data augmentation (AdauGAN) is proposed. The adaptive data augmentation module is added before the discriminant network, and a spatial transformation is carried out simultaneously at the probability distribution level of generated data and real data. To alleviate the over-fitting phenomenon in the training process, the current enhancement intensity is adjusted adaptively after the over-fitting occurs. The proposed algorithm is verified on SVHN, CelebA and CIFAR-10 data sets. The Frechet Inception Distance (FID) values of AdauGAN achieve 22.10, 23.94, 34.87, respectively, which is close to or even higher than the training results of Deep Convolution Generated Adversarial Network (DCGAN) under all data. Extensive experiment results show that the proposed Adaugan has an excellent performance in small samples. Besides, in some cases, it can catch up with the large sample results of existing algorithms.
Student Apartment Access Control System Based on MTCNN-FaceNet Algorithm
Authors: Zhang Jing
Published: August 2024

In response to the security management issues of student apartments, a study is conducted on a student apartment access control system based on multitasking cascaded convolutional networks and FaceNet. Firstly, a face detection model is built based on an improved multi-task cascaded convolutional network, and then a face recognition model is built using FaceNet. The results showed that the detection accuracy of the multi-task cascaded convolutional network using the improved non-maximum suppression algorithm was 98.7%, which was higher than the traditional multi-task cascaded convolutional network and effectively improved the detection performance of the multi-task cascaded convolutional network. The face detection model based on the improved multi-task cascaded convolutional network had the shortest average detection time of 361[Formula: see text]s, the highest average detection accuracy of 90.3%, an accuracy of 99%, a recall rate of 98.5%, and an F1 value of 99%. While maintaining high detection efficiency, it also ensured the accuracy of detection. The average accuracy of the mask detection method based on the MobileNet V2 network was relatively high, at 98.96%. The facial recognition model based on FaceNet achieved a recognition accuracy of 99.15% for faces without masks and 92.04% for faces with masks, with the highest accuracy and recall rates of 99.3% and 99.6%. The model constructed in the study has good application effects in face detection, which helps to improve the security of the student apartment access control system.
Integrating Fuzzy Graph Theory into Cryptography: A Survey of Techniques and Security Applications
Authors: Singh Rashmi , Khalid Saifullah , Nishad D. K. , Ruchira
Published: August 2024

Since the advent of networked systems, fuzzy graph theory has surfaced as a fertile paradigm for handling uncertainties and ambiguities. Among the different modes of handling challenges created by the uncertainties and ambiguities of current networked systems, integrating fuzzy graph theory with cryptography has emerged as the most promising approach. In this regard, this review paper elaborates on potentially studying fuzzy graph-based cryptographic techniques, application perspectives, and future research directions. Since the expressive power of fuzzy graphs allows the cryptographic schemes to handle imprecise information and to enhance security in many domains, several domains have benefited, such as image encryption, key management, and attribute-based encryption. The paper analyzes in depth the research landscape, mainly by focusing on the varied techniques used, such as fuzzy logic for key generation and fuzzy attribute representation for access control policies. A comparison with performance metrics unveils the trade-offs and advantages of different fuzzy graph-based approaches in efficiency, security strength, and computational overhead. Additionally, the survey explores the security applications of fuzzy graph-based cryptography and underpins potential development for secure communication in wireless sensor networks, privacy-preserving data mining, fine-grained access control in cloud computing, and blockchain security. Some challenges and research directions, such as the standardization of fuzzy logic operators, algorithmic optimization, integration with emerging technologies, and exploitation of post-quantum cryptography applications, are also brought out. This review will thus bring insight into this interdisciplinary domain and stimulate further research for the design of more robust, adaptive, and secure cryptographic systems in the wake of rising complexities and uncertainties.
A Non-Probabilistic Strategy to Investigate Imprecisely Defined Nonlinear Eigenvalue for Structural Dynamic Problems
Authors: Panigrahi Paresh Kumar , Nayak Sukanta , Edalatpanah S. A.
Published: August 2024

Structural dynamic problems are influenced by many parameters which are uncertain in nature. As such, for better estimation, the problems can be considered with epistemic-type uncertainties. Due to the epistemic type of uncertainties, the modeled system appears in the form of an imprecise nonlinear eigenvalue problem. Therefore, this paper proposes a novel nonprobabilistic approach to analyze nonlinear eigenvalue problems in structural dynamics, particularly when dealing with imprecisely defined parameters. In order to solve the fuzzy nonlinear eigenvalue models, the same is to be transformed into a fuzzy eigenvalue problem. Traditional methods often rely on probabilistic frameworks, which may not adequately capture uncertainties inherent in real-world structural systems. The proposed approach leverages concepts from fuzzy set theory to handle imprecise data, providing a more robust and realistic analysis. Then, the developed method is used to solve a case study of structural dynamic problem. Here, a spring-mass system is demonstrated through the developed technique to quantify the uncertain field variables. Based on the imprecise system parameters, different fuzzy models are discussed in various cases and results are reported here. This framework offers valuable insights for engineers and researchers working on dynamic analysis of complex structures, paving the way for more reliable and informed decision-making in structural engineering applications.
Numerical Simulation of MHD Oldroyd-B Fluid with Thermal Radiation and Chemical Reactions Using Chebyshev Wavelets
Authors: Lakshmi B. N. , Prasad Sumana Krishna , Nargund Achala L. , Rathour Laxmi , Mishra Lakshmi Narayan , Mishra Vishnu Narayan
Published: August 2024

The study has been carried out to analyze the Magnetohydrodynamic (MHD) boundary layer flow, heat and mass transfer of the two-dimensional viscoelastic Oldroyd-B fluid over a vertical stretching sheet in the presence of thermal radiation and chemical reaction with suction/injection in a steady state. The governing equations of the system are partial differential equations, which then give rise to a set of highly nonlinear coupled ordinary differential equations using similarity transformations. The nonlinearity in the differential equations is dealt with quasilinearization technique. The resultant equations are numerically solved using Chebyshev wavelet collocation method. The effects of magnetic field, radiation, chemical reaction and buoyancy parameters are investigated. The numerical values of local skin friction coefficient, local Nusselt number and local Sherwood number are also tabulated and analyzed. We observe that the increase in buoyancy parameters enhances the velocity profiles and larger values of magnetic field decrease the velocity profiles but increases the temperature and concentration profiles. Error analysis has been done to check the convergence of the numerical scheme.