bluuebunny/crossref_metadata_2025_split huggingface dataset:
Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA
Authors: Lu Zhijie , Yan Xiaoan , Wang Zhiliang , Zhang Yuyan , Sun Jianjun , Ma Chenbo 
Published: March 2024

Abstract
The intricate nature of compound fault diagnosis in rolling bearings during nonstationary operations poses a challenge. To address this, a novel technique combines adaptive variational mode decomposition (AVMD) with improved multipoint optimal minimum entropy deconvolution adjustment (IMOMEDA). The compound fault signal is isolated through AVMD, with internal parameters obtained via a new indicator termed integrated fault-impact measure index guiding the improved dung beetle optimizer. An adaptive selection method, using a weight factor, chooses the intrinsic mode function containing principal fault data. IMOMEDA whose key parameters are determined by a novel combinatorial strategy is then employed to deconvolute selected fault components, enhancing periodic fault impulses by removing complex interferences and ambient noise. The deconvoluted signal undergoes enhanced envelope spectrum processing to extract fault frequencies and identify fault types. Numerical simulations and experimental data confirm the method’s effectiveness and feasibility for compound faults diagnosis of rolling bearings, showcasing its superiority over existing techniques.
A novel robust moving horizon estimator for discrete-time linear systems subject to measurement outliers
Authors: Wang Zhongxin , Liu Zhilin , Yuan Shouzheng , Ma Yingkai , Song Simeng 
Published: March 2024

Abstract
State estimation is a crucial problem in modern industries and has been widely applied across various fields. The performance of the estimator depends on the quality of the measurement data. Measurements being corrupted by outliers is becoming an unavoidable phenomenon that leads to degradation of estimator performance. It is critical to develop estimators with outlier suppression capabilities to mitigate the adverse impact of measurement outliers. In this paper, we propose an effective outlier suppression technique for discrete-time linear systems within the framework of moving horizon estimation (MHE). The proposed estimator solves the issues of poor estimation accuracy and low computational efficiency among the existing MHE-based outlier-robust estimators. Moreover, the proposed method allows us to not only achieve robust state estimation but also detect outliers. Specifically, we propose a set of least-squares cost functions and an outlier identification mechanism to implement the estimation process. Subsequently, the stability of the estimation error of the proposed estimator is demonstrated. The estimation error can achieve exponential convergence by choosing appropriate design parameters. Lastly, the proposed estimator is applied to target tracking simulations and compared with state-of-the-art outlier-robust estimation methods, confirming the effectiveness and superiority of the proposed estimator.
Flexible light-stimulated artificial synapse based on detached (In,Ga)N thin film for neuromorphic computing
Authors: Zhang Qianyi , Hou Binbin , Zhang Jianya , Gu Xiushuo , Huang Yonglin , Pei Renjun , Zhao Yukun 
Published: March 2024

AbstractBecause of wide range of applications, the flexible artificial synapse is an indispensable part for next-generation neural morphology computing. In this work, we demonstrate a flexible synaptic device based on a lift-off (In,Ga)N thin film successfully. The synaptic device can mimic the learning, forgetting, and relearning functions of biological synapses at both flat and bent states. Furthermore, the synaptic device can simulate the transition from short-term memory to long-term memory successfully under different bending conditions. With the high flexibility, the excitatory post-synaptic current of the bent device only shows a slight decrease, leading to the high stability. Based on the experimental conductance for long-term potentiation and depression, the simulated three-layer neural network can achieve a high recognition rate up to 90.2%, indicating that the system comprising of flexible synaptic devices could have a strong learning-memory capability. Therefore, this work has a great potential for the development of wearable intelligence devices and flexible neuromorphic systems.
Stability for a system of the 2D incompressible magneto-micropolar fluid equations with partial mixed dissipation
Authors: Lin Hongxia , Liu Sen , Zhang Heng , Sun Qing 
Published: March 2024

Abstract
This paper focuses on the 2D incompressible anisotropic magneto-micropolar fluid equations with vertical dissipation, horizontal magnetic diffusion, and horizontal vortex viscosity. The goal is to investigate the stability of perturbations near a background magnetic field in the 2D magneto-micropolar fluid equations. Two main results are obtained. The first result is based on the linear system. Global existence for any large initial data and asymptotic linear stability are established. The second result explores stability for the nonlinear system. It is proven that if the initial data are sufficiently small, then the solution for some perturbations near a background magnetic field remains small. Additionally, the long-time behaviour of the solution is presented. The most challenging terms in the proof are the linear terms in the velocity equation and the micro-rotation equation that will grow with respect to time t. We are able to find some background fields to control the growth of the linear terms. Our results reveal that some background fields can stabilise electrically conducting fluids.
Schrödinger equation as a confluent Heun equation
Authors: Figueiredo Bartolomeu Donatila Bonorino 
Published: March 2024

Abstract
This article deals with two classes of quasi-exactly solvable (QES) trigonometric potentials for which the one-dimensional Schrödinger equation reduces to a confluent Heun equation (CHE) where the independent variable takes only finite values. Power series for the CHE are used to get polynomial and nonpolynomial eigenfunctions. Polynomials occur only for special sets of parameters and characterize the quasi-exact solvability. Nonpolynomial solutions occur for all admissible values of the parameters (even for values which give polynomials), and are bounded and convergent in the entire range of the independent variable. Moreover, throughout the article we examine other QES trigonometric and hyperbolic potentials. In all cases, for a polynomial solution there is a convergent nonpolynomial solution.
Investigation of illumination-dependent electrical and photodiode properties and conduction mechanism of the Al/p-Si contact with Schiff base compound (Pyr-Pic) interlayer
Authors: Oruç Pınar , Tezcan Ali Osman , Tuğluoğlu Nihat , Eymur Serkan 
Published: March 2024

Abstract
In this paper, an organic semiconductor, N-(2-((pyren-4-yl)methyleneamino)ethyl)-5- nitropyridin-2-amine (Pyr-Pic), was synthesized and was used as an interfacial organic layer to fabrication of Al/Pyr-Pic/p-Si/Al diode. The device’s characteristic parameters were determined under various operating situations using current–voltage (I-V) measurements. Thermionic emission theory (TE), the Cheung methodology, and Norde functions were the three methods used to determine the device’s electrical properties, such as the ideality factor, barrier height, and series resistance. Based on the I-V measurements conducted under illumination circumstances, the fabricated device exhibits photoresponse properties in the reverse bias region. An examination of the forward log(I)-log(V) plot of the Al/Pyr-Pic/p-Si diode indicated that ohmic conduction dominates carrier transport in the lower bias regions, while the space-charge-limited current (SCLC) governs transport in the medium bias regions, and the trap-charge limit current (TCLC) mechanism is responsible for transport in the higher bias regions. The current voltage mechanisms in the reverse bias area can be characterized by using Poole–Frenkel emission (PFE) and Schottky emission (SE) theories. The device exhibits photovoltaic characteristics when it is illuminated through an Al electrode. The typical photovoltaic parameters were estimated at room temperature and under illumination conditions.
Stretchable and recyclable In3SnBi/PU nanofiber composite as flexible sensors for human motion monitoring
Authors: Zhang Cong , Luo Yuze , Hu Xiaotao 
Published: March 2024

Abstract
Low-melting-point metal alloys (LMMAs) have great potential for application in soft sensors. However, the high cost limits the application of gallium-based liquid metals. In this paper, LMMA In3SnBi has been designed and prepared through a facile melt-mixing process followed by an ultrasonic dispersion technique. To overcome the fragility of low-melting-point alloys, flexible electrospun polyurethane (PU) matrix is used as structural support. When used as a sensor, the composite film exhibits a response time of 54.37 ms within a strain range of 15%. In addition, the composite film exhibits self-healing properties, and cracks generated by cyclic stretching can be repaired through a hot-pressing process. The current work presents a versatile fabrication strategy for LMMA-based flexible sensors, which holds great promise in the field of wearable electronic devices.
Preparation and luminescent properties of SiC nanoparticles by strong pulse discharge in organic liquid phase environment
Authors: Du Kai , Wei Ronghui , Li Gaojie 
Published: March 2024

Abstract
SiC nanoparticles with relatively uniform particle distribution were successfully prepared using a continuous strong pulse discharge method using hexamethyldisilane as the organic liquid phase environment. The samples were subjected to x-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and Raman spectroscopy (Raman) tests. XRD and Raman data indicate that the particle composition is mainly composed of β-SiC. The electron microscope image illustrates particle sizes ranging from 5–20 nm and existing in an agglomerated state. Further photoluminescence (PL) results indicate that the annealing temperature under vacuum conditions has a significant positive effect on the luminescence intensity.
Digital light 3D printing of artificial bone with star-shaped polycaprolactone-based polyurethane acrylate
Authors: Yang Xinmeng , Qiu Shuiwei , Du Zhanhui , Xing Quansheng 
Published: March 2024

Abstract
Advanced medical materials and manufacturing technologies are highly in demand in artificial bones. Herein, a four-arm star-shaped polycaprolactone polyurethane acrylate (FPCLA) was designed and synthesized. The photosensitive character of FPCLA contributed to the rapid prototyping and personalized customization under digital light processing (DLP) 3D printing technology. The FPCLA was prepared by introducing unsaturated double bonds into polycaprolactone tetraethyl alcohol (PCLT). We characterized the physico-chemical properties of the material through FTIR, H-NMR, GPC, DSC and SEM. Cell behaviors on material were observed in vitro. In addition, we employed a DLP 3D printer to evaluate the feasibility of FPCLA to fabricate artificial bone model. The photocuring star polycaprolactone was confirmed in detail by detection method. SEM analyses demonstrated that FPCLA has good tenacity. The material can be used to fabricated artificial bone with a diameter of 3.02 mm at its narrowest by DLP 3D printing technology. The cell survival rates of CCK-8 and Live/Dead fluorescence staining experiments were both above 90%, which indicated safety and feasibility of such new-generation artificial bone made of synthetic polymers.
Plant pathogen infection risk and climate change in the Nordic and Baltic countries
Authors: Strandberg G , Andersson B , Berlin A 
Published: March 2024

Abstract
Climate change and global warming are already affecting food production, and the impact is predicted to intensify in the future. Previous studies have been based on global data and have provided general information about climate change effects on food production. Regional high-resolution data are, however, needed to evaluate the effect of future scenarios of climate change to support strategic and tactical planning to safeguard food production. Here, we provide results on the future potential distribution range of fungal plant pathogens in the Nordic and Baltic countries. This is done using regional climate model data at 12.5 km horizontal resolution. The temperature dependent infection risk and species richness are calculated using data for 80 plant pathogens. Within the region the studied pathogens will in most cases thrive more and be more abundant in a warmer climate; leading to a longer infection risk season and the introduction of new pathogens. This applies to all emissions scenarios, even though the effects are stronger with high emissions. Our results indicate that plant diseases will increase, and this will negatively affect crop production and food security.
Assessing urban sustainability in Saudi Arabia: an empirical evidence from Al-Medina Al-Munawwarah
Authors: Aldegheishem Abdulaziz 
Published: March 2024

Abstract
This study aims to assess the progress towards Sustainable Development Goal 11 (SDG 11) in Al-Madinah Al-Munawwarah, Saudi Arabia. The study also examines challenges that encounter SDG 11. To this end, six targets consisting of 40 indicators of SDG 11 have been adopted, relying on literature, international and national technical reports, and the personal perspectives of twelve experts. Overall progress towards SDG 11 has shown significant improvement considerably, with a middle level, particularly after Saudi Vision 2030 was approved. The findings show that three targets including affordable housing, sustainable transportation, and sustainable urbanization, have achieved average progress, and two targets, including air quality, and waste management, and access to green areas have been demonstrated low progress, as well as a single target, which is the preservation and protection of natural and cultural heritage, which has achieved optimal progress. The results highlight several challenges that hinder progress towards SDG 11, but the level of these challenges varies from one target to another target, ranging from moderate to major challenges. These challenges should be considered in continuing urban strategies and could be reduced by establishing resource-saving and innovative community urban renewal programs.
Intelligent passive infrared sensor based on learning vector quantization for human detection
Authors: Doan Tuan Kiet , Nguyen Thi Nhat Linh , Hoang Ngoc Chau , Le Minh Thuy , Nguyen Quoc Cuong 
Published: March 2024

Abstract
Passive Infrared (PIR) Sensors have been used widely in human detection indoors nowadays due to their low cost and range. However, traditional PIR sensors may get fault detection, especially when the human is in a static pose. To overcome this limitation, a Machine Learning (ML)-based PIR sensor is proposed in this work for detection accuracy enhancement. The Learning Vector Quantization (LVQ) approach is used to be easily implemented in the embedded device (which requires a low computational complexity) to provide a real-time response. The experimental scenarios to create the datasets are conducted in two distinct locations for training and testing purposes. In each location, participants performed a series of different activities and left the room unoccupied. Data is collected via a PIR sensor and then wireless transmitted to a computer for training and testing. In the test set, the presence of humans with an accuracy of 89.25 % is obtained using the proposed LVQ algorithm prediction. Finally, the LVQ is implemented on an embedded device based on Xtensa Dual-Core 32-bit LX6 CPU to form an intelligent PIR (iPIR)-based LVQ sensor, this novel iPIR sensor then is evaluated and tested with a remarkable result.
Multivariable machine learning models for clinical prediction of subsequent hip fractures in older people using the Chinese population database
Authors: Huang Wenbo , Wang Jie , Xu Jilai , Guo Guinan , Chen Zhenlei , Xue Haolei 
Published: March 2024

Abstract

Purpose
This study aimed to develop and validate clinical prediction models using machine learning (ML) algorithms for reliable prediction of subsequent hip fractures in older individuals, who had previously sustained a first hip fracture, and facilitate early prevention and diagnosis, therefore effectively managing rapidly rising healthcare costs in China.

Methods
Data were obtained from Grade A Tertiary hospitals for older patients (age ≥ 60 years) diagnosed with hip fractures in southwest China between 1 January 2009 and 1 April 2020. The database was built by collecting clinical and administrative data from outpatients and inpatients nationwide. Data were randomly split into training (80%) and testing datasets (20%), followed by six ML-based prediction models using 19 variables for hip fracture patients within 2 years of the first fracture.

Results
A total of 40,237 patients with a median age of 66.0 years, who were admitted to acute-care hospitals for hip fractures, were randomly split into a training dataset (32,189 patients) and a testing dataset (8,048 patients). Our results indicated that three of our ML-based models delivered an excellent prediction of subsequent hip fracture outcomes (the area under the receiver operating characteristics curve: 0.92 (0.91–0.92), 0.92 (0·92–0·93), 0.92 (0·92–0·93)), outperforming previous prediction models based on claims and cohort data.

Conclusions
Our prediction models identify Chinese older people at high risk of subsequent hip fractures with specific baseline clinical and demographic variables such as length of hospital stay. These models might guide future targeted preventative treatments.
Identifying excessive chronic alcohol use with phosphatidylethanol in patients with suspected severe injury-results from the IDART study
Authors: Jørgenrud Benedicte M , Bråthen Camilla C , Steinson Stenehjem Jo , Kristiansen Thomas , Rosseland Leiv Arne , Bogstrand Stig Tore 
Published: March 2024

Abstract

Introduction
Acute and chronic alcohol use are well-known risk factors for accidents and injuries, and concurrent psychoactive drug use can increase injury risk further. Phosphatidylethanol (PEth) 16:0/18:1 is a biomarker used to determine alcohol consumption the previous 3–4 weeks. The aim was to investigate the prevalence of chronic alcohol use in trauma patients, as determined by PEth 16:0/18:1 concentrations, and how excessive chronic alcohol use relate to demographic variables, injury mechanisms and drug use.

Setting
Patients received at Norwegian trauma hospitals from March 2019 to February 2020. The study is part of the Impairing Drugs and Alcohol as Risk factors for Traumatic Injuries study.

Methods
All patients aged ≥ 16 years received with trauma team were included in the study. Data on injury date and mechanism, gender and age was registered. Blood samples were analyzed for 22 psychoactive medicinal and illicit drugs, ethanol and phosphatidylethanol 16:0/18:1. Regression analyses were conducted to assess associations between alcohol use and gender, age, injury mechanism and drug use.

Results and Conclusion
Of the 4845 patients included in the study, 10% had PEth 16:0/18:1 concentration ≥ 600 nM (~430 ng/mL), indicative of excessive chronic alcohol use. Being male, between 44-61 years old, involved in violence, and testing positive for medicinal drugs was associated with excessive chronic alcohol use.
Excessive chronic alcohol use was common among males, middle-aged, patients with violence as injury mechanism and those with medicinal drug use. These findings emphasize the need to detect and treat excessive chronic alcohol use among trauma patients.
Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects
Authors: Heiling Hillary M , Rashid Naim U , Li Quefeng , Peng Xianlu L , Yeh Jen Jen , Ibrahim Joseph G 
Published: March 2024

ABSTRACT
Modern biomedical datasets are increasingly high-dimensional and exhibit complex correlation structures. Generalized linear mixed models (GLMMs) have long been employed to account for such dependencies. However, proper specification of the fixed and random effects in GLMMs is increasingly difficult in high dimensions, and computational complexity grows with increasing dimension of the random effects. We present a novel reformulation of the GLMM using a factor model decomposition of the random effects, enabling scalable computation of GLMMs in high dimensions by reducing the latent space from a large number of random effects to a smaller set of latent factors. We also extend our prior work to estimate model parameters using a modified Monte Carlo Expectation Conditional Minimization algorithm, allowing us to perform variable selection on both the fixed and random effects simultaneously. We show through simulation that through this factor model decomposition, our method can fit high-dimensional penalized GLMMs faster than comparable methods and more easily scale to larger dimensions not previously seen in existing approaches.
Bayesian mixed model inference for genetic association under related samples with brain network phenotype
Authors: Tian Xinyuan , Wang Yiting , Wang Selena , Zhao Yi , Zhao Yize 
Published: March 2024

Abstract
Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.