bluuebunny/crossref_metadata_2025 huggingface dataset:
Small sample uncertainty evaluation of industrial robot position accuracy measurement based on grey model
Authors: Cheng Yinbao , Zhu Yanlong , Gao Hongtang , Li Yaru , Jiang Wensong , Luo Zai
Published: April 2024

The measurement of position accuracy of industrial robots has high complexity, and its uncertainty assessment is one of the important research topics. This study analyses the principle of measuring the position accuracy of industrial robots by laser tracker, the evaluation model of the measurement uncertainty of the position accuracy of industrial robots is established, and various factors affecting the measurement results are analysed, including industrial robot itself, the measurement instrument, the measurement personnel, the measurement strategy and the data processing method. There is a very small sample problem in the uncertainty evaluation process of the position accuracy of industrial robots. The literature and experiments show that the reliability of The Guide to the Expression of Uncertainty in Measurement is low under the condition of very small sample. In this study, an improved grey method is proposed to evaluate the uncertainty. The principle of the improved grey method is discussed in detail, and the general steps of the improved grey method are summarized. The experimental data prove that the standard deviation of very small sample calculated by the improved grey method has higher accuracy. Taking the position accuracy of an industrial robot measured by a laser tracker as an example, the improved grey method is successfully applied to the measurement uncertainty evaluation of the position accuracy of industrial robots.
The research on triple-coil inductive debris sensor with electromagnetic shielding
Authors: Ren Yi Jun , Gao Sheng Wei , Wen Xiao Long , Liu Yong Bin , Ju Bin
Published: April 2024

A high sensitivity inductive debris sensor is of great significance for oil condition monitoring and the fault diagnosis of mechanical equipment. However, inductive sensors are susceptible to interference from external electromagnetic fields. In this study, an inductive debris sensor with a shielding layer is studied, which can effectively reduce the influence of external magnetic fields on the sensor. Theoretical and experimental results verify the effectiveness of the shielding layer, and also show that the shielding layer impacts the magnetic field of the sensor, resulting in a decrease in the amplitude of the output signal. Detailed analysis was conducted to investigate the effect of structural parameters on the performance of inductive sensors. These parameters include the distance between the shielding layer and the coils, the thickness of the shielding layer, and the shielding layer material. Based on the theoretical and experimental results, reference suggestions are provided for the design of the shielding layer. This study ensures the performance and reliability of inductive sensors in practical applications.
A phase modulation method of sine signal for dual-active-valve piezoelectric pump
Authors: Sun Yeming , Deng Lingying , Wang Liang , Wang Junyao
Published: April 2024

A phase modulation method with wide range and high resolution for the sine signal is essential for dual-active-valve piezoelectric pump (DAVPP) control. In DAVPP, by phase modulation, the flow direction can be changed and the output flow rate and pressure can be precisely adjusted. In this article, the authors developed a phase modulation method for the sine signal. This method is characterized by both the combination of hardware and software, and the combination of digital circuits and analog circuits. In hardware, a sinxcosϕ constructing circuit and a cosxsinϕ constructing circuit are specially structured, which enables phase modulation to be achieved. In software, the output phase is determined by the digital controlling quantities sent by the main control chip and stored in the form of a table. Analytical formulas for the cosine constructing table tab_cos and the sine constructing table tab_sin are analyzed and structured. Experimental results show that the output phase can be regulated linearly and continuously, within a range of 0°–360°. Its resolution can be improved according to the requirements. Although the modulation process and circuit are simple, it can effectively solve the problem of sine signal phase modulation for DAVPP control.
An improved hidden Markov model with magnetic Barkhausen noise and optimized Gaussian mixture feature for fatigue prediction
Authors: Li Xiang , Guo Wei , Deng Xin , Guo Yitong , Zheng Yang , Zhou Jinjie , Zhan Peng
Published: April 2024

Evaluating fatigue states of metallic materials is essential for predicting their failures and ensuring structural safety. Magnetic Barkhausen noise (MBN) analysis, a non-destructive testing method, provides efficient and reliable methods for identifying and categorising material parameters such as hardness and residual stresses. To establish a quantitative relationship between MBN signals and fatigue states, an improved hidden Markov model (HMM) is proposed based on optimised Gaussian mixture features (GMFs) and the Kullback–Leibler (KL) divergence measure for fatigue prediction. The MBN-GMFs replicate the probability characteristics of MBN signals and track the fatigue degradation trend throughout the fatigue life; thus, they are superior to some widely used statistical features. A Gaussian component optimisation algorithm is proposed to automatically adjust the appropriate number of components in the Gaussian mixture model and enhance the representation of MBN-GMFs. Then, the KL divergence is introduced to quantify the similarity and accurately classify the degree of MBN-GMF migration. The HMM is constructed to obtain the probability transfer relationship between the observations and states and obtain accurate fatigue predictions. Experiments on two 20R metallic materials at three excitation frequencies are conducted to collect the MBN signals. The experimental results and comparisons indicate that the proposed HMM can accurately predict fatigue states and provide a practical and robust analysis tool for MBN-based fatigue predictions.
Isocentric fixed angle irradiation-based DRR: a novel approach to enhance x-ray and CT image registration
Authors: Shu Lixia , Li Meng , Guo Xi , Chen Yu , Pu Xin , Lin Changyan
Published: April 2024

Objective. Digitally reconstructed radiography (DRR) plays an important role in the registration of intraoperative x-ray and preoperative CT images. However, existing DRR algorithms often neglect the critical isocentric fixed angle irradiation (IFAI) principle in C-arm imaging, resulting in inaccurate simulation of x-ray images. This limitation degrades registration algorithms relying on DRR image libraries or employing DRR images (DRRs) to train neural network models. To address this issue, we propose a novel IFAI-based DRR method that accurately captures the true projection transformation during x-ray imaging of the human body. Approach. By strictly adhering to the IFAI principle and utilizing known parameters from intraoperative x-ray images paired with CT scans, our method successfully simulates the real projection transformation and generates DRRs that closely resemble actual x-ray images. Main result. Experimental results validate the effectiveness of our IFAI-based DRR method by successfully registering intraoperative x-ray images with preoperative CT images from multiple patients who underwent thoracic endovascular aortic procedures. Significance. The proposed IFAI-based DRR method enhances the quality of DRR images, significantly accelerates the construction of DRR image libraries, and thereby improves the performance of x-ray and CT image registration. Additionally, the method has the generality of registering CT and x-ray images generated by large C-arm devices.
A temporal enhanced semi-supervised training framework for needle segmentation in 3D ultrasound images
Authors: Wen Mingwei , Shcherbakov Pavel , Xu Yang , Li Jing , Hu Yi , Zhou Quan , Liang Huageng , Yuan Li , Zhang Xuming
Published: April 2024

Objective. Automated biopsy needle segmentation in 3D ultrasound images can be used for biopsy navigation, but it is quite challenging due to the low ultrasound image resolution and interference similar to the needle appearance. For 3D medical image segmentation, such deep learning networks as convolutional neural network and transformer have been investigated. However, these segmentation methods require numerous labeled data for training, have difficulty in meeting the real-time segmentation requirement and involve high memory consumption. Approach. In this paper, we have proposed the temporal information-based semi-supervised training framework for fast and accurate needle segmentation. Firstly, a novel circle transformer module based on the static and dynamic features has been designed after the encoders for extracting and fusing the temporal information. Then, the consistency constraints of the outputs before and after combining temporal information are proposed to provide the semi-supervision for the unlabeled volume. Finally, the model is trained using the loss function which combines the cross-entropy and Dice similarity coefficient (DSC) based segmentation loss with mean square error based consistency loss. The trained model with the single ultrasound volume input is applied to realize the needle segmentation in ultrasound volume. Main results. Experimental results on three needle ultrasound datasets acquired during the beagle biopsy show that our approach is superior to the most competitive mainstream temporal segmentation model and semi-supervised method by providing higher DSC (77.1% versus 76.5%), smaller needle tip position (1.28 mm versus 1.87 mm) and length (1.78 mm versus 2.19 mm) errors on the kidney dataset as well as DSC (78.5% versus 76.9%), needle tip position (0.86 mm versus 1.12 mm) and length (1.01 mm versus 1.26 mm) errors on the prostate dataset. Significance. The proposed method can significantly enhance needle segmentation accuracy by training with sequential images at no additional cost. This enhancement may further improve the effectiveness of biopsy navigation systems.
Ginzburg–Landau simulations of three-terminal operation of a superconducting nanowire cryotron
Authors: Yasukawa Naoki , Nishio Taichiro , Mawatari Yasunori
Published: April 2024

Superconducting nanowire cryotrons (nTrons) are expected to be used as interfaces for super-high-performance hybrid devices in which superconductor and semiconductor circuits are combined. However, nTrons are still under development, and diverse analyses of these devices are needed. Accordingly, we have developed a numerical technique to simulate the three-terminal operation of an nTron by using the finite element method to solve the time-dependent Ginzburg–Landau (TDGL) equation and the heat-diffusion equation. Simulations using this technique offer understanding of the dynamics of the order parameter, the thermal behavior, and the characteristics of three-terminal operation, and the TDGL model reproduces qualitatively the results of nTron experiments. In addition, we investigated how some geometric and physical parameters (the design elements) affect the operation characteristics. The TDGL model has far fewer free parameters compared with the lumped-element electrothermal model commonly used for simulating superconducting devices. Furthermore, the TDGL model provides time-dependent visual information about the superconducting state and the normal state, thereby offering insights into the relationship between nTron geometry and three-terminal operation. These simulation results offer a route to nTron optimization and the development of nTron applications.
Impregnation damage monitoring for the canted-cosine-theta magnets using time-domain reflectometry
Authors: Seok Lee Geon , Marchevsky Maxim , Teyber Reed , Arbelaez Diego , Prestemon Soren
Published: April 2024

Impregnation plays a crucial role in the performance and protection of superconducting magnets. To investigate the impregnation status during quench training, a vector network analyzer (VNA)-based time domain reflectometry (TDR) is introduced. The proposed method and analyses focus on demonstrating their applicability within canted-cosine-theta (CCT) magnets, covering both artificially induced quenches using spot heaters and naturally occurring quenches. To verify the performance of the proposed method, VNA-based TDR is applied to CCT Subscale magnets developed in the US Magnet Development Program. The findings contribute to a deeper understanding of CCT superconducting magnet behavior and inform strategies for improving their performance.
Improving the screening ability of high-temperature superconductors by combining disk-shaped bulks and closed-loop coated conductors
Authors: Rotheudt N , Brialmont S , Fagnard J-F , Hlasek T , Plechacek J , Vanderbemden P
Published: April 2024

In this work, we demonstrate experimentally the remarkable DC magnetic screening ability of hybrid superconducting screens. The configurations under investigation combine a 30 mm-diameter disk-shaped superconducting bulk, or another with a 10 mm-diameter hole, with different closed superconducting loops made from coated conductors. The loops are placed coaxially with the bulk. The bulk superconductors and the closed-loop coated conductors are made of GdBa2Cu3O7 and the experiments are performed in liquid nitrogen (77 K). The DC and inhomogeneous field to be screened is produced by a bespoke coil and reaches around 100 mT at the location of the superconducting bulk, which is much less than its full-penetration field. By mapping the magnitude of the flux density using a 3-axis cryogenic Hall probe, we show that such a hybrid superconducting screen allows the maximum shielding factor SF above the bulk to be almost doubled. For the best investigated hybrid configuration, the SF reaches

∼

45

at 2.7 mm above the bulk. In addition, the area of the spatial region for which less than half of the applied field remains (SF

>
2

) is roughly multiplied by 4 when compared to a situation involving only a disk-shaped bulk. In this work, this region corresponds to a 60 mm-diameter circle. The choice of the loop diameter is found to result from a trade-off between maximum SF and extension of the screened region. Finally, we detail why these hybrid configurations bring such improvements: the key point is that the flux lines generated by the source coil are diverted by the bulk towards a region where the closed-loop coated conductors can oppose the best to them.
Illumination dependent electrical, photosensitivity and photodetectivity properties of Au/SiO2/n-Si structures: optoelectronic situations
Authors: Karataş Şükrü , AK Aykut
Published: April 2024

This study aims to examine the electrical, photosensitivity (S), photo-response (R), and photo-detectivity (D*) properties of Au/SiO2/n-type Si structures under different illumination intensities. The illumination-dependent properties of the Au/SiO2/n-type Si structure with SiO2 interlayer were obtained using current–voltage (I-V) characteristics. The ideality factors (n) and barrier heights (Φbo) values of Au/SiO2/n-type Si structures were obtained and compared from forward and reverse bias current–voltage (I-V) measurements at room temperature. Also, barrier heights (Φbo) and series resistance (R

S
) values obtained from Norde method were compared with the values obtained from TE theory. It was observed that ideality factor and barrier height values obtained from forward bias region are higher than the values reverse bias. This means that the linearity or rectification feature in the reverse bias region is better than the forward bias region. Furthermore, photodiode values such as photo response (R), photosensitivity (S) and photodetectivity (D*) of Au/SiO2/n type Si structures were also examined depending on the light intensity. Consequently, the experimental results showed that the increase in reverse current with increasing light indicates that the Au/SiO2/n-type Si structures can be used in semiconductor technology as a photodiode, detector or sensor.
Envelope solitary waves in two-negative ions with stationary dust grains
Authors: Alharbi Abdulaziz H , Alzahrani M S J , Moslem W M , Elkamash I S
Published: April 2024

Using a multi-fluid model, we look at how modulated electrostatic dust-ion-acoustic wave packets move nonlinearly through a plasma made up of a three-ion fluid with Maxwellian electrons and stationary dust grains. A nonlinear Schrödinger (NLS) equation describes the electric potential envelope wave packet. The analysis reveals the existence of different types of localized modes, namely bright, dark, and grey solitons. We numerically analyse the coefficients of the NLS equation to identify stable or unstable regions for wave packet propagation. It is found that higher relative density ratios increase the group velocity of the wave packets. Stable pulses can become unstable when plasma parameters exceed certain relative density ratio values. Stable pulses can exist within a crucial window of the relative dust density ratio. Controlling the dust grain density ratio outside the zone can cause unstable wave packets or bright envelope solitons to propagate.
Properties of spray pyrolysis deposited Zr-doped ZnO thin films and their UV sensing properties
Authors: Alasmari Aeshah , Abd-Elraheem Ramy A , Aboud Ahmed A , Ismail Motaz
Published: April 2024

This study investigated the characteristics of Zr-doped ZnO thin films with varying Zr doping concentrations. X-ray diffraction (XRD) analysis confirmed the presence of the ZnO hexagonal phase without any additional phases detected. The crystallite size was determined using Scherrer’s equation and Halder-Wagner equation, revealing distinct trends as the Zr content increased. The impact of Zr doping concentration on structural properties such as lattice parameters was also explored. Field emission scanning electron microscopy (FE-SEM) images indicated agglomeration, with a peak value observed at Zr-5 wt% of 175 nm that decreased at higher Zr contents. Optical properties exhibited minor variations with increasing Zr content, with the maximum band gap recorded at 3.28 eV for Zr-7 wt% and Zr-10 wt% films. Utilizing the Spitzer-Fan model, the high-frequency dielectric constant peaked at 14.26 for Zr-7 wt% films. Optical mobility displayed fluctuations with rising Zr content. Direct current (DC) conductivity results unveiled two donor levels in the deposited films, showcasing minimum activation energies of 0.23 and 0.165 eV for high and low-temperature ranges in the Zr-3 wt% film. Furthermore, the response to UV light illumination at a wavelength of 365 nm was examined, revealing notable changes in rise and decay times with varying Zr content.
Design and optimization of Cs2SnI6 based inorganic perovskite solar cell model: numerical simulation
Authors: Chen Qi , Qiao Zaixiang
Published: April 2024

To overcome the drawbacks of high lead toxicity and poor corrosion resistance of lead-based perovskite solar cells (PSCs), and to compensate for the poor air stability of Sn2+ compound-based perovskite, Cs2SnI6 (Sn4+ compound) is selected as the absorber for the PSC in this study. Using FTO/ETL/Cs2SnI6/HTL/Au as the model, the high-performance non-toxic inorganic PSC structure is explored through theoretical simulation and calculation by SCAPS-1D. The conduction band offsets (CBO) and valence band offsets (VBO) of commonly used electron transport layer materials (ETMs), hole transport layer materials (HTMs), and Cs2SnI6 are calculated based on electron affinity potential (χ) and bandgap (E

g
). Then, by analyzing the pn junction composed of ETL and HTL and the bandgap structure at the n-i, i-p interfaces, the most matching n-i-p planar heterojunction model, FTO/IGZO/Cs2SnI6/Cu2BaSnS4/Au, was selected. Finally, by analyzing and adjusting the material thickness, defect density of each layer, operation temperature, the optimal performance of PSC was determined to be 30.39% power conversion efficiency (PCE), 1.27 V open circuit voltage (V

oc
), 28.46 mA cm−2 short circuit current (J

sc
), and 84.02% fill factor (FF). A new and more efficient PSC is proposed in this study, providing some terrific clues for finding high-quality alternatives to lead-based PSCs.
EEGminer: discovering interpretable features of brain activity with learnable filters
Authors: Ludwig Siegfried , Bakas Stylianos , Adamos Dimitrios A , Laskaris Nikolaos , Panagakis Yannis , Zafeiriou Stefanos
Published: April 2024

Objective. The patterns of brain activity associated with different brain processes can be used to identify different brain states and make behavioural predictions. However, the relevant features are not readily apparent and accessible. Our aim is to design a system for learning informative latent representations from multichannel recordings of ongoing EEG activity. Approach: We propose a novel differentiable decoding pipeline consisting of learnable filters and a pre-determined feature extraction module. Specifically, we introduce filters parameterized by generalized Gaussian functions that offer a smooth derivative for stable end-to-end model training and allow for learning interpretable features. For the feature module, we use signal magnitude and functional connectivity estimates. Main results. We demonstrate the utility of our model on a new EEG dataset of unprecedented size (i.e. 721 subjects), where we identify consistent trends of music perception and related individual differences. Furthermore, we train and apply our model in two additional datasets, specifically for emotion recognition on SEED and workload classification on simultaneous task EEG workload. The discovered features align well with previous neuroscience studies and offer new insights, such as marked differences in the functional connectivity profile between left and right temporal areas during music listening. This agrees with the specialisation of the temporal lobes regarding music perception proposed in the literature. Significance. The proposed method offers strong interpretability of learned features while reaching similar levels of accuracy achieved by black box deep learning models. This improved trustworthiness may promote the use of deep learning models in real world applications. The model code is available at https://github.com/SMLudwig/EEGminer/.
Wave–particle interactions in tokamaks
Authors: Shaing K.C. , Garcia-Munoz M. , Viezzer E. , Harvey R.W.
Published: April 2024

Transport consequences of the wave–particle interactions in the quasilinear plateau (QP) regime are presented. Eulerian approach is adopted to solve the drift kinetic equation that includes the physics of the nonlinear trapping (NT) and QP regimes. The localization of the perturbed distribution simplifies the test particle collision operator. It is shown that a mirror force like term responsible for the flattening of the distribution in the NT regime is subdominant in the QP regime, and controls the transition between these two regimes. Transport fluxes, flux-power relation, and nonlinear damping or growth rate are all calculated. There is no explicit collision frequency dependence in these quantities; however, the width of the resonance does. Formulas that join the asymptotic results of these two regimes to facilitate thermal and energetic particle transport, and nonlinear wave evolution of a single mode are presented.
Optical trimer: a theoretical physics approach to waveguide couplers
Authors: Stoffel A , Caballero-Benitez S F , Rodríguez-Lara B M
Published: April 2024

We study electromagnetic field propagation through an ideal, passive, three-dimensional, triangular three-waveguide coupler using a symmetry-based approach that capitalizes on the underlying su(3) symmetry. The planar version of this platform has already demonstrated its utility in photonic circuit design, enabling optical sampling, filtering, modulating, multiplexing, and switching. We aim to provide a practical tutorial on using group theory for the analysis of photonic lattices for those less familiar with abstract algebra methods. This approach serves as a powerful tool for optical designs. To illustrate this, we focus on the equilateral trimer, connected to the discrete Fourier transform, and the isosceles trimer, related to the golden ratio, providing stable single waveguide output. We also explore a scenario where the coupling in an equilateral coupler changes linearly with propagation distance. Going beyond the standard optical-quantum analogy, we show that coupled-mode equations for intensity and phase allows us to calculate envelopes for inputs within an intensity class, as well as individual input field amplitudes. This approach streamlines the design process by eliminating the need for point-to-point propagation calculations, highlighting the power of group theory in the field of photonic design.
Dual-optical-multiplexing-based multi-image invisible visual cryptography
Authors: Zhang Xinyu , Shen Xiaoshuang , Zhu Yupeng , Mi Zhaoke , Zhu Yusi , Zhang Huiliang , Ke Changjun , Shi Yishi
Published: April 2024

In earlier research, the concept of using diffractive optics to indirectly achieve invisible visual cryptography (VC) was proposed. In this approach, the extraction process does not require complex optical implementations or additional computations. However, the system’s security and the capacity still need to be improved. Correspondingly, this paper introduces a multi-image invisible VC system based on dual optical multiplexing. Under the conditions of diffraction distance multiplexing and wavelength multiplexing, the visual keys of secret images are concealed within a phase key in the Fresnel domain. This method enhances the system’s security through dual optical multiplexing and ensures a certain capacity for information concealment. Optical experiments verify that the easy extraction and the high repeatability are all obtainable in the method.
Linearizing the vertical scale of an interferometric microscope and its effect on step-height measurement
Authors: Germer Thomas A , Renegar T Brian , Griesmann Ulf , Soons Johannes A
Published: April 2024

The vertical scale calibration of an interferometric microscope is important for establishing traceability of surface topography measurements to the International System of Units (SI) unit of length, the meter. Building on the calibration procedure for the amplification coefficient developed by de Groot and Beverage [Proc. SPIE
9526, 952610 (2015)], this paper describes a calibration procedure that yields the response curve for the entire vertical scan motion of a coherent scanning interferometric microscope. The method requires only a flat mirror as an artifact, a narrow band spectral filter, an aperture to reduce the effective numerical aperture, and the ability to raise and lower the microscope head so that the center of the interferogram can be varied within the scan range. The local frequency of the interferogram is determined by fitting sections of the interferogram to a sinusoidal function. The nonlinearity determined from the local frequency data can be used to estimate the uncertainty in uncorrected vertical height measurements. We describe how optical profile data can be corrected for nonlinearity due to dynamic effects in the scan motion and show that the correction improves the reproducibility of step height measurements by at least a factor of three and close to that of the repeatability.
Impurity modes in two-dimensional strongly coupled complex plasma crystals
Authors: WU 吴 Shijie 诗杰 , XU 徐 Yunqiao 韵乔 , XIAN 冼 Shunyu 顺宇 , HE 何 Xianglei 向磊 , YANG 杨 Xiaoyi 肖易
Published: April 2024

Complex plasma fluctuation processes have been extensively studied in many aspects, especially lattice waves in strongly coupled plasma crystals, which are of great significance for understanding fundamental physical phenomena. A challenge of experimental investigations in two-dimensional strongly coupled complex plasma crystals is to keep the main body and foreign particles of different masses on the same horizontal plane. To solve the problem, we have proposed a potential well formed by two negatively biased grids to bind the negatively charged particles in a two-dimensional (2D) plane, thus achieving a 2D plasma crystal in the microgravity environment. The study of such phenomena in complex plasma crystals under microgravity environment then becomes possible. In this paper, we focus on the continuum spectrum, including both phonon and optic branches of the impurity mode in a 2D system in microgravity environments. The results show the dispersion relation of the longitudinal and transverse impurity oscillation modes and their properties. Considering the macroscopic visibility of complex mesoscopic particle lattices, theoretical and experimental studies on this kind of complex plasma systems will help us further understand the physical nature of a wide range of condensed matters.
An application of the graph approach to life-cycle optimisation of vehicle electrification
Authors: Tokito Shohei , Nakamoto Yuya , Hanaka Tesshu
Published: April 2024

Although durable goods with low energy consumption are being promoted to achieve a decarbonised society, from the perspective of life-cycle assessment, the choice of new durable goods may increase CO2 emissions. To address this problem, research has been conducted on product replacement based on life-cycle optimisation (LCO), a method for identifying a replacement life span that minimises life-cycle CO2 emissions. However, several additional assumptions complicate the analysis of replacement patterns of products and conditional formulas because cumulative emissions do not increase linearly when considering energy mix and technology improvement, and it is difficult to extend the model to optimisation methods in previous LCO studies. This study developed a new LCO approach by applying the shortest path problem to graph theory. Our methodology can contribute to the following: (i) it is computationally inexpensive; (ii) it is intuitively easy to add complex conditions, such as various policy scenarios and parameter changes; and (iii) once the graph of replacement patterns is defined, the optimal solution can be derived using existing solution methods, such as the Dijkstra algorithm. As a case study, we focused on vehicle replacement, which is a major source of CO2 emissions and is being electrified. In particular, we identified vehicle switching paths that minimise life-cycle CO2 emissions by considering changes in Japan’s energy mix and alternative fuel vehicle (AFV) characteristics. We determined that the optimal vehicle replacement path method to reduce CO2 emissions is to switch first to plug-in hybrid electric vehicles (PHEVs) and then to battery electric vehicles (BEVs). Thus, we suggest that the transition to electric vehicles requires a step-by-step process. This methodology is not only conducive to AFV deployment for decarbonisation but can also be applied to other products, such as air conditioners and lighting. Thus, various transition policies could be formulated using our methodology.
Feature selection for high-dimensional neural network potentials with the adaptive group lasso
Authors: Sandberg Johannes , Voigtmann Thomas , Devijver Emilie , Jakse Noel
Published: April 2024

Neural network potentials are a powerful tool for atomistic simulations, allowing to accurately reproduce ab initio potential energy surfaces with computational performance approaching classical force fields. A central component of such potentials is the transformation of atomic positions into a set of atomic features in a most efficient and informative way. In this work, a feature selection method is introduced for high dimensional neural network potentials, based on the adaptive group lasso (AGL) approach. It is shown that the use of an embedded method, taking into account the interplay between features and their action in the estimator, is necessary to optimize the number of features. The method’s efficiency is tested on three different monoatomic systems, including Lennard–Jones as a simple test case, Aluminium as a system characterized by predominantly radial interactions, and Boron as representative of a system with strongly directional components in the interactions. The AGL is compared with unsupervised filter methods and found to perform consistently better in reducing the number of features needed to reproduce the reference simulation data at a similar level of accuracy as the starting feature set. In particular, our results show the importance of taking into account model predictions in feature selection for interatomic potentials.
Physics-informed neural networks for an optimal counterdiabatic quantum computation
Authors: Ferrer-Sánchez Antonio , Flores-Garrigos Carlos , Hernani-Morales Carlos , Orquín-Marqués José J , Hegade Narendra N , Gomez Cadavid Alejandro , Montalban Iraitz , Solano Enrique , Vives-Gilabert Yolanda , Martín-Guerrero José D
Published: April 2024

A novel methodology that leverages physics-informed neural networks to optimize quantum circuits in systems with

N

Q

qubits by addressing the counterdiabatic (CD) protocol is introduced. The primary purpose is to employ physics-inspired deep learning techniques for accurately modeling the time evolution of various physical observables within quantum systems. To achieve this, we integrate essential physical information into an underlying neural network to effectively tackle the problem. Specifically, the imposition of the solution to meet the principle of least action, along with the hermiticity condition on all physical observables, among others, ensuring the acquisition of appropriate CD terms based on underlying physics. This approach provides a reliable alternative to previous methodologies relying on classical numerical approximations, eliminating their inherent constraints. The proposed method offers a versatile framework for optimizing physical observables relevant to the problem, such as the scheduling function, gauge potential, temporal evolution of energy levels, among others. This methodology has been successfully applied to 2-qubit representing

H

2

molecule using the STO-3G basis, demonstrating the derivation of a desirable decomposition for non-adiabatic terms through a linear combination of Pauli operators. This attribute confers significant advantages for practical implementation within quantum computing algorithms.
Militant Democracy
Authors: Schupmann Benjamin A
Published: April 2024

This chapter critically examines ‘militant democracy’, defined as the use of measures of constitutional entrenchment to defend democracy against actors intent on revolutionizing it through legal democratic means. It surveys existing normative theories to highlight the limitations and challenges in justifying and implementing those measures. One limitation is key to this chapter’s argument: the paradox of democracy’s self-defence. This paradox arises from concerns that measures of militant democracy may undermine the very democracy they aim to protect. Along with other normative challenges, this paradox casts doubts on the legitimacy of using militant democracy at all. This chapter concludes by arguing that these concerns could be more effectively addressed by developing an alternative normative theory of militant democracy.
Political Rights Restrictions
Authors: Schupmann Benjamin A
Published: April 2024

This chapter analyses political rights restrictions as the second principal mechanism of militant democracy, focusing in particular on the party ban. These restrictions are crucial for constitutional entrenchment, as antidemocrats can employ many indirect methods to undermine constitutional democracy, which do not require amending the constitution. Such methods include constitutional derogation and capturing the judiciary and government ministries. For that reason, a multifaceted approach is necessary, one that defends democracy against both antidemocratic actors and actions. This chapter demonstrates the compatibility of political rights restrictions with liberal normative theory and investigates their different forms beyond an outright party ban. It illustrates the significance of political rights restrictions in preventing antidemocratic organizations from legally obtaining and abusing state power.
‘This is the fate I choose’: Elsinore and thoughtful choice in Shakespeare games
Authors: Wold Julia
Published: April 2024

Interactivity is one of, if not the most, crucial factor in video game adaptations of literature. The 2019 point-and-click adventure game Elsinore makes the most of its interactivity by building its narrative and gameplay around structural and thematically relevant aspects of the source text, Hamlet. Specifically, the combination of knowledge and motivation, central to the philosophy of prohairesis or thoughtful choice, is also an essential element of a fulfilling game experience. Additionally, it is meaningful that Elsinore leverages its focus on choice to make Ophelia their player-character. In short, this article argues that interactive genres, specifically video games, are a natural fit for the Early Modern fixation on thoughtful choice, especially representations of characters infamous for their lack of choice. Furthermore, these adaptations continue to represent an encouraging future for the commercial success of such works and future scholarship in adaptation theory.
Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?
Authors: Galanter Nina , Carone Marco , Kessler Ronald C , Luedtke Alex
Published: April 2024

Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if outcome variability is mainly driven by factors other than variability in the treatment effect, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources. Motivated by recent meta-analyses assessing the potential of individualizing treatment for major depressive disorder using only summary statistics, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. We also offer alternate bounds for settings in which trial results are stratified by another covariate. Our upper bounds can be especially informative when they are small, as there is then little benefit to collecting additional covariate data. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package.
BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities
Authors: Blasco Telmo , Balzerani Francesco , Valcárcel Luis V , Larrañaga Pedro , Bielza Concha , Francino María Pilar , Rufián-Henares José Ángel , Planes Francisco J , Pérez-Burillo Sergio
Published: April 2024

Motivation

Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in the microbial environment. These methods, however, do not comprehensively account for microbe–microbe stimulant or inhibitory effects or for nutrient–microbe inhibitory effects, typically observed in different compounds present in the daily diet.

Results

Here, we present BN-BacArena, an extension of BacArena consisting on the incorporation within the native computational framework of a Bayesian network model that accounts for microbe–microbe and nutrient–microbe interactions. Using in vitro experiments, 16S rRNA gene sequencing data and nutritional composition of 55 foods, the output Bayesian network showed 23 significant nutrient–bacteria interactions, suggesting the importance of compounds such as polyols, ascorbic acid, polyphenols and other phytochemicals, and 40 bacteria–bacteria significant relationships. With test data, BN-BacArena demonstrates a statistically significant improvement over BacArena to predict the time-dependent relative abundance of bacterial species involved in the gut microbiota upon different nutritional interventions. As a result, BN-BacArena opens new avenues for the dynamic modeling and simulation of the human gut microbiota metabolism.

Availability and implementation

MATLAB and R code are available in https://github.com/PlanesLab/BN-BacArena
scTPC: a novel semisupervised deep clustering model for scRNA-seq data
Authors: Qiu Yushan , Yang Lingfei , Jiang Hao , Zou Quan
Published: April 2024

Motivation

Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of “false” zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging.

Results

This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework.

Availability and implementation

scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.
Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach
Authors: Chakraborty Saptarshi , Guan Zoe , Begg Colin B , Shen Ronglai
Published: April 2024

Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic “hidden genome” model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.
High-dimensional covariate-augmented overdispersed poisson factor model
Authors: Liu Wei , Zhong Qingzhi
Published: April 2024

The current Poisson factor models often assume that the factors are unknown, which overlooks the explanatory potential of certain observable covariates. This study focuses on high dimensional settings, where the number of the count response variables and/or covariates can diverge as the sample size increases. A covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. A group of identifiability conditions is provided to theoretically guarantee computational identifiability. We incorporate the interdependence of both response variables and covariates by imposing a low-rank constraint on the large coefficient matrix. To address the computation challenges posed by nonlinearity, two high-dimensional latent matrices, and the low-rank constraint, we propose a novel variational estimation scheme that combines Laplace and Taylor approximations. We also develop a criterion based on a singular value ratio to determine the number of factors and the rank of the coefficient matrix. Comprehensive simulation studies demonstrate that the proposed method outperforms the state-of-the-art methods in estimation accuracy and computational efficiency. The practical merit of our method is demonstrated by an application to the CITE-seq dataset. A flexible implementation of our proposed method is available in the R package COAP.
Cohort study on incidence of new-onset type 2 diabetes in patients after bariatric surgery and matched controls
Authors: Vahtera Viiko , Pajarinen Jukka , Kivimäki Mika , Ervasti Jenni , Pentti Jaana , Stenholm Sari , Vahtera Jussi , Salminen Paulina
Published: April 2024

Background

Metabolic bariatric surgery the reduces risk of new-onset type 2 diabetes in individuals with obesity, but it is unclear whether the benefit varies by sex, age, or socioeconomic status. The aim was to assess the risk of new-onset type 2 diabetes after metabolic bariatric surgery in these subgroups.

Methods

The Finnish Public Sector study, a follow-up study with matched controls nested in a large employee cohort, included patients without type 2 diabetes and with a diagnosis of obesity or self-reported BMI of at least 35 kg/m2. For each patient who had laparoscopic metabolic bariatric surgery (2008–2016), two propensity-score matched controls were selected. New-onset type 2 diabetes was ascertained from linked records from national health registries.

Results

The study included a total of 917 patients and 1811 matched controls with obesity. New-onset type 2 diabetes was diagnosed in 15 of the patients who had metabolic bariatric surgery (4.1 per 1000 person-years) and 164 controls (20.2 per 1000 person-years). The corresponding rate ratio (RR) was 0.20 (95% c.i. 0.12 to 0.35) and the rate difference (RD) was −16.1 (−19.8 to −12.3) per 1000 person-years. The risk reduction was more marked in individuals of low socioeconomic status (RR 0.10 (0.04 to 0.26) and RD −20.6 (−25.6 to −15.5) per 1000 person-years) than in those with higher socioeconomic status (RR 0.35 (0.18 to 0.66) and RD −11.5 (−16.9 to −6.0) per 1000 person-years) (Pinteraction = 0.017). No differences were observed between sexes or age groups.

Conclusion

Metabolic bariatric surgery was associated with a reduced risk of new-onset type 2 diabetes in men and women and in all age groups. The greatest benefit was observed in individuals of low socioeconomic status.