bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel hybrid framework for heterogeneous measurement data prediction of cable-stayed bridges using ACMD-PE-BiLSTM
Authors: Zhou Zhenwei , Xiao Liang , Zhuang Haiyang , Zhang Guangcai , Gao Shuai 
Published: December 2024

Abstract
Bridge heterogeneous measurement data prediction is a crucial aspect of structural analysis of bridge engineering, which serves as an important reference for the early warning and decision-making of the bridges. However, the nonlinearity and nonstationarity of the measurement data are common occurrences in daily monitoring operations, which affects the accuracy of the prediction of bridge health. In this context, this paper proposes a hybrid data-driven deep learning approach for predicting bridge multi-source heterogeneous data to tackle the challenges posed by the complexity and nonstationarity of cable-stayed bridge monitoring data and enhance the accuracy and efficiency of predicting measurement data. This approach leverages adaptive chirp mode decomposition (ACMD), permutation entropy (PE), and Bi-directional long short-term memory (BiLSTM). Firstly, the ACMD algorithm decomposes the bridge monitoring data into a discrete number of intrinsic mode functions (IMFs) to produce clearer signals. Then, the PE algorithm is applied to each IMF to optimize the number of IMFs and construct the new components. Finally, a BiLSTM network is present for each component to establish the prediction model, and the final prediction results are obtained by synthesizing the predictions. The effectiveness and feasibility of the proposed method are extensively evaluated using measured wind speed, displacement, and acceleration data from a cable-stayed bridge. Evaluation indicators are used to evaluate the performance, and a comparative analysis with other benchmark models is further conducted to systematically validate the reliability of the proposed approach. The proposed prediction method offers several advantages, with its stability and accuracy particularly noteworthy. The results suggest that the proposed method is superior to the compared models regarding cable-stayed bridge heterogeneous measurement data and can provide reliable results for real-world bridge engineering.
Synthetic graphs for link prediction benchmarking
Authors: Vlaskin Alexey , Altmann Eduardo G 
Published: December 2024

Abstract
Predicting missing links in complex networks requires algorithms that are able to explore statistical regularities in the existing data. Here we investigate the interplay between algorithm efficiency and network structures through the introduction of suitably-designed synthetic graphs. We propose a family of random graphs that incorporates both micro-scale motifs and meso-scale communities, two ubiquitous structures in complex networks. A key contribution is the derivation of theoretical upper bounds for link prediction performance in our synthetic graphs, allowing us to estimate the predictability of the task and obtain an improved assessment of the performance of any method. Our results on the performance of classical methods (e.g. Stochastic Block Models, Node2Vec, GraphSage) show that the performance of all methods correlate with the theoretical predictability, that no single method is universally superior, and that each of the methods exploit different characteristics known to exist in large classes of networks. Our findings underline the need for careful consideration of graph structure when selecting a link prediction method and emphasize the value of comparing performance against synthetic benchmarks. We provide open-source code for generating these synthetic graphs, enabling further research on link prediction methods.
Automation of quantum dot measurement analysis via explainable machine learning
Authors: Schug Daniel , Kovach Tyler J , Wolfe M A , Benson Jared , Park Sanghyeok , Dodson J P , Corrigan J , Eriksson M A , Zwolak Justyna P 
Published: December 2024

Abstract
The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. Many of the measurements acquired during the tuning process come in the form of images that need to be properly analyzed to guide the subsequent tuning steps. By design, features present in such images capture certain behaviors or states of the measured QD devices. When considered carefully, such features can aid the control and calibration of QD devices. An important example of such images are so-called triangle plots, which visually represent current flow and reveal characteristics important for QD device calibration. While image-based classification tools, such as convolutional neural networks (CNNs), can be used to verify whether a given measurement is good and thus warrants the initiation of the next phase of tuning, they do not provide any insights into how the device should be adjusted in the case of bad images. This is because CNNs sacrifice prediction and model intelligibility for high accuracy. To ameliorate this trade-off, a recent study introduced an image vectorization approach that relies on the Gabor wavelet transform (Schug et al 2024 Proc. XAI4Sci: Explainable Machine Learning for Sciences Workshop (AAAI 2024) (Vancouver, Canada) pp 1–6). Here we propose an alternative vectorization method that involves mathematical modeling of synthetic triangles to mimic the experimental data. Using explainable boosting machines, we show that this new method offers superior explainability of model prediction without sacrificing accuracy. This work demonstrates the feasibility and advantages of applying explainable machine learning techniques to the analysis of QD measurements, paving the way for further advances in automated and transparent QD device tuning.
A novel dynamic machine learning-based explainable fusion monitoring: application to industrial and chemical processes
Authors: Ali Husnain , Safdar Rizwan , Zhou Yuanqiang , Yao Yuan , Yao Le , Zhang Zheng , Ding Weilong , Gao Furong 
Published: December 2024

Abstract
The complexity and fusion dynamism of the modern industrial and chemical sectors have been increasing with the rapid progress of IR 4.0–5.0. The transformative characteristics of Industry 4.0–5.0 have not been fully explored in terms of the fundamental importance of explainability. Traditional monitoring techniques for automatic anomaly detection, identifying the potential variables, and root cause analysis for fault information are not intelligent enough to tackle the intricate problems of real-time practices in the industrial and chemical sectors. This study presents a novel dynamic machine learning based explainable fusion approach to address the issues of process monitoring in industrial and chemical process systems. The methodology aims to detect faults, identify their key causes and feature variables, and analyze the root path of fault propagation with the time and magnitude of one cause variable to another impact. This study proposed using a time domain multivariate granger-entropy-aided dynamic independent component analysis (DICA)—distributed canonical correlation analysis approach, incorporating the dynamics time wrapping supported time delay-signed directed graph. The proposed methodology utilized the application to industrial and chemical processes and verified using the continuous stirred tank reactor and Tennessee Eastman process as practical application benchmarks. The framework’s validations and efficiency are evaluated using established techniques such as classic computed ICA and DICA as standard model scenarios. The outcomes and results showed that the newly developed strategy is preferable to previous approaches regarding explainability and robust detection and identification of the actual root causes with high FDRs and low FARs.
Comparing AI versus optimization workflows for simulation-based inference of spatial-stochastic systems
Authors: Ramirez Sierra Michael Alexander , Sokolowski Thomas R 
Published: December 2024

Abstract
Model parameter inference is a universal problem across science. This challenge is particularly pronounced in developmental biology, where faithful mechanistic descriptions require spatial-stochastic models with numerous parameters, yet quantitative empirical data often lack sufficient granularity due to experimental limitations. Parameterizing such complex models therefore necessitates methods that elaborate on classical Bayesian inference by incorporating notions of optimality and goal-orientation through low-dimensional objective functions that quantitatively encapsulate target system behavior. In this study, we contrast two such inference workflows and apply them to biophysically inspired spatial-stochastic models. Technically, both workflows employ simulation-based inference (SBI) methods: the first leverages a modern deep-learning technique known as sequential neural posterior estimation, while the second relies on a classical optimization technique called simulated annealing. We evaluate these workflows by inferring the parameters of two complementary models for the inner cell mass (ICM) lineage differentiation in the blastocyst-stage mouse embryo. This developmental biology system serves as a paradigmatic example of a highly robust and reproducible cell-fate proportioning process that self-organizes under strongly stochastic conditions, such as intrinsic biochemical noise and cell–cell signaling delays. Our results reveal that while both methods provide consistent model parameter estimates, the modern SBI workflow yields significantly richer inferred distributions at an equivalent computational cost. We identify the computational scenarios that favor the modern SBI method over its classical counterpart, and propose a plausible strategy to exploit the complementary strengths of both workflows for enhanced parameter space exploration.
New parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithm
Authors: Ding Xiaodong , Liu FuDong , Wang Weilong , Zhu Yu , Hou Yifan , Huang Yizhen , Xu Jinchen , Shan Zheng 
Published: December 2024

Abstract
Currently, variational quantum classification algorithms (VQCAs) generally rely on traditional optimization techniques such as Powell and SLSQP in the parameter optimization session. However, the performance of these methods shows limitations in practical applications. Although the parameter-shift rule can efficiently compute the parameter gradient with quantum circuits, it needs to run the quantum circuit twice repeatedly, which significantly reduces the computation efficiency. In order to overcome this challenge, this paper innovatively integrates the principle of unitary operation in quantum mechanics with the technical characteristics of superconducting quantum chips and elaborately designs some new parameterized quantum gates (PQGs). These PQGs strictly follow the rules of unitary operation, which ensures the stability and accuracy of quantum state evolution while realizing an efficient solution to the parameter gradient. Especially for the gradient calculation of a single qubit and single-angle PQGs, the new method can be completed with only a single quantum circuit run, which greatly improves the computation efficiency. Experimental validation on benchmark datasets such as breast cancer and iris shows that the method proposed in this paper exhibits excellent performance on quantum classification tasks. Compared with the parameter-shift rule, the computation efficiency of the new method is improved by 40%. And the classification accuracy, precision, and other key performance metrics are improved by an average of 5% in comparison with traditional optimization algorithms. This work not only enriches the methodology of quantum machine learning theoretically but also demonstrates its remarkable superiority in practical applications, which indicates that the method has great potential in scientific research and industrial applications.
Anisotropy-driven topological quantum phase transition in magnetic impurities
Authors: Blesio G G , Manuel L O , Aligia A A 
Published: December 2024

Abstract
A few years ago, a topological quantum phase transition (TQPT) has been found in Anderson and Kondo 2-channel spin-1 impurity models that include a hard-axis anisotropy term

D

S
z
2

with D > 0. The most remarkable manifestation of the TQPT is a jump in the spectral density of localized electrons, at the Fermi level, from very high to very low values as D is increased. If the two conduction channels are equivalent, the transition takes place at the critical anisotropy

D

c

∼
2.5

T

K

, where

T

K

is the Kondo temperature for D = 0. This jump might be important to develop a molecular transistor. The jump is due to a corresponding one in the Luttinger integral, which has a topological non-trivial value

π

/

2

for

D
>

D

c

. Here, we review the main results for the spectral density and highlight the significance of the theory for the interpretation of measurements conducted on magnetic atoms or molecules on metallic surfaces. In these experiments, where D is held constant, the energy scale

T

K

is manipulated by some parameters. The resulting variation gives rise to a differential conductance

d

I

/

d

V

, measured by scanning-tunneling spectroscopy, which is consistent with a TQPT at an intermediate value of

T

K

. For non-equivalent channels and non-zero magnetic field, the topological phase is lost but still a peculiar behaviour in the spectral density is obtained which agrees with experimental observations. We also show that the theory can be extended to integer spin S > 1 and two-impurity systems. This is also probably true for half-integer spin and non-equivalent channels in some cases.
Continual learning with hebbian plasticity in sparse and predictive coding networks: a survey and perspective
Authors: Safa Ali 
Published: December 2024

Abstract
Recently, the use of bio-inspired learning techniques such as Hebbian learning and its closely-related spike-timing-dependent plasticity (STDP) variant have drawn significant attention for the design of compute-efficient AI systems that can continuously learn on-line at the edge. A key differentiating factor regarding this emerging class of neuromorphic continual learning system lies in the fact that learning must be carried using a data stream received in its natural order, as opposed to conventional gradient-based offline training, where a static training dataset is assumed available a priori and randomly shuffled to make the training set independent and identically distributed (i.i.d). In contrast, the emerging class of neuromorphic CL systems covered in this survey must learn to integrate new information on the fly in a non-i.i.d manner, which makes these systems subject to catastrophic forgetting. In order to build the next generation of neuromorphic AI systems that can continuously learn at the edge, a growing number of research groups are studying the use of sparse and predictive Coding (PC)-based Hebbian neural network architectures and the related spiking neural networks (SNNs) equipped with STDP learning. However, since this research field is still emerging, there is a need for providing a holistic view of the different approaches proposed in the literature so far. To this end, this survey covers a number of recent works in the field of neuromorphic CL based on state-of-the-art sparse and PC technology; provides background theory to help interested researchers quickly learn the key concepts; and discusses important future research questions in light of the different works covered in this paper. It is hoped that this survey will contribute towards future research in the field of neuromorphic CL.
Making dew in the Atacama Desert: a paradigmatic case of plant water uptake from an unsaturated atmosphere fails a test
Authors: Gersony Jess , Manandhar Anju , Hochberg Uri , Abdellaoui Nora , Llanos Paula , Dumais Jacques , Holbrook N Michele , Rockwell Fulton E 
Published: December 2024

Abstract

Background and Aims
Nolana mollis is a dominant plant species in the hyper-arid Atacama Desert. A previous hypothesis states that N. mollis owes its success to the condensation of atmospheric water from undersaturated air onto its leaf surfaces by exuded salts, and absorption of this water by its leaves, or by shallow roots following drip onto the soil surface; living roots of N. mollis were suggested to only exist near the soil surface.

Methods
We conducted a field experiment with three treatments to establish the source of water for N. mollis: control, root cutting to block uptake of all soil moisture, and plastic skirting at the soil surface to block leaf drip of atmospheric water.

Key Results
Xylem tensions monotonically increased after root cutting until the plants wilted irreversibly, diverging clearly from the skirted and control treatments showing diurnal patterns of increasing tension in the day followed by recovery overnight.

Conclusions
Hydration in N. mollis requires access to deep soil water, motivating an alternative hypothesis: imperfect salt exclusion at the root surface and salt exudation by the leaf results in less root fouling and lower xylem tensions, while during the day evaporation of the surface brine, condensed overnight, increases the water use efficiency of carbon gain.
Symbiosis, hybridization, and speciation in Mediterranean octocorals (Octocorallia, Eunicellidae)
Authors: Aurelle Didier , Haguenauer Anne , Bally Marc , Zuberer Frédéric , Guillemain Dorian , Ledoux Jean-Baptiste , Sartoretto Stéphane , Cabau Cédric , Lapeyre Rachel , Chaoui Lamya , Kara Hichem , Samadi Sarah , Pontarotti Pierre 
Published: December 2024

Abstract
Understanding how species can form and remain isolated in the marine environment still stimulates active research. Here we study the differentiation and the possibility of hybridization among three temperate octocorals: Eunicella cavolini, Eunicella singularis, and Eunicella verrucosa. Morphologically intermediate individuals have been observed between them. Among these three species, E. singularis is the only one described in mutualistic symbiosis with photosynthetic Symbiodiniaceae. The symbiosis between Symbiodiniaceae and scleractinian corals is well studied, especially in the context of the response to anthropogenic climate change. Nevertheless, the potential role of symbiotic interactions in speciation processes remains unknown in cnidaria. We tested here the possibility of hybridization between symbiotic and non-symbiotic Eunicella species. Through multivariate analyses and hybrid detection, we prove the existence of on-going gene flow between E. singularis and E. cavolini, with the observation of F1 and F2 hybrids, and backcrosses. Demographic inferences indicate a scenario of secondary contact between these two species. Despite current gene flow, these two species appear genetically well differentiated. Our data also suggest an intermediate abundance of Symbiodiniaceae in the hybrids of the two parental populations. We discuss the evolution of the Symbiodiniaceae/cnidarian symbiosis in the light of our results.
Blood–brain barrier disruption and microglial activation during hypoxia and post-hypoxic recovery in aged mice
Authors: Sapkota Arjun , Halder Sebok K , Milner Richard 
Published: December 2024

Abstract
Hypoxia triggers blood–brain barrier disruption and a strong microglial activation response around leaky cerebral blood vessels. These events are greatly amplified in aged mice which is translationally relevant because aged patients are far more likely to suffer hypoxic events from heart or lung disease, and because of the pathogenic role of blood–brain barrier breakdown in vascular dementia. Importantly, it is currently unclear if disrupted cerebral blood vessels spontaneously repair and if they do, whether surrounding microglia deactivates. In this study, we addressed these questions by exposing aged (20 months old) mice to chronic mild hypoxia (8% O2) for 7 days and then returned them to normoxic conditions for 7 or 14 days, before evaluating blood–brain barrier disruption and microglial activation at the different timepoints. Seven days chronic mild hypoxia triggered marked blood–brain barrier disruption, as measured by extravascular leak of fibrinogen and red blood cells, which led to enhanced microglial activation, as measured by Mac-1 and CD68 levels. Interestingly, while return to normoxia promoted spontaneous repair of damaged blood vessels, the surrounding microglia remained persistently activated and were slow to deactivate. Chronic mild hypoxia also triggered neuronal loss that resulted in irreversible cognitive decline as measured by the novel object recognition test. Taken together, these findings describe an important disconnect between vascular repair and microglial deactivation in aged mice, which likely contributes to prolonged neuroinflammation. As hypoxia occurs in many age-related conditions, our data have important implications for the pathogenic role of hypoxia in the induction and progression of vascular dementia.
A case report supporting early surgery in mitral valve infective endocarditis with recurrent cerebral infarcts
Authors: Kesieme Emeka B , Omoregbee Benjamin , Ngaage Dumbor L 
Published: December 2024

Abstract

Background
In patients with infective endocarditis, brain embolism portends a poor prognosis. The timing of surgery in patients who require emergency valve surgery in the setting of deteriorating level of consciousness from recurrent embolic events, and brain infarction with haemorrhagic transformation, remains controversial.

Case summary
We report a case of a 54-year-old male who presented with Streptococcus salivarius mitral valve endocarditis, recurrent episodes of cerebral embolic infarctions with haemorrhagic transformation and deteriorating level of consciousness, and successfully underwent emergency mitral valve surgery without extension of the preoperative cerebral embolic complication or worsening of neurological symptoms.

Discussion
Mitral valve surgery can be performed successfully in patients with mitral valve endocarditis and cerebral embolism earlier than the recommended 2–4 weeks, and this should be considered in deteriorating patients.
Lifetime risk of lower extremity peripheral arterial disease: a Danish nationwide longitudinal study
Authors: Søgaard Mette , Behrendt Christian-Alexander , Eldrup Nikolaj , Skjøth Flemming 
Published: December 2024

Abstract

Background and Aims
Lower extremity peripheral arterial disease (PAD) presents a substantial disease burden, yet lifetime estimates remain scant. This nationwide study quantified the lifetime risk of PAD and its clinical outcomes in Denmark.

Methods
This cohort study included 4 275 631 individuals in Denmark aged 40–99 years between 1998 and 2018. We estimated the lifetime risk using a modified survival analysis method, considering death as a competing risk event.

Results
Over a median 15.5-year follow-up, 151 846 individuals were diagnosed with PAD (median age at diagnosis 71.5 years, interquartile range 63.1–79.2). The overall lifetime risk of PAD from age 40 was 11.6% (95% confidence interval 11.6%–11.7%), decreasing from 12.9% in 1998–2002 to 10.7% in 2013–18. Males had a higher lifetime risk than females (12.8% vs. 10.5%). Socioeconomic disparities were evident, with higher risks for those with lower educational levels (risk difference 3.4%, 95% confidence interval 3.2%–3.6%) and lower income (risk difference 0.4%, 95% confidence interval 0.2%–0.5%). One year after PAD diagnosis, 21.4% had undergone lower limb revascularization, 8.0% had experienced a major amputation, and 16.2% had died. At 5 years, the corresponding proportions were 26.4%, 10.8%, and 40.8%, respectively. The risk of lower limb revascularization showed little variation by sex and socioeconomic status, whereas there was a strong socioeconomic gradient for major amputation and all-cause death.

Conclusions
More than one in 10 Danish individuals are diagnosed with symptomatic PAD during their lifetime. Peripheral arterial disease diagnosis is associated with high morbidity and mortality at 1 and 5 years.