bluuebunny/crossref_metadata_2025 huggingface dataset:
Piezo2 interacts with E-cadherin in specialized gastrointestinal epithelial mechanoreceptors
Authors: Mercado-Perez Arnaldo , Hernandez Jeric P. , Fedyshyn Yaroslav , Treichel Anthony J. , Joshi Vikram , Kossick Kimberlee , Betageri Kalpana R. , Farrugia Gianrico , Druliner Brooke , Beyder Arthur
Published: November 2024

Piezo2 is a mechanically gated ion channel most commonly expressed by specialized mechanoreceptors, such as the enteroendocrine cells (EECs) of the gastrointestinal epithelium. A subpopulation of EECs expresses Piezo2 and functionally resembles the skin’s touch sensors, called Merkel cells. Low-magnitude mechanical stimuli delivered to the mucosal layer are primarily sensed by mechanosensitive EECs in a process we term “gut touch.” Piezo2 transduces cellular forces into ionic currents, a process that is sensitive to bilayer tension and cytoskeletal depolymerization. E-cadherin is a widely expressed protein that mediates cell–cell adhesion in epithelia and interacts with scaffold proteins that anchor it to actin fibers. E-cadherin was shown to interact with Piezo2 in immortalized cell models. We hypothesized that the Piezo2–E-cadherin interaction is important for EEC mechanosensitivity. To test this, we used super-resolution imaging, co-immunoprecipitation, and functional assays in primary tissues from mice and gut organoids. In tissue EECs and intestinal organoids, we observed multiple Piezo2 cellular pools, including one that overlaps with actin and E-cadherin at the cells’ lateral walls. Further, E-cadherin co-immunoprecipitated with Piezo2 in the primary colonic epithelium. We found that E-cadherin knockdown decreases mechanosensitive calcium responses in mechanically stimulated primary EECs. In all, our results demonstrate that Piezo2 localizes to the lateral wall of EECs, where it physically interacts with E-cadherin and actin. They suggest that the Piezo2–E-cadherin–actin interaction is important for mechanosensitivity in the gut epithelium and possibly in tissues where E-cadherin and Piezo2 are co-expressed in epithelial mechanoreceptors, such as skin, lung, and bladder.
Ternary MXene/PANI/ZnO-based composite with a built-in p–n heterojunction for high-performance supercapacitor applications
Authors: Mahajan Parika , Sardana Sagar , Mahajan Aman
Published: November 2024

Two-dimensional (2D) Ti3C2T
x
MXene-based materials have attracted widespread attraction in the field of energy storage owing to their high conductivity and accordion-like structure. However, challenges such as restacking and oxidative degradation of the Ti3C2T
x
MXene structure lead to poor stability, low conductivity, low specific capacitance and, consequently, a low specific energy, hindering their extensive adoption at an industrial scale. In this study, a ternary MXene/polyaniline (PANI)/ZnO (MPZ) composite has been synthesized via surface engineering of two-dimensional (2D) MXene using one-dimensional (1D) PANI nanowires and ZnO nanoparticles to enhance its specific energy and stability while sustaining its specific power. 1D PANI nanowires and ZnO nanoparticles act as spacers to prevent restacking, while also exposing the suppressed redox active sites of 2D MXene and preventing it from being oxidized by forming a porous conductive network all over the surface of the MXene. PANI and ZnO also provide additional electroactive redox sites by forming p–n heterojunctions, thus enhancing faradaic redox reactions and the specific capacitance of the MPZ composite. As a result, the overall electrochemical performance and stability of the ternary MPZ composite are enhanced due to the synergistic interactions among the individual components within the ternary MPZ composite. At a low current density of 0.1 A g−1, the ternary MPZ composite exhibited a maximum specific capacitance of 651.96 F g−1 and a highest specific energy of 32.59 Wh Kg−1 while maintaining a specific power of 60 W Kg−1 as compared to MXene and binary MP composite. Furthermore, it showcased exceptional cyclic stability over 10 000 cycles with 94.75% and 92.95% capacitive retention at 0.6 A g−1 current density and 40 mV s−1 scan rate, respectively. Thus, this current study highlights an effective strategy to enhance the specific energy of MXene-based supercapacitors through surface engineering and the construction of p–n heterojunctions within the composite.
Huge photosensitivity gain combined with long photocurrent decay times in various polymorphs of Ga2O3: effects of carrier trapping with deep centers
Authors: Polyakov A Y , Yakimov E B , Shchemerov I V , Vasilev A A , Kochkova A I , Nikolaev V I , Pearton S J
Published: November 2024

The material system of ultra-wide bandgap Ga2O3 has already shown great promise in the field of solar-blind photodetectors with high photoresponsivity, high photoresponsivity gain and low dark current. These promising results have been achieved on Ga2O3 films of different polymorphs and by different methods, often not with particularly high crystalline quality. In fact, it would often seem the case that the lower the crystalline quality of the films, the higher the photosensitivity and its gain. This, however, is in most cases accompanied by unusually long photocurrent build-up and decay times. We show that the experimental results can be explained by models in which the high photosensitivity gain is related to the effects of holes being trapped by deep states, which, in Schottky diodes, results in a decrease of the Schottky barrier height with a consequent increase in the electron current, and in metal–semiconductor–metal (MSM) structures additionally gives rise to the usual gain increase due to the increased concentration and lifetime of electrons. We present and discuss models describing the effects in Ga2O3 Schottky diodes, MSM structures, and unipolar and bipolar heterojunctions, and we propose possible candidates for the role of the hole traps in different Ga2O3 polymorphs. We also discuss the existing results for the photocurrent build-up and decay times and offer possible explanations for the observed temperature dependences of the characteristic times where such data are present.
Programmable metasurface based phase-modulating reflector for 2.4 GHz wireless communications
Authors: Chu Wei , Cao Wei , Zhang Qiong , Gao Mandi , Ren Xiaole , Ji Zhonghang , Liu Yunqing
Published: November 2024

This paper presents an innovative programmable metasurface structure that achieves precise phase control within the 2–2.7 GHz frequency range by adjusting the state of varactor diodes embedded in the unit cells. The design employs a single diode, which simplifies the structure, reduces manufacturing costs, and minimizes reflection loss. At 2.4 GHz, the metasurface achieves 1-bit phase responses of 0° and 180°, with a reflection amplitude exceeding 0.72, demonstrating excellent reflective performance. Moreover, as the 2.4 GHz frequency is closely related to wireless communication bands, this programmable metasurface shows significant potential in the field of wireless communication encryption. By integrating dual varactor diodes, the design enables 2-bit phase control with reflection phase angles of 0°, 90°, 180°, and 270°. To validate the design, a 1-bit metasurface structure was fabricated and tested, with experimental results showing a high degree of consistency with simulations, highlighting the potential of this structure in enhancing wireless communication security.
Possible structures of skyrmion states in chiral ferromagnetic films with spatially modulated uniaxial anisotropy
Authors: Vakhitov R M , Akhmetova A A , Filippov M A
Published: November 2024

In this paper, the stabilization conditions, structure, and properties of possible vortex-like inhomogeneities, including kπ-skyrmions k = 0, 1, 2, 3, 4, in a uniaxial multilayer disk with a columnar defect in the center are investigated based on micromagnetic modeling. Their stability diagrams depending on the Dzyaloshinskii–Moriya interaction, the magnitude of magnetic anisotropy and the defect parameters are determined. New types of vortex-like inhomogeneities that can arise in such samples are found. The obtained data can be used to create artificial regions of nucleation, capture and pinning of magnetic skyrmions, which can provide greater reliability of data storage in spintronic logical devices.
A remaining useful life prediction method for rotating machinery based on interactive BiLSTM
Authors: Li Guofa , Yu Liyao , He Jialong , Zhang Wei , Liu Shaoyang , Wei Jingfeng
Published: November 2024

Rotating machinery, as a key component of mechanical equipment, plays a crucial role in ensuring the reliability and safety of the equipment. This article proposes a RUL prediction framework based on interactive bidirectional long short-term memory (IT-BiLSTM) and function fitting, aiming to improve the accuracy of RUL prediction in rotating machinery. Firstly, to unify the failure threshold under different working conditions, a data interception method based on root mean square is proposed, and empirical mode decomposition is used to reconstruct the intercepted signals; then, 16 features are extracted, and health indicator (HI) is constructed through IT-BiLSTM, which is mapped to the [0,1] interval; finally, the time when HI is 0 is found through function fitting, which is the current RUL. The accuracy of the proposed method is verified using two datasets. The results show that the proposed IT-BiLSTM can more accurately predict the RUL of rotating machinery.
Traffic signal current prediction algorithm based on CNN and LSTM
Authors: Feng Weijia , Qi Siyao , Guo Jia , Zuo Xinyu , Chen Yunhe , Zhu Yichen
Published: November 2024

Traffic signal failures can lead to severe accidents and citywide traffic disruptions, making their reliable operation essential. This study collected real-world data from intersection traffic signals and constructed a dataset. Based on the current data from traffic signals, the formatted pass-dot product attention-LSTM (FD-LSTM) model was proposed. The model utilizes one-dimensional convolution and max pooling for feature extraction, employs dual LSTM units to process forward and backward information, incorporates Dropout to prevent overfitting, and uses the dot-product attention mechanism to enhance the importance of key features. The model is trained using the Adam optimization algorithm. Comparative experiments demonstrate that the FD-LSTM model outperforms other models.
Room-temperature ferromagnetic MnGa nanoparticles in dilute magnetic semiconductor (Ga, Mn)As thin film: preparation and characterization
Authors: Duan Juanmei , Li Zichao , Begeza Viktor , Ruan Shuangchen , Zeng Yu-Jia , Tang Wei , Tsai Hsu-Sheng
Published: November 2024

The GaAs based diluted magnetic semiconductor, (Ga, Mn)As, with the unique advantage of manipulating the spin and charge was widely investigated in the scientific community and considered as a potential material for the spintronic devices. However, its Curie temperature (T
c), which is limited to around 200 K, hinders the research progress of diluted magnetic semiconductors for potential device applications. Herein, we propose an approach to prepare the MnGa nanoparticles embedded in (Ga, Mn)As matrix using the magnetron sputtering deposition of Mn on GaAs surface, followed by the nano-second pulsed laser annealing (PLA), which gives a T
c above 400 K. We demonstrate that the MnGa nanoparticles are only formed in (Ga, Mn) As thin film during the nano-second PLA under a critical range of energy density (0.4–0.5 J cm−2). The highest achieved coercivity, saturation magnetization and remanent magnetization are 760 Oe, 11.3 emu cm−3 and 9.6 emu cm−3, respectively. This method for preparing the hybrid system of ferromagnetic metal/dilute magnetic semiconductor builds a platform for exploring the interesting spin transport phenomenon and is promising for the application of spintronic devices.
Floquet theory and stability analysis for Hamiltonian PDEs
Authors: Bronski Jared C , Mikyoung Hur Vera , Marangell Robert
Published: November 2024

We analyze Floquet theory as it applies to the stability and instability of periodic traveling waves in Hamiltonian PDEs. Our investigation focuses on several examples of such PDEs, including the generalized KdV and BBM equations (third order), the nonlinear Schrödinger and Boussinesq equations (fourth order), and the Kawahara equation (fifth order). Our analysis reveals that the characteristic polynomial of the monodromy matrix inherits symmetry from the underlying PDE, enabling us to determine the essential spectrum along the imaginary axis and bifurcations of the spectrum away from the axis, employing the Floquet discriminant. We present numerical evidence to support our analytical findings.
Multi-winged Lorenz attractors due to bifurcations of a periodic orbit with multipliers

(
−
1
,
i
,
−
i
)
Authors: Karatetskaia Efrosiniia , Kazakov Alexey , Safonov Klim , Turaev Dmitry
Published: November 2024

We show that bifurcations of periodic orbits with multipliers

(
−
1
,
i
,
−
i
)

can lead to the birth of pseudohyperbolic (i.e. robustly chaotic) Lorenz-like attractors of three different types: one is a discrete analogue of the classical Lorenz attractor, and the other two are new. We call them two- and four-winged ‘Simó angels’. These three attractors exist in an orientation-reversing, three-dimensional, quadratic Hénon map. Our analysis is based on a numerical study of a normal form for this bifurcation, a three-dimensional system of differential equations with a Z4-symmetry. We investigate bifurcations in the normal form and describe those responsible for the emergence of the Lorenz attractor and the continuous-time version of the Simó angels. Both for the normal form and the 3D Hénon map, we have found open regions in the parameter space where the attractors are pseudohyperbolic, implying that for every parameter value from these regions every orbit in the attractor has positive top Lyapunov exponent.
Full 3D thermal-hydraulic and electric modelling of quench propagation in HTS conductors
Authors: Zappatore A
Published: November 2024

A fully three-dimensional multi-physics model to simulate quench propagation in high temperature superconducting (HTS) conductors for fusion applications is presented. It accounts for thermal, electric and fluid dynamics throughout the entire transient. The need for high-fidelity models for quench simulations comes from the bulky layouts of many HTS conductors that are being proposed. The model is then validated against experimental data, showing a good agreement on all the relevant quantities (local voltages and temperatures). It is shown that the detailed model improves the quality of the agreement with the measured data with respect to more simplified models. It also allows an insight on the temperature distributions in the conductor cross-section, which can be relevant for the interpretation of experimental data as well as to support the design of quench detection strategies which rely on local temperature variations.
Study on structure and magnetic properties of rare earth terbium-doped ferrite Sr1-xTbxFe12O19
Authors: Tang Yuelong , Wang Changming , Tang Jin , Luo Liang , Wang Chenchen
Published: November 2024

In this paper, Sr1-xTbxFe12O19 (x = 0.00, 0.02, 0.04, 0.06, 0.08, 0.10) ferrite was successfully prepared by ceramic method. The effects of terbium on the structure and magnetic properties of strontium ferrite were investigated. By x-ray diffractometer, it is confirmed that Sr1-xTbxFe12O19 obtained by experiment are all M-type hexagonal ferrite crystals. At the same time, Rietveld finishing was carried out on the prepared samples with fullprof software. The morphologic analysis using emission scanning electron microscope showed that the proportion of hexagonal flake crystals increased with the increase of dopant Tb. Elemental analysis of the samples was performed by EDAX/EDS spectra. The magnetic properties of powder ferrite were measured by hysteresis loop tester, and the magnetic properties of its block were measured by B-H tester. When Tb = 0.04 mol, the magnetic properties of ferrite are the best. The difference in ionic radii between Tb3+ (0.923 Å) and Sr2+ (1.12 Å) allows Tb3+ to form a unique occupancy behavior that affects the formation of the phases, which changes the structure and magnetic properties of Sr1-xTbxFe12O19.
Effect of electromagnetic radiations on the dynamics of a five-chain coupled inertial Hopfield neural network and control of multi-stability with the selection of a desired attractor
Authors: Dongmo T N Sogui , Kengne Jacques , Chedjou Jean Chamberlain
Published: November 2024

In this paper, we investigate the effect of electromagnetic radiation on the dynamics of a network consisting of five chain-coupled inertial Hopfield neurons. The study revealed that the neural system designed such a way involves several complex phenomena, namely Hopf bifurcation, chaos, hyperchaos and the coexistence of up to thirty-two attractors in phase space. The complexity specific to our system is due to the higher number of equilibrium points, namely two hundred and forty-three. Under the conditions of safe functioning of our neural system (chaos or hyperchaos), we have been able to observe that electromagnetic radiation has a harmful character for the system, because we found for a range of variation in the intensity of the electromagnetic feedback induction current a coexistence of chaotic and periodic states (epileptic state). We then controlled the multi-stability using a desired attractor selection scheme, with the aim of suppressing the pathological state. All the work carried out in this contribution is done with the help of dynamical system analysis tools such as the bifurcation diagram, the spectrum and the maximum exponent of Lyapunov, phase portraits and basins of attraction. The numerical scheme used is the Runge–Kutta-4. In order to validate the numerical results, we use analog calculation and some results were derived from the PSpice software. These results are in good accordance in amplitude and location on the plane to those of numerical simulations.
Design and synchronization control application of a new five-dimensional memristor CNN conservative hyperchaotic system
Authors: Zhang Jie , Liu Enze
Published: November 2024

Incorporating memristors into a cellular neural network (CNN) and introducing chaotic characteristics can generate highly complex and unpredictable dynamic behaviors. To advance this research area, this paper proposes a new five-dimensional memristor CNN conservative hyperchaotic system and systematically analyzes its dynamic properties. The analysis content includes equilibrium point analysis, Poincaré sections, Lyapunov exponent spectra, bifurcation diagrams, two-parameter Lyapunov exponent spectra, complexity assessment, homogeneous and heterogeneous extreme multistability, etc In addition, the simulation circuit for the new system is designed and constructed. The digital circuit of the new system is implemented using a microcontroller (MCU). After running simulations, the experimental results from the analog circuit, digital circuit, and numerical simulation are consistent with each other, demonstrating the feasibility of the circuit implementation. Finally, two different synchronization control strategies are employed to achieve synchronization control within a finite time.
Flow characteristics analysis and application research of multi-branch piping
Authors: Zhang Pengfei , Lin Yuhong , Yang Rongchao , Zeng Bo , Shen Longwu , Tian Jiali , Miao Qian
Published: November 2024

This study examines the flow characteristics of multi-branch piping and provides production guidance for tobacco-specific ventilation rate standard rods (VRSRs), focusing on VRSRs within a multi-branch pipeline structure as the research subject. The present work analyses the flow resistance, and flow distribution on the influence of each piping section volume flow. By adopting the iterative method to appropriately construct the mathematical model of the VRSR, an effective technique is developed for thoroughly examining specific VRSRs. The performance of these customized VRSRs is then methodically assessed through repeatability and stability tests, and their suitability is further analyzed by employing five types of ventilation rate test equipment. The main findings reveal: (1) Customized VRSRs exhibit high repeatability and stability; (2) Customized VRSRs are compatible with five common types of ventilation rate testing equipment. Consequently, the model offers scientific theoretical guidance for the production of tobacco-specific VRSRs. Additionally, the study serves as a practical case reference for the production of multi-branch piping systems.
Theoretical Performance Evaluation of Wide-band Diffraction-limited Coronagraph for the Next-generation Segmented Large Telescopes
Authors: Kojima Reiki , Satoshi Itoh , Ota Shunsuke , Yuji Ikeda , Bessho Taiki , Matsuo Taro
Published: November 2024

We investigate the raw-contrast mitigation ability of the spectroscopic coronagraph. We consider a mis-modulation of the complex amplitude by the transmissive focal-plane mask that is an intrinsic problem dependent on the spectral resolving power of a grating in a spectroscopic coronagraph, stellar angular radius, and non-common path error (NCP) that is due to chromaticity in the active wavefront control (WFC) as limitation factors to the performance of the spectroscopic coronagraph. Concerning the impact of the mis-modulation and stellar angular radius under the assumption with the an achromatic WFC, we found that the spectroscopic coronagraph potentially archives the raw-contrast of 1.25 × 10−10 at the spectral resolving power R greater than or equal to 2000 at

2

λ

/

D

with the 10 pc distant solar radius 0.93 mas. It corresponds to approximately 0.03λ/D where the wavelength λ is 800 nm and telescope diameter D is 6 m. The inner working angle (defined as the minimal angular distance where planetary throughput is the maximal throughput) of the spectroscopic coronagraph is

2

λ

/

D

. In addition, we found that the NCP limits the raw-contrast mitigation ability to 7.3 × 10−11 and 5.5 × 10−10 at

2

λ

/

D

for 3% and 8% spectral bandwidths, respectively. We conclude that the spectroscopic coronagraph may serve as a promising method of broadband direct imaging of potentially habitable exoplanets located at small inner working angles with the next-generation segmented large telescopes.
High-pass Filter Periodogram: An Improved Power Spectral Density Estimator for Unevenly Sampled Data
Authors: Albentosa-Ruiz Ezequiel , Marchili Nicola
Published: November 2024

Accurate time series analysis is essential for studying variable astronomical sources, where detecting periodicities and characterizing power spectral density (PSD) are crucial. The Lomb–Scargle periodogram, commonly used in astronomy for analyzing unevenly sampled time series data, often suffers from noise introduced by irregular sampling. This paper presents a new high-pass filter (HPF) periodogram, a novel implementation designed to mitigate this sampling-induced noise. By applying a frequency-dependent HPF before computing the periodogram, the HPF method enhances the precision of PSD estimates and periodicity detection across a wide range of signal characteristics. Simulations and comparisons with the Lomb–Scargle periodogram demonstrate that the HPF periodogram improves accuracy and reliability under challenging sampling conditions, making it a valuable complementary tool for more robust time series analysis in astronomy and other fields dealing with unevenly sampled data.
Tetrahedral shape and Lambda impurity effect in 80Zr with a multidimensionally constrained relativistic Hartree-Bogoliubov model*
Authors: Yang 杨 Dan 丹 , Rong 荣 Yu-Ting 宇婷
Published: November 2024

This study investigated the tetrahedral structure in 80Zr and Lambda (Λ) impurity effect in

Zr using the multidimensionally constrained relativistic Hartree-Bogoliubov model. The ground states of both 80Zr and

Zr exhibit a tetrahedral configuration, accompanied by prolate and axial-octupole shaped isomers. Our calculations reveal that there are changes in the deformation parameters

,

, and

upon Λ binding to 80Zr, except for

when Λ occupies p-orbits. Compared to the two shape isomers, the Λ particle exhibits weaker binding energy in the tetrahedral state when occupying the

or

single-particle state. In contrast, the strongest binding occurs for the Λ particle in the

state with tetrahedral shape. Besides, a large Λ separation energy may not necessarily correlate with a significant overlap between the density distributions of the Λ particle and nuclear core, particularly for tetrahedral hypernuclei.
Temporal attention fusion network with custom loss function for EEG–fNIRS classification
Authors: Bunterngchit Chayut , Wang Jiaxing , Su Jianqiang , Wang Yihan , Liu Shiqi , Hou Zeng-Guang
Published: November 2024

Objective. Methods that can detect brain activities accurately are crucial owing to the increasing prevalence of neurological disorders. In this context, a combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offers a powerful approach to understanding normal and pathological brain functions, thereby overcoming the limitations of each modality, such as susceptibility to artifacts of EEG and limited temporal resolution of fNIRS. However, challenges such as class imbalance and inter-class variability within multisubject data hinder their full potential. Approach. To address this issue, we propose a novel temporal attention fusion network (TAFN) with a custom loss function. The TAFN model incorporates attention mechanisms to its long short-term memory and temporal convolutional layers to accurately capture spatial and temporal dependencies in the EEG–fNIRS data. The custom loss function combines class weights and asymmetric loss terms to ensure the precise classification of cognitive and motor intentions, along with addressing class imbalance issues. Main results. Rigorous testing demonstrated the exceptional cross-subject accuracy of the TAFN, exceeding 99% for cognitive tasks and 97% for motor imagery (MI) tasks. Additionally, the ability of the model to detect subtle differences in epilepsy was analyzed using scalp topography in MI tasks. Significance. This study presents a technique that outperforms traditional methods for detecting high-precision brain activity with subtle differences in the associated patterns. This makes it a promising tool for applications such as epilepsy and seizure detection, in which discerning subtle pattern differences is of paramount importance.
ITER-relevant 600 s steady-state extraction of negative hydrogen ions at the test facility ELISE
Authors: Wünderlich D. , Riedl R. , Fröschle M. , Heiler A. , Navarro A. , Yordanov D. , Döring A. , Fantz U.
Published: November 2024

The neutral beam heating system for the future international fusion experiment ITER will be based on radio-frequency driven ion sources delivering a large (≈1 × 2 m2) and homogeneous negative hydrogen or deuterium ion beam of several ten Amperes for several hundred seconds. The size scaling experiment ELISE (Extraction from a Large Ion Source Experiment) is an integral part of the R&D road-map towards the ITER neutral beam heating system. Recently, 90% of the ITER target for the extracted current density was achieved in hydrogen for 600 s, increasing the pulse length over which such current densities can be achieved by a factor of more than ten. For ten second beam pulses the ITER target current density was achieved. These breakthrough results are made possible by using a steady-state capable high-voltage power supply together with an improved version of internal potential rods and a modified topology of the magnetic filter field.
Remarks on q-difference opers arising from quantum toroidal algebras
Authors: Feigin B , Jimbo M , Mukhin E
Published: November 2024

We propose a conjectural correspondence between the spectra of the Bethe algebra for the quantum toroidal

gl

2

algebra on relaxed Verma modules, and q-hypergeometric opers with apparent singularities. We introduce alongside the notion of apparent singularities for linear q-difference operators and discuss some of their properties. We also touch on a generalization to

gl

n

.
Effectiveness of a combination of laccase and green coffee extract on oral malodor: a comparative, randomized, controlled, evaluator-blind, parallel-group trial
Authors: Santos Sylvia L , Holz Caterina , Milleman Kimberly , Milleman Jeffery , Wenqian Gu , Mateo Luis R
Published: November 2024

Oral malodor negatively impacts a person’s quality of life and may affect up to 50% of the population. The aim of this randomized, placebo and no-product controlled, evaluator-blind, proof-of-concept study was to evaluate the effectiveness and safety of the single use of two experimental lozenges containing the laccase enzyme and green coffee extract (with and without flavor) in reducing intrinsic oral malodor. Following 12–16 h of avoidance of oral hygiene,156 generally healthy subjects presented at screening and baseline visits with a mean organoleptic odor intensity (OI) score of ⩾2 and an OralChromaTM reading of ⩾125 parts per billion (ppb) hydrogen sulfide (H2S) gas and were randomly assigned to receive either one of the two experimental lozenges, a placebo lozenge, or no-product. Following the supervised use of the assigned products, subjects’ oral malodor was evaluated using OI assessments and OralChromaTM measurement for volatile sulfur compounds (VSCs) immediately following product use (approximately 5 min), and at 30 min, 1 h, 2 h, 3 h and 4 h. The two experimental lozenges, with and without flavor, showed significant reductions in OI scores compared with the placebo and no-product groups at all time points (p < 0.001). At 5 min post-product use, the experimental lozenges, with and without flavor, were significantly better than the no-product group in reducing the VSCs (p < 0.04). The results of individual VSC components (hydrogen sulfide, methyl mercaptan and dimethyl sulfide) were variable; both experimental lozenges notably reduced hydrogen sulfide and methyl mercaptan levels in most post-use assessments. Four minor adverse events were reported, none of which were directly linked to the product. In conclusion, the experimental lozenges, whether flavored or not, were safe and effective in reducing oral malodor over a span of 4 h, based on organoleptic OI scores.

Clinical Trial No: NCT05950529
Validation of a sensor system for the measurement of breath ammonia using selected-ion flow-tube mass spectrometry
Authors: Wagner Michal , Saad Saliha , Killard Anthony J
Published: November 2024

The measurement of trace breath gases is of growing interest for its potential to provide non-invasive physiological information in health and disease. While instrumental techniques such as selected-ion flow-tube mass spectrometry (SIFT-MS) can achieve this, these are less suitable for clinical application. Sensitive sensor-based systems for breath ammonia could be more widely deployed, but have proven challenging to develop. This work demonstrates the sequential analytical validation of an electrochemical impedance-based sensor system for the measurement of ammonia in breath using SIFT-MS. Qualitative and relative responses between the two methods were comparable, although there were consistent differences in absolute concentration. When tested in artificial breath ammonia, sensors had a relative impedance sensitivity of 3.43 × 10−5 ppbv−1 for each breath in the range of 249–1653 ppbv (r
2 = 0.87, p< 0.05). When correlated with SIFT-MS using human breath (n = 14), ammonia was detected in the range of 100–700 ppbv (r = 0.78, p < 0.001), demonstrating acceptable sensitivity, reproducibility and dynamic range for clinical application.
Permutation-equivariant quantum convolutional neural networks
Authors: Das Sreetama , Caruso Filippo
Published: November 2024

The Symmetric groupSnmanifests itself in large classes of quantum systems as the invariance of certain characteristics of a quantum state with respect to permuting the qubits. Subgroups ofSnarise, among many other contexts, to describe label symmetry of classical images with respect to spatial transformations, such as reflection or rotation. Equipped with the formalism of geometric quantum machine learning, in this study we propose the architectures of equivariant quantum convolutional neural networks (EQCNNs) adherent toSnand its subgroups. We demonstrate that a careful choice of pixel-to-qubit embedding order can facilitate easy construction of EQCNNs for small subgroups ofSn. Our novel EQCNN architecture corresponding to the full permutation groupSnis built by applying all possible QCNNs with equal probability, which can also be conceptualized as a dropout strategy in quantum neural networks. For subgroups ofSn, our numerical results using MNIST datasets show better classification accuracy than non-equivariant QCNNs. TheSn-equivariant QCNN architecture shows significantly improved training and test performance than non-equivariant QCNN for classification of connected and non-connected graphs. When trained with sufficiently large number of data, theSn-equivariant QCNN shows better average performance compared toSn-equivariant QNN . These results contribute towards building powerful quantum machine learning architectures in permutation-symmetric systems.
Gender differences regarding forming the intent to participate in carbon neutral practices: a nationwide sample study of South Korea
Authors: Lee Jin Won , Roh Seungkook
Published: November 2024

For a society to achieve the goal of carbon neutrality, the public must also participate in practices that reduce carbon emissions in their daily lives. This study investigates how males and females differ in forming the intent to participate in such practices. Our analysis of a nationwide survey sample taken from South Korea shows that (1) females have a higher rate of intent to participate in carbon neutral practices; however, (2) the positive effect of the perceived necessity of carbon neutrality on this intent is greater among males. These findings suggest that although males are more restrained regarding their view of carbon neutral practices, their intent to participate in such practices can be greatly increased if they perceive carbon neutrality as necessary. The findings contribute to the existing research on the role of gender in environmental psychology and provide implications for communicating essential information regarding carbon neutral behavior to the general public.
Regulation of protonation in aramid nanofiber films for improved mechanical properties
Authors: Choi Yuseung , Shin Gilyong , Lee Ju Hwan , Kim Hyeong Jun , Jun so Byeong , Lee Hoyeon , Jeon Jei Gyeong , Kang Tae June
Published: November 2024

Aramid nanofibers (ANFs) are a strong and heat resistant nanomaterial that can be isolated from commercial poly(p-phenylene terephthalamide) (PPTA) fibers. These nanofibers allow bottom-up self-assembly to form macroscale structures in various form factors through a simple reprotonation process. However, the mechanical properties of these reassembled ANF structures often fall short of those of PPTA fibers, mainly due to insufficient packing, non-uniform microstructures, and low crystallinity. In this study, we present ANF films with improved mechanical properties prepared by a repeated spin-coating technique combined with a reprotonation process using deionized water and formic acid at concentrations ranging from 20 to 60 wt.%. Extensive analyses were performed on the resulting ANF films to evaluate their surface and cross-sectional morphologies, chemical bonds and compositions, thermal stabilities, and mechanical properties. The fabricated ANF films exhibited remarkable performance, with an elastic modulus of 6.7 GPa, tensile strength of 680 MPa, and toughness of 7.7 MJ m−3, while maintaining the inherent thermal stability of PPTA fibers. These properties significantly exceed those of previously reported ANF films, broadening the potential applications for ANF films in various fields.
Investigation on novel ultralight weight thermally insulated fireproof composite
Authors: Burai Suvam , Srikrishnan A R , Bhowmik Shantanu
Published: November 2024

This study highlights the performance of ultra-lightweight fireproof composite. Polyacrylonitrile based carbon fiber (CF) has been reinforced in Polyetherimide (PEI) polymer to develop the composite. The Surfac2e of CF and PEI film was modified by low-pressure plasma to improve the bonding strength between matrix and reinforcement. The polymeric composite was fabricated by compression molding with a pressure of 2 bar, temperature of 380 °C and holding time of 30 min. CF/PEI composite was used to make a hybrid composite by layering of silicone foam in between the layers. The hybrid composite was exposed to a Bunsen burner under sustained flame for a duration of 10 min. The composite panel’s flame-facing side reached 676.2 °C after 10 min of fire exposure, while the temperature on the other side only reached 58.2 °C. The fabricated hybrid composite was exposed to very low temperature in order to test its ability of thermal insulation under extreme cold temperature. Over the specific period of testing, the temperature of the dry ice decreased from 25 °C to −3.1 °C. After exposure to fire, only minimal loss of material was observed. The hybrid composite of carbon fiber and PEK film, sandwiched between silicone foam, exhibits excellent fire resistance due to its high limiting oxygen index. This composite is considered to be among the best thermally insulating and fire-resistant materials. Thermogravimetric analysis of carbon fiber and PEI-Carbon fiber composite was performed to determine the optimal processing temperature of compression molding for the composite, upon heating, it showed a modest weight decrease of 6.053%. The composite shows a significant improvement of impact resistance, compressive strength and thermal stability. A simulation model was developed under Ansys fluent software for both heating and cooling. The analysis of developed model also shows similar results.
TIA-UNet: transformer-enhanced deep learning for adolescent idiopathic scoliosis spinal x-ray image segmentation
Authors: Li Zhiwu , Deng Shuangcheng , Xue Zhilong , Zhang Jinlong , Hua Junfei , Li Gang , Yang Yang , Li Aijing , Wang Junyang
Published: November 2024

Adolescent Idiopathic Scoliosis (AIS) is a common spinal deformity where precise diagnosis is crucial for developing effective treatment strategies. Traditional manual x-ray image analysis is time-consuming and highly dependent on the operator’s expertise, thus constraining diagnostic efficiency and accuracy. This study aimed to develop an automated thoracolumbar spine segmentation method utilizing deep learning to enhance the efficiency and accuracy of AIS diagnosis. We introduced TIA-UNet, an innovative network architecture that combines Convolutional Neural Networks (CNN) with Transformer models. By integrating the IR Block and DA Block, TIA-UNet was optimized for feature extraction and multi-scale information fusion. The model underwent training and validation using our established Adolescent Scoliosis Medical Dataset (ASMD). TIA-UNet attained a Dice similarity coefficient of 90. 02%, a Mean Intersection over Union (MIoU) of 81. 96%, and a Hausdorff Distance (HD) of 4. 09, significantly surpassing current state-of-the-art methods such as UNet and TransUNet. Moreover, TIA-UNet exhibited superior computational efficiency regarding parameter count, inference time, and floating-point operations (FLOPs). As an automated medical image segmentation algorithm, TIA-UNet enhanced segmentation accuracy while maintaining high computational efficiency, demonstrating significant potential for clinical diagnostic applications. This study provides compelling evidence supporting the utilization of deep learning techniques in medical image analysis.
Exploring the thermal conductivity of green filamentous algae natural fibre: an experimental investigation
Authors: G R Sajin , S Gijoy , S Rejin
Published: November 2024

The use of non-renewable resources in thermal insulation has significant environmental impacts. Another major problem faced by the ecosystem is the invasive growth of water weeds. Making sustainable products from waterweeds helps to prevent its overgrowth that disrupts the balance of the ecosystem. This study explores the viability of Green Filamentous Algae (GFA) as an eco-friendly thermal insulation material. GFA was collected from water bodies, cleaned, and processed into two forms: untreated (UTD) and alkali-treated (TD). Experimental investigations were conducted to evaluate the physical properties of GFA, including density, self-ignition temperature, water absorption, moisture absorption, flammability, and thermal conductivity. Results show that the untreated GFA had a density of 402.52 kg m−3, while the treated GFA exhibited a slightly lower density of 392.32 kg m−3. The self-ignition temperature for both untreated and treated GFA was measured between 275 °C and 280 °C. The water absorption capacity was higher in treated GFA (654.7%) compared to untreated (493.83%). Moisture absorption capacity was 15.14% for untreated GFA and 17.47% for treated GFA. Flammability tests revealed a burning rate of 20.026 mm min−1, placing GFA in the combustibility classification 1 (CC1). Thermal conductivity values were found to be 0.308 W m−1K−1 (UTD) and 0.273 W m−1K−1 (TD), making treated GFA a promising candidate for sustainable insulation applications. This study demonstrates the potential of GFA as a bio-based insulation material and highlights future improvements to enhance its moisture resistance and thermal performance.
Environmental impacts of floating solar panels on freshwater systems and their techno-ecological synergies
Authors: M G Rocha Sofia , Armstrong Alona , Thackeray Stephen J , Hernandez Rebecca R , M Folkard Andrew
Published: November 2024

Renewable energy sources, such as floating photovoltaic systems (FPVs), are crucial to mitigating the climate crisis. FPV deployments on freshwater bodies are rapidly growing, as they avert land-use change, operate with increased efficiency, and potentially improve water availability by reducing evaporation and the frequency of algal blooms. However, understanding of the ecological consequences for water bodies is very limited despite the variable and far-reaching range of potential impacts. Here, we bring novel insight by combining an established theoretical approach—techno-ecological synergies (TES)—with robust understanding of water body functioning, to direct sustainable FPV deployments. Specifically, we show the potential to integrate consideration of both energy and ecological systems for FPV deployment decisions based on current evidence and fundamental understanding of freshwater body states and processes. Based on 422 pieces of evidence from a systematic review of known physical (n = 283), chemical (n = 96) and biological (n = 43) effects of FPV on freshwater systems, we outline eight potential TES that could be realised through FPV location, design, and operation decisions. There was most evidence (n = 114) for the Water Use Efficiency TES, which all reported decreased evaporation rates, or increased water savings, due to panel shading. We highlight a lack of understanding of chemical and biological effects of FPV on hosting water bodies, as well as a need for comprehensive studies in which physical, chemical, and biological aspects of water body dynamics are integrated. Finally, we detail research priorities to ensure future FPV deployments bring benefit for both energy and water bodies. Ultimately, integrated energy and water body system knowledge, FPV deployments could mitigate both the climate and ecological emergencies, with notable benefits for society.
Hecke eigenvalues in 𝑝-modular representations of unramified 𝑈(2,1)
Authors: Xu Peng
Published: November 2024

Let

G
G

be the unramified unitary group

U
(
2
,
1
)
(
E

/

F
)

U(2, 1)(E/F)

defined over a non-archimedean local field

F
F

of odd residue characteristic

p
p

, and let

K
K

be a maximal compact open subgroup of

G
G

. For an irreducible smooth

F

¯

p

\overline {\mathbf {F}}_p

-representation

π
\pi

of

G
G

, and a weight

σ
\sigma

of

K
K

contained in

π
\pi

, we prove that

π
\pi

admits eigenvectors for the spherical Hecke algebra

H

(
K
,
σ
)

\mathcal {H}(K, \sigma )

. Our approach is close to that of Barthel–Livné [Duke Math. J. 75 (1994), pp. 261–292] for

G

L
2

GL_2

. For

G

L
n

(
n
≥
3
)

GL_n (n\geq 3)

, we give a sketch of an essential case where the analogue of a crucial ingredient in loc.cit fails.
An unbiased comparison of immunoglobulin sequence aligners
Authors: Konstantinovsky Thomas , Peres Ayelet , Polak Pazit , Yaari Gur
Published: November 2024

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is critical for our understanding of the adaptive immune system’s dynamics in health and disease. Reliable analysis of AIRR-seq data depends on accurate rearranged immunoglobulin (Ig) sequence alignment. Various Ig sequence aligners exist, but there is no unified benchmarking standard representing the complexities of AIRR-seq data, obscuring objective comparisons of aligners across tasks. Here, we introduce GenAIRR, a modular simulation framework for generating Ig sequences alongside their ground truths. GenAIRR realistically simulates the intricacies of V(D)J recombination, somatic hypermutation, and an array of sequence corruptions. We comprehensively assessed prominent Ig sequence aligners across various metrics, unveiling unique performance characteristics for each aligner. The GenAIRR-produced datasets, combined with the proposed rigorous evaluation criteria, establish a solid basis for unbiased benchmarking of immunogenetics computational tools. It sets up the ground for further improving the crucial task of Ig sequence alignment, ultimately enhancing our understanding of adaptive immunity.
Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering
Authors: Shu Zhenqiu , Xia Min , Tan Kaiwen , Zhang Yongbing , Yu Zhengtao
Published: November 2024

Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering. Firstly, the proposed method constructs shallow views through the $k$-nearest neighbor ($k$NN) and diffusion mapping (DM) algorithms, and then deep views are generated by utilizing the graph Laplacian filters. These deep multi-view data serve as the input for representation learning. To improve the clustering performance of scRNA-seq data, contrastive learning is introduced to enhance the discrimination ability of our network. Specifically, we construct a group contrastive loss for representation features and a structural consistency contrastive loss for structural relationships. Extensive experiments on eight real scRNA-seq datasets show that the proposed method outperforms other state-of-the-art methods in scRNA-seq data clustering tasks. Our source code has already been available at https://github.com/szq0816/scMMN.