bluuebunny/crossref_metadata_2025_split huggingface dataset:
Local mean decomposition enhanced graph spectrum analysis for condition monitoring of rolling element bearings
Authors: Chen Zixu , Li Ke , Lu Guoliang 
Published: January 2025

Abstract
Early fault detection and diagnosis of rolling element bearings (REBs) is crucial for preventing unexpected machine failures, thereby ensuring operational reliability and lowering maintenance costs. Component signals decomposed by local mean decomposition (LMD) are effective tools for describing non-stationary and nonlinear signals across multiple frequency scales. Inspired by recent advancements in graph modeling, a LMD enhanced graph spectrum analysis approach is proposed for condition monitoring of REBs. Specifically, the component signals decomposed by LMD are utilized as inputs to construct the graph spectrum, through which the degradation characteristics of REB’s health status are extracted based on multi-scale correlation information, and health indicators describing the dynamic characteristics of REBs are obtained. According to the generated health indicator, a null hypothesis testing is conducted for early fault detection. Once an early warning is triggered, subsequent data will be marked as suspicious segments. The graph spectrum will be further used to provide discriminative features, and fault identification for suspicious segments will be achieved using k-Nearest Neighbors (KNN). By constructing multi-scale graph spectrum, the proposed approach effectively captures the degradation information of REBs while enabling precise fault characterization, providing a unified framework for early fault detection and fault identification. Experiments for fault detection and diagnosis are conducted using the XJTU-SY and CWRU datasets. Comparative studies with state-of-the-art methods are conducted to demonstrate the effectiveness and superiority of the proposed approach.
Automated estimation of individualized organ-specific dose and noise from clinical CT scans
Authors: Wang Sen , Medrano Maria Jose , Imran Abdullah Al Zubaer , Lee Wonkyeong , Cao Jennie Jiayi , Stevens Grant M , Tse Justin Ruey , Wang Adam S 
Published: January 2025

Abstract

Objective. Radiation dose and diagnostic image quality are opposing constraints in x-ray computed tomography (CT). Conventional methods do not fully account for organ-level radiation dose and noise when considering radiation risk and clinical task. In this work, we develop a pipeline to generate individualized organ-specific dose and noise at desired dose levels from clinical CT scans. Approach. To estimate organ-specific dose and noise, we compute dose maps, noise maps at desired dose levels and organ segmentations. In our pipeline, dose maps are generated using Monte Carlo simulation. The noise map is obtained by scaling the inserted noise in synthetic low-dose emulation in order to avoid anatomical structures, where the scaling coefficients are empirically calibrated. Organ segmentations are generated by a deep learning-based method (TotalSegmentator). The proposed noise model is evaluated on a clinical dataset of 12 CT scans, a phantom dataset of 3 uniform phantom scans, and a cross-site dataset of 26 scans. The accuracy of deep learning-based segmentations for organ-level dose and noise estimates was tested using a dataset of 41 cases with expert segmentations of six organs: lungs, liver, kidneys, bladder, spleen, and pancreas. Main results. The empirical noise model performs well, with an average RMSE approximately 1.5 HU and an average relative RMSE approximately 5% across different dose levels. The segmentation from TotalSegmentator yielded a mean Dice score of 0.8597 across the six organs (max = 0.9315 in liver, min = 0.6855 in pancreas). The resulting error in organ-level dose and noise estimation was less than 2% for most organs. Significance. The proposed pipeline can output individualized organ-specific dose and noise estimates accurately for personalized protocol evaluation and optimization. It is fully automated and can be scalable to large clinical datasets. This pipeline can be used to optimize image quality for specific organs and thus clinical tasks, without adversely affecting overall radiation dose.
Generalized, sublethal damage-based mathematical approach for improved modeling of clonogenic survival curve flattening upon hyperthermia, radiotherapy, and beyond
Authors: De Mendoza Adriana M , Michlíková Soňa , Castro Paula S , Muñoz Anni G , Eckhardt Lisa , Lange Steffen , Kunz-Schughart Leoni A 
Published: January 2025

Abstract

Objective. Mathematical modeling can offer valuable insights into the behavior of biological systems upon treatment. Different mathematical models (empirical, semi-empirical, and mechanistic) have been designed to predict the efficacy of either hyperthermia (HT), radiotherapy (RT), or their combination. However, mathematical approaches capable of modeling cell survival from shared general principles for both mono-treatments alone and their co-application are rare. Moreover, some cell cultures show dose-dependent saturation in response to HT or RT, manifesting in survival curve flattenings. An advanced survival model must, therefore, appropriately reflect such behavior. Approach. We propose a mathematical approach to model the effect of both treatments based on the general principle of sublethal damage (SLD) accumulation for the induction of cell death and irreversible proliferation arrest. Our approach extends Jung’s model on heat-induced cellular inactivation by incorporating dose-dependent recovery rates that delineate changes in SLD restoration. Main results. The resulting unified model (Umodel) accurately describes HT and RT survival outcomes, applies to simultaneous thermoradiotherapy modeling, and is particularly suited to reproduce survival curve flattening phenomena. We demonstrate the Umodel’s robust performance (R2 0.95) based on numerous clonogenic cell survival data sets from the literature and our experimental studies. Significance. The proposed Umodel allows using a single unified mathematical function based on generalized principles of accumulation of SLD with implemented radiosensitization, regardless of the type of energy deposited and the mechanism of action. It can reproduce various patterns of clonogenic survival curves, including any flattening, thus encompassing the variability of cell reactions to therapy, thereby potentially better reflecting overall tumor responses. Our approach opens a range of options for further model developments and strategic therapy outcome predictions of sequential treatments applied in different orders and varying recovery intervals between them.
Dispersive gains enhance wireless power transfer with asymmetric resonance
Authors: Hao Xianglin , Yin Ke , Cai Shiqing , Zou Jianlong , Wang Ruibin , Ma Xikui , Tse Chi Kong , Dong Tianyu 
Published: January 2025

Abstract
Parity-time (PT) symmetry is a fundamental concept in non-Hermitian physics that has recently gained attention for its potential in engineering advanced electronic systems and achieving robust wireless power transfer (WPT) even in the presence of disturbances, through the incorporation of nonlinearity. However, the current PT-symmetric scheme falls short of achieving the theoretical maximum efficiency of WPT and faces challenges when applied to non-resistive loads. In this study, we propose a theoretical framework and provide experimental evidence demonstrating that asymmetric resonance, based on dispersive gain, can greatly enhance the efficiency of WPT beyond the limits of symmetric approaches. By leveraging the gain spectrum interleaving resulting from dispersion, we observe a mode switching phenomenon in asymmetric systems similar to the symmetry-breaking effect. This phenomenon reshapes the distribution of resonance energy and enables more efficient WPT compared to conventional methods. Our findings open up new possibilities for harnessing dispersion effects in various domains such as electronics, microwaves, and optics. This work represents a significant step towards exploiting dispersion as a means to optimize WPT and lays the foundation for future advancements in these fields.
Uncoiling deployment of thin-shell composite booms with piezoelectric actuation
Authors: Daye Jacob G , Lee Andrew J 
Published: January 2025

Abstract
The efficacy of using piezoelectric actuators to initiate the dynamic deployment of bistable composite tape springs is evaluated in this paper. Ultra-thin composite booms such as tape springs and their cross-sectional variants have seen increased popularity in spacecraft structures due to enabling the precise deployment of flexible solar arrays, sails, reflectors, and antennas. They can elastically transition between the deployed ‘extended’ position and the stowed ‘coiled’ position while retaining superior stiffness, thermal properties, mass efficiency, and compactness when compared to thin-shelled metal booms and rigid articulated columns. Bistability in the coiled and extended states allows the boom to exhibit more controllable self-deployment and become reconfigurable, which could allow spacecraft to relocate, redeploy, and adapt to changing environmental conditions or mission objectives. Deployment systems commonly include motors and mechanical restraints that significantly contribute to mechanical complexity and spacecraft weight. Since bistable booms do not rely on elastic instability of packaging to initiate motion, a non-intrusive and lightweight actuation mechanism is needed to trigger deployment. This paper experimentally demonstrates how a macro fiber composite actuator can statically and dynamically excite a stowed composite tape spring to initiate unrolling into its extended state. A finite element model in Abaqus/Explicit is also developed to computationally corroborate the deployment schemes discussed.
Flexible biomimetic olfactory neurons based on organic heterojunction
Authors: Feng Tianyang , Meng Jialin , Xu Hang , Yang Yafen , Wang Tianyu , Zhu Hao , Sun Qingqing , Zhang David Wei , Chen Lin 
Published: January 2025

Abstract
Simulating the human olfactory nervous system is one of the key issues in the field of neuromorphic computing. Olfactory neurons interact with gas molecules, transmitting and storing odor information to the olfactory center of the brain. In order to emulate the complex functionalities of olfactory neurons, this study presents a flexible olfactory synapse transistor (OST) based on pentacene/C8-BTBT organic heterojunction. By modulating the interface between the energy bands of the organic semiconductor layers, this device demonstrates high sensitivity (ppb level) and memory function for NH3 sensing. Typical synaptic behaviors triggered by NH3 pulses have been successfully demonstrated, such as inhibitory postsynaptic currents (IPSC), paired-pulse depression (PPD), long-term potentiation/depression (LTP/LTD), and transition from short-term depression (STD) to long-term depression (LTD). Furthermore, this device maintains stable olfactory synaptic functions even under different bending conditions, which can present new insights and possibilities for flexible synaptic systems and bio-inspired electronic products.
The impact of plasmonic Ag−Au bimetallic nanoparticles on photocurrent enhancement in GaN-based photodetectors
Authors: Vu Thi Kim Oanh , Bui Thi Thu Phuong , Nguyen Ngoc Anh , Nguyen Thi Thanh Bao , Nguyen Thi Minh Hien , Kim Eun Kyu 
Published: January 2025

Abstract
Recently, there has been considerable interest in high-efficiency ultraviolet (UV) photodetectors for their potential practical uses. In this study, a high-quality UV photodetector was fabricated using a combination of Ag and Au NPs with GaN film. The GaN film was deposited using sputtering technique, whereas Ag and Au films were grown using thermal evaporation technique. Ag−Au bimetallic nanoparticles were formed by treating them at the various annealing temperature to improve the interaction between light and the photoactive layers of the photodetectors. The optimal annealing temperature to achieve the best performance of a photodetector is 650 °C. This led to a photoresponsivity of 98.5 A/W and the ON/OFF ratio of 705 at low bias voltage of 1 V. This work establishes the foundation for the advancement of high-performance UV photodetectors.
Ultrabroadband THz probing of anisotropic optical conductivity and plasmonic damping in graphene nanostructures
Authors: Ji Jie , Xiang Cheng , Gammelgaard Lene , Shivayogimath Abhay , Zhou Binbin , Jepsen Peter Uhd , Bøggild Peter 
Published: January 2025

Abstract
Graphene nanostructures have garnered significant attention in the terahertz (THz) range due to their exceptional plasmonic properties. Exploring the THz conductivity of graphene nanostructures is crucial for optimizing their performance in advanced devices, necessitating comprehensive research into their optical responses across a broad frequency spectrum. In this study, we experimentally investigate the optical conductivity of graphene nanostructures using ultra-broadband THz time-domain spectroscopy. Extracted from the time-dependent THz echo signals, an order-of-magnitude enhancement of localized surface plasmon resonances (LSPRs) in graphene nanostructures was observed. This led to the anisotropic behavior in optical conductivity over the broadband THz range. In addition, the analyzed results show that LSPRs in graphene nanostructures experience damping loss from the intraband transition, resulting in energy dissipation into the substrate through the dielectric space layer. The induced redshift of the LSPR and linewidth broadening, in turn, modifies the optical conductivity of the graphene nanostructures. These findings provide valuable insights for developing advanced tunable THz devices based on graphene nanostructures.
The triadic relationship between green HRM, innovation, and pro-environmental behaviour: a study of their interactions and impacts on employee productivity and organizational sustainability
Authors: Gazi Md. Abu Issa , Masud Abdullah Al , Emon Md. , Ibrahim Md. , Senathirajah Abdul Rahman bin S 
Published: January 2025

Abstract
The study aims to examine the impact of Green Human Resource Management (GHRM) and eco-consciousness on work productivity, eventually resulting in greater efficiency and sustainability. Furthermore, we are also investigating the role of green innovation, environmentally conscious behavior, and employee productivity as mediators between green HRM and societal, economic, and environmental outcomes. Through the use of an online survey, 401 samples were collected from a variety of industries in Bangladesh, and then structural equation modeling was used to analyses the data. The study found that green HRM, innovation, and pro-environmental behavior favorably and significantly influence worker productivity. Furthermore, worker productivity directly and statistically affects social, economic, and environmental performance. The originality of this study resides in its comprehensive illustration of how GHRM, green innovation, and pro-environmental behavior effectively encourage worker productivity, resulting in organizational sustainability.
Li+ diffusion in crystalline lithium silicides: influence of intrinsic point defects
Authors: Kirsch Christoph , Dreßler Christian , Sebastiani Daniel 
Published: January 2025

Abstract
Crystalline lithium silicides Li
x
Si
y
are a group of Zintl-like compounds, of which Li15Si4 is observed crystallizing in Si anodes after complete electrochemical lithiation. Here, Li diffusion in Li1Si1, Li12Si7, Li13Si4 and Li15Si4 is extensively studied by means of ab initio molecular dynamics simulations and nudged elastic band calculations considering various intrinsic point defects. Li interstitials are identified as the main vehicle of diffusion due to typically low formation and migration energies. They diffuse via an interstitialcy mechanism usually involving 2 or 3 atoms. Moreover, Frenkel defects are found to play a role in Li12Si7 and Li13Si4 only, as their formation energies are relatively high in Li1Si1 and Li15Si4. All investigated intrinsic defects have an impact on energy barriers of nearby as well as farther diffusion paths.
Dynamic characteristics analysis of high-speed motorized spindle system considering unbalanced magnetic pull and non-linear bearing restoring force
Authors: Zhang Xiangming , Ma Zhimin , Fang Miaofeng , Jiang Yongying 
Published: January 2025

Abstract
The high-speed motorized spindles play a pivotal role in modern machining, holding vast application potential and profound research significance. To analyze the dynamic characteristics of the high-speed motorized spindle system, an fourteen-degree-of-freedom dynamic model has been established using the lumped parameter method, and numerical methods have been employed for computation. Then, the bearing stiffness, the variation of the restoring force, and the unbalanced magnetic force are considered. Additionally, the parameters of the model were optimized using the genetic algorithm. Finally, the effects of spindle speed, initial eccentricity, and bearing parameters on the vibration behavior and stability of the motorized spindle were analyzed using bifurcation diagrams, time-frequency waveform, spectra, trajectories, and Poincaré. The study provides a valuable reference for the design and optimization of motorized spindle systems.
Predictive modeling of transverse cracking in continuously reinforced concrete pavement: a machine learning approach
Authors: Alnaqbi Ali , Al-Khateeb Ghazi G , Zeiada Waleed 
Published: January 2025

Abstract
Accurate prediction of transverse cracking in Continuously Reinforced Concrete Pavement (CRCP) is critical for improving infrastructure management procedures and preserving the road network’s long-term durability and safety. This paper conducts a thorough analysis into predicting transverse cracking in CRCP using machine learning approaches. The research involved meticulous data preparation, feature selection, and evaluation of various machine learning models to identify the most effective predictor. Key variables such as pavement age, total thickness, temperature, freeze index, traffic volume, precipitation, and initial International Roughness Index (IRI) were analyzed for their impact on transverse cracking occurrences. Sensitivity analysis was conducted to assess the influence of individual input variables on model predictions. Results indicated that the cubic Support Vector Machine (SVM) model outperformed other models, demonstrating exceptional predictive accuracy. Furthermore, sensitivity analysis revealed significant correlations between input variables and transverse cracking occurrences, emphasizing the importance of considering a holistic range of factors in pavement engineering and maintenance strategies. Our findings, which provide insights into the intricate interactions between input factors and pavement distress, help to create tailored treatments and methods, such as optimized crack sealing schedules, improved reinforcement strategies, and the use of high-performance materials, for minimizing transverse cracking and enhancing CRCP performance.
CFNet:CSI compression feedback network based on WiFi sensing
Authors: He Yuting , Wei Mingjun , Li Dapeng , Li Pinhao , Li Hui 
Published: January 2025

Abstract
To solve the problem of huge channel state information (CSI)data caused by a large number of antennas in large-scale MIMO systems,a CSI compressed feedback network model (CFNet)with encoder-decoder structure is proposed in this paper. Specifically, the encoder efficiently compresses the CSI data to reduce the burden of data transmission and storage. A key component, the Asymmetric Expansion Convolution (AEC) module, combines asymmetric convolution and dilated convolution to extract feature information from different directions, thereby increasing the model’s receptive field and minimizing information loss. The compressed CSI is fed into a Gated Recurrent Unit (GRU), which captures long-term dependencies in the CSI sequence using the encoder’s output and the hidden state of the upper window. Additionally, on the decoder side, the proposed Asymmetric Transpose Convolution reconstructs the compressed CSI data, enhancing the network’s ability to capture and express CSI features through two complementary convolution operations, ultimately reducing communication costs. Thanks to the proposed encoder and decoder, CFNet can accurately identify fine-grained features of human activities recognition(HAR) while leveraging compressed sensing techniques, further improving data processing efficiency and recognition accuracy. The experiments conducted on two public datasets demonstrate that the evaluation metrics of the proposed network outperform those of advanced methods in the relevant field.
Infectious disease time series modelling using transformer self-attention based network
Authors: Prakash Satya , Jalal Anand Singh , Pathak Pooja 
Published: January 2025

Abstract
Researchers are focusing on improving time series forecasting methods to address real-world problems like COVID-19. Current methods show decreased accuracy due to unpredictable seasonality, and enhancing models to handle long-term dependencies is crucial for better forecasting accuracy. This paper presents a Transformer self-attention based novel approach for infectious disease time series forecasting, specifically for COVID-19. The proposed method utilizes the Ensemble Empirical Mode Decomposition (EEMD) and Local Outlier Factor (LOF) methods for data pre-processing and to detect outliers. Next, a modified self-attention model based on Transformer neural network is introduced for predicting COVID-19 time series forecasting for the first time. The research specifically investigates the application of encoder/decoder networks with an enhanced Positional Encoding approach. This involves using a novel time encoding technique on the input pattern to achieve more precise intended output. Consequently, the parameters in the Transformer model are adjusted using the Arithmetic Optimization Algorithm (AOA) to enhance the accuracy of the prediction. The model generates more accurate predictions over broader time intervals, with the lowest MAE of 371.92 and RMSE of 674.61, indicating superior predictive accuracy by approximately 30% compared to other state-of-the-art methods. The proposed Transformer model has demonstrated significant improvements in robustness and forecasting accuracy compared to standard approaches such as LSTM, RNN, Exponential Smoothing, AutoARIMA, and TBATS for the COVID-19 time series of India, USA, and Brazil. The suggested model, due to its superior predictive accuracy, is applicable in diverse time series forecasting domains such as stock market trends, sales, and industrial consumption forecasting etc.
Huxley
Authors: Klein Alexander Mugar 
Published: January 2025

Abstract
This chapter examines the 1870s automatism controversy with a focus on T.H. Huxley. It reconstructs his central argument for epiphenomenalism, his favored form of automatism. His argument depended on the mechanistic idea that physiological explanation should only appeal to physical and chemical causes. Huxley supported the plausibility of this mechanistic program by appealing to purposive behavior in decapitated frogs, creatures he took to be unconscious. In contrast, anti-automatist opponents like G. H. Lewes had premised their arguments on a rival interpretation of the decapitated frog experiments according to which such creatures retain sentience. When the two sides stalemated, one cause was an inability to resolve rival interpretations of these experiments. This chapter uses the nineteenth-century dispute over decapitated frogs to develop a philosophical analysis of the so-called measurement problem, which is the problem of determining empirically whether a given creature is conscious.