bluuebunny/crossref_metadata_2025 huggingface dataset:
Synchronization, clustering, and weak chimeras in a densely coupled transcription-based oscillator model for split circadian rhythms
Authors: Ocampo-Espindola Jorge Luis , Nikhil K. L. , Li Jr-Shin , Herzog Erik D. , Kiss István Z.
Published: August 2023

The synchronization dynamics for the circadian gene expression in the suprachiasmatic nucleus is investigated using a transcriptional circadian clock gene oscillator model. With global coupling in constant dark (DD) conditions, the model exhibits a one-cluster phase synchronized state, in dim light (dim LL), bistability between one- and two-cluster states and in bright LL, a two-cluster state. The two-cluster phase synchronized state, where some oscillator pairs synchronize in-phase, and some anti-phase, can explain the splitting of the circadian clock, i.e., generation of two bouts of daily activities with certain species, e.g., with hamsters. The one- and two-cluster states can be reached by transferring the animal from DD or bright LL to dim LL, i.e., the circadian synchrony has a memory effect. The stability of the one- and two-cluster states was interpreted analytically by extracting phase models from the ordinary differential equation models. In a modular network with two strongly coupled oscillator populations with weak intragroup coupling, with appropriate initial conditions, one group is synchronized to the one-cluster state and the other group to the two-cluster state, resulting in a weak-chimera state. Computational modeling suggests that the daily rhythms in sleep–wake depend on light intensity acting on bilateral networks of suprachiasmatic nucleus (SCN) oscillators. Addition of a network heterogeneity (coupling between the left and right SCN) allowed the system to exhibit chimera states. The simulations can guide experiments in the circadian rhythm research to explore the effect of light intensity on the complexities of circadian desynchronization.
Full three-dimensional computational fluid dynamics simulation and optimization of a swirling jet-induced cavitation reactor
Authors: Poorbaba Mohammad , Soleimani Mansooreh
Published: August 2023

Hydrodynamic cavitation is an efficient method in terms of energy consumption that can be used to intensify the pollutant degradation processes in wastewater treatment. Among various kinds of cavitation reactors, a swirling jet-induced cavitation reactor that has been less studied was investigated in detail for the first time in this work. Recently, researchers have focused more on investigating the parameters that affect the process and control its intensity. In the present work, the efficacy of the operating pressure on the performance of the cavitating device was investigated by calculating the cavitational efficacy ratio (CER) using full three-dimensional computational fluid dynamics simulations. Also, preliminary studies were carried out for the first time to optimize the curvature correction coefficient of the shear stress transport k–ω viscous model to sensitize it to streamline curvature to obtain convergence and stability of the simulations. The optimum operating pressure was found by solving the cavity dynamics equations and calculating the CER parameter. The Rayleigh–Plesset cavity dynamics equation was applied to the cavity trajectory obtained from solving the discrete phase model to track the cavity radius and inside pressure variations. Finally, the validation of the simulation and estimated optimum operating pressure were done by the experimental data reported in the literature that there was reasonable agreement between them.
Quantifying population dynamics via a geometric mean predator–prey model
Authors: da Silva S. L. , Carbone A. , Kaniadakis G.
Published: August 2023

An integrable Hamiltonian variant of the two species Lotka–Volterra (LV) predator–prey model, shortly referred to as geometric mean (GM) predator–prey model, has been recently introduced. Here, we perform a systematic comparison of the dynamics underlying the GM and LV models. Though the two models share several common features, the geometric mean dynamics exhibits a few peculiarities of interest. The structure of the scaled-population variables reduces to the simple harmonic oscillator with dimensionless natural time TGM varying as ωGMt with ωGM=c12c21. We found that the natural timescales of the evolution dynamics are amplified in the GM model compared to the LV one. Since the GM dynamics is ruled by the inter-species rather than the intra-species coefficients, the proposed model might be of interest when the interactions among the species, rather than the individual demography, rule the evolution of the ecosystems.
Stability of a particle-laden planar jet in the dilute suspension limit
Authors: Warrier Srikumar , Hemchandra Santosh , Tomar Gaurav
Published: August 2023

Particle laden flows are commonly seen in many industrial applications, such as fluidized beds in process industry, air laden with abrasive particles in abrasive machining, and particle laden plumes in chemical industries. In the present work, we perform local analysis of a particle laden planar jet in the dilute suspension regime. Unladen parallel planar jets have been extensively studied using normal modes and are shown to have two unstable modes, namely, sinuous and varicose modes. Sinuous modes are found to be more unstable compared to the varicose modes. In the present study, we investigate the effect of particles on the stability of planar jets. Addition of particles at low Stokes numbers (St) (fine particles) results in higher growth rates than that of the unladen jet. In the intermediate Stokes number regime, addition of particles has a stabilizing effect on both the sinuous and the varicose modes. Interestingly, for St∼10, the unstable varicose mode is completely damped. Increasing the Stokes number by increasing the particle size, both sinuous and varicose modes, shows increasing growth rates, while increasing the density ratio has a stabilizing effect on the flow. For non-uniform particle loading, additional modes apart from the sinuous and varicose modes are observed. These modes suggest the occurrence of compositional instability with an increased particle accumulation in the shear layer that is an order of magnitude higher compared to that of the sinuous and varicose modes.
Plasmonic metasurface superscatters driven by infrared surface lattice resonances
Authors: Sadeghi Seyed M. , Roberts Dustin T. , Knox Harrison , Gutha Rithvik R.
Published: August 2023

We have demonstrated that plasmonic metasurfaces composed of arrays of Au bowtie nanoantennas can support an infrared bidirectional superscattering state. This state arises when the nanoantennas are coherently coupled together, forming a surface lattice resonance that efficiently guides the infrared range (1–1.6 μm) of incident broadband white light along the plane of the arrays. This process exhibits strong polarization dependence, offering an “OFF” state where a 90° rotation of the incident light polarization effectively suppresses in-plane scattering from all sides. Stokes parameters analysis is used to study the states of polarization of the scattering, demonstrating transformation into a complete depolarized state. The results emphasize the significant influence of the multipolar modes of these nanoantennas on the interference processes associated with such scattering phenomena, and their potential applications in polarization optical switching and unique beamsplitting.
Further rotation reversal studies in C-Mod L-mode plasmas
Authors: Rice J. E. , Cao N. M. , Diamond P. H. , Greenwald M. J. , Hubbard A. E. , Marmar E. S. , Reinke M. L. , Rodriguez-Fernandez P.
Published: August 2023

Studies of core toroidal rotation reversal phenomenology in C-Mod deuterium L-mode plasmas have been expanded to include details of the dependences on plasma current and toroidal magnetic field. Rotation reversal occurs at a critical density, and universal scaling indicates that the product of ncritq95R ∼ BT/2, with ncrit in 1020/m3, R in m, and BT in T. Measurements in H and He plasmas exhibit similar behavior, including a connection with the linear Ohmic confinement/saturated Ohmic confinement transition and the cutoff for non-diffusive heat transport. Electron density and ion cyclotron range of frequencies power modulation experiments suggest that the collisionality ν* is a unifying parameter. Strong impurity puffing causes the critical density to increase, indicating that the situation is more complicated than only collisionality, perhaps involving the details of the effects of dilution on ion temperature gradient mode stability.
Photo-excited charge carrier lifetime enhanced by slow cation molecular dynamics in lead iodide perovskite FAPbI3
Authors: Hiraishi M. , Koda A. , Okabe H. , Kadono R. , Dagnall K. A. , Choi J. J. , Lee S.-H.
Published: August 2023

Using muon spin relaxation measurements on formamidinium lead iodide [FAPbI3, where FA denotes HC(NH2)2], we show that, among the five structurally distinct phases of FAPbI3 exhibited through two different temperature hysteresis, the reorientation motion of FA molecules is quasi-static below ≈50 K over the time scale of 10−6 s in the low-temperature (LT) hexagonal (Hex-LT, <160 K) phase, which has a relatively longer photo-excited charge carrier lifetime (τc∼10−6 s). In contrast, a sharp increase in the FA molecular motion was found above ≈50 K in the Hex-LT phase, LT-tetragonal phase (Tet-LT, <140 K), the high-temperature (HT) hexagonal phase (Hex-HT, 160–380 K), and the HT-tetragonal phase (Tet-HT, 140–280 K), where τc decreases with increasing temperature. More interestingly, the reorientation motion is further promoted in the cubic phase at higher temperatures (>380/280 K), while τc is recovered to comparable or larger than that of the LT phases. These results indicate that there are two factors that determine τc, one related to the local reorientation of cationic molecules that is not unencumbered by phonons and the other to the high symmetry of the bulk crystal structure.
Bifurcation cascade, self-similarity, and duality in the three-rotor problem
Authors: Krishnaswami Govind S. , Yadav Ankit
Published: August 2023

The three-rotor system concerns equally massive point particles moving on a circle subject to attractive cosine potentials of strength g. The quantum theory models chains of coupled Josephson junctions. Classically, it displays order–chaos–order behavior with increasing energy E along with a seemingly globally chaotic phase for 5.33g≲E≲5.6g. It is also known to admit pendulum and isosceles breather families of periodic orbits at all energies. While pendula display a doubly infinite sequence of stability transitions accumulating at their libration to rotation threshold at E=4g, breathers undergo only one stability transition. Here, we show that these stability transitions are associated with forward and reverse fork-like isochronous and period-doubling bifurcations. The new family of periodic orbits born at each of these bifurcations is found using an efficient search algorithm starting from a transverse perturbation to the parent orbit. The graphs of stability indices of various classes of orbits born at pendulum bifurcations meet at E=4g forming “fans.” The transitions in the librational and rotational phases are related by an asymptotic duality between bifurcation energies and shapes of newly born periodic orbits. The latter are captured by solutions to a Lamé equation. We also find and numerically validate values of scaling constants for self-similarity in (a) stability indices of librational and rotational pendula and (b) shapes of newly born orbits as E→4g. Finally, we argue that none of the infinitely many families of periodic orbits we have found is stable for 5.33g≲E≲5.6g, providing further evidence for global chaos in this energy band.
Numerical simulation of lateral jet interaction with rarefied hypersonic flow over a two-dimensional blunt body
Authors: Zhao Guang , Zhong Chengwen , Liu Sha , Chen Jianfeng , Zhuo Congshan
Published: August 2023

Reaction Control System (RCS) is a direct force control system that successfully adjusts a craft's attitude or orbit using the reaction force created by jet flow. RCS is frequently employed in the management of near-space vehicles due to its properties of fast response time and effective control efficiency. When the near-space vehicle is navigating at high altitude in a low density atmosphere, the Navier–Stokes equation is no longer applicable. The numerical approach utilized in this study is known as the Conserved Discrete Unified Gas Kinetic Scheme, and the governing equation is the Boltzmann equation, which is not constrained by the continuum hypothesis. In velocity space, an unstructured mesh is utilized, which minimizes the amount of discrete velocity points and considerably increases computation efficiency. The numerical results are in good agreement with the direct simulation Monte Carlo code DS2V when modeling large Knudsen number lateral jet flow. The interaction flow field between hypersonic free stream and lateral jet is then simulated at altitudes of 60–90 km using argon as the working gas and a two-dimensional blunt cone with lateral jet as the study object. Under a fixed jet pressure ratio, preliminary research was conducted on the variation of the lateral jet interference flow field characteristics with the freestream Knudsen number and angle of attack. The differences in surface pressure and heat flux caused by jet opening and shutting are compared. Under rarefied atmospheric conditions, the variation of the force/moment amplification coefficient is given. The numerical results show that when the angle of attack is 0°, the separation area in front of the nozzle and a pair of opposite vortices, which are common in the jet interference flow field, gradually disappear with increasing altitude, but the separation vortex reappears when the angle of attack of the freestream is increased. The high-pressure region generated upstream of the nozzle is the primary cause of the extra force/moment. The density of the main flow decreases as altitude increases, various shock wave patterns of the interference flow field gradually dissipate and the force/moment amplification factor changes considerably. The rarefied gas effect has a significant effect on the lateral jet interference flow.
Translation–rotation coupling and the kinematics of non-slip boundary conditions: A rough sphere between two sliding walls
Authors: Wang Yueran , Harrowell Peter
Published: August 2023

A non-slip constraint between a particle and a wall is applied at the microscopic level of collision dynamics using the rough sphere model. We analyze the consequences of the translation–rotation coupling of a rough sphere confined between two parallel planar walls and establish that shearing the walls past each other (i) preferentially deposits energy into the rotational degree of freedom and (ii) results in a bounded oscillation of the energy of the confined particle.
Ultrafast and terahertz spintronics: Guest editorial
Authors: Kampfrath Tobias , Kirilyuk Andrei , Mangin Stéphane , Sharma Sangeeta , Weinelt Martin
Published: August 2023

Spin-based electronics (spintronics) aims at extending electronic functionalities, which rely on the electron charge as information carrier, by the spin of the electron. To make spintronics competitive and compatible with other information carriers like photons and electrons, their speed needs to be pushed to femtosecond time scales and, thus, terahertz frequencies. In ultrafast and terahertz spintronics, femtosecond optical and terahertz electromagnetic pulses are used to induce spin torque and spin transport and to monitor the subsequent time evolution. The two approaches, sometimes referred to as femto-magnetism and terahertz magnetism, have provided new, surprising, and relevant insight as well as applications for spintronics. Examples include the ultrafast optical switching of magnetic order and the generation of broadband terahertz electromagnetic fields. This APL Special Topic Collection is dedicated to provide a platform for the newest developments and future trends in the very active, dynamic, and exciting research field of ultrafast and terahertz spintronics.
Does shadow education discourage or encourage creative thinking? Evidence from South Korea (La educación en la sombra, ¿fomenta o desincentiva el pensamiento creativo? Evidencia de Corea del Sur)
Authors: Byun Soo-yong , Jung Jilli , Shin Tae-Seob
Published: August 2023

Using longitudinal data for a nationally representative sample of fifth-graders from the Korean Educational Longitudinal Study of 2013, this study examined the effect of shadow education — referred to as academically oriented extracurricular activities mainly aimed to prepare for examinations — on students’ creative thinking. To estimate the effect of shadow education, we used propensity score matching approaches. We found no significant shadow education effect on gains (or losses) in students’ creative thinking between the fifth and sixth grades. This finding does not support the arguments that students’ participation in shadow education discourages or encourages their creative thinking. Implications, limitations and future research directions are discussed.
Microstructure and properties of AlCrxFeNi1.2Cu0.8 high-entropy alloys at high temperature
Authors: Li Chunyan , Tang Zihui , Lu Yu , Tian Lin , Zhai Jianshu , Li Xiaocheng , Kou Shengzhong
Published: August 2023

With the development of space technology, high-temperature resistant materials have become one of the hot spots in space research. The research of new high-temperature materials has become the key to breakthrough in aviation field. Research on microstructure and properties of AlCrxFeNi1.2Cu0.8 high-entropy alloys at high temperature (900, 1000 and 1100°C) were studied in this paper. After high-temperature annealing, the structural stability of alloys is maintained and the microstructure of alloys is dendrite. The grains are refined with the increase of Cr content. The alloy exhibits excellent comprehensive mechanical properties after heat treatment. The compressive strength of the alloy is above 1000 MPa and the plasticity of the alloy is above 10%. The alloy changes from brittle to tough with the increase in temperature. The hardness of Cr1.25 specimen is doubled after annealing, and the corrosion resistance of Cr 0.5 specimen is the best.
Women in STEM becoming independent: Embracing every experience as part of the learning process
Authors: Van Emmenis Lucie
Published: August 2023

In this Viewpoint, we hear from a cross section of women, across multiple research fields, discussing their science and the process of setting up a lab as an independent researcher. As well as being able to celebrate the positives of becoming an independent researcher, we would also like to use this platform to discuss the unique challenges they face as women scientists in their respective scientific environments.
Wavelength-actuated varifocal and polarization-insensitive metalenses assisted by monolayer single-celled phase-change metasurfaces
Authors: Xu Junwei , Xu Yaning , Zhang Shenglan , Huang Yafeng , Ding Pei , Jiang Tao , Tian Ximin , Li Zhi-Yuan , Ji Yuhang , Pan Xiaoyu
Published: August 2023

Varifocal metalenses with tunable focal lengths allowing for transforming the complex and bulky tunable lens kit into a single flat lens, show great potential applications in tomography techniques, 3D displays, multi-imaging processing, among others. However, varifocal metalenses that integrate key properties, such as modest efficiency, large numerical aperture (NA) as well as polarization insensitivity into one remain elusive. Here, we present and numerically demonstrate a wavelength-actuated varifocal and polarization-insensitive metalens. This metalens is based on a novel generic design principle that relies on independently tailoring two sets of Ge2Sb2Se4Te1 (GSST) anisotropic nanofins corresponding to two resonant wavelengths of λ
1 and λ
2, respectively. Via intelligently integrating two sets of anisotropic nanofins into a monolayer single-celled phase-change metasurface in the form of ‘cross’ or ‘straight’ composite, the metalens design not only enables the crosstalks between two wavelength channels minimized, but also is endowed with polarization-insensitive and varifocal performance, while maintaining high focusing efficiency and diffraction-limited performance. Due to the noninterleaved arrangement making the meta-structure more compact, another prototype of wavelength-actuated varifocal metalens with ultrahigh-NAs (0.89 and 0.95, respectively corresponding to the target wavelengths of λ
1 and λ
2) are also implemented with a field of view of 4° based on this principle. We also demonstrate the versatility of our approach by successfully implementing the generation of the polarization-insensitive varifocal focusing optical vortex. Additionally, the phase transition of GSST enables the proposed meta-devices to switch between ON and OFF states for spot focusing. Our design can be easily extended to visible or THz wavebands and will enable enormous applications ranging from switchable optical scanners, dynamic optical tweezers and spanners, to miniaturized polarization-insensitive imaging devices.
Energetic contributions to deformation twinning in magnesium
Authors: Kapan Enver , Alkan Sertan , Can Aydıner C , K Mason Jeremy
Published: August 2023

Modeling deformation twin nucleation in magnesium has proven to be a challenging task. In particular, the absence of a heterogeneous twin nucleation model which provides accurate energetic descriptions for twin-related structures indicates a need to more deeply understand twin energetics. To address this problem, molecular dynamics simulations are performed to follow the energetic evolution of

{
10

1
‾

2
}

tension twin embryos nucleating from an asymmetrically-tilted grain boundary. The line, surface and volumetric terms associated with twin nucleation are identified. A micromechanical model is proposed where the stress field around the twin nucleus is estimated using the Eshelby formalism, and the contributions of the various twin-related structures to the total energy of the twin are evaluated. The reduction in the grain boundary energy arising from the change in character of the prior grain boundary is found to be able to offset the energy costs of creating the other interfaces. The defect structures bounding the stacking faults that form inside the twin are also found to possibly have significant energetic contributions. These results suggest that both of these effects could be critical considerations when predicting twin nucleation sites in magnesium.
Real-time detection of isoprene marker gas based on micro-integrated chromatography system with GOQDs-modified μGC column and metal oxide gas detector
Authors: Han Baoqing , Zhang Xinyu , Wang Yan , Wang Wenjuan , Wang Benben , Li Shuai , Wang Hairong , Yan Yuefei , Han Jiusheng , Wang Chuanliu , Wang Congsi
Published: August 2023

Isoprene is a typical physiological marker that can be used to screen for chronic liver disease. This work developed a portable micro-integrated chromatography analysis system based on micro-electromechanical system technology, nanomaterials technology and embedded microcontroller technology. The system integrated components such as graphene oxide quantum dots modified semi-packed microcolumn, In2O3 nanoflower (NF) gas-sensitive detector and 3D printed miniature solenoid valve group. The effectiveness of the separation effect of the micro-integrated system was verified by gas mixture test; the laws of the influence of carrier gas pressure and column temperature on the chromatographic separation performance, respectively, were investigated, and the working conditions (column temperature 90 °C and carrier gas pressure 7.5 kPa) for system testing were determined. The percentages of relative standard deviation of the peak areas and retention times obtained for the separated gases were in the range of 0.95%–6.06%, indicating the good reproducibility of the system. Meanwhile, the microintegrated system could detect isoprene down to 50 ppb at small injection volume (1 ml). The system response increased with increasing isoprene concentration and was linearly correlated with isoprene concentration (R
2 = 0.986), indicating that the system was expected to be used for trace detection of isoprene, a marker gas for liver disease, in the future.
Topological phase transition between non-high symmetry critical phases and curvature function renormalization group
Authors: R Kumar Ranjith , Kartik Y R , Sarkar Sujit
Published: August 2023

The interplay between topology and criticality has been a recent interest of study in condensed matter physics. A unique topological transition between certain critical phases has been observed as a consequence of the edge modes living at criticalities. In this work, we generalize this phenomenon by investigating possible transitions between critical phases which are non-high symmetry (non-HS) in nature. We find the triviality and non-triviality of these critical phases in terms of the decay length of the edge modes and also characterize them using the winding numbers. The distinct non-HS critical phases are separated by multicritical points with linear dispersion at which the winding number exhibits the quantized jump, indicating a change in the topology (number of edge modes) at the critical phases. Moreover, we reframe the scaling theory based on the curvature function, i.e. curvature function renormalization group method to efficiently address the non-HS criticalities and multicriticalities. Using this we identify the conventional topological transition between gapped phases through non-HS critical points, and also the unique topological transition between critical phases through multicritical points. The renormalization group flow, critical exponents and correlation function of Wannier states enable the characterization of non-HS criticalities along with multicriticalities.
Time-dependent probability density function for partial resetting dynamics
Authors: Di Bello Costantino , Chechkin Aleksei V , Hartmann Alexander K , Palmowski Zbigniew , Metzler Ralf
Published: August 2023

Stochastic resetting is a rapidly developing topic in the field of stochastic processes and their applications. It denotes the occasional reset of a diffusing particle to its starting point and effects, inter alia, optimal first-passage times to a target. Recently the concept of partial resetting, in which the particle is reset to a given fraction of the current value of the process, has been established and the associated search behaviour analysed. Here we go one step further and we develop a general technique to determine the time-dependent probability density function (PDF) for Markov processes with partial resetting. We obtain an exact representation of the PDF in the case of general symmetric Lévy flights with stable index

0
<
α
⩽
2

. For Cauchy and Brownian motions (i.e.

α
=
1
,
2

), this PDF can be expressed in terms of elementary functions in position space. We also determine the stationary PDF. Our numerical analysis of the PDF demonstrates intricate crossover behaviours as function of time.
Numerical investigation of the freezing property of impinging water droplets on inclined cold superhydrophobic surfaces
Authors: Hou Jianqiang , Gong Jianying , Wu Xin
Published: August 2023

We numerically study the freezing process of impinging water droplets on inclined cold superhydrophobic surfaces, concerning the dynamic and heat transfer properties of impinging water droplets. Increasing the surface inclination angle and decreasing the contact angle hysteresis (CAH) is found to promote the rebound of impinging water droplets at a higher cold superhydrophobic temperature (such as −10 °C). The influence of the cold surface temperature on the receding side of impinging water droplets is stronger than that on the front side, and so is the CAH. The frozen region inside impinging water droplets first appears on the receding side, and this becomes more obvious as the surface inclination angle increases. The relatively intensive heat transfer process appears early in the impinging process of water droplets on an inclined cold superhydrophobic surface. Increasing the surface inclination angle could weaken the heat transfer process of impinging water droplets at a higher cold surface temperature (such as −10 °C), but it is not remarkable at a lower cold surface temperature (such as −30 °C). By increasing the supercooling degree, impinging Weber number, and CAH, the freezing process of impinging water droplets is enhanced.
Classical and quantum correlations between two qubits interacting with a field in thermal spin states
Authors: Algarni M , Abdel-Khalek S , Berrada K
Published: August 2023

In this manuscript, we introduce a quantum physical model consisting of two-atom system that interact with a quantized field initially prepared in the thermal spin states (TSSs). We study the effect of the main parameters of the model on the dynamical behavior atom–atom coherence, atoms-field entanglement, atom–atom entanglement and classical correlation. We show how the quantumness measures can be influenced by the spin number and thermal noise in the absence and presence of time-dependent coupling effect. We obtain that, despite the destructive influence of thermal noise, a considerable amount of coherence, entanglement and classical correlation still remain during the time evolution according the values of spin number. The results also show that the TSSs can offer the advantage of generating the maximal amount of coherence, entanglement and classical correlation during the dynamics.
Auditory neural correlates and neuroergonomics of driving assistance in a simulated virtual environment
Authors: Baqapuri Halim I , Roecher Erik , Zweerings Jana , Wolter Stefan , Schmidt Eike A , Gur Ruben C , Mathiak Klaus
Published: August 2023

Objective. Driver assistance systems play an increasingly important role in modern vehicles. In the current level of technology, the driver must continuously supervise the driving and intervene whenever necessary when using driving assistance systems. The driver’s attentiveness plays an important role in this human–machine interaction. Our aim was to design a simplistic technical framework for studying neural correlates of driving situations in a functional magnetic resonance imaging (fMRI) setting. In this work we assessed the feasibility of our proposed platform. Methods. We proposed a virtual environment (VE) simulation of driver assistance as a framework to investigate brain states related to partially automated driving. We focused on the processing of auditory signals during different driving scenarios as they have been shown to be advantageous as warning stimuli in driving situations. This provided the necessary groundwork to study brain auditory attentional networks under varying environmental demands in an fMRI setting. To this end, we conducted a study with 20 healthy participants to assess the feasibility of the VE simulation. Results. We demonstrated that the proposed VE can elicit driving related brain activation patterns. Relevant driving events evoked, in particular, responses in the bilateral auditory, sensory-motor, visual and insular cortices, which are related to perceptual and behavioral processes during driving assistance. Conceivably, attentional mechanisms increased somatosensory integration and reduced interoception, which are relevant for requesting interactions during partially automated driving. Significance. In modern vehicles, driver assistance technologies are playing an increasingly prevalent role. It is important to study the interaction between these systems and drivers’ attentional responses to aid in future optimizations of the assistance systems. The proposed VE provides a foundational first step in this endeavor. Such simulated VEs provide a safe setting for experimentation with driving behaviors in a semi-naturalistic environment.
Modulating individual axons and axonal populations in the peripheral nerve using transverse intrafascicular multichannel electrodes
Authors: Xie Yuyang , Qin Peijun , Guo Tianruo , Al Abed Amr , Lovell Nigel H , Tsai David
Published: August 2023

Objective. A transverse intrafascicular multichannel electrode (TIME) may offer advantages over more conventional cuff electrodes including higher spatial selectivity and reduced stimulation charge requirements. However, the performance of TIME, especially in the context of non-conventional stimulation waveforms, remains relatively unexplored. As part of our overarching goal of investigating stimulation efficacy of TIME, we developed a computational toolkit that automates the creation and usage of in silico nerve models with TIME setup, which solves nerve responses using cable equations and computes extracellular potentials using finite element method. Approach. We began by implementing a flexible and scalable Python/MATLAB-based toolkit for automatically creating models of nerve stimulation in the hybrid NEURON/COMSOL ecosystems. We then developed a sciatic nerve model containing 14 fascicles with 1,170 myelinated (A-type, 30%) and unmyelinated (C-type, 70%) fibers to study fiber responses over a variety of TIME arrangements (monopolar and hexapolar) and stimulation waveforms (kilohertz stimulation and cathodic ramp modulation). Main results. Our toolkit obviates the conventional need to re-create the same nerve in two disparate modeling environments and automates bi-directional transfer of results. Our population-based simulations suggested that kilohertz stimuli provide selective activation of targeted C fibers near the stimulating electrodes but also tended to activate non-targeted A fibers further away. However, C fiber selectivity can be enhanced by hexapolar TIME arrangements that confined the spatial extent of electrical stimuli. Improved upon prior findings, we devised a high-frequency waveform that incorporates cathodic DC ramp to completely remove undesirable onset responses. Conclusion. Our toolkit allows agile, iterative design cycles involving the nerve and TIME, while minimizing the potential operator errors during complex simulation. The nerve model created by our toolkit allowed us to study and optimize the design of next-generation intrafascicular implants for improved spatial and fiber-type selectivity.
Automated sleep classification with chronic neural implants in freely behaving canines
Authors: Mivalt Filip , Sladky Vladimir , Worrell Samuel , Gregg Nicholas M , Balzekas Irena , Kim Inyong , Chang Su-youne , Montonye Daniel R , Duque-Lopez Andrea , Krakorova Martina , Pridalova Tereza , Lepkova Kamila , Brinkmann Benjamin H , Miller Kai J , Van Gompel Jamie J , Denison Timothy , Kaufmann Timothy J , Messina Steven A , St Louis Erik K , Kremen Vaclav , Worrell Gregory A
Published: August 2023

Objective. Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function. Approach. Here we develop and validate an automated iEEG-based sleep–wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep–wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep–wake classifier in freely behaving canines. Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen’s Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night; p < 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night; p < 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night; p < 0.001). Significance. These results support the feasibility and accuracy of automated iEEG sleep–wake classifiers for canine behavior investigations.
Intelligent Path Planning of Mobile Robot Based on Genetic Algorithm
Authors: Wang JiaQi
Published: August 2023

In this study, in order to deal with the path planning problems with genetic algorithm (GA) and improve its time efficiency, a new method combined with K-Nearest Neighbor (KNN) is proposed. GA is a well-known approach to figure out a path between two designated points while avoiding obstacles in the way. In traditional GA process, a set of solutions from the starting node to the destination are randomly generated in the beginning, which are called initial population. Although not every solution is feasible and the feasible ones may not be ideal, optimal path is likely to be obtained during the iterations. A new method is designed to reduce the number of iterations by constructing a dataset using traditional GA and selecting elite solutions as initial population when facing a new map. The selection criteria is the similarity between the distribution of obstacles and previous ones. Relevant experiments are organized to test the performance of the proposed method by comparing it with the traditional GA. According to the simulation results, the proposed method with elite initial population tends to converge more quickly.
Data Generation Using Simulation Technology to Improve Perception Mechanism of Autonomous Vehicles
Authors: Cao Minh , Ramezani Ramin
Published: August 2023

Recent advancements in computer graphics technology allow more realistic rendering of car driving environments. They have enabled self-driving car simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large amounts of synthetic data that can complement the existing real-world dataset in training autonomous car perception. Furthermore, since self-driving car simulators allow full control of the environments, they can generate dangerous driving scenarios that the real-world dataset lacks such as bad weather and accident scenarios. In this paper, we will demonstrate the effectiveness of combining data gathered from the real-world with data generated in simulated world to train perception system on object detection and localization task. We will also propose a multi-level deep learning perception framework that aims to emulate a human learning experience in which series of tasks from simple to more difficult ones are learnt in a certain domain. The autonomous car perceptron can learn from easy-to-drive scenarios to more challenging ones customized by simulation software.
Machine Learning and Deep Learning Methods on Breast Cancer Metastases Detection
Authors: Zheng Wentao , Liu Kan , Che Fuqi , Zheng Yichen
Published: August 2023

There is a lot of room for research in how to detect breast cancer metastases in whole slide images more quickly and precisely. This work contrasts several machine learning and deep learning models on the breast cancer metastases detection problem and experiments the impacts of feature extraction, dimension reduction and the sizes of the datasets on these models. This work also establishes a breast cancer metastases detection pipeline based on the most accurate model in the experiments. Among the models, Dense Model using features trained from DenseNet201 with weights trained by ImageNet is of the best performance. This work develops a computer aided method to examine the cancerous regions in the whole slide images of breast cancer more quickly and precisely than an experienced doctor does.
Image Data Visualization Using T-SNE for Urban Pavement Disease Recognition
Authors: Huang Siyu
Published: August 2023

Urban pavement disease recognition is for the most part, a mission performed manually. Recently, video analysis task has been one of the most important applications in various fields. Aims to renovate on the automated vision-based disease recognition and to experiment new methods of road disease detection, this paper analyzes the images took from a city and performed image data visualization of road issues. From image features extracted from histogram of oriented gradient, we perform principal components analysis to reduce the dimensionality of features. Results are presented by visualizing datapoints using t-distributed stochastic neighbor embedding. The experiment shows that the image data visualization using t-SNE is suitable for the growing field of urban road disease recognition.
Lane Detection Methods Survey for Automatic Driving
Authors: Yang Mingyue
Published: August 2023

In realizing automatic driving, environment perception is an important task, and the research of lane detection algorithms occupies the leading position in environment perception. After many years of research on lane detection by researchers, many effective lane detection methods are now available. This article will research and analyze mainstream lane detection methods, including traditional methods and learning-based methods. Later, through an in-depth analysis of these methods, the advantages and disadvantages of each method are evaluated, and the direction of lane detection is discussed. It is hoped to help choose suitable lane detection methods in different scenes.
Causal Fusion for Recommender System
Authors: Sun Liwen , Zhang Chi , Su Zilin , Zhang Feiran , Zhou Yuen
Published: August 2023

Recommender system suffers from huge selection bias from users, which makes utilizing causal inference to solve this problem becomes a necessary problem. However, traditional rating model only utilizing biased dataset and unbiased estimator cannot remarkably be improved. Thus, we fused the unbiased dataset into training model and combine it with jointly learning method to better improve the original baselines. Extensive experiments verify that the proposed doubly robust joint leaning method after fusing unbiased, missing rating data can significantly outperform the state-of-the-art methods and can dramatically reduce the error from rating model with the increasing number of unbiased data.
Cooperative Spectrum Sensing for IoT System
Authors: Guo Mengxi
Published: August 2023

This paper it mainly writes how to make better use of spectrum and propose different algorithms to reduce excess spectrum waste in the case of spectrum shortage. Using Randomly distribute PU and SU’s location in certain area, with the help of graphs mark the PU and SU’s, we calculate the SNR considering channel fading, using formula to find the path loss. To perform energy detection, we generated an observation period of H1 signals and run simulations and evaluated it based on the graph of probability of detection (Pd) vs probability of false alarm (Pf). We also made some improvement on the algorithm to deal with sudden impulsive change in the environment variables. We subsequently used OR rule to decide presence of PUs. To get good performance and affordable work, we divided PU sensors into groups to detect energy of different PUs. When grouping according to SNR between PU and SU pairs, we achieved stable performance and less burden.
Computer Vision and Deep Learning for Precise Agriculture: A Case Study of Lemon Leaf Image Classification
Authors: Yuan Yang
Published: August 2023

Crop protection, an crucial field of precise agriculture, requires attention and improvement, as it secures sustainability and safety of crop and food production. There are various threats to crops in which pest is one of the severest. Computer vision technologies based on deep learning have shown great advantages on image classification as they enable real-time pest recognition on devices with cameras, such as drones. Thus, it is promising for pest monitoring and control and many DL models have been developed. Furthermore, early and accurate diagnosis is need as it minimizes pest damage. However, traditional models are limited on speed because the massive parameters require huge computing resource. In this work, we investigate the capability of lightweight model based on DL for the task of leaf disease classification on uncontrolled environment and compare it with traditional DL model. Lightweight models, in general, are designed to reduce computation on convolution layers with acceptable accuracy lose. We use an open database named LeLePhid, which contains lemon leave images, healthy or affected by aphid. The damage caused by aphid is general as the pest makes obvious changes to leaf outlooks. We focus on two typical DL models: the traditional, DenseNet and the lightweight, MobileNet, and discuss the balance between speed and accuracy, in order to support real-time analytics. Finally, we discuss the challenges and opportunities in practice.
Short-term Load Forecasting with Deep Learning Techniques
Authors: Dai Shijie
Published: August 2023

Short-term load forecasting is essential to the modern power system, which is the basis to guarantee the equilibrium of power supply and demand. It not only ensures the secure and stable operation of power grid, but also provides an important basis for power load dispatch, which can effectively reduce the economic loss caused by energy surplus or energy shortage in a short term. In the previous research work, traditional linear regression models, typical machine learning models and other models have been explored, but there are still problems of low accuracy or single influence factors. In this paper, a short-term forecasting model based on Temporal Convolutional Network (TCN) is proposed. Historical power load data, weather conditions, date information and their combinations are separately used to forecast the power load in different prediction models. Compared with other models, the evaluation metric of TCN model shows that its prediction accuracy is higher, and it is more suitable for short-term load forecasting.
Diversity of Models Based on Sequence to Sequence for Neural Machine Translation Tasks
Authors: Lu JiaNing
Published: August 2023

One of the most critical areas of Natural Language Processing study is machine translation, which realizes the translation process from source language to target language. After a rapid development, neural machine translation has become mainstream. Most of the tasks of natural language generation are implemented based on sequence to sequence model. The current technology of machine translation is the use of recurrent neural networks and attention mechanisms, which is essentially a set of encoding and decoding techniques. In order to handle the issue of excessive information compression introduced by decoder, attention mechanism is proposed. Transformer is also built with attention, which not only computes faster, but also achieves better results on translation tasks. In this paper, a series of comparison of these models and description of the performance on the dataset were shown.