Miracle-dz/UniFAR-FTUs huggingface dataset:
Relaxation back to equilibrium after cessation of shear for confined colloidal bilayers

Crystalline bilayers of charged colloidal suspensions which are confined between two parallel plates and sheared via a relative motion of the two plates are studied by extensive Brownian dynamics computer simulations. The charge-stabilized suspension is modeled by a Yukawa pair potential. The unsheared equilibrium configuration are two crystalline layers with a nested quadratic in-plane structure. For increasing shear rates $\dot \gamma$, we find the following steady states: first, there is a static solid which is elastically sheared until a yield-stress limit is reached. Then there are two crystalline layers sliding on top of each other with a registration procedure. Higher shear rates melt the crystalline bilayers and even higher shear rates lead to a reentrant solid stratified in the shear direction. This qualitative scenario is similar to that found in previous bulk simulations. We have then studied the relaxation of the sheared steady state back to equilibrium after an instantaneous cessation of shear and found a nonmonotonic behavior of the typical relaxation time as a function of the shear rate $\dot \gamma$. In particular, application of high shear rates accelerates the relaxation back to equilibrium since shear-ordering facilitates the growth of the equilibrium crystal. This mechanism can be used to grow defect-free colloidal crystals from strongly sheared suspensions. Our theoretical predictions can be verified in real-space experiments of strongly confined charged suspensions.
Impact of spanwise effective slope upon rough-wall turbulent channel flow

Abstract Whereas streamwise effective slope ($ES_{x}$) is accepted as a key topographical parameter in the context of rough-wall turbulent flows, the significance of its spanwise counterpart ($ES_{y}$) remains largely unexplored. Here, the response of turbulent channel flow over irregular, three-dimensional rough walls with systematically varied values of $ES_{y}$ is studied using direct numerical simulation. All simulations were performed at a fixed friction Reynolds number 395, corresponding to a viscous-scaled roughness height $k^{+}\approx 65.8$ (where $k$ is the mean peak-to-valley height). A surface generation algorithm is used to synthesise a set of ten irregular surfaces with specified $ES_{y}$ for three different values of $ES_{x}$. All surfaces share a common mean peak-to-valley height and are near-Gaussian, which allows this study to focus on the impact of varying $ES_{y}$, since roughness amplitude, skewness and $ES_{x}$ can be eliminated simultaneously as parameters. Based on an analysis of first- and second-order velocity statistics, as well as turbulence co-spectra and the fractional contribution of pressure and viscous drag, the study shows that $ES_{y}$ can strongly affect the roughness drag penalty – particularly for low-$ES_{x}$ surfaces. A secondary observation is that particular low-$ES_{y}$ surfaces in this study can lead to diminished levels of outer-layer similarity in both mean flow and turbulence statistics, which is attributed to insufficient scale separation between the outer length scale and the in-plane spanwise roughness wavelength.
Understanding and Identifying ‘Themes’ in Qualitative Case Study Research

Themes are at the heart of any qualitative research approach. Themes cannot be observed as they are perceptions, experiences, feelings, values and emotions residing in the minds of participants/respondents of a research. If a researcher feels that a theme is visible and no extra effort will be needed to extract, she may be confusing with the context. Because the themes are invisible perceptions and experiences embedded in the minds of respondents, to extract them the researcher needs to ‘ask’ questions. In-depth interviews or focus group discussions are easier processes to detect the themes. Only after collecting data with either interviews or focus group discussions or both, a researcher can turn to secondary sources of data. Identification of themes that connect with the theory determines the contribution of research. Themes should be far away from the description of any facet of the context. Themes should be closer to explaining the endogenous constructs of a research. Further, often the contribution of a qualitative case study research (QCSR) emerges from the ‘extension of a theory’ or ‘developing deeper understanding—fresh meaning of a phenomenon’. However, the lack of knowledge on how to identify themes results in shallow findings with limited to no contribution towards literature. This editorial is thus dedicated to explaining the process of identifying themes to make significant contributions through a QCSR. Themes are identified with any form of qualitative research method, be it phenomenology, narrative analysis, grounded theory, thematic analysis or any other form. However, the purpose and process of identifying themes may differ based not only on the methodology but also the research questions (Braun & Clarke, 2006). For instance, a thematic analysis helps to identify common patterns emerging from data, hence it is not an appropriate method if the objective is to search for unique themes (Kiger & Varpio, 2020). While the use of grounded theory is proper when the researcher looks for significant themes which can be idiosyncratic. We are not advocating any particular analysis process for identifying themes, rather we shall focus on explaining what to look for in data so that appropriate themes can be identified.
The Search for the Origins of M Theory : Loop Quantum Mechanics, Loops/Strings and Bulk/Boundary Dualities

The construction of a $covariant$ Loop Wave functional equation in a 4D spacetime is attained by introducing a generalized $eleven$ dimensional categorical {\bf C}-space comprised of $8\times 8$ antisymmetric matrices. The latter matrices encode the generalized coordinates of the histories of points, loops and surfaces $combined$. Spacetime Topology change and the Holographic principle are natural consequences of imposing the principle of $covariance$ in {\bf C}-space. The Planck length is introduced as a necessary rescaling parameter to establish the correspondence limit with the physics of point-histories in ordinary Minkowski space, in the limit $l_P\to 0$. Spacetime quantization should appear in discrete units of Planck length, area, volume ,. All this seems to suggest that the generalized principle of covariance, representing invariance of proper $area$ intervals in {\bf C}-space, under matrix-coordinate transformations, could be relevant in discovering the underlying principle behind the origins of $M$ theory. We construct an ansatz for the $SU(\infty)$ Yang-Mills vacuum wavefunctional as a solution of the Schroedinger Loop Wave equation associated with the Loop Quantum Mechanical formulation of the Eguchi-Schild String . The Strings/Loops ($SU(\infty)$ gauge field) correspondence implements one form of the Bulk/Boundary duality conjecture in this case.
The VIMOS Public Extragalactic Redshift Survey (VIPERS): - A quiescent formation of massive red-sequence galaxies over the past 9 Gyr

We explore the evolution of the Colour-Magnitude Relation (CMR) and Luminosity Function (LF) at 0.4 10^11 M_sun) and expeditious RS formation over a short period of ~1.5 Gyr starting before z=1. This is supported by the detection of ongoing SF in ETGs at 0.9<z<1.0, in contrast with the quiescent red stellar populations of ETGs at 0.5<z<0.6. There is an increase in the observed CMR scatter with redshift, two times larger than in galaxy clusters and at variance with theoretical models. We discuss possible physical mechanisms that support the observed evolution of the red galaxy population. Our findings point out that massive galaxies have experienced a sharp SF quenching at z~1 with only limited additional merging. In contrast, less-massive galaxies experience a mix of SF truncation and minor mergers which build-up the low- and intermediate-mass end of the CMR.
Implementation of cascaded H-bridge DC-link inverter for marine electric propulsion drives

Abstract This paper presents a single-phase seven level Cascaded H-Bridge DC-Link (CHBDCL) inverter for marine electric propulsion drives. The speed of propulsion drive can be changed by CHBDCL inverter by converting DC output from the rectifier to variable output voltage with or without change in frequency. The proposed CHBDCL inverter generates more output voltage with minimum harmonic content than classical Multilevel Inverters (MLIs). The CHBDCL inverter necessitates only ‘m + 3’ power devices for ‘m’ number of levels, although the classical MLIs requires ‘(2m − 1)’ power devices. CHBDCL inverter can have the effective performance by utilizing unipolar Sine Pulse Width Modulation (SPWM) and unipolar Space Vector PWM (SVPWM) with sine carrier. The effectiveness of the proposed CHBDCL inverter topology has been verified by using MATLAB/SIMULINK for various modulation indices in terms of voltage levels and harmonic analysis. Furthermore, an experimental setup involving pulse generation from a Field Programmable Gate Array (FPGA) has been used to test the performance of the proposed CHBDCL inverter.
Prediction model of deflection based on wavelet and LSTM+GAN

Bridge deflection is an important indicator to evaluate bridge safety, and accurate deflection prediction is an important way to ensure the healthy operation of bridges. At present, the methods applied to bridge deflection prediction are mainly long-term short-term memory networks (LSTM), but for bridge deflection data, the prediction effect of LSTM in the case of large mutation data often does not meet the ideal standard. In this paper, this paper proposes an LSTM+GAN hybrid network based on wavelet transform, which decomposes the deflection of the bridge into flat data and live load data by the wavelet transform, uses LSTM to predict the flat data, and the GAN network generates the predicted live load data, and finally sums the predicted values of LSTM and GAN to obtain the final predicted value. Experiments show that the proposed method has a better prediction accuracy improvement than LSTM.
Automatic image registration of optoacoustic tomography and magnetic resonance imaging based on deep learning

Multi-spectral optoacoustic tomography (MSOT) combines rich contrast of optical imaging and high resolution of ultrasound, and becomes an attractive biomedical research tool in the last decade. Aligning MSOT images with anatomical map provided by magnetic resonance imaging (MRI) can potentially enhance the interpretation of optoacoustic signal which mainly reflects molecular and functional information. Therefore, developing an automated algorithm of image registration between MSOT and MRI is crucial. Existing MSOT-MRI registration algorithms mostly relied on manual segmentation, which requires user-dependent experience. Herein, we developed a fully automated algorithm for MSOT-MRI registration based on deep learning (DL). This workflow consists of DL-based segmentation and image transformation. We have experimentally demonstrated the accuracy and computational efficiency of the method, paving the way towards high-throughput MSOT data analysis in close future.
Research on scent multidimensional AI empowers of audio and video of smart home users

Under the general trend of "intelligent networking" and "digital life", this paper makes an in-depth analysis of the latest trends and research hotspots of smell and smell empowerment of home TV and smart speaker, and combines the latest development trends of content traceability, big data analysis and AI technology. Combined with the achievements and shortcomings of communication operators in smart home TV and smart home speakers, this paper discusses some ideas about the operation and development of olfactory empowerment.
Three-dimensional cavity-assisted spontaneous emission as a single-photon source: Two cavity modes and Rabi resonance

Within the framework of exact quantum electrodynamics in dispersing and absorbing media, we have studied the emission from an initially in the upper state prepared emitter in a high-quality cavity in the case when there are two cavity modes that resonantly interact with the emitter. In the case when one of the modes is in resonance with the emitter transition and the other mode is tuned to the frequency equal to the Rabi splitting of the first mode, the effect of Rabi resonance is observed. In particular, the second mode exhibits a substantial increase of the one-photon Fock state efficiency and enhancement of the emission spectrum in comparison to the exact resonant case.
High speed moving object location based on UWB ranging calibration

In the traditional ultra-wideband (UWB) positioning technology, the influence of the ranging error on the positioning model and the influence of the motion parameters of the moving object on the positioning accuracy are not considered, especially for high-speed moving objects, it is difficult to achieve real-time, stable and exact positioning. Therefore this paper proposes a ranging calibration algorithm to minimize the distance error. The algorithm constructs a cost function based on the distance error, and combines the acceleration gradient descent (NAG) method to screen the value of the large distance error in the cost function. For the positioning of high-speed moving objects, the speed parameters of the moving objects are introduced, the vector equations about the anchor and the moving objects are established, and the positioning solution model of the moving objects is obtained, so as to realize the precise positioning of the high-speed moving objects.
Coexistence of LTE-U and Wi-Fi sharing unlicensed spectrum in ultra-dense networks

Spectrum sharing is an important approach to meet the requirements of the exponential increase in the demand for capacity in wireless communication. Long-Term Evolution (LTE) and Wi-Fi are two of the most remarkable wireless technologies at present. LTE-Unlicensed (LTE-U) bands have been introduced by the 3rd Generation Partnership Project (3GPP) to increase throughput and offload data from crowded licensed bands. Researches indicate that LTE-U exerts significant interference on Wi-Fi in dense and ultra-dense scenarios, preventing the harmonious coexistence of LTE-U and Wi-Fi. Therefore, it is necessary to adopt the coexistence scheme for restraining interference between LTE-U and Wi-Fi in unlicensed bands. In this paper, a dynamic on/off scheme is proposed to address the coexistence issues of LTEU and Wi-Fi when sharing unlicensed spectrum. We choose GNU Radio and Universal Software Radio Peripheral (USRP) in this study to construct a real-time test platform for investigating the performance of coexistence schemes based on software-defined radio (SDR). Using this platform, we conducted real-time experiments on the coexistence schemes of LTE-U and Wi-Fi in real wireless environments. The test results showed that the dynamic on/off scheme has better performance than the static on/off scheme in dense and ultra-dense scenarios. Harmonious coexistence can be achieved by adopting the dynamic on/off scheme in dense and ultra-dense scenarios.
Software test task assignment in agile development based on deep learning algorithms

Agile development is an efficient development mode, which has a short iteration cycle and high iteration frequency. To maintain the stability and quality of software in the high-frequency iterations, test engineers require to assign and perform many test tasks while it is hard to design an accurate and efficient test assignment. In order to mitigate the problem, we designed a BERT-based test task assignment prediction model, which uses the software requirement description text and testing records as dataset to train out an accurate task assignment model. For each iteration, this model assigns the case design tasks to different testers and automatically dispatches regression test cases to the appropriate testers. As a result, it accelerates the test management process, and improves the test efficiency and quality.
Research on quality monitoring technology of fertilizer granulation based on deep learning

Fertilizer is the food of grain, which has a decisive impact on grain yield. The granulation particle size of fertilizers is one of the important factors affecting product quality. Today, fertilizer production plants still observe the granulation particle size manually. This method is timeconsuming, labor-intensive, and unstable. For this reason, this study uses computer vision technology and high-speed cameras to quickly capture fertilizer granulation particles, uses a control system to control industrial cameras to capture particle size, and then train and optimize the neural network model. Achieve the monitoring of fertilizer granulation production quality, adjust the production factors in real time, and improve the production quality. Finally, it can replace manual work and accomplish unmanned and intelligent granulation detection.
Using an Iterative Algorithm to Predict Topography From Vertical Gravity Gradients and Ship Soundings

With the help of the analytical expression of the vertical gravity gradient (VGG) generated by a rectangular prism, an analytical algorithm for predicting seafloor topography using the VGG data has been studied. Nevertheless, ship sounding data are an essential constraint in solving the seafloor topography. This paper combines ship sounding data with VGG anomaly to predict the seafloor topography. The main research contents include the following: Using the ship soundings and VGG data in the study area, the observation equations about sea depth are established, and the stability of the equations are studied; furthermore, considering the influence of seafloor topography outside the study area on the observation equations, these effects are divided into boundary effects and far‐field effects, and different processing methods are proposed. Finally, the method is tested on the East Pacific Rise, only using VGG anomaly and adding the mean value to fix the boundary region, the RMS error of the results is 108.8 m; SIO's model is added to the boundary region and the seven maximum absolute errors are replaced by ship sounding data, the RMS error of the results can reach 94.2 m and the accuracy improvement is 13.42%.
Conventional dendritic cells 2, the targeted antigen-presenting-cell, induces enhanced type 1 immune responses in mice immunized with CVC1302 in oil formulation

Multifunctional CD4+ T helper 1 (Th1) cells, producing IFN-γ, TNF-α and IL-2, define a correlate of vaccine-mediated protection against intracellular infection. In our previous study, we found that CVC1302 in oil formulation promoted the differentiation of IFN-γ+/TNF-α+/IL-2+ Th1 cells. In order to extend the application of CVC1302 in oil formulation, this study aimed to elucidate the mechanism of action in improving the Th1 immune response. Considering the signals required for the differentiation of CD4+ T cells to Th1 cells, we detected the distribution of innate immune cells and the model antigen OVA-FITC in lymph node (LN), as well as the quantity of cytokines produced by the innate immune cells. The results of these experiments show that, cDC2 and OVA-FITC localized to inter-follicular region (IFR) of the draining lymph nodes, inflammatory monocytes localized to both IFR and T cell zone, which mainly infiltrate from the blood. In this inflammatory niche within LN, CD4+ T cells were attracted into IFR by CXCL10, secreting by inflammatory monocytes, then activated by IL-12-secreting cDC2. Above all, CVC1302 in oil formulation, on the one hand, targeted antigen and inflammatory monocyte into the LN IFN in order to attract CD4+ T cells, on the other hand, targeted cDC2 to produce IL-12 in order to promote optimal Th1 differentiation. The new finding will provide a blueprint for application of immunopotentiators in optimal formulations. Importance Along with the development of veterinary immunology, immunopotentiator and delivery system were not simply mixed with antigen, more and more attentions paid on the optimal compatibility in order to induce multifunctional immune responses. As reported, LPS formulated in IFA targeted antigens to the IFR of the LN, and recruited Mo into the IFR, then attracted antigen-specific CD4+ T cells, which differentiated into Th1 cells under the IL-12 produced by DC-SIGN+ DC(1). Herein, we further found that CVC1302 formulated with Marcol 52 induced enhanced Th1 immunity. Combined with our previous finding that CVC1302 in oil formulation induced improved humoral immunity, we concluded that CVC1302 in oil formulation provided multifunctional immunity, not only higher antibody titers to prevent pathogen infection, but also cellular immunity cytokine to prevent viral shedding.
Monitoring of the eclipsing Wolf-Rayet ULX in the Circinus galaxy

Abstract . We studied the eclipsing ultraluminous X-ray source CG X-1 in the Circinus galaxy, re-examining two decades of Chandra and XMM-Newton observations. The short binary period (7.21 hr) and high luminosity (LX ≈ 1040 erg s-1) suggest a Wolf-Rayet donor, close to filling its Roche lobe; this is the most luminous Wolf-Rayet X-ray binary known to-date, and a potential progenitor of a gravitational-wave merger. We phase-connect all observations, and show an intriguing dipping pattern in the X-ray lightcurve, variable from orbit to orbit. We interpret the dips as partial occultation of the X-ray emitting region by fast-moving clumps of Compton-thick gas. We suggest that the occulting clouds are fragments of the dense shell swept-up by a bow shock ahead of the compact object, as it orbits in the wind of the more massive donor.
Numerical simulation of the formation of shock waves in thin accretion disks and the resulting angular momentum transport

La formation et la propagation des ondes de choc dans un disque keplecien sont etudiees par une simulation numerique 2D. Cette simulation confirme l'idee que les perturbations non axisymetriques sont capables de developper des ondes de choc en forme de spirales dans les disques d'accretion et montre que la viscosite resultante peut etre calculee a partir de transport du moment cinetique
Neural dynamics underlying self-control in the primate subthalamic nucleus

The subthalamic nucleus (STN) is hypothesized to play a central role in neural processes that regulate self-control. Still uncertain, however, is how that brain structure participates in the dynamically evolving representation of value that underlies the ability to delay gratification and wait patiently for a gain. To address that gap in knowledge, we studied the spiking activity of neurons in the STN of monkeys during a task in which animals were required to remain motionless for varying periods of time in order to obtain food reward. At the single-neuron and population levels, we found a cost-benefit integration between the desirability of the expected reward and the imposed delay to reward delivery, with STN signals that dynamically combined both attributes of the reward to form a single integrated estimate of value. This neural representation of subjective value evolved dynamically across the waiting period that intervened between instruction cue and reward delivery. Moreover, this representation was gradually distributed along the antero-posterior axis of the STN such that the most posterior-placed neurons represented the temporal discounting of value most strongly. These findings highlight the selective involvement of the posterior STN in a dynamic internal process that dynamically estimates the ongoing cost-benefit balance of the current context. Such a process is essential for self-control, promoting goal pursuit and the willingness to bear the costs of time delays.