bluuebunny/crossref_metadata_2025 huggingface dataset:
Systems-level analyses dissociate genetic regulators of reactive oxygen species and energy production
Authors: Bennett Neal K. , Lee Megan , Orr Adam L. , Nakamura Ken
Published: January 2024

Respiratory chain dysfunction can decrease ATP and increase reactive oxygen species (ROS) levels. Despite the importance of these metabolic parameters to a wide range of cellular functions and disease, we lack an integrated understanding of how they are differentially regulated. To address this question, we adapted a CRISPRi- and FACS-based platform to compare the effects of respiratory gene knockdown on ROS to their effects on ATP. Focusing on genes whose knockdown is known to decrease mitochondria-derived ATP, we showed that knockdown of genes in specific respiratory chain complexes (I, III, and CoQ10 biosynthesis) increased ROS, whereas knockdown of other low ATP hits either had no impact (mitochondrial ribosomal proteins) or actually decreased ROS (complex IV). Moreover, although shifting metabolic conditions profoundly altered mitochondria-derived ATP levels, it had little impact on mitochondrial or cytosolic ROS. In addition, knockdown of a subset of complex I subunits—including
NDUFA8
,
NDUFB4
, and
NDUFS
8—decreased complex I activity, mitochondria-derived ATP, and supercomplex level, but knockdown of these genes had differential effects on ROS. Conversely, we found an essential role for ether lipids in the dynamic regulation of mitochondrial ROS levels independent of ATP. Thus, our results identify specific metabolic regulators of cellular ATP and ROS balance that may help dissect the roles of these processes in disease and identify therapeutic strategies to independently target energy failure and oxidative stress.
The pedagogy for teaching suprasegmental features
Authors: Shamsi Esmat , Bozorgian Hossein
Published: January 2024

Suprasegmental (prosody) deficiencies seriously affect English as a foreign language (EFL) learners’
comprehensibility and intelligibility. Among suprasegmental features, the most problematic for Iranian learners is rhythm, due to
fundamental differences between English and Farsi language timing patterns. To improve learners’ pronunciation, computer-assisted
pronunciation training (CAPT) is commonly applied to provide a private, individualized learning environment with unlimited input
and visual, instant, and precise feedback. In our previous study, a specific kind of CAPT application, (MyET™) was used to enhance
suprasegmental features of five Iranian EFL learners. The results showed that CAPT helped them improve their pronunciation
skills. They also had positive attitudes toward taking this approach. In this short paper, we describe how prosody teaching
actually occurred in the classroom, how we used the material, and how we reported the results. Suggestions and recommendations for
further research are also presented.
How direct is the link between words and images?
Authors: Shahmohammadi Hassan , Heitmeier Maria , Shafaei-Bajestan Elnaz , Lensch Hendrik P. A. , Baayen R. Harald
Published: January 2024

Günther et al. (2022) investigated the relationship between words and images in which they concluded the possibility of a direct link between words and embodied experience. In their study, participants were presented with a target noun and a pair of images, one chosen by their model and another chosen randomly. Participants were asked to select the image that best matched the target noun. Building upon their work, we addressed the following questions. 1. Apart from utilizing visually embodied simulation, what other strategies subjects might have used? How much does this setup rely on visual information? Can it be solved using textual representations? 2. Do current visually-grounded embeddings explain subjects’ selection behavior better than textual embeddings? 3. Does visual grounding improve the representations of both concrete and abstract words? For this aim, we designed novel experiments based on pre-trained word embeddings. Our experiments reveal that subjects’ selection behavior is explained to a large extend on text-based embeddings and word-based similarities. Visually grounded embeddings offered modest advantages over textual embeddings in certain cases. These findings indicate that the experiment by Günther et al. (2022) may not be well suited for tapping into the perceptual experience of participants, and the extent to which it measures visually grounded knowledge is unclear.
High-Performance Organic Field-Effect Transistors Based on Two-Dimensional Vat Orange 3 Crystals
Authors: Yan 闫 Ning 宁 , Xiong 熊 Zhiren 志仁 , Qin 秦 Chengbing 成兵 , Li 李 Xiaoxi 小茜
Published: January 2024

The exploration and research of low-cost, environmentally friendly, and sustainable organic semiconductor materials are of immense significance in various fields, including electronics, optoelectronics, and energy conversion. Unfortunately, these semiconductors have almost poor charge transport properties, which range from ∼ 10−4 cm2⋅V−1⋅s−1 to ∼ 10−2 cm2⋅V−1⋅s−1. Vat orange 3, as one of these organic semiconductors, has great potential due to its highly conjugated structure. We obtain high-quality multilayered Vat orange 3 crystals with two-dimensional (2D) growth on h-BN surfaces with thickness of 10–100 nm using physical vapor transport. Raman’s results confirm the stability of the chemical structure of Vat orange 3 during growth. Furthermore, by leveraging the structural advantages of 2D materials, an organic field-effect transistor with a 2D vdW vertical heterostructure is further realized with h-BN encapsulation and multilayered graphene contact electrodes, resulting in an excellent transistor performance with On/Off ratio of 104 and high field-effect mobility of 0.14 cm2⋅V−1⋅s−1. Our results show the great potential of Vat orange 3 with 2D structures in future nano-electronic applications. Furthermore, we showcase an approach that integrates organic semiconductors with 2D materials, aiming to offer new insights into the study of organic semiconductors.
A transparent broadband flexible metamaterial absorber for radar infrared-compatible stealth
Authors: Gao Yulong , Jing Huihui , Wang Jiayun , Kang Jinfeng , Zhao Ling , Chen Linyue , Wang Yongsheng , Duan Junping , Qu Zeng , Zhang Binzhen
Published: January 2024

The iterative upgrading of the means of warfare has promoted the development of detection technology, and military activities are increasingly demanding target stealth. In this paper, a multifunctional metamaterial absorber is recommended, which simultaneously realizes the integrated stealth functions of ultra-wideband radar wave absorption, low infrared emission, and optically transparent. The structure is composed of an infrared shield, a radar-absorbing layer, and a reflective layer. Simulation results indicate that the absorption bandwidth in the radar band (1.81–20.4 GHz) is more than 90%, and the infrared emissivity is as low as 0.271 in the IR band (3–14 μm). Moreover, it has the features of flexibility, angle stability (0–45°), polarization insensitivity (0–90°), and so on. Furthermore, the electric field current distribution and equivalent circuit model of the structure are analyzed, and a 180 mm*180 mm sample is built and tested; the results show that the simulated and actual measurements are in agreement, and it is expected that such absorbers will be used in areas such as aircraft.
Monolayer Cu2Se: a topological catalysis in CO2 electroreduction
Authors: Li Zihan , Liu Ying , He Zeqing , Wang Lirong , Dai Xuefang , Liu Guodong
Published: January 2024

This investigation provides a comprehensive exploration into the intricate interplay between topological surface states (TSS) and catalytic performance in two-dimensional (2D) materials, with specific emphasis on monolayer Cu2Se. Leveraging the unique characteristics of nodal loop semimetals (NLSMs), we delve into the precise influence of TSS on catalytic activity, particularly in the domain of CO2 electrochemical reduction. Our findings illuminate the central role played by these TSS, arising from the underlying NLSM framework, in sculpting catalytic efficiency. The length of these surface states emerges as a critical determinant of surface density of states (DOSs), a fundamental factor governing catalytic behavior. Extension of these surface states correlates with heightened surface DOSs, yielding lower Gibbs free energies and consequently enhancing catalytic performance, particularly in the electrochemical reduction of CO2. Moreover, we underscore the profound importance of preserving symmetries that protect the nodal loop. The disruption of these symmetries is found to result in a significant degradation of catalytic efficacy, underscoring the paramount significance of topological features in facilitating catalytic processes. Therefore, this study not only elucidates the fundamental role of TSS in dictating the catalytic performance of topological 2D materials but also paves the way for harnessing these unique attributes to drive sustainable and highly efficient catalysis across a diverse spectrum of chemical processes.
MPFANet: a multipath feature aggregation network for steel surface defect detection
Authors: Li Zhongyang , Tai Yichun , Huang Zhenzhen , Peng Tao , Zhang Zhijiang
Published: January 2024

As a crucial component of product quality surveillance in the industrial manufacturing field, surface defect detection plays a pivotal role in achieving industrial automation. Although numerous defect detection methods have been widely used in steel production, complicated defect characteristics still severely affect the accuracy and efficiency of defect detection methods. To solve this problem, we propose a multi-path feature aggregation network that can significantly improve steel defect detection performance. Firstly, we design an efficient multi-path aggregation feature extraction module combining gradient path with data path propagation design paradigm as the backbone feature extraction unit based on YOLOv5. Secondly, in the branch path of the backbone, deformable convolution is introduced to expand the effective receptive field and enhance the capability of extracting irregularly distributed defect features. Furthermore, we propose a chained feature enhanced pooling module that refines feature information to obtain feature maps containing more complicated defect details in the pooling layer. Experimental results on the publicly available steel surface defect datasets show that our proposed model achieves a mean average precision of 81.9

%

on Northeastern University-DET and 72.1

%

on GC10-DET improving by 4.6

%

and 3.4

%

over the baseline, respectively. The proposed defect detection network can achieve a balance between accuracy and efficiency, and meet the demand in practical production. The detection speed of our work reaches 41.1 frames per second (FPS) and 40.0 FPS on the above two datasets, which demonstrates that our method has achieved comprehensive performance in steel surface defect detection.
Nonlinear and nonstationary detection and quantification of multi-scale measured signals for bridge structure
Authors: Shan Deshan , Yu Zhongru , Long Qinchun , Zhang Erhua
Published: January 2024

The assessment of nonlinear and nonstationary levels in measured bridge signals is a vital step in system identification and long-term health monitoring for the bridge structure. The field-measured signals from the bridge structure are inherently weak and multiscale, so a specific adaptive variational mode decomposition (AVMD) is proposed to decompose them and extract their included multi-scale features. Combination the adaptability of empirical mode decomposition with the dimensionality reduction of principal component analysis, the number of inherent mode functions (IMFs) that need to be given in the conventional variational mode decomposition is adaptively determined in the proposed AVMD. The original measured signals from the bridge structure multiscale are subsequently decomposed by AVMD into the multiscale IMFs with the lowest cross-correlation. Then, the recurrence plot and recurrence quantification analysis are introduced into the detection and quantification of the measured signals, and the nonlinear and nonstationary quantification indexes are constructed to describe quantitatively the nonlinear and nonstationary levels. The stabilities and accuracies of three nonlinear and three nonstationary quantification indexes are comparatively discussed by the nonlinear and nonstationary detection and quantification of three well-defined simulated signals. The Shannon entropy and trapping time indexes are subsequently determined to quantify the nonlinear and nonstationary levels of the measured signals, respectively. Finally, the proposed algorithm and quantification indexes are applied to the nonlinear and nonstationary detection and quantification of the measured signals from the real-world bridge structures. It is shown from the validation and discussion that the proposed algorithm is available to detect and quantify the nonlinear and nonstationary levels of the measured multiscale signal from the real-world bridge structure.
Improved performance of BDS-3 time and frequency transfer based on an epoch differenced model with receiver clock estimation
Authors: Zhang Pengfei , Yang Yuanxi , Tu Rui , Gao Yuping , Wang Bing
Published: January 2024

The carrier phase (CP) technique based on the BeiDou Global Satellite Navigation System (BDS-3) has proven to be a crucial spatial tool for remote time and frequency transfer. The current CP technique models the receiver clock offset as a white noise stochastic process and easily absorbs some unmodeled errors, thus compromising the time and frequency transfer performance. To further improve the performance of time and frequency transfer, a new BDS-3 receiver clock estimation algorithm based on the epoch difference (ED) model is presented, and the mathematical principles and applied modes are discussed. The algorithm makes full use of both observations of the current epoch and practical variations of the receiver clock offset, further improving the performance of time and frequency transfer. Five Multi-Global Navigation Satellite System Experiment network stations equipped with various types of receivers and antennas with dual-frequency BDS-3 signals were used to establish four time transfer links (i.e., AMC4–PTBB, BRUX–PTBB, OP71–PTBB, and WTZS–PTBB) to evaluate their effectiveness. The ED model improves all the four time links in terms of noise level, with improvements of 17.0%, 18.3%, 20.3%, and 5.9%, respectively, when compared with the results from a non-ED model. The ED model outputs were better than the raw solutions in terms of frequency stability at all time links, particularly for average time intervals (tau) < 1000 s. The mean improvement was 8.1% for AMC4–PTBB, 16.1% for BRUX–PTBB, 10.0% for OP71–PTBB, and 18.6% for WTZS–PTBB when the average time (tau) was less than 1000 s.
Coronary vessel segmentation in coronary angiography with a multi-scale U-shaped transformer incorporating boundary aggregation and topology preservation
Authors: Wang Guangpu , Zhou Peng , Gao Hui , Qin Zewei , Wang Shuo , Sun Jinglai , Yu Hui
Published: January 2024

Coronary vessel segmentation plays a pivotal role in automating the auxiliary diagnosis of coronary heart disease. The continuity and boundary accuracy of the segmented vessels directly affect the subsequent processing. Notably, during segmentation, vessels with severe stenosis can easily cause boundary errors and breakage, resulting in isolated islands. To address these issues, we propose a novel multi-scale U-shaped transformer with boundary aggregation and topology preservation (UT-BTNet) for coronary vessel segmentation in coronary angiography. Specifically, considering the characteristics of coronary vessels, we first develop the UT-BTNet for coronary vessels segmentation, which combines the advantages of a convolutional neural networks (CNN) and a transformer, and is able to effectively extract the local and global features of angiographic images. Secondly, we innovatively employ boundary loss and topological loss in two stages, in addition to the traditional losses. In the first stage, boundary loss is adopted, which has the effect of boundary aggregation. In the second stage, topological loss is applied to preserve the topology of the vessels, after the network converges. In the experiment, in addition to the two metrics of Dice and intersection over union (IoU), we specifically propose two metrics of boundary intersection over union (BIoU) and Betti error to evaluate boundary accuracy and the continuity of segmentation results. The results show that the Dice is 0.9291, the IoU is 0.8687, the BIoU is 0.5094, and the Betti error is 0.3400. Compared with the other state-of-the-art methods, UT-BTNet achieves better segmentation results, while ensuring the continuity and boundary accuracy of the vessels, indicating its potential clinical value.
Integrating microdosimetric in vitro RBE models for particle therapy into TOPAS MC using the MicrOdosimetry-based modeliNg for RBE ASsessment (MONAS) tool
Authors: Cartechini Giorgio , Missiaggia Marta , Scifoni Emanuele , La Tessa Chiara , Cordoni Francesco G
Published: January 2024

Objective. In this paper, we present MONAS (MicrOdosimetry-based modelliNg for relative biological effectiveness (RBE) ASsessment) toolkit. MONAS is a TOPAS Monte Carlo extension, that combines simulations of microdosimetric distributions with radiobiological microdosimetry-based models for predicting cell survival curves and dose-dependent RBE. Approach. MONAS expands TOPAS microdosimetric extension, by including novel specific energy scorers to calculate the single- and multi-event specific energy microdosimetric distributions at different micrometer scales. These spectra are used as physical input to three different formulations of the microdosimetric kinetic m
odel, and to the generalized stochastic microdosimetric model (GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra and in vitro survival fraction data. To show the MONAS features, we present two different applications of the code: (i) the depth-RBE curve calculation from a passively scattered proton SOBP and monoenergetic 12C-ion beam by using experimentally validated spectra as physical input, and (ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons. Main results. MONAS can estimate dose-dependent RBE and cell survival curves from experimentally validated microdosimetric spectra with four clinically relevant radiobiological models. From the radiobiological characterization of a proton SOBP and 12C fields, we observe the well-known trend of increasing RBE values at the distal edge of the radiation field. The 3D RBE map calculated confirmed the trend observed in the analysis of the SOBP, with the highest RBE values found in the distal edge of the target. Significance. MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, pushing closer the experimental physics-based description to the biological damage assessment, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE.
First experimental evaluation of count-rate performance for micrometre resolution deep silicon detector
Authors: Jin Zihui , Brunskog Rickard , Danielsson Mats , Persson Mats
Published: January 2024

Objective. An ultra-fine-pitch deep silicon detector has been developed for clinical photon-counting computed tomography (CT). With a small pixel size of 14 × 650 μm2, it has shown potential to reach micrometre spatial resolution in previous simulation studies. A detector prototype with such geometry has been manufactured, and we report on the first experimental evaluation of its count-rate performance. Approach. The measurement was carried out at MAX IV synchrotron laboratory with 35 keV monochromatic x-rays. By inserting tungsten attenuators of 50, 75, 100, 150, 200, 225, 325

μ

m-thicknesses into the beam, the response of the detector to fluence rates from 3.3 × 107 to 1.3 × 1011 mm−2 s−1 was characterized. Main results. The measurement result showed that the detector exhibited count rate linearity up to 6.66 × 108 mm−2 s−1 with 13% count loss and was still functional at count rate up to 2.9 × 1010 mm−2 s−1. A semi-nonparalyzable dead-time model was fitted to the count-rate behaviour of the detector, showing great agreement with the measured data, with an estimated nonparalyzable dead time of 2.9 ns. Significance. This is the first experimental evaluation of the count-rate performance for a deep silicon detector with such small pixel geometry. The results suggest that this type of detector shows the potential to be used at fluence rates encountered in clinical CT with little count loss due to pile-up.
A generalized model for monitor units determination in ocular proton therapy using machine learning: A proof-of-concept study
Authors: Fleury Emmanuelle , Herault Joël , Spruijt Kees , Kouwenberg Jasper , Angellier Gaëlle , Hofverberg Petter , Horwacik Tomasz , Kajdrowicz Tomasz , Pignol Jean-Philippe , Hoogeman Mischa , Trnková Petra
Published: January 2024

Objective. Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pre-treatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments. Approach. To cope with the small number of patients being treated in proton centers, three European institutes participated in this study. Measurements data were collected to address output factor differences across the institutes, especially as function of field size, spread-out Bragg peak modulation width, residual range, and air gap. A generic model for monitor units prediction using a large number of 3748 patients and broad diversity in tumor patterns, was evaluated using six popular machine learning algorithms: (i) decision tree; (ii) random forest, (iii) extra trees, (iv) K-nearest neighbors, (v) gradient boosting, and (vi) the support vector regression. Features used as inputs into each machine learning pipeline were: Spread-out Bragg peak width, range, air gap, fraction and calibration doses. Performance measure was scored using the mean absolute error, which was the difference between predicted and real monitor units, as collected from institutional gold-standard methods. Main results. Predictions across algorithms were accurate within 3% uncertainty for up to 85.2% of the plans and within 10% uncertainty for up to 98.6% of the plans with the extra trees algorithm. Significance. A proof-of-concept of using machine learning-based generic monitor units determination in ocular proton therapy has been demonstrated. This could trigger the development of an independent monitor units calculation tool for clinical use.
LVONet: automatic classification model for large vessel occlusion based on the difference information between left and right hemispheres
Authors: Ma Yuqi , Chen Shanxiong , Xiong Hailing , Yao Rui , Zhang Wang , Yuan Jiang , Duan Haowei
Published: January 2024

Objective. Stroke is a highly lethal condition, with intracranial vessel occlusion being one of its primary causes. Intracranial vessel occlusion can typically be categorized into four types, each requiring different intervention measures. Therefore, the automatic and accurate classification of intracranial vessel occlusions holds significant clinical importance for assessing vessel occlusion conditions. However, due to the visual similarities in shape and size among different vessels and variations in the degree of vessel occlusion, the automated classification of intracranial vessel occlusions remains a challenging task. Our study proposes an automatic classification model for large vessel occlusion (LVO) based on the difference information between the left and right hemispheres. Approach. Our approach is as follows. We first introduce a dual-branch attention module to learn long-range dependencies through spatial and channel attention, guiding the model to focus on vessel-specific features. Subsequently, based on the symmetry of vessel distribution, we design a differential information classification module to dynamically learn and fuse the differential information of vessel features between the two hemispheres, enhancing the sensitivity of the classification model to occluded vessels. To optimize the feature differential information among similar vessels, we further propose a novel cooperative learning loss function to minimize changes within classes and similarities between classes. Main results. We evaluate our proposed model on an intracranial LVO data set. Compared to state-of-the-art deep learning models, our model performs optimally, achieving a classification sensitivity of 93.73%, precision of 83.33%, accuracy of 89.91% and Macro-F1 score of 87.13%. Significance. This method can adaptively focus on occluded vessel regions and effectively train in scenarios with high inter-class similarity and intra-class variability, thereby improving the performance of LVO classification.
LRENet: a location-related enhancement network for liver lesions in CT images
Authors: Guo Shuli , Wang Hui , Agaian Sos , Han Lina , Song Xiaowei
Published: January 2024

Objective. Liver cancer is a major global health problem expected to increase by more than 55% by 2040. Accurate segmentation of liver tumors from computed tomography (CT) images is essential for diagnosis and treatment planning. However, this task is challenging due to the variations in liver size, the low contrast between tumor and normal tissue, and the noise in the images. Approach. In this study, we propose a novel method called location-related enhancement network (LRENet) which can enhance the contrast of liver lesions in CT images and facilitate their segmentation. LRENet consists of two steps: (1) locating the lesions and the surrounding tissues using a morphological approach and (2) enhancing the lesions and smoothing the other regions using a new loss function. Main results. We evaluated LRENet on two public datasets (LiTS and 3Dircadb01) and one dataset collected from a collaborative hospital (Liver cancer dateset), and compared it with state-of-the-art methods regarding several metrics. The results of the experiments showed that our proposed method outperformed the compared methods on three datasets in several metrics. We also trained the Swin-Transformer network on the enhanced datasets and showed that our method could improve the segmentation performance of both liver and lesions. Significance. Our method has potential applications in clinical diagnosis and treatment planning, as it can provide more reliable and informative CT images of liver tumors.
Ultrafast diffusion tensor imaging based on deep learning and multi-slice information sharing
Authors: Wang Jiechao , Chen Zunquan , Cai Congbo , Cai Shuhui
Published: January 2024

Objective. Diffusion tensor imaging (DTI) is excellent for non-invasively quantifying tissue microstructure. Theoretically DTI can be achieved with six different diffusion weighted images and one reference image, but the tensor estimation accuracy is poor in this case. Increasing the number of diffusion directions has benefits for the tensor estimation accuracy, which results in long scan time and makes DTI sensitive to motion. It would be beneficial to decrease the scan time of DTI by using fewer diffusion-weighted images without compromising reconstruction quality. Approach. A novel DTI scan scheme was proposed to achieve fast DTI, where only three diffusion directions per slice was required under a specific direction switching manner, and a deep-learning based reconstruction method was utilized using multi-slice information sharing and corresponding T
1-weighted image for high-quality DTI reconstruction. A network with two encoders developed from U-Net was implemented for better utilizing the diffusion data redundancy between neighboring slices. The method performed direct nonlinear mapping from diffusion-weighted images to diffusion tensor. Main results. The performance of the proposed method was verified on the Human Connectome Project public data and clinical patient data. High-quality mean diffusivity, fractional anisotropy, and directionally encoded colormap can be achieved with only three diffusion directions per slice. Significance. High-quality DTI-derived maps can be achieved in less than one minute of scan time. The great reduction of scan time will help push the wider application of DTI in clinical practice.
Reconfigurable phononic crystal sensor for liquid detection
Authors: Zhu Tong , Wang Ting-Ting , Zhou Hong-Tao , Wang Yan-Feng , Wang Yue-Sheng
Published: January 2024

We propose a reconfigurable phononic crystal (PnC) for detecting the concentration of solutes in liquids. The designed PnC consists of liquid-filled hollow pillars and connecting bars. The finite element method is used to calculate the transmission spectra and band structures of PnC filled with various liquids. We fabricate 3D printed samples and conduct corresponding experiments. The results show that sound velocity is the key parameter affecting the frequency of the passing band. As the sound velocity increases, the resonance frequency shifts down. For both NaCl solution and ethanol solution, good linear relationships between the resonance frequency and liquid concentration are established. Experimental results show good agreement with simulations, and stable detection capabilities are maintained in the presence of interference. The impact of fabrication tolerances on sensor performance has also been discussed, with a greater impact on sensitivity and a smaller impact on Q-factor. The reconfigurability also shows the potential of the design of multi-liquid PnC sensors.
Modelling and simulation of a commercially available dielectric elastomer actuator
Authors: Sohlbach Lukas , Hobbani Hamza , Blase Christopher , Perez-Peña Fernando , Schmidt Karsten
Published: January 2024

To fully harness the potential of dielectric elastomer actuators (DEAs) in soft robots, advanced control methods are needed. An important groundwork for this is the development of a control-oriented model that can adequately describe the underlying dynamics of a DEA. Existing models commonly focus on custom-made DEAs, simplifying the modelling process due to well-known specifications and actuator structures. However, for commercial actuators, only information from the manufacturer is available, necessitating verification or completion during the modelling process. The aim of this paper is to explore how a commercial stacked silicone-based DEA can be modelled and how complex the model should be to properly replicate the features of the actuator. The static description has demonstrated the suitability of Hooke’s law. In the case of dynamic description, it is shown that no viscoelastic model is needed for control-oriented modelling. However, if all features of the DEA are considered, the generalised Kelvin–Maxwell model with three Maxwell elements shows good results, stability and computational efficiency.
Studies on the Arrott plots of inhomogeneous first order magnetic phase transitions
Authors: Vinod K , Sathyanarayana A T , Gangopadhyay P , Mani Awadhesh
Published: January 2024

First order ferromagnetic to paramagnetic phase transition is studied using the Bean-Rodbell model within the mean-field framework. The presence of sample inhomogeneities is modeled by considering distributions of the Bean-Rodbell parameter. The corresponding Arrott plots with and without distributions of the Bean-Rodbell parameter are generated and compared. Results indicate that in the presence of inhomogeneities, ferromagnetic to paramagnetic phase transition is broadened. The initial negative slopes in the Arrott plots due to first order phase transition changes to positive slopes for temperatures lying in the broadened tail region of the ferromagnetic to paramagnetic phase transitions. However, the initial negative slopes in the paramagnetic regime (for temperatures above the broadened tail region) are preserved even in the presence of sample inhomogeneities. Also, the effect of inhomogeneities on the magnetic entropy change near the first order transition is studied.
Thermodynamics, phase structure and quasinormal modes for AdS Heyward massive black hole
Authors: Singh Bhupendra , Veer Singh Dharm , Kumar Singh Benoy
Published: January 2024

In this paper, we find the new exact AdS black hole (BH) solution in the presence of massive gravity and nonlinear electrodynamics (NED). The obtained black hole solution (known as AdS Heyward massive BH ) interpolates with the AdS Heyward BH in the absence of graviton mass (m) and massive BH when the magnetic charge is switched off. We calculate the exact expression of thermodynamics quantities including local (heat capacity), global (free energy), and dynamical stability (quasinormal modes) of the obtained BH solution. The heat capacity of the BH diverges where the temperature is maximum and free energy is minimum. We also study the extended thermodynamics of the BH when the cosmological constant (Λ) is treated as the thermodynamics pressure (P = –Λ/8π). We analyze the first and second-order phase transition by studying the behavior of Gibbs free energy and these phase transitions are similar to the van der Walls phase transition. The effect of magnetic charge and graviton mass are opposite to each other on critical values.
Adsorption of NO and NO2 on MoSeS/GaN heterojunction: a first-principles study
Authors: Chen Hui , Pang Jianhua , Zhang Jiwei , Wei Guang , Wei Songrui , Wang Kuanyi , Yan Jin
Published: January 2024

The adsorption behaviors of gas molecules such as Cl2, CO, NO, NO2 and SO2 on MoSSe/GaN, as well as their adsorption energies, charge transfer and adsorption distances are investigated based on the first-principles calculation. The large adsorption energies of Cl2, NO, NO2 and SO2 on MoSSe/GaN indicate that the gas molecules have strong interaction forces with MoSSe/GaN. Probing the changes in the electronic structure, the adsorption of NO and NO2 on MoSSe/GaN introduces magnetism into their systems and reduces the band gaps. The magnetism mainly originates from the O atoms in NO and NO2, while the decrease of the band gap is due to the hybridization of orbitals near the bottom of the conduction band and the top of the valence band with the p orbitals of Ga atoms in MoSSe/GaN. This suggests that MoSSe/GaN can be applied to detect NO and NO2 gas molecules and has potential as a semiconductor gas sensor for these gases.
Proca stars with dark photons from spontaneous symmetry breaking of the scalar field dark matter
Authors: San.-Hernandez Leonardo , Matos Tonatiuh
Published: January 2024

Recently, the Scalar Field Dark Matter (SFDM) model (also known as Fuzzy, Wave, Bose-Einstein, Ultra-light
Dark Matter) has gained a lot of attention because it has provided simpler and more natural explanations for various phenomena observed in galaxies, as a natural explanation for the center of galaxies, the number of satellite galaxies around their host and, more recently, a natural explanation for anomalous trajectories of satellite galaxies called Vast Polar Orbits (VPO) observed in various galaxies. In the present work we study the assumption that the SFDM is a type of charged dark boson whose gauge charge is associated with the Dark Photon (DP). Inspired by these results, we study the formation of compact bosonic objects, such as Boson Stars (BS) and focus on the possibility that, due to spontaneous U(1) SFDM symmetry breaking, the DP may acquire mass and form compact objects like Proca Stars (PS). If this is true, we can expect measurable effects on the electromagnetic field of the Standard Model (SM) of particles due to their interaction with the DP on the formation of compact objects.
From regular black holes to horizonless objects: quasi-normal modes, instabilities and spectroscopy
Authors: Franzin Edgardo , Liberati Stefano , Vellucci Vania
Published: January 2024

We study gravitational and test-field perturbations for the two possible families of spherically symmetric black-hole mimickers that smoothly interpolate between regular black holes and horizonless compact objects accordingly to the value of a regularization parameter.
One family can be described by the Bardeen-like metrics, and the other by the Simpson-Visser metric.
We compute the spectrum of quasi-normal modes (QNMs) of these spacetimes enlightening a common misunderstanding regarding this computation present in the recent literature.
In both families, we observe long-living modes for values of the regularization parameter corresponding to ultracompact, horizonless configurations. Such modes appear to be associated with the presence of a stable photon sphere and are indicative of potential non-linear instabilities.
In general, the QNM spectra of both families display deviations from the standard spectrum of GR singular BHs.
In order to address the future detectability of such deviations in the gravitational-wave ringdown signal, we perform a preliminary study, finding that third generation ground-based detectors might be sensible to macroscopic values of the regularization parameter.
How effective is N
eff at discovering dark radiation in a cosmology with heavy particle decay?
Authors: Bleau Katarina , Bramante Joseph , Cappiello Christopher
Published: January 2024

Any light relic which was in thermal equilibrium with the Standard Model before it freezes out results in a shift in the effective number of neutrino species, N
eff. This quantity is being measured with increasing precision, and planned experiments would seemingly rule out light particles beyond the Standard Model, even for rather high temperature light particle freeze out. Here we explore how these bounds are loosened if the energy density of the light particles is diluted with respect to that of Standard Model radiation. This can happen if a heavy particle that is decoupled from the Standard Model decays into the Standard Model bath after the light particle freezes out. After calculating how heavy state decays alter N
eff for light particles beyond the Standard Model, we focus in particular on the case that the heavy decaying particle is a gravitino, and use current bounds on N
eff to place constraints on the gravitino mass and the branching ratio into light particles for different values of the reheating temperature of the Universe.
Coercivity mechanism of La–Nd–Fe–B films with Y spacer layer
Authors: Ma 马 Jun 俊 , Zhao 赵 Xiao-Tian 晓天 , Liu 刘 Wei 伟 , Li 李 Yang 阳 , Liu 刘 Long 龙 , Zhao 赵 Xin-Guo 新国 , Zhang 张 Zhi-Dong 志东
Published: January 2024

The effect of the Y spacer layer on the phase composition, coercivity, and magnetization reversal processes of La–Nd–Fe–B films has been investigated. The addition of a 10 nm Y spacer layer increases the coercivity of the film to 1.36 T at 300 K and remains 0.938 T at 380 K. As the thickness of the Y spacer layer increases, Y participates in the formation of the main phase in the film, and further regulates the formation of La–B phases. The results of the first-order reversal curve (FORC) and micromagnetic fitting show that the coercivity of all the films is dominated by nucleation mechanism. The c-axis preferred orientation, good magnetic microstructure parameters and the largest dipole interaction enhance the coercivity. Therefore, the introduction of the Y spacer layer can be an effective way to improve the coercivity of La–Nd–Fe–B film over a wide temperature range of 150 K–380 K.
Fish robotics: multi-fin propulsion and the coupling of fin phase, spacing, and compliance
Authors: Mignano Anthony P , Kadapa Shraman , Drago Anthony C , Lauder George V , Kwatny Harry G , Tangorra James L
Published: January 2024

Fish coordinate the motion of their fins and body to create the time-varying forces required for swimming and agile maneuvers. To effectively adapt this biological strategy for underwater robots, it is necessary to understand how the location and coordination of interacting fish-like fins affect the production of propulsive forces. In this study, the impact that phase difference, horizontal and vertical spacing, and compliance of paired fins had on net thrust and lateral forces was investigated using two fish-like robotic swimmers and a series of computational fluid dynamic simulations. The results demonstrated that the propulsive forces created by pairs of fins that interact through wake flows are highly dependent on the fins’ spacing and compliance. Changes to fin separation of less than one fin length had a dramatic effect on forces, and on the phase difference at which desired forces would occur. These findings have clear implications when designing multi-finned swimming robots. Well-designed, interacting fins can potentially produce several times more propulsive force than a poorly tuned robot with seemingly small differences in the kinematic, geometric, and mechanical properties.
Influence of posture during gliding flight in the flying lizard Draco volans
Authors: Buffa Valentin , Salaün William , Cinnella Paola
Published: January 2024

The agamid lizards of the genus Draco are undoubtedly the most renown reptilian gliders, using their rib-supported patagial wings as lifting surfaces while airborne. Recent investigations into these reptiles highlighted the role of body posture during gliding, however, the aerodynamics of postural changes in Draco remain unclear. Here, we examine the aerodynamics and gliding performances of Draco volans using a numerical approach focusing on three postural changes: wing expansion, body camber, and limb positioning. To this aim, we conducted 70 three-dimensional steady-state computational fluid dynamics simulations of gliding flight and 240 two-dimensional glide trajectory calculations. Our results demonstrate that while airborne, D. volans generates a separated turbulent boundary layer over its wings characterized by a large recirculation cell that is kept attached to the wing surface by interaction with wing–tip vortices, increasing lift generation. This lift generating mechanism may be controlled by changing wing expansion and shape to modulate the generation of aerodynamic force. Furthermore, our trajectory simulations highlight the influence of body camber and orientation on glide range. This sheds light on how D. volans controls its gliding performance, and conforms to the observation that these animals plan their glide paths prior to take off. Lastly, D. volans is mostly neutral in pitch and highly maneuverable, similar to other vertebrate gliders. The numerical study presented here thus provides a better understanding of the lift generating mechanism and the influence of postural changes in flight in this emblematic animal and will facilitate the study of gliding flight in analogous gliding reptiles for which direct observations are unavailable.
In vitro analysis of insoluble salt formation mechanism associated with Mg corrosion—variations depending on the diffusion environment in model tissue
Authors: Hayashi Masanobu , Yamamoto Akiko , Aizawa Takayuki , Yusa Yu , Shimizu Yoshinaka , Imai Yoshimichi
Published: January 2024

Magnesium (Mg) alloys have attracted attention as biodegradable metals, but the details of their corrosion behavior under biological environment have not been elucidated. Previous studies have suggested that diffusion through blood flow may influence Mg corrosion. Therefore, to understand the degradation behaviors of Mg, we analyzed insoluble salt precipitation associated with Mg corrosion in model tissue with different diffusion rates. A pure Mg specimen was immersed into a model tissue prepared with cell culture medium supplemented by a thickener at a different concentration (0.2%–0.5%) to form the gel. Micro-focus x-ray computed tomography of the gel was performed to observe gas cavity formation around the specimen. The insoluble salt layer formed on the specimen surface were analyzed by scanning electron microscopy with energy-dispersive x-ray spectroscopy, and Raman spectroscopy. As results, gas cavity formation was observed for all specimens. At day 7, the gas cavity volume was the highest at 0.5% thickener gel followed by 0.3% thickener gel. The insoluble salts were classified into three types based on their morphology; plate-like, granular-like, and crater-like salts. The crater-like salts were observed to cover 16.8 ± 3.9% of the specimen surface immersed in the 0.5% thickener gel, at the specimen area contacted to the gas cavity. The crater-like salts were composed by Mg hydroxide and carbonate from the deepest to the top layer. In plate-like or granular-like salts, Mg carbonate was formed in the deepest layer, but phosphates and carbonates, mainly containing calcium not Mg, were formed on the surface layer. In conclusion, the increase in the thickener concentration increased the gas cavity volume contacting to the specimen surface, resulting in the increase in precipitation of Mg hydroxide and carbonate, composing crater-like salts. Mg hydroxide and carbonate precipitation suggests the local increase in OH− concentration, which may be attributed to the decrease in diffusion rate.
Regional patterns and trends of compound hot extremes in mainland China: a new objective approach
Authors: Li Yiping , Wu Zhiwei
Published: January 2024

Compound hot extremes (CHEs) are receiving increasing attention due to their significant impacts on human health, ecosystems, and society compared to individual hot days or nights. While previous studies have focused on the characteristics of CHEs at individual points or stations, assessments of features for regional CHEs (RCHEs), which have a specific impact area and duration, are still lacking. This study aimed to investigate the climatic characteristics of RCHEs in mainland China by applying an objective identification technique for regional extreme events based on a compound index. The results show that 379 RCHEs were identified during 1961–2020, most of the events had a duration of 5–11 d and a maximum impacted area of approximately 460 104 km2. Long-duration RCHEs were found to have vigorous extreme intensity and large maximum impacted area. The middle and lower reaches of the Yangtze River were most susceptible to RCHEs, while the Yellow River Valley had the most robust positive trend of frequency for RCHEs, suggesting a significant risk of compound temperature disasters in this region. Furthermore, RCHEs in mainland China showed significant increasing trends in several aspects, such as annual frequency, integrated index, and single indices (e.g. duration, accumulated intensity, accumulated impacted area, and extreme intensity). These upward trends were accompanied by evident interdecadal variations, with low values before 1992 and high values after 1992. This study provides valuable insights into understanding and monitoring CHEs in China from the perspective of regional extremes.