bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Effect of Cracks in Diaphragm Couplings on the Dynamic Characteristics of Shaft System
Authors: Liao Meijun , Zhang Lan , Nong Su , Zhang Chao , Zhu Rupeng , Chen Weifang
Published: October 2024

Diaphragm couplings are prone to developing diaphragm cracks under prolonged high-speed operating conditions, which can lead to degradation in the performance of the transmission system and affect the dynamics of the shafting system. To investigate the effects of diaphragm cracks on the dynamics of couplings and the shafting system, a finite element model of a diaphragm coupling with a crack failure is established using ANSYS finite element software to analyze the time-varying characteristics of the diaphragm coupling’s angular and radial stiffness. A shaft dynamics model of the diaphragm coupling with a crack is developed using Timoshenko beam elements to analyze the impact of different crack lengths and locations on the dynamics of the shafting system. The validity of the dynamic model for a diaphragm coupling with a crack is verified through a constant speed experiment conducted on a rotor test bench. The results indicate that diaphragm crack failure causes a change in the periodicity of the time-varying stiffness of the diaphragm coupling, leading to a distinct 2× component appearing in the frequency domain of the transmission shaft system.
An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge
Authors: Bonci Andrea , Kermenov Renat , Longarini Lorenzo , Longhi Sauro , Pompei Geremia , Prist Mariorosario , Verdini Carlo
Published: October 2024

Production efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artificial intelligence and the growing significance of identifying and mitigating anomalies across different fields, approaches based on artificial neural networks facilitate the recognition of intricate types of anomalies by taking into account both temporal and contextual attributes. In this paper, a lightweight framework based on the Echo State Network (ESN) model running at the edge is introduced for online AD. Compared to other AD methods, such as Long Short-Term Memory (LSTM), it achieves superior precision, accuracy, and recall metrics while reducing training time, CO2 emissions, and the need for high computational resources. The preliminary evaluation of the proposed solution was conducted using a low-resource computing device at the edge of the real production machine through an Industrial Internet of Things (IIoT) smart meter module. The machine used to test the proposed solution was provided by the Italian company SIFIM Srl, which manufactures filter mats for industrial kitchens. Experimental results demonstrate the feasibility of developing an AD method that achieves high accuracy, with the ESN-based framework reaching 85% compared to 80.88% for the LSTM-based model. Furthermore, this method requires minimal hardware resources, with a training time of 9.5 s compared to 2.100 s for the other model.
Comprehensive Characterization of Bi1.34Fe0.66Nb1.34O6.35 Ceramics: Structural, Morphological, Electrical, and Magnetic Properties
Authors: Devesa Susana , Amorim Carlos Oliveira , Belo João Horta , Araújo João P. , Teixeira Sílvia Soreto , Graça Manuel P. F. , Costa Luís Cadillon
Published: October 2024

Recent research in solid-state physics and materials engineering focuses on the development of new dielectric materials, with bismuth-based pyrochlores being already extensively applied in communications technology for their excellent dielectric properties and relatively low sintering temperatures. Herein, the structural, morphological, electrical, and magnetic properties of Bi1.34Fe0.66Nb1.34O6.35 ceramic, prepared by the sol–gel method and sintered at 500 °C, are investigated. The Rietveld refinement of the XRD pattern showed a cubic phase belonging to the space group Fd-3m and a crystallite size of 42 nm. Transmission electron microscopy further confirmed the crystallite size and the homogeneous distribution of Bi, Fe, Nb, and O elements, as evidenced by high-angle annular dark field imaging and STEM-EDX mapping. The morphology of the sample, assessed by scanning electron microscopy, is characterized by submicron-sized spherical particles. Dielectric spectroscopic studies revealed that the dielectric properties, strongly influenced by frequency and temperature, indicate the material’s potential for energy storage due to lower dielectric loss compared to the dielectric constant. The observed relaxation phenomena, confirmed through variations in dielectric loss and loss tangent, highlight the influence of grain boundaries and temperature on electron hopping and charge carrier dynamics. Using SQUID magnetometry, we identified two distinct magnetic phases. The primary phase, corresponding to the Bi1.34Fe0.66Nb1.34O6.35 ceramic, exhibits an antiferromagnetic behavior below its Néel temperature at around 8.8 K. A secondary high-Curie temperature ferrimagnetic phase, likely vestigial maghemite and/or magnetite, was also detected, indicating an estimated fraction below 0.02 wt.%.
Convergence of Graph-Based Fixed Point Results with Application to Fredholm Integral Equation
Authors: Ahmad Haroon , Riaz Aqsa , Öztürk Mahpeyker , Ud Din Fahim , Köksal Mehmet Emir , Girgin Ekber
Published: October 2024

In this manuscript, we present a novel concept termed graphical Θc-Kannan contraction within the context of graphically controlled metric-type spaces. Unlike traditional Kannan contraction, this novel concept presents a modified method of contraction mapping. We discuss the significance and the existence of fixed point results within the framework of this novel contraction. To strengthen the credibility of our theoretical remarks, we provide a comparison example demonstrating the efficiency of our suggested framework. Our study not only broadens the theoretical foundations inside graphically controlled metric-type spaces by introducing and examining visual Θc-Kannan contraction, but it also demonstrates the practical significance of our innovations through significant examples. Furthermore, applying our findings to second-order differential equations by constructing integral equations into the domain of Fredholm sheds light on the broader implications of our research in the field of mathematical analysis and contributes to the advancement of this field.
Optimizing Supply Chain Design under Demand Uncertainty with Quantity Discount Policy
Authors: Tsai Jung-Fa , Tan Peng-Nan , Truong Nguyen-Thao , Tran Dinh-Hieu , Lin Ming-Hua
Published: October 2024

In typical business situations, sellers usually offer discount schemes to buyers to increase overall profitability. This study aims to design a supply chain network under uncertainty of demand by integrating an all-unit quantity discount policy. The objective is to maximize the profit of the entire supply chain. The proposed model is formulated as a mixed integer nonlinear programming model, which is subsequently linearized into a mixed integer linear programming model and hence able to obtain a global solution. Numerical examples in the manufacturing supply chain where customer demand follows normal distributions are used to assess the effect of quantity discount policies. Key findings demonstrate that the integration of quantity discount policies significantly reduces total supply chain costs and improves inventory management under demand uncertainty, and decision makers need to decide a balance level between service levels and profits.
A Novel Normal Contact Stiffness Model of Bi-Fractal Surface Joints
Authors: Xue Pengsheng , Zhu Lida , Cao Xiangang
Published: October 2024

The contact stiffness of the mechanical joint usually becomes the weakest part of the stiffness for the whole machinery equipment, which is one of the important parameters affecting the dynamic characteristics of the engineering machinery. Based on the three-dimensional Weierstrass–Mandelbrot (WM) function, the novel normal contact stiffness model of the joint with the bi-fractal surface is proposed, which comprehensively considers the effects of elastoplastic deformation of asperity and friction factor. The effect of various parameters (fractal dimension, scaling parameter, material parameter, friction factor) on the normal contact stiffness of the joint is analyzed by numerical simulation. The normal contact stiffness of the joint increases with an increase in the fractal dimension, normal load, and material properties and decreases with an increase in the scaling parameter. Meanwhile, the fractal parameters of the equivalent rough surface of the joint are calculated by the structural function method. The experimental results show that when the load is between 14 and 38 N∙m, the error of the model is within 20%. The normal contact stiffness model of the bi-fractal surface joint can provide a theoretical basis for the analysis of the dynamic characteristics of the whole machine at the design stage.
Lifted Codes with Construction of Echelon-Ferrers for Constant Dimension Codes
Authors: Niu Yongfeng , Wang Xuan
Published: October 2024

Finding the highest possible cardinality, Aq(n,d;k), of the set of k-dimensional subspaces in Fqn, also known as codewords, is a fundamental problem in constant dimension codes (CDCs). CDCs find applications in a number of domains, including distributed storage, cryptography, and random linear network coding. The goal of recent research papers has been to establish Aq(n,d;k). We further improved the echelon-Ferrers construction with an algorithm, and enhanced the inserting construction by swapping specified columns of the generator matrix to obtain new lower bounds.
Comparative Analysis of Machine Learning Models for Predicting Student Success in Online Programming Courses: A Study Based on LMS Data and External Factors
Authors: Arévalo-Cordovilla Felipe Emiliano , Peña Marta
Published: October 2024

Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning Management System (LMS) and external factors of 591 students enrolled in online object-oriented programming courses at the Universidad Estatal de Milagro (UNEMI) between 2022 and 2023. The data were preprocessed to address class imbalance using the synthetic minority oversampling technique (SMOTE), and relevant features were selected based on Random Forest importance rankings. The models were trained and optimized using Grid Search with cross-validation. Logistic Regression achieved the highest Area Under the Receiver Operating Characteristic Curve (AUC-ROC) on the test set (0.9354), indicating strong generalization capability. SVM and Neural Network models performed adequately but were slightly outperformed by the simpler models. These findings suggest that integrating LMS data with external factors enhances early prediction of student success. Logistic Regression is a practical and interpretable tool for educational institutions to identify at-risk students, and to implement personalized interventions.
Efficient Solutions for Stochastic Fractional Differential Equations with a Neutral Delay Using Jacobi Poly-Fractonomials
Authors: Babaei Afshin , Banihashemi Sedigheh , Parsa Moghaddam Behrouz , Dabiri Arman , Galhano Alexandra
Published: October 2024

This paper introduces a novel numerical technique for solving fractional stochastic differential equations with neutral delays. The method employs a stepwise collocation scheme with Jacobi poly-fractonomials to consider unknown stochastic processes. For this purpose, the delay differential equations are transformed into augmented ones without delays. This transformation makes it possible to use a collocation scheme improved with Jacobi poly-fractonomials to solve the changed equations repeatedly. At each iteration, a system of nonlinear equations is generated. Next, the convergence properties of the proposed method are rigorously analyzed. Afterward, the practical utility of the proposed numerical technique is validated through a series of test examples. These examples illustrate the method’s capability to produce accurate and efficient solutions.
Dynamics and Control of a Novel Discrete Internet Rumor Propagation Model in a Multilingual Environment
Authors: Lei Nan , Xia Yang , Fu Weinan , Zhang Xinyue , Jiang Haijun
Published: October 2024

In the Internet age, the development of intelligent software has broken the limits of multilingual communication. Recognizing that the data collected on rumor propagation are inherently discrete, this study introduces a novel SIR discrete Internet rumor propagation model with the general nonlinear propagation function in a multilingual environment. Then, the propagation threshold R0 is obtained by the next-generation matrix method. Besides, the criteria determining the spread or demise of rumors are obtained by the stability theory of difference equations. Furthermore, combined with optimal control theory, prevention and refutation mechanisms are proposed to curb rumors. Finally, the validity and applicability of the model are demonstrated by numerical simulations and a real bilingual rumor case study.
A Two-Stage Facial Kinematic Control Strategy for Humanoid Robots Based on Keyframe Detection and Keypoint Cubic Spline Interpolation
Authors: Yuan Ye , Li Jiahao , Yu Qi , Liu Jian , Li Zongdao , Li Qingdu , Liu Na
Published: October 2024

A plentiful number of facial expressions is the basis of natural human–robot interaction for high-fidelity humanoid robots. The facial expression imitation of humanoid robots involves the transmission of human facial expression data to servos situated within the robot’s head. These data drive the servos to manipulate the skin, thereby enabling the robot to exhibit various facial expressions. However, since the mechanical transmission rate cannot keep up with the data processing rate, humanoid robots often suffer from jitters in the imitation. We conducted a thorough analysis of the transmitted facial expression sequence data and discovered that they are extremely redundant. Therefore, we designed a two-stage strategy for humanoid robots based on facial keyframe detection and facial keypoint detection to achieve more natural and smooth expression imitation. We first built a facial keyframe detection model based on ResNet-50, combined with optical flow estimation, which can identify key expression frames in the sequence. Then, a facial keypoint detection model is used on the keyframes to obtain the facial keypoint coordinates. Based on the coordinates, the cubic spline interpolation method is used to obtain the motion trajectory parameters of the servos, thus realizing the robust control of the humanoid robot’s facial expression. Experiments show that, unlike before where the robot’s imitation would stutter at frame rates above 25 fps, our strategy allows the robot to maintain good facial expression imitation similarity (cosine similarity of 0.7226), even at higher frame rates.
Shift-Invariance Robustness of Convolutional Neural Networks in Side-Channel Analysis
Authors: Krček Marina , Wu Lichao , Perin Guilherme , Picek Stjepan
Published: October 2024

Convolutional neural networks (CNNs) offer unrivaled performance in profiling side-channel analysis. This claim is corroborated by numerous results where CNNs break targets protected with masking and hiding countermeasures. One hiding countermeasure commonly investigated in related works is desynchronization (misalignment). The conclusions usually state that CNNs can break desynchronization as they are shift-invariant. This paper investigates that claim in more detail and reveals that the situation is more complex. While CNNs have certain shift-invariance, it is insufficient for commonly encountered scenarios in deep learning-based side-channel analysis. We investigate data augmentation to improve the shift-invariance and, in a more powerful version, ensembles of data augmentation. Our results show that the proposed techniques work very well and improve the attack significantly, even for an order of magnitude.
Variational Autoencoding with Conditional Iterative Sampling for Missing Data Imputation
Authors: Kuang Shenfen , Song Jie , Wang Shangjiu , Zhu Huafeng
Published: October 2024

Variational autoencoders (VAEs) are popular for their robust nonlinear representation capabilities and have recently achieved notable advancements in the problem of missing data imputation. However, existing imputation methods often exhibit instability due to the inherent randomness in the sampling process, leading to either underestimation or overfitting, particularly when handling complex missing data types such as images. To address this challenge, we introduce a conditional iterative sampling imputation method. Initially, we employ an importance-weighted beta variational autoencoder to learn the conditional distribution from the observed data. Subsequently, leveraging the importance-weighted resampling strategy, samples are drawn iteratively from the conditional distribution to compute the conditional expectation of the missing data. The proposed method has been experimentally evaluated using classical generative datasets and compared with various well-known imputation methods to validate its effectiveness.
An Efficient Fusion Network for Fake News Classification
Authors: Alzaidi Muhammad Swaileh A. , Alshammari Alya , Hassan Abdulkhaleq Q. A. , Yousafzai Samia Nawaz , Thaljaoui Adel , Fitriyani Norma Latif , Kim Changgyun , Syafrudin Muhammad
Published: October 2024

Nowadays, it is very tough to differentiate between real news and fake news due to fast-growing social networks and technological progress. Manipulative news is defined as calculated misinformation with the aim of creating false beliefs. This kind of fake news is highly detrimental to society since it deepens political division and weakens trust in authorities and institutions. Therefore, the identification of fake news has emerged as a major field of research that seeks to validate content. The proposed model operates in two stages: First, TF-IDF is applied to an entire document to obtain its global features, and its spatial and temporal features are simultaneously obtained by employing Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory with a Gated Recurrent Unit. The Fast Learning Network efficiently classifies the extracted features. Comparative experiments were conducted on three easily and publicly obtainable large-scale datasets for the purposes of analyzing the efficiency of the approach proposed. The results also show how well the model performs compared with past methods of classification.
Diffusion Cascades and Mutually Coupled Diffusion Processes
Authors: Barna Imre Ferenc , Mátyás László
Published: October 2024

In this paper, we define and investigate a system of coupled regular diffusion equations in which each concentration acts as a driving term in the next diffusion equation. Such systems can be understood as a kind of cascade process which appear in different fields of physics like diffusion and reaction processes or turbulence. As a solution, we apply the time-dependent self-similar Ansatz method, the obtained solutions can be expressed as the product of a Gaussian and a Kummer’s function. This model physically means that the first diffusion works as a catalyst in the second diffusion system. The coupling of these diffusion systems is only one way. In the second part of the study we investigate mutually coupled diffusion equations which also have the self-similar trial function. The derived solutions show some similarities to the former one. To make our investigation more complete, different kinds of couplings were examined like the linear, the power-law, and the Lorentzian. Finally, a special coupling was investigated which is capable of describing isomerization with temporal decay.
Generalized Mean Square Exponential Stability for Stochastic Functional Differential Equations
Authors: He Tianyu , Li Zhi , Feng Tianquan
Published: October 2024

This work focuses on a class of stochastic functional differential equations and neutral stochastic differential functional equations. By using a new approach, some sufficient conditions are obtained to guarantee the generalized mean square exponential stability for the equation under consideration. Certain existing results are refined and extended. Lastly, the validity of the main results is confirmed through several simulation examples.
Dynamic and Stable R&D Strategies for Green Technology Based on Cooperative Differential Games
Authors: Jiang Hui , Yao Fanjun , Gao Hongwei
Published: October 2024

As the “carbon neutrality” strategy is implemented, green technology R&D, a core competitive strength for sustainable enterprise development, is an essential pathway for China’s transformation and green growth. Green technology, a breakthrough over traditional production technologies, involves lengthy and costly R&D processes with high risks typically beyond the reach of a single enterprise. It requires the heterogeneous functions of enterprises, universities, and research institutions to complement each other’s advantages and establish an “industry–university–research” collaborative innovation alliance for green technologies. This paper constructs differential game models for non-cooperative and cooperative green technology R&D involving a green manufacturer and a research institution. We solve and compare the profits for both parties under these scenarios, apply a time-consistent payment distribution mechanism to allocate cooperative profits, and ensure that neither party deviates from the optimal cooperative trajectory over a prolonged period, achieving Pareto improvement and enhancing social welfare.
A Time Series Synthetic Control Causal Evaluation of the UK’s Mini-Budget Policy on Stock Market
Authors: Zhang Yan , Lu Zudi
Published: October 2024

In this paper, we propose a modified synthetic control causal analysis for time series data with volatility in terms of absolute value of return outcomes taken into account in constructing the prediction of potential outcomes for time series causal analysis. The consistency property of the synthetic weight parameter estimators is developed theoretically under a time series data-generating process framework. The application to evaluate the UK’s mini-budget policy, announced by the then Chancellor on 23 September 2022, which had significant implications for the stock market, is examined and analysed. Comparisons with traditional synthetic control and synthetic difference in difference (DID) methods for evaluation of the effect of the mini-budget policy on the UK’s stock market are also discussed.
Hybrid Extreme Learning for Reliable Short-Term Traffic Flow Forecasting
Authors: Chen Huayuan , Lin Zhizhe , Yao Yamin , Xie Hai , Song Youyi , Zhou Teng
Published: October 2024

Reliable forecasting of short-term traffic flow is an essential component of modern intelligent transport systems. However, existing methods fail to deal with the non-linear nature of short-term traffic flow, often making the forecasting unreliable. Herein, we propose a reliable short-term traffic flow forecasting method, termed hybrid extreme learning, that effectively learns the non-linear representation of traffic flow, boosting forecasting reliability. This new algorithm probes the non-linear nature of short-term traffic data by exploiting the artificial bee colony that selects the best-implied layer deviation and input weight matrix to enhance the multi-structural information perception capability. It speeds up the forecasting time by calculating the output weight matrix, which guarantees the real usage of the forecasting method, boosting the time reliability. We extensively evaluate the proposed hybrid extreme learning method on well-known short-term traffic flow forecasting datasets. The experimental results show that our method outperforms existing methods by a large margin in both forecasting accuracy and time, effectively demonstrating the reliability improvement of the proposed method. This reliable method may open the avenue of deep learning techniques in short-term traffic flow forecasting in real scenarios.
Wave Speeds for a Time-Periodic Bistable Three-Species Lattice Competition System
Authors: Pan Chaohong , Zhan Jiali , Wang Hongyong
Published: October 2024

In this paper, we consider propagation direction (which can be used to predict which species will occupy the habitat or win the competition eventually) of a bistable wave for a three-species time-periodic lattice competition system with bistable nonlinearity, aiming to address an open problem. As a first step, by transforming the competition system to a cooperative one, we study the asymptotic behavior for the bistable wave profile and then prove the uniqueness of the bistable wave speed. Secondly, we utilize comparison principle and build up two couples of upper and lower solutions to judge the sign of the bistable wave speed which partially provides the answer to the open problem. As an application, we reduce the time-periodic system to a space–time homogeneous system, we obtain the corresponding criteria and carry out numerical simulations to illustrate the availability of our results. Moreover, an interesting phenomenon we have found is that the two weak competitors can wipe out the strong competitor under some circumstances.
Real-World Data Mining for Signal Detection of Antipsychotics-Associated Adverse Events Using the Korea Adverse Event Reporting System (KAERS) Database
Authors: Moon Suhyeon , Ko Minjung , Choi Yeo-Jin , Shin Sooyoung
Published: October 2024

Background and Objectives: Recent studies suggest that the binary categorization of first-generation antipsychotics (FGAs) as being primarily responsible for extrapyramidal symptoms (EPSs) and second-generation antipsychotics (SGAs) for cardiometabolic abnormalities is an oversimplification. SGAs also demonstrate antagonistic affinity for D2 receptors, indicating their potential to induce EPSs. This study utilized the Korea Adverse Event Reporting System (KAERS) database to explore adverse drug event (ADE) signals related to both FGAs and SGAs. Materials and Methods: Relevant ADE reports from January 2013 to December 2022 were extracted from the KAERS database and analyzed using disproportionality analysis, employing the proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC) with its 95% lower confidence interval (LCI) indices. Results: Of the initial dataset of 2,890,702 ADE reports, those with insufficient data and duplicates were removed, resulting in a final dataset of 5249 reports for analysis. Aripiprazole, an SGA, showed signals for movement disorders, including EPSs (PRR 4.7, ROR 4.8, IC 2.2), tremors (PRR 5.3, ROR 5.4, IC 2.4), and akathisia (PRR 18.6, ROR 19.3, IC 3.5). Notably, for quetiapine, cardiovascular signals were detected, including increased blood pressure (PRR 2.1, ROR 2.3, IC 0.5), and tachyarrhythmia (PRR 13.9, ROR 14.1, IC 1.8), along with peripheral edema (PRR 2.5, ROR 2.5, IC 0.2). Metabolic abnormalities, such as weight gain and increased appetite, were identified for four SGAs: aripiprazole, olanzapine, quetiapine, and risperidone. Safety signals related to movement disorders were not detectable for FGAs, likely due to the limited number of ADE reports available for analysis. Conclusions: Our study findings support that the distribution of ADEs between FGAs and SGAs is not strictly binary. Aripiprazole, despite being an SGA, showed signals for extrapyramidal movement disorders. Four SGAs (aripiprazole, olanzapine, quetiapine, and risperidone) were linked to metabolic side effects, while quetiapine was associated with cardiovascular safety signals.
Functional Magnetic Neuromuscular Stimulation vs. Routine Physiotherapy in the Critically Ill for Prevention of ICU Acquired Muscle Loss: A Randomised Controlled Trial
Authors: Skočir Anej , Jevšnik Alja , Plaskan Lidija , Podbregar Matej
Published: October 2024

Background and Objectives: Muscle loss is a known complication of ICU admission. The aim of the study was to investigate the effect of neuromuscular functional magnetic stimulation (FMS) on quadriceps muscle thickness in critically ill patients. Materials and Methods: Among ICU patients one quadriceps was randomized to FMS (Tesla Stym, Iskra Medical, Ljubljana, Slovenia) stimulation and the other to control care. Quadriceps thickness was measured by ultrasound (US) in transversal and longitudinal planes at enrolment, Days 3–5, and Days 9–12. The trial stopped early following an interim analysis comparing muscle thickness differences between groups using repeated measures ANOVA. Results: Of 18 patients randomized, 2 died before completing the trial. The final analysis reported included 16 patients (female 38%, age 68 ± 10 years, SOFA 10.8 ± 2.7). Three mild skin thermal injuries were noted initially, which were later avoided with proper positioning of FMS probe. Primary outcome comparison showed that quadriceps thickness in transversal and longitudinal planes decreased in the non-stimulated legs and, but it did not change in FMS legs (−4.1 mm (95%CI: −9.4 to −0.6) vs. −0.7 mm (95%CI: −4.1 to −0.7) (p = 0.03) and −4.4 mm (95%CI: −8.9 to −1.1) vs. −1.5 mm (95%CI: −2.6 to −2.2) (p = 0.02), respectively) (ANOVA difference between groups p = 0.036 and 0.01, respectively). Conclusions: In the critically ill, neuromuscular FMS is feasible and safe with precautions applied to avoid possible skin thermal injury. FMS decreases the loss of quadriceps muscle thickness.
The Application of TiO2/ZrO2-Modified Nanocomposite PES Membrane for Improved Permeability of Textile Dye in Water
Authors: Nhlengethwa Sibukiso Thobani , Tshangana Charmaine Sesethu , Mamba Bhekie Brilliance , Muleja Adolph Anga
Published: October 2024

This study investigates the modification of polyethersulfone (PES) membranes with 1 wt% titanium dioxide (TiO2), zirconium dioxide (ZrO2) and a nanocomposite of TiO2/ZrO2. The aim was to efficiently remove Rhodamine B (RhB) from water using a threefold approach of adsorption, filtration and photodegradation. Among the modified membranes (TiO2, ZrO2 and TiO2/ZrO2), the TiO2/ZrO2-PES nanocomposite membrane showed a better performance in rejection of RhB than other membranes with the rejection efficiency of 96.5%. The TiO2/ZrO2-PES membrane was found to possess a thicker selective layer and reduced mean pore radius, which contributed to its improved rejection. The TiO2/ZrO2 nanocomposite membrane also showed high bulk porosity and a slightly lower contact angle of 69.88° compared to pristine PES with a value of 73°, indicating an improvement in hydrophilicity. Additionally, the TiO2/ZrO2-PES nanocomposite membrane demonstrated a relatively lower surface roughness (Sa) of 8.53 nm, which offers the membrane antifouling properties. The TiO2/ZrO2-PES membrane showed flux recovery ratio (FRR), total fouling (Rt), reversible fouling (Rr) and irreversible fouling (Rir) of 48.0%, 88.7%, 36,8% and 52.9%, respectively. For the photocatalytic degradation performance, the removal efficiency of RhB followed this order TiO2 > TiO2/ZrO2 > ZrO2 (87.6%, 85.7%, 67.8%). The tensile strength and elongation were found to be compromised with the addition of nanoparticles and nanocomposites. This indicates the necessity to further modify and optimise membrane fabrication to achieve improved mechanical strength of the membranes. At low pressure, the overall findings suggest that the TiO2/ZrO2 nanocomposite has the potential to offer significant improvements in membrane performance (water flux) compared to other modifications.
Microstructural Evolution of P92 Steel with Different Creep Life Consumptions After Long-Term Service
Authors: Zhang Zhen , Yang Zheyi , Tang Liying
Published: October 2024

P92 steel is widely used in ultra-supercritical units due to its excellent high-temperature performance. This paper studies the microstructure of P92 steel steam pipes in three conditions: as-supplied, after 80,000 h of service at 67.06 MPa stress, and after 100,000 h of service at 80.28 MPa stress. After prolonged service, the P92 steel retains its martensitic structure, but the lath width increases and the dislocation density decreases. In addition to M23C6, MX, and Laves phases, Z phase was also observed among the precipitates. The results indicate that the sizes of M23C6 and Laves phases increase with the progression of creep life consumption, with the coarsening rate of Laves phase being significantly higher than that of M23C6. However, the coarsening of MX phase is not evident. Compared to the Laves phase, the formation of the Z phase requires a longer period of time. The precipitation of the Z phase consumes MX carbonitrides, and it has been observed that the Z phase precipitates from the MX phase, with the two phases exhibiting a coexisting state.
Investigating the Effects and Mechanisms of Thermal–Vibration-Coupled Stress Relief Treatment on Residual Stress in SiC/Al Composites
Authors: Li Bianhong , Ouyang Wu , Dong Yushuang
Published: October 2024

Aluminum matrix composites reinforced with particles (PRAMCs) frequently develop considerable residual stresses post-quenching, which can negatively affect fatigue life and dimensional accuracy. Traditional stress relief methods for aluminum alloys are only partially effective. This study examined thermal stress relief (TSR), vibratory stress relief (VSR), and a combined thermal–vibratory stress relief (TVSR) approach for SiC/Al composites. All treatments proved successful in diminishing residual stresses, with the most significant reduction along the direction of peak dynamic stress. Additionally, this study analyzed micro-residual stresses via a macro–micro-residual stress finite element model to understand differences in stress relief outcomes. Optimizing the TVSR process could be key to more effectively reducing residual stresses in SiC/Al composites.
Multi-Wavelength Narrow-Spacing Laser Frequency Stabilization Technology Based on Fabry-Perot Etalon
Authors: Wang Ju , Gao Ye , Yu Jinlong , Luo Hao , Su Xuemin , Zhang Shiyu , Zhang Ruize , Ma Chuang
Published: October 2024

Classical frequency-stabilized lasers have achieved high-frequency stability and reproducibility; however, their extensive wavelength spacing limits their utility in various scenarios. This study introduces a novel frequency-stabilized laser scheme that integrates a Fabry-Perot etalon (FPE) with digital control technology and wavelength modulation techniques. The FPE, characterized by multiple transmission peaks at minimal frequency intervals, provides stable frequency references for different lasers, thereby enhancing the system’s flexibility and adaptability. An error signal is derived from the first-order differentiation of the FPE’s transmission curve. A 180° phase difference was observed in the feedback output signal when the laser’s central frequency diverged from the reference, determining that the direction of the frequency control was accordingly determined.Employing feedback control, the laser’s output frequency is stabilized at the transmission peak frequency of the FPE. Experimental results demonstrate that this stabilization scheme effectively locks the laser’s output wavelength to different transmission peak frequencies of the FPE, achieving 25 GHz wavelength spacing. The frequency stability is improved by two orders of magnitude on a second-level timescale, maintained within hundreds of kHz, equating to a frequency stability level of 10−10.
Temperature Compensation Method for Tunnel Magnetoresistance Micro-Magnetic Sensors Through Reference Magnetic Field
Authors: Kuai Tao , Du Qingfa , Hu Jiafei , Shi Shilong , Li Peisen , Chen Dixiang , Pan Mengchun
Published: October 2024

The sensitivity of Tunnel Magnetoresistance (TMR) sensors is characterized by significant temperature drift and poor sensitivity drift repeatability, which severely impairs measurement accuracy. Conventional temperature compensation techniques are often hindered by low compensation precision, inadequate real-time performance, and an inability to effectively address the issue of poor repeatability in temperature drift characteristics. To overcome these challenges, this paper introduces a novel method for suppressing temperature drift in TMR sensors. In this method, an alternating reference magnetic field is applied to TMR sensors, and the output amplitude at the frequency of the reference magnetic field is calculated to compensate the sensitivity temperature drift in real time. Temperature characteristic tests were conducted in a non-magnetic temperature test chamber, and the results revealed that the proposed method significantly reduced the TMR sensitivity drift coefficient from 985.39 ppm/°C to 59.08 ppm/°C. Additionally, the repeatability of sensitivity temperature characteristic curves was enhanced, with a reduction in root mean square error from 0.84 to 0.21. This approach effectively mitigates temperature-induced sensitivity drift without necessitating the use of a temperature sensor, and has the advantages of real-time performance and repeatability, providing a new approach for the high-precision temperature drift suppression of TMR.
A Sub-1 ppm/°C Reference Voltage Source with a Wide Input Range
Authors: Xiao Yuchi , Wang Chunlai , Hou Hongyang , Han Weihua
Published: October 2024

With the continuous advancement of electronic technology, the application of high-voltage integrated circuits is becoming increasingly prevalent in fields such as power systems, medical devices, and industrial automation. The reference circuit within high-voltage integrated circuits must not only exhibit insensitivity to temperature variations but also maintain stability across a broad voltage supply. This paper presents a bandgap reference (BGR) source capable of operating over a wide input range. This BGR employs a high-order curvature compensation method to eliminate nonlinear voltage terms, resulting in minimal temperature drift. The circuit achieves an impressive temperature coefficient (TC) of 0.88 ppm/°C over a temperature range from −40 °C to 130 °C. To ensure stable operation within a 4–40 V range, the design incorporates a pre-regulation circuit that stabilizes the supply voltage of the BGR core at a fixed value, thereby enhancing the ability to withstand variations in power supply voltage.
A Thorough Review of Emerging Technologies in Micro- and Nanochannel Fabrication: Limitations, Applications, and Comparison
Authors: Karimi Koosha , Fardoost Ali , Mhatre Nikhil , Rajan Jay , Boisvert David , Javanmard Mehdi
Published: October 2024

In recent years, the field of micro- and nanochannel fabrication has seen significant advancements driven by the need for precision in biomedical, environmental, and industrial applications. This review provides a comprehensive analysis of emerging fabrication technologies, including photolithography, soft lithography, 3D printing, electron-beam lithography (EBL), wet/dry etching, injection molding, focused ion beam (FIB) milling, laser micromachining, and micro-milling. Each of these methods offers unique advantages in terms of scalability, precision, and cost-effectiveness, enabling the creation of highly customized micro- and nanochannel structures. Challenges related to scalability, resolution, and the high cost of traditional techniques are addressed through innovations such as deep reactive ion etching (DRIE) and multipass micro-milling. This paper also explores the application potential of these technologies in areas such as lab-on-a-chip devices, biomedical diagnostics, and energy-efficient cooling systems. With continued research and technological refinement, these methods are poised to significantly impact the future of microfluidic and nanofluidic systems.
Strain-Induced Frequency Splitting in PT Symmetric Coupled Silicon Resonators
Authors: Wang Lifeng , Zhang Shangyang , Yuan Qunce
Published: October 2024

When two resonators of coupled silicon resonators are identical and the gain on one side is equal to the loss on the other side, a parity-time (PT) symmetric-coupled silicon resonator is formed. As non-Hermitian systems, the PT-symmetric systems have exhibited many special properties and interesting phenomena. This paper proposes the strain-induced frequency splitting in PT symmetry-coupled silicon resonators. The frequency splitting of the PT system caused by strain perturbations is derived and simulated. Theory and simulation both indicate that the PT system is more sensitive to strain perturbation near the exceptional point (EP) point. Then, a feedback circuit is designed to achieve the negative damping required for PT symmetry. Based on a simple silicon-on-insulator (SOI) process, the silicon resonator chip is successfully fabricated. After that, the PT-symmetric-coupled silicon resonators are successfully constructed, and the frequency splitting phenomenon caused by strain is observed experimentally.
Research on Optimization of Height Difference of a Precision Horizontal Machining Center
Authors: Han Lin , Zhang Zhenyun , Tian Xueguang , Qi Houjun , Liu Fucong , Qi Yang
Published: October 2024

This work proposes a methodology to determine the height difference of the guideways of machine tools where two guideways are not placed on the same horizontal plane. Firstly, a rigid–flexible coupling system consisting of a moving rigid mass and an elastic overhanging beam is presented as an equivalent mechanical model of a column and a spindle box. Then, the relationship between the deviation of a reference point and the height difference or the spindle box’s stroke is modeled. Next, the natural frequency and mode shape function of the overhanging beam, and the frequency response functions of the coupling system, are derived. The results indicate that there always exists an optimal height difference minimizing the relative deflection over the stroke of the moving part, and the optimal value depends on the loads in two directions and the stroke of the moving part. Similarly, there is also an optimal choice maximizing the first-order resonant frequency of the coupling system; however, the optimal solutions for both static and dynamic cases are not the same. This work provides beneficial instruction for choosing the height difference of machine tools with two guideways on a bed that are not on the same plane.
Three-Year Monitoring of Microorganisms’ Composition and Concentration in Atmospheric Aerosols of Novosibirsk City and Suburbs
Authors: Andreeva Irina , Safatov Aleksandr , Totmenina Olga , Olkin Sergei , Rebus Maxim , Buryak Galina , Alikina Tatiana , Baturina Olga , Kabilov Marsel
Published: October 2024

The atmospheric environment is formed under the influence of local and distant sources as a result of horizontal and vertical transport. In the present work, microbiological analysis of 604 samples of atmospheric aerosol collected in the period from September 2020 to September 2023 at four sites differing in anthropogenic load, located in Novosibirsk and the region, was carried out. Day and night aerosol samples were collected during 12 h every two weeks by filtration using Sartorius reinforced Teflon membranes, then sown on a set of nutrient media. The taxonomic affiliation of the isolated microbial isolates was determined based on phenotypic characteristics and analysis of 16S rRNA gene nucleotide sequences. Changes in the composition and concentration of culturable microorganisms depending on the season, time of day, and site of aerosol sampling were observed. In winter, lower fungi and bacteria of the genera Bacillus, Staphylococcus, Micrococcus dominated with an average concentration from zero to 12.5 CFU/m3 of aerosol. In the warm period, the concentration and diversity of cocci, spore-forming and non-spore-forming bacteria, actinomycetes, and fungi (up to 1970 CFU/m3), among which pathogenic microorganisms were found, increased sharply in aerosols. The use of 16S metabarcoding techniques has greatly expanded the range of aerosols’ microbial diversity detectable.
Co-Regulation Mechanism of Host p53 and Fos in Transcriptional Activation of ILTV Immediate-Early Gene ICP4
Authors: Liu Zheyi , Li Xuefeng , Cui Lu , Feng Shufeng , Han Zongxi , Zhang Yu , Liu Shengwang , Li Hai
Published: October 2024

Infectious laryngotracheitis virus (ILTV) exhibits a cascade expression pattern of encoded genes, and ICP4 is the only immediate-early gene of ILTV, which plays a crucial role in initiating the subsequent viral genes. Therefore, studying the transcriptional regulation mechanism of ICP4 holds promise for effectively blocking ILTV infection and spread. Host transcriptional factors p53 and Fos are proven to regulate a variety of viral infections, and our previous studies have demonstrated their synergistic effects in regulating ILTV infection. In this study, we constructed eukaryotic expression vectors for p53 and Fos as well as their specific siRNAs and transfected them into a chicken hepatoma cell line. The results showed that knocking down p53 or Fos significantly inhibited ICP4 transcription, while overexpressing p53 or Fos had an opposite effect. A further CoIP and ChIP-qPCR assay suggested p53 and Fos physically interacted with each other, and jointly bound to the upstream transcriptional regulatory region of ICP4. To elucidate the specific mechanisms of p53 and Fos in regulating ICP4 transcription, we designed p53 and Fos protein mutants by mutating their DNA binding domains, which significantly reduced their binding ability to DNA without affecting their interaction. The results showed that Fos directly bound to the promoter region of ICP4 as a binding target of p53, and the p53–Fos protein complex acted as a transcriptional co-regulator of ICP4. Studying the transcriptional process and regulatory pattern of ICP4 is of great significance for understanding the molecular mechanism of ILTV infection, and thus for finding effective methods to control and prevent it.
The Effect of Aloe vera on Skin and Its Commensals: Contribution of Acemannan in Curing Acne Caused by Propionibacterium acnes
Authors: Pal Suraj , Raj Mayank , Singh Medha , Saurav Kumar , Paliwal Chetan , Saha Subhasish , Sharma Anil Kumar , Singh Manoj
Published: October 2024

Aloe vera is one of the most significant therapeutical plant species that belongs to the family Liliaceae. Aloe vera is composed of a high amount of water, with the remainder being dry matter. The dry matter contains a lot of bioactive compounds like carbohydrates, fats, and enzymes, with various therapeutic and antimicrobial properties. It can enhance the proliferation of cells and prevent cell damage by anti-oxidative properties (stimulating the secretion of superoxide dismutase and peroxidase). Human skin is colonized by microbes like fungi (Candida albicans), bacteria (Propionibacterium acnes, Staphylococcus aureus), and mites. These commensals are responsible for skin characteristics such as acidic pH, the pungent smell of sweat, etc. Human fetuses lack skin microbiota, and their skin is colonized after birth. Commensals present on the skin have a crucial role in training the human immune system against other pathogenic microbes. Propionibacterium acnes act as an opportunistic pathogen when the balance between the commensals is disturbed. We also emphasize the recent progress in identifying the aloe metabolite biosynthesis pathways and the associated enzyme machinery. The hyperproliferation of Propionibacterium acnes causes acne, and acemannan plays a significant role in its cure. Hence, we need to consider a new treatment approach based on the root cause of this dysbiosis.