bluuebunny/crossref_metadata_2025_split huggingface dataset:
Autonomous vehicle path planning for smart logistics mobile applications based on modified heuristic algorithm
Authors: Fusic S Julius , Sitharthan R , Masthan SAR Sheik , Hariharan K 
Published: November 2022

Abstract
In this study, a heuristic algorithm is used to find an optimal route for smart logistics loading and unloading applications. Various environments, such as traditional building blocks, satellite images, terrain environments, and Google map environments are developed by converting into a binary occupancy grid and used to optimize the viable path in the smart mobile logistics application. The proposed autonomous vehicle (AV) route planning navigation approach is to forecast the AV’s path until it detects an imminent obstacle, at which point it should turn to the safest area before continuing on its route. To demonstrate the path navigation results of proposed algorithms, a navigational model is developed in the MATLAB/Simulink 2D virtual environment. The particle swarm optimization (PSO) method, the Bat search algorithm, and its proposed variants are used to identify a smooth and violation-free path for a given application environment. The proposed variants improve the algorithm’s effectiveness in finding a violation-free path while requiring less time complexity by using cubic spline curve interpolation and its improved constriction factor. Extensive simulation and benchmark validation results show the proposed standard PSO has a significantly shorter violation-free path, quick convergence rate and takes less time to compute the distance between loading and unloading environment locations than the cooperative coevolving PSO, Bat algorithm, or modified frequency Bat algorithms.
Load and release of gambogic acid via dual-target ellipsoidal-Fe3O4@SiO2@mSiO2-C18@dopamine hydrochloride -graphene quantum dots-folic acid and its inhibition to VX2 tumor cells
Authors: Dong Mengyang , Liu Wenwen , Yang Yuxiang , Xie Meng , Yuan Hongming , Ni Chaoying 
Published: November 2022

Abstract
Ellipsoidal-Fe3O4@SiO2@mSiO2-C18@dopamine hydrochloride-graphene quantum dots-folic acid (ellipsoidal-HMNPs@PDA-GQDs-FA), a dual-functional drug carrier, was stepwise constructed. The α-Fe2O3 ellipsoidal nanoparticles were prepared by a hydrothermal method, and then coated with SiO2 by Stöber method. The resulting core–shell structure, Fe3O4@SiO2@mSiO2-C18 magnetic nano hollow spheres, abbreviated as HMNPs, was finally grafted with graphene quantum dots (GQDs), dopamine hydrochloride (PDA) and folic acid (FA) by amide reaction to obtain HMNPs@PDA-GQDs-FA. Transmission electron microscopy, Fourier transform infrared spectroscopy, fluorescence spectroscopy and element analysis proved the successful construction of the HMNPs@PDA-GQDs-FA nanoscale carrier-cargo composite. The carrier HMNPs@PDA-GQDs-FA has higher load (51.63 ± 1.53%) and release (38.56 ± 1.95%) capacity for gambogic acid (GA). Cytotoxicity test showed that the cell survival rate was above 95%, suggesting the cytotoxicity of the carrier-cargo was very low. The cell lethality (74.91 ± 1.2%) is greatly improved after loading GA because of the magnetic targeting of HMNPs, the targeting performance of FA to tumor cells, and the pH response to the surrounding environment of tumor cells of PDA. All results showed that HMNPs@PDA-GQDs-FA had good biocompatibility and could be used in the treatment of VX2 tumor cells after loading GA.
Automated multi-modal Transformer network (AMTNet) for 3D medical images segmentation
Authors: Zheng Shenhai , Tan Jiaxin , Jiang Chuangbo , Li Laquan 
Published: November 2022

Abstract

Objective. Over the past years, convolutional neural networks based methods have dominated the field of medical image segmentation. But the main drawback of these methods is that they have difficulty representing long-range dependencies. Recently, the Transformer has demonstrated super performance in computer vision and has also been successfully applied to medical image segmentation because of the self-attention mechanism and long-range dependencies encoding on images. To the best of our knowledge, only a few works focus on cross-modalities of image segmentation using the Transformer. Hence, the main objective of this study was to design, propose and validate a deep learning method to extend the application of Transformer to multi-modality medical image segmentation. Approach. This paper proposes a novel automated multi-modal Transformer network termed AMTNet for 3D medical image segmentation. Especially, the network is a well-modeled U-shaped network architecture where many effective and significant changes have been made in the feature encoding, fusion, and decoding parts. The encoding part comprises 3D embedding, 3D multi-modal Transformer, and 3D Co-learn down-sampling blocks. Symmetrically, the 3D Transformer block, upsampling block, and 3D-expanding blocks are included in the decoding part. In addition, a Transformer-based adaptive channel interleaved Transformer feature fusion module is designed to fully fuse features of different modalities. Main results. We provide a comprehensive experimental analysis of the Prostate and BraTS2021 datasets. The results show that our method achieves an average DSC of 0.907 and 0.851 (0.734 for ET, 0.895 for TC, and 0.924 for WT) on these two datasets, respectively. These values show that AMTNet yielded significant improvements over the state-of-the-art segmentation networks. Significance. The proposed 3D segmentation network exploits complementary features of different modalities during the feature extraction process at multiple scales to increase the 3D feature representations and improve the segmentation efficiency. This powerful network enriches the research of the Transformer to multi-modal medical image segmentation.
Hybrid Cerenkov-scintillation detector validation using Monte Carlo simulations
Authors: Jean Emilie , Lambert-Girard Simon , Therriault-Proulx François , Beaulieu Luc 
Published: November 2022

Abstract

Objective. This study aimed at investigating through Monte Carlo simulations the limitations of a novel hybrid Cerenkov-scintillation detector and the associated method for irradiation angle measurements. Approach. Using Monte Carlo simulations, previous experimental irradiations of the hybrid detector with a linear accelerator were replicated to evaluate its general performances and limitations. Cerenkov angular calibration curves and irradiation angle measurements were then compared. Furthermore, the impact of the Cerenkov light energy dependency on the detector accuracy was investigated using the energy spectra of electrons travelling through the detector. Main results. Monte Carlo simulations were found to be in good agreement with experimental values. The irradiation angle absolute mean error was found to be less than what was obtained experimentally, with a maximum value of 1.12° for the 9 MeV beam. A 0.4% increase of the ratio of electrons having an energy below 1 MeV to the total electrons was found to impact the Cerenkov light intensity collected as a function of the incident angle. The effect of the Cerenkov intensity variation on the measured angle was determined to vary according to the slope of the angular calibration curve. While the contribution of scattered electrons with a lower energy affects the detector accuracy, the greatest discrepancies result from the limitations of the calculation method and the calibration curve itself. Significance. A precise knowledge of the limitations of the hybrid detector and the irradiation angle calculation method is crucial for a clinical implementation. Moreover, the simulations performed in this study also corroborate hypotheses made regarding the relations between multiple Cerenkov dependencies and observations from the experimental measurements.
Collection efficiencies of ionization chambers in pulsed radiation beams: an exact solution of an ion recombination model including free electron effects
Authors: Fenwick John D , Kumar Sudhir 
Published: November 2022

Abstract

Objective. Boag et al (1996) formulated a key model of collection efficiency for ionization chambers in pulsed radiation beams, in which some free electrons form negatively charged ions with a density that initially varies exponentially across the chamber. This non-uniform density complicates ion recombination calculations, in comparison with Boag’s 1950 work in which a collection efficiency formula, f, was straightforwardly obtained assuming a uniform negative ion cloud. Boag et al (1996) therefore derived collection efficiency formulae f′, f″ and f′″ based on three approximate descriptions of the exponentially-varying negative ion cloud, each uniform within a region. Collection efficiencies calculated by Boag et al (1996) using these formulae differed by a maximum of 5.1% relative (at 144 mGy dose-per-pulse with 212 V applied over a 1 mm electrode separation) and all three formulae are often used together. Here an exact solution of the exponentially-varying model is obtained. Approach. The exact solution was derived from a differential equation relating the number of negative ions collected from within some distance of the anode to numbers of ions initially located within that region. Using the resulting formula, f
exp, collection efficiencies were calculated for a range of ionization chamber properties and doses-per-pulse, and compared with f, f′, f″ and f″′ values and results from an ion transport code. Main results. f
exp values agreed to 5 decimal places with ion transport code results. The maximum relative difference between f
exp and f″′, which was often closest to f
exp, was 0.78% for the chamber properties and doses-per-pulse studied by Boag et al (1996), rising to 6.1% at 1 Gy dose-per-pulse and 2 mm electrode separation. Significance. Use of f
exp should reduce ambiguities in collection efficiencies calculated using the approximate formulae, although like them f
exp does not account for electric field distortion, which becomes substantial at doses-per-pulse ≥100 mGy.
Limitations in evaluating COVID-19 protective face masks using open circuit spirometry systems: respiratory measurement mask introduces bias in breathing pressure and perceived respiratory effort
Authors: Seibt Robert , Bär Mona , Rieger Monika A , Steinhilber Benjamin 
Published: November 2022

Abstract

Objective. In response to the COVID-19 pandemic and the resulting widespread use of protective face masks, studies have been and are being conducted to investigate potential side effects of wearing masks on the performance and physiological parameters of wearers. The purpose of the present study is to determine whether and to what extent the use of a respiratory measurement (RM) mask—which is normally used during open-circuit spirometry—influences the results of these types of studies. Approach. 34 subjects were involved in this intra-subject study with a cross-over design. Four different protective face masks, Community Mask, medical Mouth-Nose-Protection Mask, Filtering Face Piece Mask Class 2 (FFP2), and FFP2 with exhalation valve (FFP2ex), were tested at rest and during deep breathing by using or not using a RM mask (RM versus noRM). Breathing pressure inside the protective face masks was measured during inhalation and exhalation, and subjects rated breathing effort using an 11-stage Borg scale. Main results. The use of an additional RM mask—worn over the protective face masks—significantly increased inspiratory pressures under all mask conditions. The respiratory pressure rises to a level that substantially distorts the results. Expiratory pressure was also significantly increased except for the FFP2ex mask condition. The perceived respiratory effort was significantly increased by 1.0 to 2.8 steps on the Borgs scale for all mask conditions compared with noRM. Significance. We strongly recommend avoiding the use of RM masks for evaluating the effects of protective face masks on human physiology and subjective perception.
Lung volumes measurement using novel pressure derived method in participants with obstructive, restrictive and healthy lungs
Authors: Zac Jacob , Zac Salomon , Pérez-Padilla Rogelio , Remigio-Luna Arantxa , Guzmán-Boulloud Nicolas , Gochicoa-Rangel Laura , Guzmán-Valderrábano Carlos , Thirión-Romero Ireri 
Published: November 2022

Abstract

Background. Lung volumes can be measured by body plethysmography (BP), by inert gas dilution during a single-breath or multiple breaths and by radiographic methods based on chest roentgenogram or CT scanning. Our objective was to analyze the concordance between several methods including a new pressure-derived method (PDM) in a variety of pulmonary conditions. Methods. We recruited four groups of adult volunteers at the chronic obstructive pulmonary disease and tobacco clinic of a respiratory referral hospital: patients with lung bullae, with obstructive lung diseases, with restrictive lung diseases and healthy controls; all subjects underwent lung volume measurements according to ATS/ERS standards in random order with each method and then CT scanning. Differences among groups were estimated by Kruskal–Wallis tests. Concordance correlation coefficients (CCC) and Bland–Altman plots were performed. Results. Sixty-two patients were studied including 15 with lung bullae, 14 with obstructive lung diseases, 12 with restrictive lung disease and 21 healthy subjects. Highest concordance was obtained between BP and CT scanning (CCC 0.95, mean difference −0.35 l) and the lowest, with TLC-DLCOsb (CCC 0.65, difference −1.05 l). TLC measured by BP had a moderate concordance with the PDM (CCC = 0.91, mean difference −0.19 l). The PDM on the other hand had the lowest intra-test repeatability (2.7%) of all tested methods. Conclusions. Lung volumes measured by BP and CT had high concordance in the scenario of varied pulmonary conditions including lung bullae, restrictive and obstructive diseases. The new PDM device, had low intra-test variability, and was easy to perform, with a reasonable concordance with BP.
Information geometry for multiparameter models: new perspectives on the origin of simplicity
Authors: Quinn Katherine N , Abbott Michael C , Transtrum Mark K , Machta Benjamin B , Sethna James P 
Published: November 2022

Abstract
Complex models in physics, biology, economics, and engineering are often sloppy, meaning that the model parameters are not well determined by the model predictions for collective behavior. Many parameter combinations can vary over decades without significant changes in the predictions. This review uses information geometry to explore sloppiness and its deep relation to emergent theories. We introduce the model manifold of predictions, whose coordinates are the model parameters. Its hyperribbon structure explains why only a few parameter combinations matter for the behavior. We review recent rigorous results that connect the hierarchy of hyperribbon widths to approximation theory, and to the smoothness of model predictions under changes of the control variables. We discuss recent geodesic methods to find simpler models on nearby boundaries of the model manifold—emergent theories with fewer parameters that explain the behavior equally well. We discuss a Bayesian prior which optimizes the mutual information between model parameters and experimental data, naturally favoring points on the emergent boundary theories and thus simpler models. We introduce a ‘projected maximum likelihood’ prior that efficiently approximates this optimal prior, and contrast both to the poor behavior of the traditional Jeffreys prior. We discuss the way the renormalization group coarse-graining in statistical mechanics introduces a flow of the model manifold, and connect stiff and sloppy directions along the model manifold with relevant and irrelevant eigendirections of the renormalization group. Finally, we discuss recently developed ‘intensive’ embedding methods, allowing one to visualize the predictions of arbitrary probabilistic models as low-dimensional projections of an isometric embedding, and illustrate our method by generating the model manifold of the Ising model.
A small piezoelectric stack motor designed for vacuum and micro-dust environment
Authors: Wan Hao , Peng Hanmin , Tang Xiongfeng , Chen Wangyi 
Published: November 2022

Abstract
With the development of moon exploration, higher requirements have been proposed for the adaptability of small motors under vacuum and micro-dust conditions. In this paper, a rotational motor based on piezoelectric stacks is proposed, which can be driven by a single-phase direct current with a simple drive circuit. The motor dimensions are 24 mm × 24 mm × 24 mm (length × width × height), and the mass is 11.4 g. In air environment, the maximum output rotational speed of the motor is 1372 rpm, and the maximum stall torque is 3.2 mN m at 40 V
DC and 5.93 kHz. An open-loop control system is established to drive the stepping motion of the motor, and the minimum stepping angle is measured as 0.122 mrad. An environmental experiment of the motor is conducted in a lunar simulator module, which can simulate a high vacuum (6.7 × 10−5 Pa) and micro-dust (average 75 μm) environment of the lunar surface. The result shows that the motor can work steadily in the vacuum and micro-dust environment with an average speed of 1723 rpm, which demonstrates its potential applications in the space environment.
Frequency dependent sensitivity of hydrogel iontronic sensor
Authors: Liu Haiyang , Guo Haoyu , Yang Meng , Liu Jianxing , Lu Tongqing 
Published: November 2022

Abstract
The hydrogel iontronic sensor (HIS) has attracted much attention in recent years due to its high sensitivity. The physical model to analyze the effects of various parameters on the sensitivity of the HIS is still lacking. In this work, we conduct experiments to study the effects of voltage frequency, sensor size, and ion concentration on the sensitivity of the HIS. The experimental results show that the sensitivity is highly dependent on frequency in the range of 20–1 MHz. We establish a theoretical model consisting of Possion–Nerust–Planck equations to describe the ion migration and incompressible Neo-Hookean constitutive equation to describe the hydrogel deformation. The theoretical results divide the sensitivity into three regions of frequency: the region dominated by fully formed electric double layers at the electrode-hydrogel interfaces, the region dominated by ionic relaxation of the hydrogel, and the region dominated by the dielectric property of hydrogel. The model agrees well with the experiments on the frequency dependence of sensitivity as well as the effect of size and ion concentration. This work may provide a guidance for the design of highly sensitive HIS.
Initial correlations in open quantum systems: constructing linear dynamical maps and master equations
Authors: Colla Alessandra , Neubrand Niklas , Breuer Heinz-Peter 
Published: November 2022

Abstract
We investigate the dynamics of open quantum systems which are initially correlated with their environment. The strategy of our approach is to analyze how given, fixed initial correlations modify the evolution of the open system with respect to the corresponding uncorrelated dynamical behavior with the same fixed initial environmental state, described by a completely positive dynamical map. We show that, for any predetermined initial correlations, one can introduce a linear dynamical map on the space of operators of the open system which acts like the proper dynamical map on the set of physical states and represents its unique linear extension. Furthermore, we demonstrate that this construction leads to a linear, time-local quantum master equation with generalized Lindblad structure involving time-dependent, possibly negative transition rates. Thus, the general non-Markovian dynamics of an open quantum system can be described by means of a time-local master equation even in the case of arbitrary, fixed initial system–environment correlations. We present some illustrative examples and explain the relation of our approach to several other approaches proposed in the literature.
Inferring oscillator’s phase and amplitude response from a scalar signal exploiting test stimulation
Authors: Cestnik Rok , Mau Erik T K , Rosenblum Michael 
Published: November 2022

Abstract
The phase sensitivity curve or phase response curve (PRC) quantifies the oscillator’s reaction to stimulation at a specific phase and is a primary characteristic of a self-sustained oscillatory unit. Knowledge of this curve yields a phase dynamics description of the oscillator for arbitrary weak forcing. Similar, though much less studied characteristic, is the amplitude response that can be defined either using an ad hoc approach to amplitude estimation or via the isostable variables. Here, we discuss the problem of the phase and amplitude response inference from observations using test stimulation. Although PRC determination for noise-free neuronal-like oscillators perturbed by narrow pulses is a well-known task, the general case remains a challenging problem. Even more challenging is the inference of the amplitude response. This characteristic is crucial, e.g. for controlling the amplitude of the collective mode in a network of interacting units—a task relevant to neuroscience. Here, we compare the performance of different techniques suitable for inferring the phase and amplitude response, particularly with application to macroscopic oscillators. We suggest improvements to these techniques, e.g. demonstrating how to obtain the PRC in case of stimuli of arbitrary shape. Our main result is a novel technique denoted by IPID-1, based on the direct reconstruction of the Winfree equation and the analogous first-order equation for isostable dynamics. The technique works for signals with or without well-pronounced marker events and pulses of arbitrary shape; in particular, we consider charge-balanced pulses typical in neuroscience applications. Moreover, this technique is superior for noisy and high-dimensional systems. Additionally, we describe an error measure that can be computed solely from data and complements any inference technique.
Effect of the electrical double layer on the electrical conductivity of suspensions
Authors: Sushko M Ya , Balika S D 
Published: November 2022

Abstract
We study the role of the electrical double layer (EDL) in the formation of the quasistatic electrical conductivity of suspensions of nanosized particles. A suspension is viewed as a system of hard-core–penetrable-shell particles. The shells are electrically inhomogeneous, with a radially symmetrical conductivity profile. It is assumed that the real microstructure of the suspension can be reflected in terms of this profile and also the rule of dominance for overlapping regions that the local conductivity in the system is determined by the nearest particle. Using our earlier rigorous results for systems with this morphology, we derive general integral relations for the desired conductivity which incorporate the effect of the EDL and make it possible to look into the contributions from its different parts and parameters. Specific features, internal consistency, and flexibility of the model are demonstrated by further elaborating it to describe experimental data for latex suspensions in aqueous electrolyte solutions with high ionic strength.