bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic detection of CO2 rebreathing during BiPAP ventilation
Authors: Szkulmowski Zbigniew , Robert Dominique , Karłowska-Pik Joanna , Argaud Laurent 
Published: August 2024

AbstractCarbon dioxide rebreathing (CO2 rebreathing) significantly influences respiratory drive and the work of breathing during BiPAP ventilation. We analyzed CO2 movement during BiPAP ventilation to find a method of real time detection of CO2 rebreathing without the need of CO2 concentration measurement sampled from the circuit (method expensive and not routinely used). Observational study during routine care in 15 bed university hospital ICU. At 18 patients who required BiPAP ventilation, intubated or during noninvasive ventilation, during weaning period airflow, pressure and CO2 concentration signals were registered on both sides of venting port and 17 respiratory parameters were measured or calculated for each of 4747 respiratory cycles analyzed. Based on CO2 movement (expiration–inspiration sequences) 3 types of cycle were identified, type I and II do not induce rebreathing but type III does. To test differences between the 3 types ANOVA, t-tests, and canonical discriminant analysis (CDA) were used. Then a multilayer perceptron (MLP) network, a type of artificial neural network, using the above parameters (excluding CO2 concentration) was applied to automatically identify the three types of respiratory cycles. Of the 4747 respiratory cycles, 1849 were type I, 1545 type II, and 1353 type III. ANOVA and t-tests showed significant differences between the types of respiratory cycles. CDA confirmed a correct apportionment of 93.9% of the cycles; notably, of 97.9% of type III. MLP automatically classified the respiratory cycles into the three types with 98.8% accuracy. Three types of respiratory cycles could be distinguished based on CO2 movement during BiPAP ventilation. Artificial neural networks can be used to automatically detect respiratory cycle type III, the only inducing CO2 rebreathing.
Carbon paste based sensor for sensitive Cr(III) ion determination in different water samples and anti-diabetic supplement
Authors: Ali Aya E. , Mohamed Asmaa M. , Mohamed Gehad G. 
Published: August 2024

AbstractA modified carbon paste sensor based on N,N′-(((ethane-1,2-diylbis(oxy))bis(2,1-phenylene))bis(methanylylidene))bis(pyridine-2-amine; BPA Schiff base as Cr(III) selective carrier was fabricated and studied in this work. The proposed sensor homogenization and mechanism of action was studied by infra-red (IR) and scanning electron microscope (SEM) with energy dispersive X-ray (EDX) tools. The sensor covered 1.0 × 10−7–1.0 × 10–1 mol L−1 linear range and a detection limit of 7.22 × 10–8 mol L−1 for Cr(III) with 20.17 ± 0.13 mV decade−1 Nernstian slope. 5 s was the response time of the prepared sensor and it was reproducible and stable for 3 months. The working pH range was 3.3–6.0 and it also works well to determine Cr(III) ion in presence of water miscible solvents up to 12.5% content of the methanol and 17.5% of ethanol. The electrode’s selectivity was studied using separate and mixed solution methods for selectivity coefficients determination and the sensor showed good selectivity relative to a variety of metal ions (selectivity coefficients = 1.01 × 10−5–8.57 × 10−3). In addition, the practical analysis value of the sensor was demonstrated by measurement of Cr(III) quantitatively in mineral water, supplement and also as an indicator electrode in Cr(III) against EDTA potentiometric titration with good reproducibility (RSDs of 0.91–2.15%).
Structure and dimerization properties of the plant-specific copper chaperone CCH
Authors: Dluhosch Dominik , Kersten Lisa Sophie , Schott-Verdugo Stephan , Hoppen Claudia , Schwarten Melanie , Willbold Dieter , Gohlke Holger , Groth Georg 
Published: August 2024

AbstractCopper chaperones of the ATX1 family are found in a wide range of organisms where these essential soluble carriers strictly control the transport of monovalent copper across the cytoplasm to various targets in diverse cellular compartments thereby preventing detrimental radical formation catalyzed by the free metal ion. Notably, the ATX1 family in plants contains two distinct forms of the cellular copper carrier. In addition to ATX1 having orthologs in other species, they also contain the copper chaperone CCH. The latter features an extra C-terminal extension whose function is still unknown. The secondary structure of this extension was predicted to be disordered in previous studies, although this has not been experimentally confirmed. Solution NMR studies on purified CCH presented in this study disclose that this region is intrinsically disordered regardless of the chaperone’s copper loading state. Further biophysical analyses of the purified metallochaperone provide evidence that the C-terminal extension stabilizes chaperone dimerization in the copper-free and copper-bound states. A variant of CCH lacking the C-terminal extension, termed CCHΔ, shows weaker dimerization but similar copper binding. Computational studies further corroborate the stabilizing role of the C-terminal extension in chaperone dimerization and identify key residues that are vital to maintaining dimer stability.
Quantitative and qualitative assessment of airborne microorganisms during gross anatomical class and the bacterial and fungal load on formalin-embalmed corpses
Authors: Keiler Jonas , Bast Antje , Reimer Jessy , Kipp Markus , Warnke Philipp 
Published: August 2024

AbstractMold growth on body donations remains an underreported yet serious issue in anatomical teaching. Bacterial and fungal growth pose health risks to lecturers and students, alongside with ethical and aesthetic concerns. However, limited information exists on the presence of bacteria and fungi on body donations and their underlying causes. To investigate the potential impact of airborne germs on body donation contamination, we conducted indoor air measurements before, during, and after our anatomical dissection course, with outdoor measurements serving as a control. Tissue samples from the dissected body donations were collected to assess the germ load, with qualitative and quantitative microbiological analyses. Air samples from the dissection hall contained no fungi, but various fungal species were identified in the adjacent stairways and outdoors which implies that fungal occurrence in the dissection hall air was independent of lecturers’ and students’ presence. Moreover, our results indicate that adequate ventilation filters can effectively reduce indoor fungal germs during courses, while the bacterial load in room air appears to increase, likely due to the presence of lecturers and students. Additionally, the tissue samples revealed no bacterial or fungal germs which implies that our ethanol-formalin-based embalming solution demonstrates an effective long-term antimicrobial preservation of corpses.
Mapping fine-scale seagrass disturbance using bi-temporal UAV-acquired images and multivariate alteration detection
Authors: Simpson Jamie , Davies Kevin P. , Barber Paul , Bruce Eleanor 
Published: August 2024

AbstractSeagrasses provide critical ecosystem services but cumulative human pressure on coastal environments has seen a global decline in their health and extent. Key processes of anthropogenic disturbance can operate at local spatio-temporal scales that are not captured by conventional satellite imaging. Seagrass management strategies to prevent longer-term loss and ensure successful restoration require effective methods for monitoring these fine-scale changes. Current seagrass monitoring methods involve resource-intensive fieldwork or recurrent image classification. This study presents an alternative method using iteratively reweighted multivariate alteration detection (IR-MAD), an unsupervised change detection technique originally developed for satellite images. We investigate the application of IR-MAD to image data acquired using an unoccupied aerial vehicle (UAV). UAV images were captured at a 14-week interval over two seagrass beds in Brisbane Water, NSW, Australia using a 10-band Micasense RedEdge-MX Dual camera system. To guide sensor selection, a further three band subsets representing simpler sensor configurations (6, 5 and 3 bands) were also analysed using eight categories of seagrass change. The ability of the IR-MAD method, and for the four different sensor configurations, to distinguish the categories of change were compared using the Jeffreys-Matusita (JM) distance measure of spectral separability. IR-MAD based on the full 10-band sensor images produced the highest separability values indicating that human disturbances (propeller scars and other seagrass damage) were distinguishable from all other change categories. IR-MAD results for the 6-band and 5-band sensors also distinguished key seagrass change features. The IR-MAD results for the simplest 3-band sensor (an RGB camera) detected change features, but change categories were not strongly separable from each other. Analysis of IR-MAD weights indicated that additional visible bands, including a coastal blue band and a second red band, improve change detection. IR-MAD is an effective method for seagrass monitoring, and this study demonstrates the potential for multispectral sensors with additional visible bands to improve seagrass change detection.
Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas
Authors: Kudus Kareem , Wagner Matthias W. , Namdar Khashayar , Bennett Julie , Nobre Liana , Tabori Uri , Hawkins Cynthia , Ertl-Wagner Birgit Betina , Khalvati Farzad 
Published: August 2024

AbstractThe use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features or convolutional neural networks (CNNs). We aimed to build and assess a combined radiomics and CNN non-invasive pLGG molecular status identification model. This retrospective study used the tumor regions, manually segmented from T2-FLAIR MR images, of 336 patients treated for pLGG between 1999 and 2018. We designed a CNN and Random Forest radiomics model, along with a model relying on a combination of CNN and radiomic features, to predict the genetic status of pLGG. Additionally, we investigated whether CNNs could predict radiomic feature values from MR images. The combined model (mean AUC: 0.824) outperformed the radiomics model (0.802) and CNN (0.764). The differences in model performance were statistically significant (p-values < 0.05). The CNN was able to learn predictive radiomic features such as surface-to-volume ratio (average correlation: 0.864), and difference matrix dependence non-uniformity normalized (0.924) well but was unable to learn others such as run-length matrix variance (− 0.017) and non-uniformity normalized (− 0.042). Our results show that a model relying on both CNN and radiomic-based features performs better than either approach separately in differentiating the genetic status of pLGGs, and that CNNs are unable to express all handcrafted features.
Organocatalytic synthetic route to esters and their application in hydrosilylation process
Authors: Mermela Aleksandra , Bołt Małgorzata , Mrzygłód Aleksandra , Żak Patrycja 
Published: August 2024

AbstractA facile esterification of α,β-unsaturated aldehydes with alcohols has been developed for the synthesis of esters by using bulky N-heterocyclic (NHC) carbene as a metal-free and eco-friendly organocatalyst. This new protocol has been proved to be effective with a wide substrate scope, giving selective esters in yields greater than 84% under mild conditions. Moreover, proposed synthetic strategy enables modification of various types of silsesquioxanes (SQ) which cannot or are technically difficult to be carried out with known protocols. For the first time, a one-pot sequential esterification/hydrosilylation has been successfully carried out.
Polarization-diverse soliton transitions and deterministic switching dynamics in strongly-coupled and self-stabilized microresonator frequency combs
Authors: Wang Wenting , Aldhafeeri Alwaleed , Zhou Heng , Melton Tristan , Jiang Xinghe , Vinod Abhinav Kumar , Yu Mingbin , Lo Guo-Qiang , Kwong Dim-Lee , Wong Chee Wei 
Published: August 2024

AbstractDissipative Kerr soliton microcombs in microresonators have enabled fundamental advances in chip-scale precision metrology, communication, spectroscopy, and parallel signal processing. Here we demonstrate polarization-diverse soliton transitions and deterministic switching dynamics of a self-stabilized microcomb in a strongly-coupled dispersion-managed microresonator driven with a single pump laser. The switching dynamics are induced by the differential thermorefractivity between coupled transverse-magnetic and transverse-electric supermodes during the forward-backward pump detunings. The achieved large soliton existence range and deterministic transitions benefit from the switching dynamics, leading to the cross-polarized soliton microcomb formation when driven in the transverse-magnetic supermode of the single resonator. Secondly, we demonstrate two distinct polarization-diverse soliton formation routes – arising from chaotic or periodically-modulated waveforms via pump power selection. Thirdly, to observe the cross-polarized supermode transition dynamics, we develop a parametric temporal magnifier with picosecond resolution, MHz frame rate and sub-ns temporal windows. We construct picosecond temporal transition portraits in 100-ns recording length of the strongly-coupled solitons, mapping the transitions from multiple soliton molecular states to singlet solitons. This study underpins polarization-diverse soliton microcombs for chip-scale ultrashort pulse generation, supporting applications in frequency and precision metrology, communications, spectroscopy and information processing.
An accurate and transferable machine learning interatomic potential for nickel
Authors: Gong Xiaoguo , Li Zhuoyuan , Pattamatta A. S. L. Subrahmanyam , Wen Tongqi , Srolovitz David J. 
Published: August 2024

AbstractNickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural applications. Magnetism affects many mechanical and defect properties, but spin-polarized density functional theory (DFT) calculations are computationally inefficient for studying material behavior requiring large system sizes and/or long simulation times. Here we develop a “magnetism-hidden” machine-learning Deep Potential (DP) model for Ni without a descriptor for magnetic moments, using training datasets derived from spin-polarized DFT calculations. The DP-Ni model exhibits excellent transferability and representability for a wide-range of FCC and HCP properties, including (finite-temperature) lattice parameters, elastic constants, phonon spectra, and many defects. As an example of its applicability, we investigate the Ni FCC-HCP allotropic phase transition under (high-stress) uniaxial tensile loading. The high accurate DP model for magnetic Ni facilitates accurate large-scale atomistic simulations for complex phase transformation behavior and may serve as a foundation for developing interatomic potentials for Ni-based superalloys and other multi-principal component alloys.
Explore human parsing modality for action recognition
Authors: Liu Jinfu , Ding Runwei , Wen Yuhang , Dai Nan , Meng Fanyang , Zhang Fang‐Lue , Zhao Shen , Liu Mengyuan 
Published: August 2024

AbstractMultimodal‐based action recognition methods have achieved high success using pose and RGB modality. However, skeletons sequences lack appearance depiction and RGB images suffer irrelevant noise due to modality limitations. To address this, the authors introduce human parsing feature map as a novel modality, since it can selectively retain effective semantic features of the body parts while filtering out most irrelevant noise. The authors propose a new dual‐branch framework called ensemble human parsing and pose network (EPP‐Net), which is the first to leverage both skeletons and human parsing modalities for action recognition. The first human pose branch feeds robust skeletons in the graph convolutional network to model pose features, while the second human parsing branch also leverages depictive parsing feature maps to model parsing features via convolutional backbones. The two high‐level features will be effectively combined through a late fusion strategy for better action recognition. Extensive experiments on NTU RGB + D and NTU RGB + D 120 benchmarks consistently verify the effectiveness of our proposed EPP‐Net, which outperforms the existing action recognition methods. Our code is available at https://github.com/liujf69/EPP‐Net‐Action.
A robust low‐rank tensor completion model with sparse noise for higher‐order data recovery
Authors: Wang Min , Chen Zhuying , Zhang Shuyi 
Published: August 2024

AbstractThe tensor singular value decomposition‐based model has garnered increasing attention in addressing tensor recovery challenges. However, existing tensor recovery methods exhibit certain inherent limitations. Some ignore the simultaneous effects of noise and missing values, while most can't handle higher‐order tensors, which are not reflective of real‐world scenarios. The information redundancy within tensor data often leads to a prevailing low‐rank structure, making low‐rankness a vital prior in the tensor recovery process. To tackle this pressing issue, a robust low‐rank tensor recovery framework is proposed to rehabilitate higher‐order tensors corrupted by sparse noise and missing entries. In the model, the tensor nuclear norm derived for order‐d tensors (d 4) are employed as a representation of the low‐rank prior, while utilizing the ‐norm to model the sparse noise. To solve the proposed model, an efficient Alternating direction method of multipliers algorithm is developed. A series of experiments are performed on synthetic and real‐world datasets. The results show that the superior performance of the method compared with other algorithms dedicated to addressing order‐d tensor recovery challenges. Notably, in scenarios where the data is severely compromised (noise ratio 40%, sample ratio 70%), the algorithm consistently outperforms its competitors, achieving significantly improved results.
Coupled piecewise tri-stable stochastic resonance system driven by dual inputs and joint EMD/VMD analysis
Authors: Zhang Gang , Xiong Qing , Bi Lujie , Li Zhaorui 
Published: August 2024

Abstract
A novel piecewise tri-stable stochastic resonance (NPTSR) system is proposed to address the issue of output saturation caused by high-order terms limitation in this paper. Building upon this, the exploration is extended to a coupled piecewise tri-stable stochastic resonance system driven by dual inputs (DCPTSR). First, we uncover the influence of dual input interaction on output quality, finding that when the fRequencies of the two input signals are consistent, varying the amplitude of the driving signal can effectively enhance the output performance of the target signal. Secondly, by utilizing the adiabatic approximation theory, the steady-state probability density (SPD) and signal-to-noise ratio (SNR) of the DCPTSR system are derived, which allows us to analyze the effects of various parameters on both SPD and SNR. Next, three combined denoising systems, namely EMD-DCPTSR, VMD-DCPTSR, and SDCPTSR, are constructed by utilizing empirical mode decomposition (EMD), variational mode decomposition (VMD), and the stochastic resonance (SR). Through numerical simulations, we demonstrate that the combined denoising system outperforms the stand-alone SR system, and we analyze the stochastic resonance phenomenon of the DCPTSR system using the spectral amplification (SA) coefficient as an evaluation index. Finally, to assess practical applicability, these systems are deployed for bearing fault detection. The experimental results exhibit notable signal-to-noise gain improvements for the DCPTSR system compared to standalone SR systems by 0.7699 ∼ 9.4541 dB. The EMD-DCPTSR system shows signal-to-noise gain improvements of 0.3245 ∼ 1.1709 dB compared to the VMD-DCPTSR and SDCPTSR systems. Moreover, all three combined denoising systems outperform the standalone SR system in terms of signal processing capabilities. In conclusion, this paper extensively investigates the interaction between the two input signals in a dual-input system and studies the output performance of using EMD, VMD, and SR as preprocessing methods for the SR system. Through numerical simulations and practical engineering applications, we highlight the substantial advantages of combined denoising systems. These findings offer essential theoretical insights and promising prospects for engineering applications.
A dual-parameter Na-based PCF sensor with wide sensing bandwidth based SPR effect
Authors: Hu Linchuan , Li Jianshe , Yin Zhiyong , Gao Zhigang , Li Hongwei , Li Kaifeng , Zhang Zhibing , Li Shuguang 
Published: August 2024

Abstract
Wide sensing bandwidth is a prerequisite for dual-channel sensors to have a wide detection range and high sensitivity. To obtain a dual-channel optical fiber sensor with wide sensing bandwidth, a Na-based photonic crystal fiber sensor based on the surface plasmon resonance effect is proposed. The proposed sensor structure is numerically analyzed by finite element method. The sensor builds the fiber core in the center of the fiber, chooses alkali metal Na as the plasma material to stimulate the SPR effect, and polymethyl methacrylate and polydimethylsiloxane (PDMS) will be coated on the Na film as the protective layer. As a temperature-sensitive material, PDMS also takes into account the role of constructing temperature-sensing channels. The sensor bandwidth covers 600–1700 nm. The results show that when the RI range of the measured object is 1.33–1.42, the maximum sensitivity of the refractive index sensor is 11100 nm/RIU, and when the temperature range of the measured object is −40–20 °C, the maximum sensitivity of the temperature sensor is 6.4 nm/°C. Meanwhile, the loss peak of the sensor has a lower half-height width and greater detection accuracy. It has a good application prospect in the fields of biosensing and environmental monitoring.
Parametric controllable planar multi-scroll chaotic attractors in a 3-D memristive tabu learning single neuron model
Authors: Ding Ruoyu , Bao Han , Wang Kai , Xu Dan , Huang Liping 
Published: August 2024

Abstract
In this paper, a three-dimensional (3D) autonomous tabu learning single neuron model is proposed, which is achieved by using a sinusoidal activation function and introducing a memristor synapse. This model exhibits the remarkable capability to produce a series of planar multi-scroll chaotic attractors, and its unique feature lies in the ability to control the number of scrolls. The investigation of the planar multi-scroll chaotic attractors and its dynamical behaviors is conducted through the analysis of phase plane portraits, bifurcation diagrams, and spectral entropies. The numerical simulations unveil a compelling relationship between the number of chaotic scrolls and specific control parameters governing the model. To further validate the findings, a 3D autonomous tabu learning single neuron model is implemented on a digital hardware platform. In an effort to extend the practical significance of this research, the multi-scroll chaotic phenomenon generated by the proposed model is deployed for image encryption. The fusion of mathematical modeling, digital hardware implementation, and practical application underscores the universality and significance of the proposed single neuron model in the fields of chaotic systems and engineering applications.