bluuebunny/crossref_metadata_2025_split huggingface dataset:
What can we learn from the experiment of electrostatic conveyor belt for excitons?
Authors: Zhao T T , Li Rui , Liu C S 
Published: September 2024

Abstract
Motivated by the experiment of electrostatic conveyor belt for indirect excitons (Winbow et al 2011 Phys. Rev. Lett.
106 196806), we studied the exciton patterns for understanding the exciton dynamics. By analyzing the exciton diffusion, we found that the patterns mainly came from the photoluminescence of two kinds of excitons. The patterns near the laser spot came from the hot excitons which can be regarded as the classical particles. However, the patterns far from the laser spot come from the cooled or coherent excitons. Considering the finite lifetime of Bosonic excitons and of the interactions between them, we built a time-dependent nonlinear Schrödinger equation including the non-Hermitian dissipation to describe the coherent exciton dynamics. The real-time and imaginary-time evolutions were used alternately to solve the Schrödinger equation to simulate the exciton diffusion accompanied by the exciton cooling in the moving lattices. By calculating the escape probability, we obtained the transport distances of the coherent excitons in the conveyor, consistent with the experimental data. The cooling speed of excitons was found to be important in coherent exciton transport. Moreover, the plateau in the average transport distance cannot be explained by the dynamical localization-delocalization transition induced by the disorders.
The Fusiongram: a periodic weak fault feature extraction strategy and its application in bearing fault diagnosis
Authors: Xue Zhengkun , Zhang Wanyang , Xue Linlin , Shi Jinchuan , Shan Xiaoming , Luo Huageng 
Published: September 2024

Abstract
The weak periodic transient impact responses caused by localized defects in rolling bearings are often obscured by complex interferences, such as white noise, random transient impact responses, and periodic responses from system operations. Meanwhile, the fault feature information contributing to damage detection may be distributed across different frequency bands in the vibration signal. Therefore, under the influence of complex interference, it is a challenging problem to accurately select frequency bands containing rich fault feature information and utilize the useful information from multiple frequency bands to serve fault diagnosis. To overcome this problem, this research introduces a novel signal processing strategy, termed as Fusiongram, for extracting weak periodic fault features amidst the influence of complex interferences. Firstly, the method of complementary hierarchical decomposition is proposed, in which the signal is decomposed into multiple components with overlapping frequency contents. Then, an index with interference resistance is constructed to select the components carrying rich damage feature information. Finally, the adaptive threshold denoising and multicomponent normalized averaging techniques are employed to fuse the information from the squared envelope spectra (SES) of the selected components, thus obtaining the reconstructed SES for fault diagnosis. The Fusiongram is able to achieve the goal of weak fault feature extraction from signals with complex interference. The analysis results of numerical simulation and experimental testing verify the effectiveness and advantages of the proposed strategy.
Fault diagnosis of the harmonic reducer based on dual-path convolutional network with multi-channel hybrid attention mechanism
Authors: Li Kai , Yang Ronggang , Wei Tianci , Yang Yiwen , Xiang Jiawei 
Published: September 2024

Abstract
Harmonic reducers are prone to wear and other failures due to continuous deformation of the flexure wheel during operation. To evaluate the sorts of faults in harmonic reducers under various operating situations, a fault diagnosis method (MADCNN-BiGRU) based on dual-path convolution neural network (DCNN) with multi-channel hybrid attention mechanism (MCHAM) and bidirectional gated recurrent unit (BiGRU) was proposed. Firstly, a novel MCHAM focuses on the critical information part of the vibration signal and can effectively suppress the noise and redundant information. Secondly, a new soft threshold function is constructed to regulate the attention weights dynamically. Thirdly, BiGRU obtains feature information for different time series locations to identify the faults correctly. Fourthly, a harmonic reducer test rig is established to collect vibration signals of harmonic reducers with different faults under various operating situations. The comparative experimental results demonstrate that MADCNN-BiGRU has excellent capacity for robustness and generalization, and can effectively diagnose under various operating situations.
Long-time prediction of multi-indicator water quality based on the improved WaveNet under time series decomposition and phase space reconstruction
Authors: Chen Ying , Wu Zhen , Shen Zhen , Zhang Hongbo , Yuan Junjun , Cai Mingfa 
Published: September 2024

Abstract
Long–time prediction of water quality indicator such as chlorophyll–a (Chl–a) is crucial for water process engineering and environmental management. In order to capture the characteristics of long–time series and reduce the limitations of traditional long–time prediction strategies, this paper proposes a novel hybrid model by combining data decomposition, phase space reconstruction, feature fusion and improved WaveNet. Firstly, the original data is decomposed into several subsequences through time series decomposition. Then, the subsequences with chaotic characteristics are integrated with multiple features for phase space reconstruction. Next, the decomposed and reconstructed subsequences are fed back into the improved WaveNet model separately. Finally, the prediction results are obtained by summing the predicted values of the subsequences. In this paper, the reliability of the method is assessed using the dissolved oxygen, water temperature, pH and Chl–a data of a monitoring station in the Beihai coastal sea area, ablation experiments are conducted to demonstrate the effectiveness of each module in the proposed model, and comparisons with multiple benchmark and hybrid models show that the proposed model exhibits better performance in long–time prediction of coastal water quality in the next fourteen days.
Three-dimensional visualization of large-area, nanoscale topography measurements
Authors: Natinsky Eva , Connolly Liam G , Cullinan Michael 
Published: September 2024

Abstract
High-resolution metrology is a critical area of development for nanoscale manufacturing, especially as it affects production throughput and fabrication quality. Atomic force microscopy (AFM) is one of the most popular tools for nanometrology, and high-resolution AFM often requires a significant time commitment and produces datasets of several million points. It is therefore critical for the development of data processing techniques to keep pace with the requirements of analyzing this type of data, and for these techniques to be portable as miniaturization in AFM is becoming more common. This work presents a data fitting algorithm designed for reducing the parameters of large-area data sets which utilizes well-established spline fitting techniques. In this paper we show that basis-spline fitting can be used to accurately represent large AFM data sets, including data sets with noisy data and sharp features, while achieving at least 90% parameter reduction in all test cases.
Plasma edge and scrape-off layer turbulence in gyrokinetic simulations of negative triangularity plasmas
Authors: Bernard T N , Halpern F D , Francisquez M , Hammett G W , Marinoni A 
Published: September 2024

Abstract
Gyrokinetic simulations in the long-wavelength or drift-kinetic limit are carried out of DIII-D inner-wall-limited (IWL) plasmas to investigate the effect of triangularity on edge and scrape-off layer (SOL) turbulence. The effect of neutral interactions and triangularity on plasma blobs is explored due to the impact blobs can have in setting the SOL width or introducing impurities through interactions with plasma-facing components. Seeded blob simulations with neutrals in shaped SOL scenarios demonstrate that increasing elongation, triangularity, or Shafranov shift decreases radial blob velocities, but neutral interactions have a minor effect. Fully turbulent simulations of DIII-D IWL plasmas include both open- and closed-field-line regions. The negative triangularity (NT) simulation has lower average core Te
, lower normalized Te
fluctuations, and lower fluxes, but a greater number of coherent structures (blobs) identified with increased size and velocity, on average. Density and electron temperature profiles are within a factor of 2 of experimental values. The increased trapped electron particle fraction in NT simulations is consistent with previous studies.
Voltametric detection of methylene blue dye in water at green and chemically synthesized Ag/MWCNT modified electrode
Authors: Mokole Seleke J , Fayemi Omolola E 
Published: September 2024

Abstract
A well-known textile dye, methylene blue (MB) was electrochemically detected by using glassy carbon electrode (GCE), modified with green and chemically synthesized silver nanoparticles (AgNPs) decorating the surface of functionalized multiwalled carbon nanotubes (fMWCNT). The green and chemical methods were used to synthesize AgNPs, which decorated MWCNT forming MWCNT/Agchm and MWCNT/Aggrn nanocomposite. Comprehensive characterization of the nanomaterials was carried out using energy-dispersive x-ray spectroscopy (EDX) detector-equipped scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), UV–visible spectroscopy (UV), and x-ray diffraction (XRD). Using XRD, particle sizes were found to be 26.81, 10.05, 5.36, 19.26, and 17.48 nm for Agchm, Aggrn, MWCNT, Agchm/MWCNT, and Aggrn/MWCNT, respectively. Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and square wave voltammetry (SWV) were employed for the investigation of the electrochemical properties and behaviour of MWCNT, Agchm, Aggrn, Agchm/MWCNT, and Aggrn/MWCNT electrodes, and higher electron transport capabilities and improved electrochemical activity towards MB on Agchm/MWCNT electrode were demonstrated by the results. Electroanalysis of methylene blue at the modified electrodes with square wave technique SWV was successful. At Agchm/MWCNT modified electrode, a low limit of detection (LOD) of 4.684 and limit of quantification (LOQ) of 14.194 pM, for MB, while at Aggrn/MWCNT modified electrode , an LOD and LOQ of 2.935 and 8.895 pM, were recorded respectively. In real sample analysis, the recovery percentage for Agchm/MWCNT ranged from 90 to 98% (n =3), and Aggrn/MWCNT showed a recovery percentage ranging from 97 to 103% (n = 3). Both electrodes’ remarkable recovery rate attest to their dependability and sensitivity in MB detection.
Plasma P-tau217 Demonstrates Excellent Diagnostic and Prognostic Performance as a Blood-Based Biomarker for Alzheimer Disease in Older Adults
Authors: Dyer Adam , Dolphin Helena , Morrison Laura , O'Connor Antoinette , Sedgwick Gavin , Young Conor , Killeen Emily , Gallagher Conal , McFeely Aoife , Connolly Eimear , Davey Naomi , Doyle Paddy , Claffey Paul , Lyons Shane , Gaffney Christine , Ennis Ruth , McHale Cathy , Joseph Jasmine , Knight Graham , Kelly Emmet , O'Farrelly Cliona , Fallon Aoife , O'Dowd Sean , Lawlor Brian , Bourke Nollaig , Kennelly Sean 
Published: September 2024

Abstract

Background
There has been unprecedented progress in the development of blood-based biomarkers (BBMs such as p-tau217 to detect Alzheimer Disease (AD) pathology – characterised by the accumulation of Amyloid-Beta (Aβ) and hyper-phosphorylated tau (T). However, BBM performance in “real-world” memory clinic contexts remains unclear.

Methods
Using high-sensitivity immunoassays, plasma p-tau217 was assessed in 554 participants. Two cohorts were studied: (i) a memory clinic validation cohort of 108 older adults (69 ± 6.5 years; 54.6% female) with early cognitive symptoms and paired plasma/cerebrospinal fluid (CSF) at time of diagnostic Lumbar Puncture (LP) and (ii) a broader replication cohort of 446 individuals ranging from cognitively-unimpaired middle-aged adults to older adults with established AD with 18-month follow-up. Plasma P-tau217 performance was examined against clinically established CSF Aβ+/T+ cut-offs using Area-Under the Curve (AUC) analysis. Plasma cut-offs were optimised vs CSF based on maximal Youden index.

Results
In the memory clinic cohort, plasma p-tau217 exhibited excellent performance for the detection of Aβ pathology (AUC: 0.91, 0.86-0.97). Plasma p-tau217 was nearly 4-fold higher in Aβ+ (13.89; 7.36-19.0pg/mL) vs Aβ- (3.72; 2.80-4.09pg/mL, U = 230, p&lt;0.001) participants. Plasma p-tau217 was superior in the identification of Aβ vs T pathology (p&lt;0.05, DeLong Test) and outperformed p-tau181 and other BBMs(all p&lt;0.05, DeLong Test). In the replication cohort, plasma p-tau217 maintained &gt;90% accuracy for clinical AD and was significantly associated with clinically meaningful cognitive decline over 18 months (Odds Ratio 1.40; 1.06-1.85, p=0.02). In the initial memory clinic cohort, application of plasma p-tau217 as a diagnostic test would have reduced the need for LPs by over half (56.5%).

Conclusion
Plasma p-217 demonstrates excellent diagnostic and prognostic performance in older adults with AD, representing an amyloid-responsive measure which also predicts meaningful cognitive decline in established AD. Incorporation of plasma p-tau217 in memory clinic settings may substantially reduce the need for over half of diagnostic LPs.
Irish Hip Fracture Database Frailty Index Predicts Delirium Incidence and Length of Stay in Older Adults Admitted with Hip Fractures
Authors: Bradley Eoin , Fitzpatrick Donal , Lannon Rosaleen , McCarroll Kevin , Romero-Ortuno Roman 
Published: September 2024

Abstract

Background
The Irish Hip Fracture Database (IHFD) Frailty Index was developed with the National Office for Clinical Audit has been shown to predict adverse outcomes in older adults with fractures in retrospective review of national data. Here we examined the tool prospectively in clinical practice and its ability to predict delirium and length of stay.

Methods
We prospectively collected data on all hip fracture patients admitted to an acute orthopaedic floor from October 2022 to May 2024. Patients aged &lt; 60 years or those with atypical femoral or peri-prosthetic fractures were excluded. The 21-item frailty index was applied to all eligible patients. Delirium diagnosis was based on a composite of clinical diagnosis and a 4AT score ≥ 4. Regression analysis was used to examine the relationship between delirium and length of stay with Frailty Index, age and sex.

Results
A total of 260 patients were included in the study, of whom 72% were female. The mean age was 80 ± 10 years, and 7.7% were nursing home residents. Additionally, 24.2% of patients were discharged directly home, while 40.3% were discharged to rehabilitation. Delirium developed in 24.6% of patients, and 24.9% of patients had a LOS exceeding 30 days. Higher frailty index scores were significantly associated with both LOS (p = 0.001) and delirium (p = 0.013) in both unadjusted and adjusted analyses. There was no significant association between age or sex and LOS or delirium in adjusted analysis.

Conclusion
The IHFD Frailty Index facilitates a pro-active risk stratification that is based on comorbidities and functional status. IHFD Frailty index independently predicted delirium and length of stay while age did not. Further studies integrating this tool into everyday practice in the care of older patients with hip fractures is warranted.
Elevated Bone Turnover Markers After One Infusion Of Zoledronic Acid Post Denosumab Cessation
Authors: Jamil Intan Juliana , Fitzpatrick Donal , Bradley Eoin , Sherry Sophie , O'Carroll Claire , Carraher Jennifer , Steen Geogina , Fallon Nessa , Maher Niamh , Lannon Rosaleen , McCarroll Kevin 
Published: September 2024

Abstract

Background
Denosumab is a commonly used to treat osteoporosis though should not be stopped without transitioning to bisphosphonates to reduce the risk of rebound bone loss and fracture. Recent NOGG guidelines recommend IV zoledronic acid (ZA) six months after the last denosumab injection with further infusions based on bone turnover markers (BTM).

Methods
We collated data (over the past 5 years) on the first follow up BTM measured after zoledronic acid treatment given to patients where denosumab was stopped. ZA was administered between 6-7 months after the last denosumab injection. The bone resorption marker (C-Telopeptide type I collagen, CTX ng/ml) was measured at different time points: 4 months, 4-6 months, and between 6-12 months. A subtherapeutic CTX was defined as &gt; 0.300 ng/ml.

Results
There were 95 patients, 93% female, mean age 67.8 years and duration of denosumab therapy 4.6 years. Bone markers were significantly elevated from baseline at all timepoints. Mean CTX at 4 months was 0.450, 6 months 0.470 and 6-12 months 0.65. Overall, 45% had a subtherapeutic CTX at 4 months, 45% at 6 months and 70% between 6-12 (mean 9 months) and this was predicted by time from first ZA infusion (P=0.03).

Conclusion
Nearly half of patients had subtherapeutic BTM at 4-6 months and the majority at 6-12 months after receiving ZA. For patients on denosumab therapy for a similar duration (4-5 years), our results support the routine administration of a second ZA six months after their first ZA. Delays in administering a second infusion may result in a substantial rise in bone resorption markers and significant bone loss. This highlights the need for timely access to ZA for such patients transitioning off denosumab therapy.
Investigating Transcutaneous Vagus Nerve Stimulation in Amnestic Mild Cognitive Impairment
Authors: Dolphin Helena , Dyer Adam H , Dukelow Tim , Finucane Ciaran , Gomollo Amparo Zamora , Commins Sean , Kennelly Sean P 
Published: September 2024

Abstract

Background
New treatments are urgently needed for individuals with Mild Cognitive Impairment (MCI) - in particular for those with amnestic MCI, of whom a high proportion have underlying Alzheimer's Disease (AD). Transcutaneous Vagus Nerve Stimulation (tVNS) is a non-invasive neuro-modulatory treatment which has not been extensively examined in older adults with amnestic MCI.

Methods
A single site, single-blind, randomised three-arm crossover pilot trial of acute (60 minutes) tVNS (baseline, sham or active stimulation) was conducted a Regional Specialist Memory Service. Forty participants (age 71.7 ±6.9; 22/40 male) with diagnosed amnestic MCI were recruited. Given the links between AD and neuro-cardiovascular instability, potential adverse effects of active tVNS were assessed using beat-to-beat peripheral (Blood Pressure (BP) and Heart Rate [HR]) and central (via Near Infra-red Spectroscopy) haemodynamic responses to Active Stand (AS). Cognition was assessed between 21.3 ±4.9 and 60.5 ±4.4 minutes using a domain-specific cognitive performance battery with results analysed using mixed-effects linear regression.

Results
In older adults with amnestic MCI, tVNS was safe, tolerable and acceptable with 98% of participants stating they would use the device again. There was no significant effect on BP, or HR responses to AS and cerebral oxygenation remained stable during AS. After tVNS stimulation, performance on tests of spatial navigation were significantly improved compared to both baseline (ß= -8.76; [-14.91, -2.56]; p=0.01) and sham (ß= -4.15; [-7.32, -0.99]; p=0.01) conditions.

Conclusion
tVNS is a safe and tolerable treatment modality in older adults with amnestic MCI. Future studies should explore sustained effects and feasibility of domiciliary use.
Bone-A-Fide Breakthrough: Machine Learning Cracks the Code on Osteoporosis Treatment Using the Irish Hip Fracture Database
Authors: Asri Nur Atikah Mohd , Muslim Azrin , O'Regan Niamh , McEliggott Annette 
Published: September 2024

Abstract

Background
Osteoporosis is a metabolic bone disorder characterised by decreased bone mineral density and mass. Due to its asymptomatic nature, it often remains undiagnosed and untreated until a fracture occurs. Traditionally, treatment decisions for osteoporosis are based on clinical appropriateness while balancing the treatment's risks and benefits. Machine learning (ML) is revolutionising healthcare domains through pattern recognition of previously “unseen” observations. Presently, its application in osteoporosis is limited to early diagnosis. More research is needed to examine its role in guiding osteoporosis treatment. This study aims to identify new predictive attributes for osteoporosis treatment using ML techniques on data from the Irish Hip Fracture Database (IHFD).

Methods
Datasets from January to March 2023 in University Hospital Waterford were sourced from the IHFD. Osteoporosis treatment decisions were obtained from discharge letters. Preliminary data cleaning was performed in Excel with zero-variance and near-zero predictor. Attributes excluded. The dataset was entered into the WEKA 3.8.6 environment for ML processing.

Results
The initial dataset containing 141 instances and 32 attributes was refined using the Correlation Feature Selection and Ranker Search Method, identifying key osteoporosis treatment predictors. The highest correlation attributes are pre-fracture total score, pre-fracture indoor score, and age. Moderately positive correlations are discharge destination, pre-fracture outdoor and shopping score, ASA grade, Length-of-stay, admission code, Admission 4AT score, Frailty scale, and fracture type. The implemented J48 Tree ML-trained model revealed Correctly Classified Instances and Incorrectly Classified Instances of 98.24% and 1.7%, respectively, indicating a high prediction accuracy rate.

Conclusion
This study demonstrates the potential of ML in enhancing osteoporosis treatment decision-making by leveraging datasets from the IHFD. Integrating ML algorithms with traditional approaches can provide a comprehensive, nuanced and personal approach to osteoporosis treatment and patient care. The study opens avenues for future research in applying big data and advanced analytics in healthcare, underscoring the evolving landscape of medical decision-making.