bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling method of a class of 5D Hamiltonian conservative hyperchaotic systems with adjustable signal amplitude
Authors: Zhang Zefeng , Huang Lilian , Liu Jin , Guo Qiang , Du Xiuli , Yu Changdong 
Published: September 2023

Abstract
Compared to dissipative chaotic systems, conservative chaotic systems have gained attention because they can avoid reconstruction attacks due to the absence of attractors. This paper reports a general method for constructing 5D Hamiltonian conservative hyperchaotic systems, mainly by coupling three 5D sub-rigid bodies with two identical axes to obtain 5D Euler equations, and then combining Hamiltonian energy and Casimir energy analysis to obtain a 5D conservative hyperchaotic system. This method is general and convenient, and the constructed conservative hyperchaotic system has good performance. In addition, this paper investigates the impact of parameters and initial values on system performance using energy analysis and proposes a simple signal amplitude adjustment method. This method has no restrictions on the mathematical models of chaotic systems, can quickly adjust signal amplitudes, and enhances the hyperchaotic characteristics of the system based on this method. Finally, the correctness of the theoretical and simulation analysis is verified using a DSP hardware platform.
A design of novel Gudermannian neural networks for the nonlinear multi-pantograph delay differential singular model
Authors: Sabir Zulqurnain , Alhazmi Sharifah E 
Published: September 2023

Abstract
In this paper, a new stochastic numerical platform through the Gudermannian neural network (GNN) based intelligent computing solver (GNNICS) is accessible for solving the nonlinear singular multi-pantograph delay differential (MP-DD) systems. In GNNICS, Gudermannian kernel is exploited to construct the neural network models of differential operators with different neurons for the nonlinear system along with the hybrid computing via global genetic algorithm (GA) and local refinements based active set (AS), i.e., GNN-GAAS method. A fitness function with GNN models is formulated for solving the MP-DD equation along with the optimization of design variables of the network using GAAS. To investigate the performance of the designed GNNICS based GNN-GAAS algorithm, three different variants of the MP-DD systems are used to assess the correctness, effectiveness, and robustness. The statistical investigations based on different performance are presented to authenticate the consistent accuracy, convergence, and stability of the designed GNN-GAAS algorithm. Furthermore, the negligible absolute error that are performed as 10−06 to 10−08 for solving the GNNICS based on GNN-GAAS algorithm.
Functional analysis in the body environment for a circularly polarized hybrid reconfigurable textile antenna
Authors: Sandeep D Ram , Madhav B T P , Salma S , Rao K Srinivasa , Govinda L , Hasitha Yepuri Aarthi , Rao M C 
Published: September 2023

Abstract
This paper presents the functional analysis of a hybrid reconfigurable circularly polarized (CP) textile antenna in different on-body environments. The Tai-Chi sign inspires the current textile antenna design, where the primary radiator and ground elements are sickle-shaped structures. Hybrid reconfigurability was achieved by successfully operating two RF PIN-Diodes in the basal ground plane’s elliptical and rectangular structures. Thus, four switching states are attained by operating the textenna at 3.5, 4.5 and 5.4 GHz in the S1. 4.39 and 5.8 GHz in S2, 5 and 5.9 GHz in S3 and 2.4, 4.3, 5.8, and 9.4 GHz in S4 state. The reconfigurable textenna was devised to function on the human body. Its functional behavior in proximity to the human body was rigorously validated through testing involving bending at various vertical and horizontal angles. Additionally, a Specific Absorption Rate (SAR) analysis was conducted using a three-layered human phantom. The results revealed that the maximum SAR value is below 1.6 W Kg−1 for 1 gram of tissue and 2 W kg−1 for 10 grams of tissue. These findings establish the safe operational parameters for the proposed model. The developed antenna functionalities were also tested in other circumstances like body sweating environment and interaction with body fluids (Blood, Plasma, SBF and DMEM). In these body fluid conditions, a peak gain of 4.88 dBi at S1, 6.97 dBi at S2, 7.99 dBi at S3, and 4.13 dBi at S4, was observed while interacting with Plasma, and the measured efficiency in the above configuration is between 77%–79%. The textenna durability analysis was measured using an electrochemical workstation while interacting with different body fluids. All these results confessed the applicability of the developed antennas for on-body wireless communication applications in different on-body conditions.
Extended Korteweg-de Vries equation for long gravity waves in incompressible fluid without strong limitation to surface deviation
Authors: Kruglov Vladimir I 
Published: September 2023

Abstract
We have derived the extended Korteweg-de Vries equation describing the long gravity waves without limitation to surface deviation. The only restriction to the surface deviation is connected with the stability condition for appropriate solutions. The derivation of extended KdV equation is based on the Euler equations for inviscid irrotational and incompressible fluid. It is shown that the extended KdV equation reduces to standard KdV equation for small amplitude of the waves. We have also generalized the extended KdV equation for describing the decaying effect of the waves. Quasi-periodic and solitary wave solutions for extended KdV equation with decaying effect are found as well. We also demonstrate that the fundamental approach based on the inverse scattering method is applicable for solving the extended KdV equation in the case when decaying effect is negligibly small. Such case always occur for restricted propagation distances of the waves.
Bayesian evaluation of energy dependent neutron induced fission yields*
Authors: Xiao 肖 Ming-Xiang 名翔 , Bao 包 Xiao-Jun 小军 , Wei 韦 Zheng 峥 , Yao 姚 Ze-En 泽恩 
Published: September 2023

Abstract
From both the fundamental and applied perspectives, fragment mass distributions are important observables of fission. We apply the Bayesian neural network (BNN) approach to learn the existing neutron induced fission yields and predict unknowns with uncertainty quantification. Comparing the predicted results with experimental data, the BNN evaluation results are found to be satisfactory for the distribution positions and energy dependencies of fission yields. Predictions are made for the fragment mass distributions of several actinides, which may be useful for future experiments.
SincMSNet: a Sinc filter convolutional neural network for EEG motor imagery classification
Authors: Liu Ke , Yang Mingzhao , Xing Xin , Yu Zhuliang , Wu Wei 
Published: September 2023

Abstract

Objective. Motor imagery (MI) is widely used in brain-computer interfaces (BCIs). However, the decode of MI-EEG using convolutional neural networks (CNNs) remains a challenge due to individual variability. Approach. We propose a fully end-to-end CNN called SincMSNet to address this issue. SincMSNet employs the Sinc filter to extract subject-specific frequency band information and utilizes mixed-depth convolution to extract multi-scale temporal information for each band. It then applies a spatial convolutional block to extract spatial features and uses a temporal log-variance block to obtain classification features. The model of SincMSNet is trained under the joint supervision of cross-entropy and center loss to achieve inter-class separable and intra-class compact representations of EEG signals. Main results. We evaluated the performance of SincMSNet on the BCIC-IV-2a (four-class) and OpenBMI (two-class) datasets. SincMSNet achieves impressive results, surpassing benchmark methods. In four-class and two-class inter-session analysis, it achieves average accuracies of 80.70% and 71.50% respectively. In four-class and two-class single-session analysis, it achieves average accuracies of 84.69% and 76.99% respectively. Additionally, visualizations of the learned band-pass filter bands by Sinc filters demonstrate the network’s ability to extract subject-specific frequency band information from EEG. Significance. This study highlights the potential of SincMSNet in improving the performance of MI-EEG decoding and designing more robust MI-BCIs. The source code for SincMSNet can be found at: https://github.com/Want2Vanish/SincMSNet.
A data expansion technique based on training and testing sample to boost the detection of SSVEPs for brain-computer interfaces
Authors: Xiao Xiaolin , Wang Lijie , Xu Minpeng , Wang Kun , Jung Tzyy-Ping , Ming Dong 
Published: September 2023

Abstract

Objective. Currently, steady-state visual evoked potentials (SSVEPs)-based brain-computer interfaces (BCIs) have achieved the highest interaction accuracy and speed among all BCI paradigms. However, its decoding efficacy depends deeply on the number of training samples, and the system performance would have a dramatic drop when the training dataset decreased to a small size. To date, no study has been reported to incorporate the unsupervised learning information from testing trails into the construction of supervised classification model, which is a potential way to mitigate the overfitting effect of limited samples. Approach. This study proposed a novel method for SSVEPs detection, i.e. cyclic shift trials (CSTs), which could combine unsupervised learning information from test trials and supervised learning information from train trials. Furthermore, since SSVEPs are time-locked and phase-locked to the onset of specific flashes, CST could also expand training samples on the basis of its regularity and periodicity. In order to verify the effectiveness of CST, we designed an online SSVEP-BCI system, and tested this system combined CST with two common classification algorithms, i.e. extended canonical correlation analysis and ensemble task-related component analysis. Main results. CST could significantly enhance the signal to noise ratios of SSVEPs and improve the performance of systems especially for the condition of few training samples and short stimulus time. The online information transfer rate could reach up to 236.19 bits min−1 using 36 s calibration time of only one training sample for each category. Significance. The proposed CST method can take full advantages of supervised learning information from training samples and unsupervised learning information of testing samples. Furthermore, it is a data expansion technique, which can enhance the SSVEP characteristics and reduce dependence on sample size. Above all, CST is a promising method to improve the performance of SSVEP-based BCI without any additional experimental burden.
Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient
Authors: Tang Jianpeng , Xi Xugang , Wang Ting , Wang Junhong , Li Lihua , Lü Zhong 
Published: September 2023

Abstract

Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system. Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model. Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals’ local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices. Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.
Lock-in: origination and significance within infrastructure systems
Authors: Helmrich Alysha , Chester Mikhail , Miller Thaddeus R , Allenby Braden 
Published: September 2023

Abstract
Infrastructure systems have legacies that continue to define their priorities, goals, flexibility, and ability to make sense of their environments. These legacies may or may not align with future needs, but regardless of alignment, they may restrict viable pathways forward. Infrastructure ‘lock-in’ has not been sufficiently confronted in infrastructure systems. Lock-in can loosely be interpreted as internal and external pressures that constrain a system, and it encourages self-reinforcing feedback where the system becomes resistant to change. By acknowledging and recognizing that lock-in exists at small and large scales, perpetuated by individuals, organizations, and institutions, infrastructure managers can critically reflect upon biases, assumptions, and decision-making approaches. This article describes six distinct domains of lock-in: technological, social, economic, individual, institutional, and epistemic. Following this description, strategies for unlocking lock-in, broadly and by domain, are explored before being contextualized to infrastructure systems. Ultimately, infrastructure managers must make a decision between a locked in and faltering but familiar system or a changing and responsive but unfamiliar system, where both are, inevitably, accepting higher levels of risk than typically accustomed.
Is Hypertension Diagnostic Testing and Diagnosis Associated With Psychological Distress?
Authors: Green B B , Anderson M L , McClure J B , Ehrlich K , Hall Y N , Hansell L , Hsu C , Margolis K L , Munson S A , Thompson M J 
Published: September 2023

Abstract

BACKGROUND
Psychological impacts of hypertension diagnostic testing and new hypertension diagnoses are unclear.

METHODS
BP-CHECK was a randomized diagnostic study conducted in 2017–2019 in an integrated healthcare system. Participants with no hypertension diagnosis or medications and elevated blood pressure (BP) were randomized to one of three diagnostic regimens: (i) Clinic, (ii) Home, or (iii) Kiosk. Participants completed questionnaires at baseline, after completion of the diagnostic regimens, and at 6 months. Outcomes included changes from baseline in health-related quality of life (HRQOL), BP-related worry, and thoughts about having a stroke or heart attack.

RESULTS
Participants (n = 482) were mostly over age 50 (77.0%), and White race (80.3%). HRQOL did not significantly change from baseline to 3 weeks or 6 months. Among all participants, BP-related worry and concerns about having a heart attack or stroke increased significantly from baseline to 3 weeks, with heart attack and stroke concerns significantly higher in the Kiosk compared Clinic and Home groups. At 6 months, thoughts about having a heart attack or stroke returned to baseline overall and in the Kiosk group, however BP-related worry was significantly higher among those with, compared to those without, a new hypertension diagnosis.

CONCLUSIONS
The hypertension diagnostic process did not lead to short-term or intermediate-term changes in self-reported HRQOL. However, BP-related worry increased short-term and persisted at 6 months among individuals with a new hypertension diagnosis. Results warrant validation in more representative populations and additional exploration of the impacts of this worry on psychological well-being and hypertension control.

ClinicalTrials.gov Identifier
NCT03130257.
KAS-Analyzer: a novel computational framework for exploring KAS-seq data
Authors: Lyu Ruitu , Wu Tong , Park Gayoung , He Yu-Ying , Chen Mengjie , He Chuan 
Published: September 2023

Abstract

Motivation
Kethoxal-assisted ssDNA sequencing (KAS-seq) is rapidly gaining popularity as a robust and effective approach to study the nascent dynamics of transcriptionally engaged RNA polymerases through profiling of genome-wide single-stranded DNA (ssDNA). Its latest variant, spKAS-seq, a strand-specific version of KAS-seq, has been developed to map genome-wide R-loop structures by detecting imbalances of ssDNA on two strands. However, user-friendly, open-source computational tools tailored for KAS-seq data are still lacking.

Results
Here, we introduce KAS-Analyzer, the first comprehensive computational framework aimed at streamlining and enhancing the analysis and interpretation of KAS-seq and spKAS-seq data. In addition to standard analyses, KAS-Analyzer offers many novel tools specifically designed for KAS-seq data, including, but not limited to: calculation of transcription-related metrics, identification of single-stranded transcribing (SST) enhancers, high-resolution mapping of R-loops, and differential RNA polymerase activity analysis. We provided a detailed overview of KAS-seq data and its diverse applications through the implementation of KAS-Analyzer. Using the example time-course KAS-seq datasets, we further showcase the robust capabilities of KAS-Analyzer for investigating dynamic transcriptional regulatory programs in response to UVB radiation.

Availability and implementation
KAS-Analyzer is available at https://github.com/Ruitulyu/KAS-Analyzer.
WSIC: a Python package and command-line interface for fast whole slide image conversion
Authors: Pocock Johnathan , Raza Shan E Ahmed , Minhas Fayyaz , Rajpoot Nasir 
Published: September 2023

Abstract

Summary
Whole slide images (WSIs) are multi-gigapixel images of tissue sections, which are used in digital and computational pathology workflows. WSI datasets are commonly heterogeneous collections of proprietary or niche specialized formats which are challenging to handle. This note describes an open-source Python application for efficiently converting between WSI formats, including common, open, and emerging cloud-friendly formats. WSIC is a software tool that can quickly convert WSI files across various formats. It has a high performance and maintains the resolution metadata of the original images. WSIC is ideal for pre-processing large-scale WSI datasets with different file types.

Availability and implementation
Source code is available on GitHub at https://github.com/John-P/wsic/ under a permissive licence. WSIC is also available as a package on PyPI at https://pypi.org/project/WSIC/.
DrForna: visualization of cotranscriptional folding
Authors: Tănasie Anda Ramona , Kerpedjiev Peter , Hammer Stefan , Badelt Stefan 
Published: September 2023

Abstract

Motivation
Understanding RNA folding at the level of secondary structures can give important insights concerning the function of a molecule. We are interested to learn how secondary structures change dynamically during transcription, as well as whether particular secondary structures form already during or only after transcription. While different approaches exist to simulate cotranscriptional folding, the current strategies for visualization are lagging behind. New, more suitable approaches are necessary to help with exploring the generated data from cotranscriptional folding simulations.

Results
We present DrForna, an interactive visualization app for viewing the time course of a cotranscriptional RNA folding simulation. Specifically, users can scroll along the time axis and see the population of structures that are present at any particular time point.

Availability and implementation
DrForna is a JavaScript project available on Github at https://github.com/ViennaRNA/drforna and deployed at https://viennarna.github.io/drforna
An extensive benchmark study on biomedical text generation and mining with ChatGPT
Authors: Chen Qijie , Sun Haotong , Liu Haoyang , Jiang Yinghui , Ran Ting , Jin Xurui , Xiao Xianglu , Lin Zhimin , Chen Hongming , Niu Zhangmin 
Published: September 2023

Abstract

Motivation
In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models (LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5 and GPT-4, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP-related tasks and benchmarks and got excellent results. With exciting performance on daily chat, researchers began to explore the capacity of ChatGPT on expertise that requires professional education for human and we are interested in the biomedical domain.

Results
To evaluate the performance of ChatGPT on biomedical-related tasks, this article presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions, and so on. Typical NLP tasks like named entity recognization, relation extraction, sentence similarity, question and answering, and document classification are included. Overall, ChatGPT got a BLURB score of 58.50 while the state-of-the-art model had a score of 84.30. Through a series of experiments, we demonstrated the effectiveness and versatility of ChatGPT in biomedical text understanding, reasoning and generation, and the limitation of ChatGPT build on GPT-3.5.

Availability and implementation
All the datasets are available from BLURB benchmark https://microsoft.github.io/BLURB/index.html. The prompts are described in the article.
Evidence of egg polymorphism in a host of Klaas’s cuckoo
Authors: Nahid Mominul Islam , Abernathy Virginia E , Liang Wei 
Published: September 2023

Abstract
Hosts may evolve more than one egg type within a population (egg polymorphism) to make mimicry of any one individual more difficult. This process of co-evolution between cuckoos and their hosts has been well studied in certain species but has been ignored in other, lesser-known species. Using avian visual modelling, reflectance spectrophotometry, digital image analysis and egg volume, we examined the degree of egg mimicry between Klaas’s cuckoo (Chrysococcyx klaas) and its host, the red-chested sunbird (Cinnyris erythrocercus), using museum collections. We found that the sunbird has evolved two distinct egg types based on luminance, colour and pattern, which we refer to as the light morph and the dark morph. Cuckoo eggs were significantly more similar to the dark morph eggs in luminance compared with the light morph eggs but were not more similar to dark morph eggs in colour, pattern or volume. To our knowledge, this is the first time that egg mimicry by Klaas’s cuckoo has been studied objectively, and egg polymorphism was described in the red-chested sunbird host. Future studies focused on living populations would help us to gain a better understanding of the co-evolutionary interactions between this cuckoo and its host species.