bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved Rotational Vector Summation Algorithm for Fade Suppression in Distributed Acoustic Sensing
Authors: Liu Xu , Tang Wei , Xie Lang , Wang Yingshu , Shi Ji , Liu Kang 
Published: October 2023

Abstract
Signal fading severely affects the performance of optical fiber distributed acoustic sensing (DAS) systems based on coherent optical time domain reflectometry (COTDR), especially in terms of differentiated responses between channels. To eliminate fading, a polarization diversity COTDR system is established, which generates two pulse feedbacks with different fading components for P and S polarization. The rayleigh backscattered (RBS) signals from the two separate probing pulses exhibit different fading positions under the influence of external interference but have the same phase change rate. Through an improved rotational vector summation (IRVS) algorithm, the complex RBS signals of the two polarization states are effectively synthesized, simultaneously suppressing fading phenomena caused by orthogonal polarization. Experimental results demonstrate that the demodulated signal-to-noise ratio (SNR) can reach 65 dB@200 Hz. Moreover, by using 20% of the mean amplitude as a threshold, the probability of fade events decreases from 4.9% to 0.12%. The proposed fade mitigation distributed sensing system exhibits excellent performance and plays a crucial role in practical DAS systems.
Dynamic Modeling of Nox Emission from Power Plants Considering Input Variable Time-Delay and Feature Extraction
Authors: Ma Ning , Zhang Wei , Chen Xiaofeng , You Mo , Yang Zhenyong , Liu Lei , Kang Jingqiu 
Published: October 2023

Abstract
Aiming at the problem that it is difficult to effectively predict the concentration of nitrogen oxide emissions from boilers under the changing load conditions of power plants, this paper proposes a dynamic modeling method of nitrogen oxide (NOx) emissions from power plants, which considers the delay-feature extraction of input variables. This method combines principal component analysis and Gaussian regression modeling, and uses principal component analysis to extract the feature information extracted from the input variable data, and the current value and historical sequence value of extracted information are used as the input of Gaussian regression model. In addition, the historical time series value of boiler NOx concentration is added to the input of the model as feedback data. Taking the combustion system of a 1,000MW ultra-supercritical unit boiler as an object, and combining with the actual operation data on site, a dynamic model of boiler NOx emission is established. The experimental results show that the built boiler NOx emission dynamic model has high prediction accuracy and strong generalization performance, which has a certain reference value for boiler NOx emission modeling and intelligent control research.
Why adiabatic quantum annealing is unlikely to yield speed-up
Authors: Villanueva Aarón , Najafi Peyman , Kappen Hilbert J 
Published: October 2023

Abstract
We study quantum annealing for combinatorial optimization with Hamiltonian

H
=

H
0

+
z

H
f

where Hf
is diagonal,

H
0

=
−
|
ϕ
⟩
⟨
ϕ
|

is the equal superposition state projector and z the annealing parameter. We analytically compute the minimal spectral gap, which is

O

1

/

N

with N the total number of states, and its location

z
∗

. We show that quantum speed-up requires an annealing schedule which demands a precise knowledge of

z
∗

, which can be computed only if the density of states of the optimization problem is known. However, in general the density of states is intractable to compute, making quadratic speed-up unfeasible for any practical combinatorial optimization problems. We conjecture that it is likely that this negative result also applies for any other instance independent transverse Hamiltonians such as

H
0

=
−

∑

i
=
1

n

σ
i
x

.
Influence of cumulative damage on synchronization of Kuramoto oscillators on networks
Authors: Eraso-Hernandez L K , Riascos A P 
Published: October 2023

Abstract
In this paper, we study the synchronization of identical Kuramoto phase oscillators under cumulative stochastic damage to the edges of networks. We analyze the capacity of coupled oscillators to reach a coherent state from initial random phases. The process of synchronization is a global function performed by a system that gradually changes when the damage weakens individual connections of the network. We explore diverse structures characterized by different topologies. Among these are deterministic networks as a wheel or the lattice formed by the movements of the knight on a chess board, and random networks generated with the Erdős–Rényi and Barabási–Albert algorithms. In addition, we study the synchronization times of 109 non-isomorphic graphs with six nodes. The synchronization times and other introduced quantities are sensitive to the impact of damage, allowing us to measure the reduction of the capacity of synchronization and classify the effect of damage in the systems under study. This approach is general and paves the way for the exploration of the effect of damage accumulation in diverse dynamical processes in complex systems.
Entanglement dynamics via periodic modulations in an optomechanical system with embedded double quantum dots
Authors: Kumar Singh Madhav , Bhatt Vijay , K Jha Pradip , B Bhattacherjee Aranya 
Published: October 2023

Abstract
In our theoretical study, we explore the dynamic behavior and entanglement properties of a hybrid optomechanical system containing double quantum dots (QDs). The system is driven by a laser with amplitude modulation and we consider the modulation of the resonance frequency of the two QDs. Through the application of modulation techniques, we can achieve the targeted mean field value and effectively control the entanglement between various degrees of freedom. This innovative approach shows great potential for the development of efficient quantum memories suitable for continuous variable quantum information processing.
Controllable growth of wafer-scale two-dimensional WS2 with outstanding optoelectronic properties
Authors: Zhang Shiwei , Hao Yulong , Gao Fenglin , Wu Xiongqing , Hao Shijie , Qiu Mengchun , Zheng Xiaoming , Wei Yuehua , Hao Guolin 
Published: October 2023

Abstract
As one of two-dimensional (2D) semiconductor materials, transition metal dichalcogenides (TMDs) have sparked enormous potential in next-generation electronics due to their unique and excellent physical, electronic and optical properties. Controllable growth of wafer-scale 2D TMDs is essential to realize the various high-end applications of TMDs, while it remains challenging. Herein, 2 inch 2D WS2 films were successfully synthesized by ambient pressure chemical vapor deposition based on substrate engineering and space-confined strategies. WS2 nucleation density can be effectively modulated depending on the annealing conditions of sapphire substrate. The thickness of WS2 films can be controllably fabricated by adjusting the space-confined height. Moreover, our strategies are demonstrated to be universal for the growth of other 2D TMD semiconductors. WS2-based photodetectors with different thicknesses were systematically investigated. Monolayer WS2 photodetector displays large responsivity of 0.355 A W−1 and high specific detectivity of 1.48 × 1011 Jones. Multilayer WS2 device exhibits negative self-powered photoresponse. Our work provides a new route for the synthesis of wafer-scale 2D TMD materials, paving the way for high performance integrated optoelectronic devices.
Revolutionizing energy storage: the emergence of MOF/MXene composites as promising supercapacitors
Authors: Khosroshahi Negin , Bakhtian Mahnaz , Asadi Ali , Safarifard Vahid 
Published: October 2023

Abstract
As the world becomes increasingly concerned with environmental preservation and the effects of fossil fuel consumption, it is essential to find new and innovative ways of providing energy. Supercapacitors are among the most promising devices for energy storage. Finding materials that can enhance their efficiency is still a major challenge. Research is currently underway to fabricate composite materials with specific properties that can improve the performance of supercapacitors. One class of materials that has shown great promise is MXenes, which are two-dimensional layers of carbides, nitrides, and carbonitrides of transition metals. These materials possess unique features such as high electrical conductivity, flexibility, and hydrophilic surfaces, which make them suitable for a range of electrochemical applications. Adding MXenes to metal–organic frameworks (MOFs) or MOF derivatives has been shown to enhance the output yield of supercapacitors. MOFs are widely used in various energy systems because of their adjustable porosity and high surface area. The addition of MXenes can prevent the stacking of MXene sheets on top of each other, leading to improved results due to the synergistic effect. In particular, MOF/MXene composites have shown significant promise for use in supercapacitor applications. This review provides a comprehensive overview of the recent advances in MOF/MXene composites, including their synthesis, properties, and potential applications. We also highlight the challenges and opportunities for future research in this field.
Increasing precipitation variability and climate-growth responses of five tree species in North Carolina, USA
Authors: Catherwood Avery A , Knapp Paul A 
Published: October 2023

Abstract
We examined the effects of increasing summertime precipitation variability from 1950 to 2022 on the radial-growth responses of five tree species native to central North Carolina, USA. Tree-ring data were collected from chestnut oak, post oak, longleaf pine, shortleaf pine, and Virginia pine and processed following standard dendrochronology procedures. Adjusted latewood chronologies for each species were created and correlated with either monthly or multi-monthly combinations of summertime precipitation for above average (AA, > 1 σ above mean), below average (BA, <−1 σ below mean), and near average (NA, <−1, and 1 > σ) precipitation years. June–September precipitation variability and other summertime monthly combinations significantly increased during the study period, with a 10.2% increase in AA/BA years during the 21st century. Climate-growth correlations ranged from 0.40–0.51 using all years within the study period. However, using AA and BA years exclusively, climate-growth responses ranged from 0.44–0.71, with post oak and longleaf pine experiencing significantly higher correlations. No significant changes in climate-growth responses occurred for chestnut oak, shortleaf pine, and Virginia pine. These findings suggest the effects of increased precipitation variability on climate-growth responses are species-dependent and affected by the precipitation classification (i.e. AA or BA years). These responses help explain temporal variations in the strength of climate-growth responses, particularly for some species, and offer additional considerations for dendroclimatological research.
New horizons in Parkinson’s disease in older populations
Authors: Chhetri Jagadish K , Mei Shanshan , Wang Chaodong , Chan Piu 
Published: October 2023

AbstractParkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease. Ageing is considered to be the greatest risk factor for PD, with a complex interplay between genetics and the environment. With population ageing, the prevalence of PD is expected to escalate worldwide; thus, it is of utmost importance to reduce the burden of PD. To date, there are no therapies to cure the disease, and current treatment strategies focus on the management of symptoms. Older adults often have multiple chronic diseases and geriatric syndromes, which further complicates the management of PD. Healthcare systems and care models necessary to address the broad needs of older PD patients are largely unavailable. In this New Horizon article, we discuss various aspects of PD from an ageing perspective, including disease management. We highlight recent advancements in PD therapies and discuss new care models with the potential to improve patient’s quality of life.
Preventable deaths involving falls in England and Wales, 2013–22: a systematic case series of coroners’ reports
Authors: Song Kaiyang , Portwood Clara , Jindal Jessy , Launer David , France Harrison , Hey Molly , Richards Georgia , Dernie Francesco 
Published: October 2023

Abstract

Background
Falls in older people are common, leading to significant harm including death. Coroners have a duty to report cases where action should be taken to prevent future deaths, but dissemination of their findings remains poor.

Objective
To identify preventable fall-related deaths, classify coroner concerns and explore organisational responses.

Design
A retrospective systematic case series of coroners’ Prevention of Future Deaths (PFD) reports, from July 2013 (inception) to November 2022.

Setting
England and Wales.

Methods
Reproducible data collection methods were used to web-scrape and read PFD reports. Demographic information, coroner concerns and responses from organisations were extracted and descriptive statistics used to synthesise data.

Results
Five hundred and twenty-seven PFDs (12.5% of PFDs) involved a fall that contributed to death. These deaths predominantly affected older people (median 82 years) in the community (72%), with subsequent death in hospital (70.8%). A high proportion of cases experienced fractures (51.6%), major bleeding (35.9%) or head injury (38.7%). Coroners frequently raised concerns regarding falls risks assessments (20.9%), failures in communication (20.3%) and documentation issues (17.5%). Only 56.7% of PFDs received a response from organisations to whom they were addressed. Organisations tended to produce new protocols (58.5%), improve training (44.6%) and commence audits (34.3%) in response to PFDs.

Conclusions
One in eight preventable deaths in England and Wales involved a fall. Addressing concerns raised by coroners should improve falls prevention and care following falls especially for older adults, but the poor response rate may indicate that lessons are not being learned. Wider dissemination of PFD findings may help reduce preventable fall-related deaths in the future.
New horizons in the diagnosis and management of dehydration
Authors: Frith James 
Published: October 2023

Abstract
Hydration is a fundamental aspect of clinical practice and yet it is an under-researched topic, particularly in older people, leading to many areas of uncertainty. There are two types of dehydration; hypertonic, which is a water deficit, and isotonic, which is a deficit of both water and salt. Individual clinical signs and bedside tests are poor diagnostic tools, making dehydration difficult to identify. However, the diagnostic value of a holistic clinical approach is not known. The gold-standard clinical test for dehydration is serum osmolality, but this cannot diagnose isotonic dehydration and may delay diagnosis in acute situations. Salivary osmolality point-of-care testing is a promising and rapid new diagnostic test capable of detecting both hypertonic and isotonic dehydration in older people, but further evidence to support its clinical utility is needed. Daily fluid requirements may be less than previously thought in adults, but the evidence specific to older people remains limited. Hydration via the subcutaneous route is safer and easier to initiate than the intravenous route but is limited by infusion speed and volume. Prompting older adults more frequently to drink, offering a wider selection of drinks and using drinking vessels with particular features can result in small increases in oral intake in the short-term. The ongoing clinically-assisted hydration at end of life (CHELsea II) trial will hopefully provide more evidence for the emotive issue of hydration at the end of life.
Considerations for Use of Blood-Based Biomarkers in Epidemiologic Dementia Research
Authors: Hayes-Larson Eleanor , Ackley Sarah F , Turney Indira C , La Joie Renaud , Mayeda Elizabeth Rose , Glymour M Maria ,   for the Alzheimer's Disease Neuroimaging Initiative
Published: October 2023

Abstract
Dementia represents a growing public health burden with large social, racial, and ethnic disparities. The etiology of dementia is poorly understood, and the lack of robust biomarkers in diverse, population-representative samples is a barrier to moving dementia research forward. Existing biomarkers and other measures of pathology—derived from neuropathology, neuroimaging, and cerebrospinal fluid samples—are commonly collected from predominantly White and highly educated samples drawn from academic medical centers in urban settings. Blood-based biomarkers are noninvasive and less expensive, offering promise to expand our understanding of the pathophysiology of dementia, including in participants from historically excluded groups. Although largely not yet approved by the Food and Drug Administration or used in clinical settings, blood-based biomarkers are increasingly included in epidemiologic studies on dementia. Blood-based biomarkers in epidemiologic research may allow the field to more accurately understand the multifactorial etiology and sequence of events that characterize dementia-related pathophysiological changes. As blood-based dementia biomarkers continue to be developed and incorporated into research and practice, we outline considerations for using them in dementia epidemiology, and illustrate key concepts with Alzheimer’s Disease Neuroimaging Initiative (2003–present) data. We focus on measurement, including both validity and reliability, and on the use of dementia blood-based biomarkers to promote equity in dementia research and cognitive aging.
This article is part of a Special Collection on Mental Health.
Evaluating Mixtures of Urinary Phthalate Metabolites and Serum Per-/Polyfluoroalkyl Substances in Relation to Adolescent Hair Cortisol: The HOME Study
Authors: Sears Clara G , Liu Yun , Lanphear Bruce P , Buckley Jessie P , Meyer Jerrold , Xu Yingying , Chen Aimin , Yolton Kimberly , Braun Joseph M 
Published: October 2023

Abstract
Results of toxicological studies indicate that phthalates and per-/polyfluoroalkyl substances (PFAS), 2 classes of endocrine-disrupting chemicals, may alter the functioning of the hypothalamic-pituitary-adrenocortical (HPA) axis. We evaluated the associations of urinary phthalate metabolites and serum PFAS during gestation and childhood with adolescent hair cortisol concentrations (pg/mg hair) at age 12 years, an integrative marker of HPA axis activity (n = 205 mother-child pairs; Cincinnati, Ohio; enrolled 2003–2006). We used quantile-based g-computation to estimate associations between mixtures of urinary phthalate metabolites or serum PFAS and hair cortisol. We also examined whether associations of individual phthalate metabolites or PFAS with cortisol varied by the timing of exposure. We found that a 1-quartile increase in all childhood phthalate metabolites was associated with 35% higher adolescent hair cortisol (phthalate mixture ψ = 0.13; 95% confidence interval: 0.03, 0.22); these associations were driven by monoethyl phthalate, monoisobutyl phthalate, and monobenzyl phthalate. We did not find evidence that phthalate metabolites during gestation or serum PFAS mixtures were related to adolescent hair cortisol concentrations. We found suggestive evidence that higher childhood concentrations of individual PFAS were related to higher and lower adolescent hair cortisol concentrations. Our results suggest that phthalate exposure during childhood may contribute to higher levels of chronic HPA axis activity.
Evidential meta-model for molecular property prediction
Authors: Ham Kyung Pyo , Sael Lee 
Published: October 2023

Abstract

Motivation
The usefulness of supervised molecular property prediction (MPP) is well-recognized in many applications. However, the insufficiency and the imbalance of labeled data make the learning problem difficult. Moreover, the reliability of the predictions is also a huddle in the deployment of MPP models in safety-critical fields.

Results
We propose the Evidential Meta-model for Molecular Property Prediction (EM3P2) method that returns uncertainty estimates along with its predictions. Our EM3P2 trains an evidential graph isomorphism network classifier using multi-task molecular property datasets under the model-agnostic meta-learning (MAML) framework while addressing the problem of data imbalance.

Our results showed better prediction performances compared to existing meta-MPP models. Furthermore, we showed that the uncertainty estimates returned by our EM3P2 can be used to reject uncertain predictions for applications that require higher confidence.

Availability and implementation
Source code available for download at https://github.com/Ajou-DILab/EM3P2.
iLSGRN: inference of large-scale gene regulatory networks based on multi-model fusion
Authors: Wu Yiming , Qian Bing , Wang Anqi , Dong Heng , Zhu Enqiang , Ma Baoshan 
Published: October 2023

Abstract

Motivation
Gene regulatory networks (GRNs) are a way of describing the interaction between genes, which contribute to revealing the different biological mechanisms in the cell. Reconstructing GRNs based on gene expression data has been a central computational problem in systems biology. However, due to the high dimensionality and non-linearity of large-scale GRNs, accurately and efficiently inferring GRNs is still a challenging task.

Results
In this article, we propose a new approach, iLSGRN, to reconstruct large-scale GRNs from steady-state and time-series gene expression data based on non-linear ordinary differential equations. Firstly, the regulatory gene recognition algorithm calculates the Maximal Information Coefficient between genes and excludes redundant regulatory relationships to achieve dimensionality reduction. Then, the feature fusion algorithm constructs a model leveraging the feature importance derived from XGBoost (eXtreme Gradient Boosting) and RF (Random Forest) models, which can effectively train the non-linear ordinary differential equations model of GRNs and improve the accuracy and stability of the inference algorithm. The extensive experiments on different scale datasets show that our method makes sensible improvement compared with the state-of-the-art methods. Furthermore, we perform cross-validation experiments on the real gene datasets to validate the robustness and effectiveness of the proposed method.

Availability and implementation
The proposed method is written in the Python language, and is available at: https://github.com/lab319/iLSGRN.