bluuebunny/crossref_metadata_2025 huggingface dataset:
Cooperative Care Does Not Scare – Use of Cooperative Care Training in Routine Husbandry in Dogs
Authors: Sydänheimo Anna , Freeman Marianne , Hunt Kerry
Published: August 2023

Empowering animals with choice, which increases predictability and control during husbandry procedures, has the potential to increase their well-being and the safety of the handler. This case study will consider differences in dogs’ clinical signs indicating stress during nail clipping when done with or without choice and control. Dogs were in two independent groups based on owner’s current husbandry routine – group 1, where the dogs had been trained with choice and were not restrained during nail clipping and group 2, where the dogs were restrained during nail clipping. Group 2 showed more stress signals. These findings suggest that offering choice and control to a dog during husbandry procedures will most likely lower the clinical sign of stress, possibly having a beneficial impact on their own and their handler’s welfare.

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Introducing Two Adult Cats
Authors: Chamberlain Sally
Published: August 2023

This case study describes the process of introducing two adult cats slowly and carefully to provide the greatest chance of long-term success in their integration. The cats in this case study, Bella and Lola, were first introduced using a method that involved feeding them on either side of a glass door. The cats did not respond well to this and there was some tension and aggressive behaviour between them, particularly with Lola chasing and attacking Bella when her back was turned. By changing the way the cats feel about one another using a different introduction method, they are now at the stage where they can spend time together in the same room quite comfortably.

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© The Author 2023
A novel micro-device for simultaneous separation-trapping and double-trapping of particles by using dielectrophoresis: numerical and experimental study
Authors: Aghdasi M , Nazari M , Yonesi Sareh
Published: August 2023

Dielectrophoretic (DEP) force is a useful tool for manipulating particles in microfluidic systems. It is affected by the frequency of the applied electric field, which can be varied to produce repellent and attractive forces depending on the dielectric properties of particles and the media. In this work, two electric fields with different frequency are used to simultaneously separate and trap particles as well as double-trap particles by utilizing the DEP force. Initially, a single-vial microchannel was proposed to study the impact of the frequency and voltage on three types of electrodes: concentrator, repellent, and absorbing. The goal was to examine their efficacy in trapping a group of particles within the vial while separating and ejecting another group of particles from the microchannel. Performance graphs were used to determine the optimal voltages for the electrodes. Subsequently, an additional vial is incorporated into the microchannel to enable the double-trapping of particles with varying sizes and properties. With the optimal design, particles of varying sizes and properties can be trapped in separate vials within the microchannel. For the first time, the performance cartography of the proposed system has been assessed, enabling the identification of the optimal values and intelligent separations. Validation is conducted in two steps. Firstly, numerical findings are compared to previous experimental results to verify the accuracy of the numerical approach. Secondly, a microchip is fabricated, tested, and compared to numerical results using yeast cells to assess system efficiency and enhance the reliability of the numerical technique.
Tunable electronic and optical properties of ferroelectric WS2/Ga2O3 heterostructures
Authors: Wei Dong , Li Yi , Guo Gaofu , Yu Heng , Ma Yaqiang , Tang Yanan , Feng Zhen , Dai Xianqi
Published: August 2023

To integrate two-dimensional (2D) materials into van der Waals heterostructures (vdWHs) is regarded as an effective strategy to achieve multifunctional devices. The vdWHs with strong intrinsic ferroelectricity is promising for applications in the design of new electronic devices. The polarization reversal transitions of 2D ferroelectric Ga2O3 layers provide a new approach to explore the electronic structure and optical properties of modulated WS2/Ga2O3 vdWHs. The WS2/Ga2O3↑ and WS2/Ga2O3↓ vdWHs are designed to explore possible characteristics through the electric field and biaxial strain. The biaxial strain can effectively modulate the mutual transition of two mode vdWHs in type II and type I band alignment. The strain engineering enhances the optical absorption properties of vdWHs, encompassing excellent optical absorption properties in the range from infrared to visible to ultraviolet, ensuring promising applications in flexible electronics and optical devices. Based on the highly modifiable physical properties of the WS2/Ga2O3 vdWHs, we have further explored the potential applications for the field-controlled switching of the channel in MOSFET devices.
Tightly coupled laser-inertial pose estimation and map building based on B-spline curves
Authors: He Guojian , Liu Yisha , Li Chengxiang
Published: August 2023

Simultaneous localization and mapping (SLAM) plays a key role in 3D environment modeling and mobile robot environment perception. However, the traditional discrete-time Laser-inertial SLAM methods are not robust due to the imbalanced registration steps between a single LiDAR frame and the global map. This paper proposes a tightly coupled laser-inertial pose estimation and map building method that uses B-spline curves to represent continuous-time trajectory and achieve high robustness of the registration steps. To ensure efficiency, the proposed method separates the SLAM task into an odometer module and a mapping module. The odometer module performs a coarse pose estimation, while the mapping module performs a fine one and builds a global map with 3D LiDAR points. B-spline curves are utilized to integrate both IMU measurement constraints and LiDAR point constraints in the proposed mapping module, which can enhance the association of consecutive LiDAR frames in the optimization step. Besides, the explicit expression of the Jacobi matrix derivation for B-spline-based laser residuals is also introduced to furtherly improve the computation efficiency. Both indoor and outdoor experiments are conducted on a self-collected dataset and a public dataset. Experimental results show that the proposed method can achieve superior performance than the baseline method LIO-mapping.
Similarity evaluation method of single flow point energy consumption mapping based on Fréchet distance
Authors: Chen Guoyu , Lan Haibei , Yan Yipu , Peng Yanhua
Published: August 2023

Aiming at the problem of inaccurate measurement results caused by factors such as gas leakage during the calibration of the standard meter gas flow standard device of the same type and small diameter, this paper proposes a similarity assessment method of single flow point energy consumption mapping based on Fréchet distance (FD). First, through the statistical analysis of the historical energy consumption data of gas flow standard device, a discrete standard energy consumption mapping is formed. Next, the energy consumption mapping to be tested is standardized and the similarity of the energy consumption mapping is determined by the binary matrix accelerated search of the FD between the energy consumption mapping to be tested and the standard energy consumption mapping. Finally, the similarity threshold is calculated by combining the standard deviation of the FD and the reliability of the gas flowmeter calibration process is evaluated accordingly. The test shows that the method in this paper can effectively determine the reliability of the gas flowmeters calibration results.
Field-effect transistors engineered via solution-based layer-by-layer nanoarchitectonics
Authors: Azzaroni Omar , Piccinini Esteban , Fenoy Gonzalo , Marmisollé Waldemar , Ariga Katsuhiko
Published: August 2023

The layer-by-layer (LbL) technique has been proven to be one of the most versatile approaches in order to fabricate functional nanofilms. The use of simple and inexpensive procedures as well as the possibility to incorporate a very wide range of materials through different interactions have driven its application in a wide range of fields. On the other hand, field-effect transistors (FETs) are certainly among the most important elements in electronics. The ability to modulate the flowing current between a source and a drain electrode via the voltage applied to the gate electrode endow these devices to switch or amplify electronic signals, being vital in all of our everyday electronic devices. In this topical review, we highlight different research efforts to engineer field-effect transistors using the LbL assembly approach. We firstly discuss on the engineering of the channel material of transistors via the LbL technique. Next, the deposition of dielectric materials through this approach is reviewed, allowing the development of high-performance electronic components. Finally, the application of the LbL approach to fabricate FETs-based biosensing devices is also discussed, as well as the improvement of the transistor’s interfacial sensitivity by the engineering of the semiconductor with polyelectrolyte multilayers.
Pd/Ni bimetallic modification of SrTiO3 for enhancement of photocatalytic water splitting
Authors: Bai Penghui , Lei Kai , Xie Juan , Wang Hu , Kang Xiaolan , Wang Xia
Published: August 2023

This paper investigates the impact of Pd/Ni modification on the photocatalytic hydrogen production performance of SrTiO3 (STO). STO catalysts were synthesized using a hydrothermal method, and Pd/Ni modification was applied on the surface of STO through chemical deposition. Experimental results demonstrate that the hydrogen evolution rate of Pd/Ni-modified STO (Pd/Ni-STO) reaches 2232.14 μmol g−1 h−1. X-ray absorption fine structure spectroscopy analysis reveals substitutional doping of Ni with Ti and coordination of Pd with surface O. X-ray photoelectron spectroscopy analysis indicates the introduction of oxygen vacancies due to Pd/Ni doping. Density functional theory calculations suggest that Ni doping activates neighboring Ti atoms, leading to the formation of bimetallic catalytic sites composed of oxygen vacancies and Ti atoms, greatly enhancing the photocatalytic hydrogen evolution performance. This study not only provides an effective catalyst for photocatalytic applications but also offers insights into the underlying mechanism, which may stimulate the development of metal-doped catalytic materials and have implications for a range of other applications.
Hyperparameter optimization and development of an advanced CNN-based technique for lung nodule assessment
Authors: Shivwanshi Resham Raj , Nirala Neelamshobha
Published: August 2023

Objective. This paper aims to propose an advanced methodology for assessing lung nodules using automated techniques with computed tomography (CT) images to detect lung cancer at an early stage. Approach. The proposed methodology utilizes a fixed-size 3 × 3 kernel in a convolution neural network (CNN) for relevant feature extraction. The network architecture comprises 13 layers, including six convolution layers for deep local and global feature extraction. The nodule detection architecture is enhanced by incorporating a transfer learning-based EfficientNetV_2 network (TLEV2N) to improve training performance. The classification of nodules is achieved by integrating the EfficientNet_V2 architecture of CNN for more accurate benign and malignant classification. The network architecture is fine-tuned to extract relevant features using a deep network while maintaining performance through suitable hyperparameters. Main results. The proposed method significantly reduces the false-negative rate, with the network achieving an accuracy of 97.56% and a specificity of 98.4%. Using the 3 × 3 kernel provides valuable insights into minute pixel variation and enables the extraction of information at a broader morphological level. The continuous responsiveness of the network to fine-tune initial values allows for further optimization possibilities, leading to the design of a standardized system capable of assessing diversified thoracic CT datasets. Significance. This paper highlights the potential of non-invasive techniques for the early detection of lung cancer through the analysis of low-dose CT images. The proposed methodology offers improved accuracy in detecting lung nodules and has the potential to enhance the overall performance of early lung cancer detection. By reconfiguring the proposed method, further advancements can be made to optimize outcomes and contribute to developing a standardized system for assessing diverse thoracic CT datasets.
Analysis of surface morphology at leakage current sources on large-area GaN-based p-i-n diodes
Authors: Yang Qian , Yang Jing , Liu Zongshun , Duan Lihong , Zhao Degang
Published: August 2023

This paper investigated the relationship between the leakage current of GaN-based diodes with the large size of 8 × 8 mm2 under reverse bias and their responsivity, and it was found that reducing leakage current is an important way to improve the conversion efficiency of diodes. Leakage analysis was performed separately for two sizes of diodes. The location of the leakage was found by emission microscope, and the morphology of the leakage point was analyzed using a focused ion beam and a scanning electron microscope. It was found that structural defects, which may be caused by dropping substances during material growth, can form large leakage channels.
A soft piezoelectric elastomer with enhanced piezoelastic response
Authors: Nicolini Lorenzo , Sorrentino Andrea , Castagnetti Davide
Published: August 2023

This work aims to study, develop, and validate a soft piezo-polymer with enhanced piezo-elastic response and easy castable in a free shape through a single and easy process. The work identified a novel formulation for soft piezopolymers based on ambient temperature polymerizable silicone rubber, easily fabricable in 3D printed plastic moulds. Combining polymerizable silicone with a barium titanate (BaTiO3) ceramic powder and defining a detailed fabrication procedure of casting, curing and high voltage poling, we defined how to obtain a promising soft piezoelectric elastomer for countless sensing applications. This study includes information about the mould design used to realize, cure and polarize cylindric elastomeric specimens. This piezopolymer stands out for its flexibility, softness, easy fabrication at ambient temperature and obtainability in multiple shapes and bulky 3D geometries. Finally, we investigated different configurations of the piezopolymer formulation analysing the powder concentration and voltage polarization effects over the mechanical, piezoelectric and morphological characteristics. The specimens exhibit a high induced polarization

d

33

with values up to 22.5 pC N−1, comparable with poled

β

-phase polyvinylidene difluoride. We finally underlined limits encountered in the most extreme configurations.
Breakdown of quantization in nonlinear Thouless pumping
Authors: Tuloup T , Bomantara R W , Gong J
Published: August 2023

The dynamics of solitons driven in a nonlinear Thouless pump and its connection with the system’s topology were recently explored for both weak and strong nonlinear strength. Using both a self-consistent algorithm and 4th order Runge Kutta method, this work uncovers the fate of nonlinear Thouless pumping in the regime of intermediate nonlinearity, thus establishing a fascinating crossover from the observation of nonzero and quantized pumping at weak nonlinearity to zero pumping at strong nonlinearity. We identify the presence of critical nonlinearity strength at which quantized pumping of solitons breaks down regardless of the protocol time scale. Such an obstruction to pumping quantization is attributed to the presence of self-crossing in nonlinear topological bands. By considering another type of pumping involving Bloch states, we further show how the presence of self-crossing bands also leads to breakdown of quantization, but in a completely different manner from that in the case of soliton pumping. Our results not only unveil a missing piece of physics in nonlinear Thouless pumping, but also provide a means to detect loop structures of nonlinear systems investigated in real space and momentum space.
Learning phase transitions from regression uncertainty: a new regression-based machine learning approach for automated detection of phases of matter
Authors: Guo Wei-chen , He Liang
Published: August 2023

For performing regression tasks involved in various physics problems, enhancing the precision or equivalently reducing the uncertainty of regression results is undoubtedly one of the central goals. Here, somewhat surprisingly, the unfavorable regression uncertainty in performing the regression tasks of inverse statistical problems is found to contain hidden information concerning the phase transitions of the system under consideration. By utilizing this hidden information, a new unsupervised machine learning approach was developed in this work for automated detection of phases of matter, dubbed learning from regression uncertainty. This is achieved by revealing an intrinsic connection between regression uncertainty and response properties of the system, thus making the outputs of this machine learning approach directly interpretable via conventional notions of physics. It is demonstrated by identifying the critical points of the ferromagnetic Ising model and the three-state clock model, and revealing the existence of the intermediate phase in the six-state and seven-state clock models. Comparing to the widely-used classification-based approaches developed so far, although successful, their recognized classes of patterns are essentially abstract, which hinders their straightforward relation to conventional notions of physics. These challenges persist even when one employs the state-of-the-art deep neural networks (NNs) that excel at classification tasks. In contrast, with the core working horse being an NN performing regression tasks, our new approach is not only practically more efficient, but also paves the way towards intriguing possibilities for unveiling new physics via machine learning in a physically interpretable manner.
Deviations and similarities between landauer’s approach and the multi-electronic classical master equation in describing nanoscale transport
Authors: Moreira Augusto C L , de Melo Celso P
Published: August 2023

In this work, we show that the classical master equation (CME) treatment—with the rates obtained via the Fermi golden rule—and the elastic scattering (ES) approach give the same results for a system composed of two states/one level when considering the approximations of (i) non-interacting limit, i.e., the electronic structure of the N-particle states remains frozen even in the presence of an extra particle, (ii) wide-band limit (WBL) approximation, and (iii) excited states are discarded. Although the predictions of these two approaches ‘deviate’ from each other when more states and/or levels are considered, under the conditions of strong coupling limit and symmetric contacts both treatments capture the same physics involved in the transport process. For other situations—such as asymmetric coupling and/or weak metal–organic coupling—the predictions of these two theories do not agree with each other. Finally, even considering that in our treatment the electronic structure of the system is described at a tight binding level, the corresponding results clearly show the situations where the similarities and differences between the CME and ES approaches can be identified.
Shake-off effect on the portions of energy reemitted by gold atom upon the cascade decay of vacancies in its K to O shells
Authors: Chaynikov A P , Kochur A G , Dudenko A I , Yavna V A
Published: August 2023

Cascade decays of single vacancies in K to O shells of the gold atom are simulated by straightforward construction and analysis of the cascade decay trees. Portions of energies (a) absorbed by initially ionized Au atom, (b) reemitted by cascade-produced electrons, and (c) reemitted by cascade-produced photons are calculated with taking into account the shake-off processes caused by the change of atomic core potential during cascade transitions. Au atom absorbs noticeable amount of energy acquired in photoionization act only when ionized in its N and O shells, 33%–45% and 41%–54%, respectively. Except for the case of K-ionization, most of the energy is reemitted by cascade electrons: 57%–64%, 77%–85%, 55%–67%, and 46%–59% during the decay of vacancies in L, M, N, and O shells. Energies carried away by the cascade photons are only important for initial vacancies in K (89%) and L (29%–39%) shells. Inclusion of the shake-off branches in the decay trees increase substantially their complexity, however it leads to comparatively small corrections to calculated portions of energy redistributed through channels a, b, and c. This supports earlier qualitative prediction of small possible effect of the SO processes on the cascades in heavy atoms.
Sensing and control the static deflection of a cantilever beam by using piezoelectric patches
Authors: Kumar Lokesh , Bagha Ashok Kumar , Singh Kiran , Kumar Sanjay , Kumar Sanjeev
Published: August 2023

Different mechanical structures when comes under static and dynamic loadings then they respond. It is very important to sense it so that the dynamic behavior of the structure in terms of natural frequencies, its mode shapes, and viscous damping coefficients can be successfully predicted or measured. The accurate prediction of the dynamic response of the oscillating structure always plays an important role to design its physical parameters. However, in this paper, the simulated and experimental study is conducted to accurately sense the static deflection of the cantilever beam by using piezoelectric patches. The accurate prediction of the static deflection of the beam and its control through piezoelectric patches is the novelty of the present work. Mostly, the finite element model of the beam is developed to predict its dynamic behavior. The finite element modeling provides an approximate solution. In this regard, the finite element model updating technique such as the direct updating algorithm is used in this study to correct the simulated finite element model of the beam. The updated simulated finite element model is then used to predict the accurate value of the static deflection. It is observed that the maximum percentage error between the predicted and measured static deflection is 29.1%. The updated simulated finite element model is also used to design the controller. The direct output feedback controller is developed and it is found that the static deflection of the beam has been reduced by 46.65%.
Some physical analysis of quaternary CuNiSbS powder for optoelectronic applications
Authors: Saleh Hisham A , Azab A A , Dahy T M
Published: August 2023

Novel quaternary CuNiSbS chalcogenide material has been successfully prepared using a simple co-precipitation method using a mixture of solutions of the constituent elements in a chemical formula.The structure and morphological characterizations of the prepared material in powder form wereperformed via x-ray diffraction, a High-resolution transmission electron microscope (HRTEM), and a Scanning electron microscope (SEM). FTIR and diffuse reflectance spectrum of the prepared material were conducted to provide valuable information about the main absorption bands besides the energy optical band gap of the material. ACconductivity and the dielectric properties of the prepared materialusingbroadband dielectricspectroscopy (BDS) besides a magnetic analysis via a vibrating sample magnetometer (VSM) were also investigated.
A foreground-immune CMB-cluster lensing estimator
Authors: Levy Kevin , Raghunathan Srinivasan , Basu Kaustuv
Published: August 2023

Galaxy clusters induce a distinct dipole pattern in the cosmic microwave back-ground (CMB) through the effect of gravitational lensing.
Extracting this lensing signal will enable us to constrain cluster masses, even for high redshift clusters (z ≳ 1) that are expected to be detected by future CMB surveys.
However, cluster-correlated foreground signals, like the kinematic and thermal Sunyaev-Zel'dovich (kSZ and tSZ) signals, present a challenge when extracting the lensing signal from CMB temperature data.
While CMB polarization-based lensing reconstruction is one way to mitigate these foreground biases, the sensitivity from CMB temperature-based reconstruction is expected to be similar to or higher than polarization for future surveys.
In this work, we extend the cluster lensing estimator developed in [1] to CMB temperature and test its robustness against systematic biases from foreground signals.
We find that the kSZ signal only acts as an additional source of variance and provide a simple stacking-based approach to mitigate the bias from the tSZ signal.
Additionally, we study the bias induced due to uncertainties in the cluster positions and show that they can be easily mitigated.
The estimated signal-to-noise ratio (SNR) of this estimator is comparable to other standard lensing estimators such as the maximum likelihood (MLE) and quadratic (QE) estimators.
We predict the cluster mass uncertainties from CMB temperature data for current and future cluster samples to be: 6.6% for SPT-3G with 7,000 clusters, 4.1% for SO and 3.9% for SO + FYST with 25,000 clusters, and 1.8% for CMB-S4 with 100,000 clusters.
Rotating black holes embedded in a cosmological background for scalar-tensor theories
Authors: Babichev Eugeny , Charmousis Christos , Lecoeur Nicolas
Published: August 2023

We present solutions of DHOST theories describing a rotating black hole embedded in an
expanding universe. The solution is constructed by conformal transformation of a stealth Kerr(-de
Sitter) black hole. The conformal factor depends explicitly on the scalar field — but not on its
derivative — and defines the new theory. The scalar field of the stealth Kerr(-de Sitter)
solution depends on time, leading to the time-dependence of the obtained conformal metric, with
cosmological asymptotics at large distances. We study the properties of the obtained metric by
considering regular null geodesic congruences, and identify trapping black hole and
cosmological horizons.
Terahertz toroidal dipole metamaterial sensors for detection of aflatoxin B1
Authors: Xu 徐 Jianwei 建伟 , Ouyang 欧 Shoujian 阳收剑 , Duan 段 Shouxin 守鑫 , Zou 邹 Liner 林儿 , Ye 叶 Danni 丹妮 , Yang 杨 Sijia 思嘉 , Deng 邓 Xiaohua 晓华
Published: August 2023

Terahertz metamaterial biosensors have attracted significant attention in the biological field due to their advantages of label-free, real-time and in situ detection. In this paper, a highly sensitive metamaterial sensor with semi-ring mirror symmetry based on toroidal dipole resonance is designed for a new metamaterial biosensor. It is shown that a refractive index sensitivity of 337.5 GHz per refractive index unit can be achieved under an analyte of saturated thickness near a 1.33 THz transmission dip. For biosensor samples where aflatoxin B1 is dropped on the metamaterial surface in our experiment, dip amplitudes of transmission varying from 0.1904 to 0.203 and 0.2093 are observed as aflatoxin B1 concentrations are altered from 0 to 0.001 μg ⋅ml−1 and to 0.01 μg ⋅ml−1, respectively. Furthermore, when aflatoxin B1 concentrations are 0.1 μg ⋅ml−1, 1 μg ⋅ml−1, 10 μg ⋅ml−1 and 100 μg ⋅ml−1, dip amplitudes of 0.2179, 0.226, 0.2384 and 0.2527 and dip redshifts of 10.1 GHz, 20.1 GHz, 27.7 GHz and 37.6 GHz are respectively observed. These results illustrate high-sensitivity, label-free detection of aflatoxin B1, enriching the applications of sensors in the terahertz domain.
Reconstructing in vivo spatially offset Raman spectroscopy of human skin tissue using a GPU-accelerated Monte Carlo platform
Authors: Zhang 张 Yun-He 云鹤 , Zhu 朱 Huan-Zheng 桓正 , Dong 董 Yong-Jiang 泳江 , Zeng 曾 Jia 佳 , Han 韩 Xin-Peng 新鹏 , A. Bratchenko Ivan , Zhang 张 Fu-Rong 富荣 , Xu 许 Si-Yuan 思源 , Wang 王 Shuang 爽
Published: August 2023

As one type of spatially offset Raman spectroscopy (SORS), inverse SORS is particularly suited to in vivo biomedical measurements due to its ring-shaped illumination scheme. To explain inhomogeneous Raman scattering during in vivo inverse SORS measurements, the light–tissue interactions when excitation and regenerated Raman photons propagate in skin tissue were studied using Monte Carlo simulation. An eight-layered skin model was first built based on the latest transmission parameters. Then, an open-source platform, Monte Carlo eXtreme (MCX), was adapted to study the distribution of 785 nm excitation photons inside the model with an inverse spatially shifted annular beam. The excitation photons were converted to emission photons by an inverse distribution method based on excitation flux with spatial offsets Δs of 1 mm, 2 mm, 3 mm and 5 mm. The intrinsic Raman spectra from separated skin layers were measured by continuous linear scanning to improve the simulation accuracy. The obtained results explain why the spectral detection depth gradually increases with increasing spatial offset, and address how the intrinsic Raman spectrum from deep skin layers is distorted by the reabsorption and scattering of the superficial tissue constituents. Meanwhile, it is demonstrated that the spectral contribution from subcutaneous fat will be improved when the offset increases to 5 mm, and the highest detection efficiency for dermal layer spectral detection could be achieved when Δs = 2 mm. Reasonably good matching between the calculated spectrum and the measured in vivo inverse SORS was achieved, thus demonstrating great utility of our modeling method and an approach to help understand the clinical measurements.
Localized estimation of electromagnetic sources underlying event-related fields using recurrent neural networks
Authors: O’Reilly Jamie A , Zhu Judy D , Sowman Paul F
Published: August 2023

Objective. To use a recurrent neural network (RNN) to reconstruct neural activity responsible for generating noninvasively measured electromagnetic signals. Approach. Output weights of an RNN were fixed as the lead field matrix from volumetric source space computed using the boundary element method with co-registered structural magnetic resonance images and magnetoencephalography (MEG). Initially, the network was trained to minimise mean-squared-error loss between its outputs and MEG signals, causing activations in the penultimate layer to converge towards putative neural source activations. Subsequently, L1 regularisation was applied to the final hidden layer, and the model was fine-tuned, causing it to favour more focused activations. Estimated source signals were then obtained from the outputs of the last hidden layer. We developed and validated this approach with simulations before applying it to real MEG data, comparing performance with beamformers, minimum-norm estimate, and mixed-norm estimate source reconstruction methods. Main results. The proposed RNN method had higher output signal-to-noise ratios and comparable correlation and error between estimated and simulated sources. Reconstructed MEG signals were also equal or superior to the other methods regarding their similarity to ground-truth. When applied to MEG data recorded during an auditory roving oddball experiment, source signals estimated with the RNN were generally biophysically plausible and consistent with expectations from the literature. Significance. This work builds on recent developments of RNNs for modelling event-related neural responses by incorporating biophysical constraints from the forward model, thus taking a significant step towards greater biological realism and introducing the possibility of exploring how input manipulations may influence localised neural activity.
Core transport modelling of the DTT full power scenario using different fuelling strategies
Authors: Baiocchi B. , Aucone L. , Casiraghi I. , Figini L. , Koechl F. , Mantica P.
Published: August 2023

A theory-based integrated modelling work of plasma response to deuterium fuelling in the new Divertor Tokamak Test facility (DTT) is performed, using the 1.5D transport code JETTO with the quasi-linear anomalous transport model QuaLiKiz for the core region. The full power DTT scenario E1 is investigated. It is characterised by 28.8 MW of Electron Cyclotron Resonance Heating, 10 MW of Neutral Beam Injection and 6 MW of Ion Cyclotron Resonance Heating to the plasma. Plasma density and temperature profile evolution is calculated up to the separatrix using two different fuelling methods, gas puffing and pellet injection, and two different seeding gases, argon and neon. To sustain the desired pedestal density level with gas puffing a big amount of neutral flux at the separatrix is needed. The feasibility limits of the pumping system are exceeded, regardless of the type of impurity introduced, thus making the use of pellets mandatory. The simulations performed with pellet injection as fuelling method predict that the pedestal density is well sustained with realistic parameters foreseen for the DTT pellet injector. Strong dependence of the core density on the electron cyclotron resonance (ECR) power deposition profile is found. Trapped Electron Modes dominance, low outward flux and strongly hollow density in the inner core region are foreseen with central peaked ECR power deposition profile. Ion Temperature Gradient modes dominance, inward flux and robust density sustainment on the whole radial interval are predicted for spread ECR power deposition, though with central density close to the ECR cut-off limit and with peaked impurity densities. An intermediate deposition extension is found to sustain the whole density profile and to obtain flatter core densities, as previously predicted for the reference full power DTT scenario by fixed pedestal simulations. The ECR deposition is negligibly modified by refraction changes both during a single pellet cycle and after several pellet cycles, indicating full compatibility between the ECR system and the pellet injection system.
Transition in particle transport under resonant magnetic perturbations in a tokamak
Authors: Kim S.K. , Logan N.C. , Becoulet M. , Hoelzl M. , Hu Q. , Huijsmans G.T.A. , Pamela S.J.P. , Yu Q. , Yang S.M. , Paz-soldan C. , Kolemen E. , Park J.-K.
Published: August 2023

Nonlinear 3D MHD simulations and validations reveal that the hybrid particle-MHD transport is a key process for driving the pump-out in the presence of Resonant Magnetic Perturbations (RMPs) in the KSTAR tokamak. Particle transport and the resulting density pump-out by RMPs are shown to be composed of not only the classical flow convection near magnetic islands due to polarization but also the neoclassical ion diffusion across perturbed magnetic surfaces. The latter is known as the Neoclassical Toroidal Viscosity (NTV) and is integrated into nonlinear MHD simulations here for the first time, revealing that the two-stage pump-outs observed in KSTAR experiments are reproduced only with such integrated nonlinear MHD and transport evolution. Near-resonant responses, which have received less attention than the resonant response, play distinct roles in the pump-out along with the island formation. In addition, this modeling is used to investigate the pump-outs in double-null-like plasmas and numerically capture the effect of the double-null shape on the pump-outs, which may explain the difficulty of Edge Localized Mode (ELM) suppression access in double-like plasmas. This reveals new aspects of the impact toroidal geometry and mode coupling have on 3D physics and reveals the importance of near-resonant components in suppressing ELMs.
3D vascularized microphysiological system for investigation of tumor-endothelial crosstalk in anti-cancer drug resistance
Authors: Kim Seunggyu , Park Joonha , Ho Jin-Nyoung , Kim Danhyo , Lee Sangchul , Jeon Jessie S
Published: August 2023

Despite the advantages of microfluidic system in drug screening, vascular systems responsible for the transport of drugs and nutrients have been hardly considered in the microfluidic-based chemotherapeutic screening. Considering the physiological characteristics of highly vascularized urinary tumors, we here investigated the chemotherapeutic response of bladder tumor cells using a vascularized tumor on a chip. The microfluidic chip was designed to have open-top region for tumor sample introduction and hydrophilic rail for spontaneous hydrogel patterning, which contributed to the construction of tumor-hydrogel-endothelium interfaces in a spatiotemporal on-demand manner. Utilizing the chip where intravascularly injected cisplatin diffuse across the endothelium and transport into tumor samples, chemotherapeutic responses of cisplatin-resistant or -susceptible bladder tumor cells were evaluated, showing the preservation of cellular drug resistance even within the chip. The open-top structure also enabled the direct harvest of tumor samples and post analysis in terms of secretome and gene expressions. Comparing the cisplatin efficacy of the cisplatin-resistant tumor cells in the presence or absence of endothelium, we found that the proliferation rates of tumor cells were increased in the vasculature-incorporated chip. These have suggested that our vascularized tumor chip allows the establishment of vascular-gel-tumor interfaces in spatiotemporal manners and further enables investigations of chemotherapeutic screening.
15 MeV proton damage in NiO/β-Ga2O3vertical rectifiers
Authors: Li Jian-Sian , Chiang Chao-Ching , Xia Xinyi , Wan Hsiao-Hsuan , Kim Jihyun , Ren Fan , Pearton S J
Published: August 2023

15 MeV proton irradiation of vertical geometry NiO/β-Ga2O3heterojunction rectifiers produced reductions in reverse breakdown voltage from 4.3 kV to 3.7 kV for a fluence of 1013ions·cm−2and 1.93 kV for 1014ions·cm−2. The forward current density was also decreased by 1–2 orders of magnitude under these conditions, with associated increase in on-state resistanceRON. These changes are due to a reduction in carrier density and mobility in the drift region. The reverse leakage current increased by a factor of ∼2 for the higher fluence. Subsequent annealing up to 400 °C further increased reverse leakage due to deterioration of the contacts, but the initial carrier density of 2.2 × 1016cm−3was almost fully restored by this annealing in the lower fluence samples and by more than 50% in the 1014cm−2irradiated devices. Carrier removal rates in the Ga2O3were in the range 190–1200 for the fluence range employed, similar to Schottky rectifiers without the NiO.
CMOS-compatible neuromorphic devices for neuromorphic perception and computing: a review
Authors: Zhu Yixin , Mao Huiwu , Zhu Ying , Wang Xiangjing , Fu Chuanyu , Ke Shuo , Wan Changjin , Wan Qing
Published: August 2023

Neuromorphic computing is a brain-inspired computing paradigm that aims to construct efficient, low-power, and adaptive computing systems by emulating the information processing mechanisms of biological neural systems. At the core of neuromorphic computing are neuromorphic devices that mimic the functions and dynamics of neurons and synapses, enabling the hardware implementation of artificial neural networks. Various types of neuromorphic devices have been proposed based on different physical mechanisms such as resistive switching devices and electric-double-layer transistors. These devices have demonstrated a range of neuromorphic functions such as multistate storage, spike-timing-dependent plasticity, dynamic filtering, etc. To achieve high performance neuromorphic computing systems, it is essential to fabricate neuromorphic devices compatible with the complementary metal oxide semiconductor (CMOS) manufacturing process. This improves the device’s reliability and stability and is favorable for achieving neuromorphic chips with higher integration density and low power consumption. This review summarizes CMOS-compatible neuromorphic devices and discusses their emulation of synaptic and neuronal functions as well as their applications in neuromorphic perception and computing. We highlight challenges and opportunities for further development of CMOS-compatible neuromorphic devices and systems.
Spherical acquisition trajectories for x-ray computed tomography with a robotic sample holder
Authors: Pekel Erdal , Dierolf Martin , Pfeiffer Franz , Lasser Tobias
Published: August 2023

This work presents methods for the seamless execution of arbitrary spherical trajectories with a seven-degree-of-freedom robotic arm as a sample holder. The sample holder is integrated into an existing x-ray computed tomography setup. We optimized the path planning and robot control algorithms for the seamless execution of spherical trajectories. A precision-manufactured sample holder part is attached to the robotic arm for the calibration procedure. Different designs of this part are tested and compared to each other for optimal coverage of trajectories and reconstruction image quality. We present experimental results with the robotic sample holder where a sample measurement on a spherical trajectory achieves improved reconstruction quality compared to a conventional circular trajectory. Our results demonstrate the superiority of the discussed system as it outperforms single-axis systems by reaching nearly 82% of all possible rotations. The proposed system is a step towards higher image reconstruction quality in flexible x-ray CT systems. It will enable reduced scan times and radiation dose exposure with task-specific trajectories in the future, as it can capture information from various sample angles.
Stochastic robust predictive fault-tolerant control for discrete systems with actuator faults
Authors: Shi Huiyuan , Zuo Lanshuang , Wang Shiqi , Su Chengli , Li Ping
Published: August 2023

A stochastic robust predictive fault-tolerant control approach is proposed for a class of industrial processes with uncertainties, external disturbances and the random failure of actuators. First, a process model of the above system is established in terms of state space. It is further converted into a new augmented form containing the state deviation and output tracking error. By means of the augmented model, the designed control law is given. Then the related theorem and corollary in the form of linear matrix inequality constraints are provided for solving the gain of the control law under different probabilities of failure. Under a certain probability, the occurrence of a failure is regarded as a switching condition. Conventional control is adopted when the failure does not occur, while fault-tolerant control is performed when the failure occurs. Finally, a pressure holding phase case is provided for verifying the feasibility of the given approach.
Exploring 3D community inconsistency in human chromosome contact networks
Authors: Bernenko Dolores , Lee Sang Hoon , Lizana Ludvig
Published: August 2023

Researchers have developed chromosome capture methods such as Hi-C to better understand DNA’s 3D folding in nuclei. The Hi-C method captures contact frequencies between DNA segment pairs across the genome. When analyzing Hi-C data sets, it is common to group these pairs using standard bioinformatics methods (e.g. PCA). Other approaches handle Hi-C data as weighted networks, where connected node pairs represent DNA segments in 3D proximity. In this representation, one can leverage community detection techniques developed in complex network theory to group nodes into mesoscale communities containing nodes with similar connection patterns. While there are several successful attempts to analyze Hi-C data in this way, it is common to report and study the most typical community structure. But in reality, there are often several valid candidates. Therefore, depending on algorithm design, different community detection methods focusing on slightly different connectivity features may have differing views on the ideal node groupings. In fact, even the same community detection method may yield different results if using a stochastic algorithm. This ambiguity is fundamental to community detection and shared by most complex networks whenever interactions span all scales in the network. This is known as community inconsistency. This paper explores this inconsistency of 3D communities in Hi-C data for all human chromosomes. We base our analysis on two inconsistency metrics, one local and one global, and quantify the network scales where the community separation is most variable. For example, we find that TADs are less reliable than A/B compartments and that nodes with highly variable node-community memberships are associated with open chromatin. Overall, our study provides a helpful framework for data-driven researchers and increases awareness of some inherent challenges when clustering Hi-C data into 3D communities.
Molecular machine learning with conformer ensembles
Authors: Axelrod Simon , Gómez-Bombarelli Rafael
Published: August 2023

Virtual screening can accelerate drug discovery by identifying promising candidates for experimental evaluation. Machine learning is a powerful method for screening, as it can learn complex structure–property relationships from experimental data and make rapid predictions over virtual libraries. Molecules inherently exist as a three-dimensional ensemble and their biological action typically occurs through supramolecular recognition. However, most deep learning approaches to molecular property prediction use a 2D graph representation as input, and in some cases a single 3D conformation. Here we investigate how the 3D information of multiple conformers, traditionally known as 4D information in the cheminformatics community, can improve molecular property prediction in deep learning models. We introduce multiple deep learning models that expand upon key architectures such as ChemProp and SchNet, adding elements such as multiple-conformer inputs and conformer attention. We then benchmark the performance trade-offs of these models on 2D, 3D and 4D representations in the prediction of drug activity using a large training set of geometrically resolved molecules. The new architectures perform significantly better than 2D models, but their performance is often just as strong with a single conformer as with many. We also find that 4D deep learning models learn interpretable attention weights for each conformer.
Estimating Gibbs free energies via isobaric-isothermal flows
Authors: Wirnsberger Peter , Ibarz Borja , Papamakarios George
Published: August 2023

We present a machine-learning model based on normalizing flows that is trained to sample from the isobaric-isothermal ensemble. In our approach, we approximate the joint distribution of a fully-flexible triclinic simulation box and particle coordinates to achieve a desired internal pressure. This novel extension of flow-based sampling to the isobaric-isothermal ensemble yields direct estimates of Gibbs free energies. We test our NPT-flow on monatomic water in the cubic and hexagonal ice phases and find excellent agreement of Gibbs free energies and other observables compared with established baselines.