Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-dimensional surface gravity waves of a broad bandwidth on deep water

Abstract A new nonlinear Schrödinger equation (NLSE) is presented for ocean surface waves. Earlier derivations of NLSEs that describe the evolution of deep-water waves have been limited to a narrow bandwidth, for which the bound waves at second order in wave steepness are described in leading-order approximations. This work generalizes these earlier works to allow for deep-water waves of a broad bandwidth with large directional spreading. The new NLSE permits simple numerical implementations and can be extended in a straightforward manner in order to account for waves on water of finite depth. For the description of second-order waves, this paper proposes a semianalytical approach that can provide accurate and computationally efficient predictions. With a leading-order approximation to the new NLSE, the instability region and energy growth rate of Stokes waves are investigated. Compared with the exact results based on McLean (J. Fluid Mech., vol. 511, 1982, p. 135), predictions by the new NLSE show better agreement than by Trulsen et al. (Phys. Fluids, vol. 12, 2000, pp. 2432–2437). With numerical implementations of the new NLSE, the effects of wave directionality are investigated by examining the evolution of a directionally spread focused wave group. A downward shift of the spectral peak is observed, owing to the asymmetry in the change rate of energy in a more complex manner than that for uniform Stokes waves. Rapid oblique energy transfers near the group at linear focus are observed, likely arising from the instability of uniform Stokes waves appearing in a narrow spectrum subject to oblique sideband disturbances.
Direct numerical simulation of inertio-elastic turbulent Taylor–Couette flow

Abstract The flow physics of inertio-elastic turbulent Taylor–Couette flow for a radius ratio of $0.5$ in the Reynolds number ($Re$) range of $500$ to $8000$ is investigated via direct numerical simulation. It is shown that as $Re$ is increased the turbulence dynamics can be subdivided into two distinct regimes: (i) a low $Re \leqslant 1000$ regime where the flow physics is essentially dominated by nonlinear elastic forces and the main contribution to transport and mixing of momentum, stress and energy comes from large-scale flow structures in the bulk region and (ii) a high $Re \geqslant 5000$ regime where inertial forces govern the flow physics and the flow dynamics is mainly governed by small-scale flow structures in the near-wall region. Flow–microstructure coupling analysis reveals that the elastic Görtler instability in the near-wall region is triggered via significant polymer extension and commensurately high hoop stresses. This instability gives rise to small-scale elastic vortical structures identified as elastic Görtler vortices which are present at all $Re$ considered. In fact, these vortices develop herringbone streaks near the inner wall that have a longer average life span than their Newtonian counterparts due to their elastic origin. Examination of the budgets of mean streamwise enstrophy, mean kinetic energy, turbulent kinetic energy and Reynolds shear stress demonstrates that increasing fluid inertia hinders the generation of elastic stresses, leading to a monotonic reduction of the elastic-related effects on the flow physics.
Balanced ellipsoidal vortex equilibria in a background shear flow at finite Rossby number

Abstract We consider a uniform ellipsoid of potential vorticity (PV), where we exploit analytical solutions derived for a balanced model at the second order in the Rossby number, the next order to quasi-geostrophic (QG) theory, the so-called QG+1 model. We consider this vortex in the presence of an external background shear flow, acting as a proxy for the effect of external vortices. For the QG model the system depends on four parameters, the height-to-width aspect ratio of the vortex, $h/r$, as well as three parameters characterising the background flow, the strain rate, $\gamma$, the ratio of the background rotation rate to the strain, $\beta$, and the angle from which the flow is applied, $\theta$. However, the QG+1 model also depends on the PV, as well as the Prandtl ratio, $f/N$ ($f$ and $N$ are the Coriolis and buoyancy frequencies, respectively). For QG and QG+1 we determine equilibria for different values of the background flow parameters for increasing values of the imposed strain rate up to the critical strain rate, $\gamma _c$, beyond which equilibria do not exist. We also compute the linear stability of this vortex to second-order modes, determining the marginal strain $\gamma _m$ at which ellipsoidal instability erupts. The results show that for QG+1 the most resilient cyclonic ellipsoids are slightly prolate, while anticyclonic ellipsoids tend to be more oblate. The highest values of $\gamma _m$ occur as $\beta \to 1$. For large values of $f/N$, changes in the marginal strain rates occur, stabilising anticyclonic ellipsoids while destabilising cyclonic ellipsoids.
Cognitive Impact of Anthropomorphized Robot Gaze

Attentional control does not have fix functioning and can be strongly impacted by the presence of other human beings or humanoid robots. In two studies, this phenomenon was investigated while focusing exclusively on robot gaze as a potential determinant of attentional control along with the role of participants’ anthropomorphic inferences toward the robot. In study 1, we expected and found higher interference in trials including a direct robot gaze compared to an averted gaze on a task measuring attentional control (Eriksen flanker task). Participants’ anthropomorphic inferences about the social robot mediated this interference. In study 2, we found that averted gazes congruent with the correct answer (same task as study 1) facilitated performance. Again, this effect was mediated by anthropomorphic inferences. These two studies show the importance of anthropomorphic robotic gaze on human cognitive processing, especially attentional control, and also open new avenues of research in social robotics.
Glucose controls co-translation of structurally related mRNAs via the mTOR and eIF2 pathways in human pancreatic beta cells

Pancreatic beta cell response to glucose is critical for the maintenance of normoglycemia. A strong transcriptional response was classically described in rodent models but, interestingly, not in human cells. In this study, we exposed human pancreatic beta cells to an increased concentration of glucose and analysed at a global level the mRNAs steady state levels and their translationalability. Polysome profiling analysis showed an early acute increase in protein synthesis and a specific translation regulation of more than 400 mRNAs, independently of their transcriptional regulation. We clustered the co-regulated mRNAs according to their behaviour in translation in response to glucose and discovered common structural and sequence mRNA features. Among them mTOR- and eIF2-sensitive elements have a predominant role to increase mostly the translation of mRNAs encoding for proteins of the translational machinery. Furthermore, we show that mTOR and eIF2α pathways are independently regulated in response to glucose, participating to a translational reshaping to adapt beta cell metabolism. The early acute increase in the translation machinery components prepare the beta cell for further protein demand due to glucose-mediated metabolism changes. AUTHOR SUMMARY Adaptation and response to glucose of pancreatic beta cells is critical for the maintenance of normoglycemia. Its deregulation is associated to Diabetic Mellitus (DM), a significant public health concern worldwide with an increased incidence of morbidity and mortality. Despite extensive research in rodent models, gene expression regulation in response to glucose remains largely unexplored in human cells. In our work, we have tackled this question by exposing human EndoC-BH1 cells to high glucose concentration. Using polysome profiling, the gold standard technique to analyse cellular translation activity, we observed a global protein synthesis increase, independent from transcription activity. Among the specific differentially translated mRNAs, we found transcripts coding for ribosomal proteins, allowing the cell machinery to be engaged in a metabolic response to glucose. Therefore, the regulation in response to glucose occurs mainly at the translational level in human cells, and not at the transcriptional level as described in the classically used rodent models. Furthermore, by comparing the features of the differentially translated mRNAs, and classifying them according to their translational response, we show that the early response to glucose occurs through the coupling of mRNA structure and sequence features impacting translation and regulation of specific signalling pathways. Collectively, our results support a new paradigm of gene expression regulation on the translation level in human beta cells.
Reciprocal differentiation via GABAergic components and ASD-related phenotypes in hES with 1q21.1 CNV

Copy number variations (CNVs) in the distal 1q21.1 region, both deletion (1q del) and duplication (1q dup), are associated with autism spectrum disorder, epilepsy and schizophrenia. Besides common phenotypes, 1q del and 1q dup manifest opposite clinical phenotypes—e.g., microcephaly in 1q del and macrocephaly in 1q dup. However, molecular and cellular mechanisms are still elusive. We generate isogenic human ES (hES) cell lines with reciprocal 1q21.1 CNVs using CRISPR/Cas9 system and differentiate them into 2-dimensional (2-D) neurons and 3-D cortical organoids. Our study recapitulates opposite organoid size and shows dosage-dependent differentiation changes i.e., more mature and GABAergic components in 1q del and more proliferative state in 1q dup. In contrast, both CNVs show hyperexcitability and altered expressions of glutamate system as common features. These results demonstrate that 1q21.1 CNVs dramatically affect cell fate in the early neurodevelopmental periods. This is the first isogenic model of hES CNVs and our findings provide new insights into the underlying mechanisms of neurodevelopmental disorders.
Structural architecture of a dimeric paramyxovirus polymerase complex

Human parainfluenza virus type 3 (hPIV3), a member of non-segmented, negative-strand RNA viruses (nsNSVs), is the second most common cause of severe respiratory disease in pediatrics. The transcription and replication processes of nsNSVs are catalyzed by a multi-functional RNA-dependent RNA polymerase (RdRp) complex composed of the large protein (L) and the phosphoprotein (P). Previous studies have shown that the polymerase can adopt a dimeric form, however, the structure of the dimer and how it functions are not understood. Here we determined the cryo-EM structure of hPIV3 L-P complex at 2.7 Å with substantial structural details. A putative catalytic magnesium ion could be built in our structure, and structural comparison revealed atomic features conserved with other RNA viruses. Interactions identified between the two priming and intrusion loops and the connector domain potentially trigger the spatial movement of three C-terminal L domains for different steps of transcription and replication. Structural comparison with other nsNSV RdRps suggests common features of L-P binding. Furthermore, we report for the first time the structural basis of the L-L interaction in the partially modelled dimeric L-P structure, in which the connector domain of one L is positioned at the putative RNA template entry of the other L. Based on these findings, we propose a model by which L dimerization promotes efficient conversion of nascent RNA into a template.
The model arbuscular mycorrhizal fungus Rhizophagus irregularis harbours endosymbiotic bacteria with a highly reduce genome

Arbuscular mycorrhizal fungi (AMF; Glomeromycotina) are symbionts of most plant species that are known to possess unique intracytoplasmic endosymbiotic bacteria with an enigmatic role. Candidatus Moeniiplasma glomeromycotorum (CaMg) was shown to be widespread along the AMF phylogeny and present in most AMF species and isolates of those species. The model AMF species, Rhizophagus irregularis, that can be cultivated in vitro and for which a lot of genomic information now exists, would be the ideal model to study the true nature of the CaMg-AMF symbiosis. However, R. irregularis was never found to host endobacteria. Here we show by DNA sequencing that R. irregularis can, indeed, host CaMg (Ri-CaMg). However, this appears rare as only one R. irregularis isolate out of 58 hosted CaMg. In that isolate, the endosymbiotic bacterial population was genetically homogenous. By sequencing the complete genome of the bacteria, we found that its genome is among the smallest of all known CaMg and Mycoplasma-like genomes, with a highly reduced gene repertoire, suggesting a strong adaptation to the intracellular life. We discuss our findings in the light of previous literature on CaMg and on the same AMF isolates and suggest that these endosymbionts are more likely parasites than non-obligatory mutualists.
Visual and auditory brain areas share a representational structure that supports emotion perception

Emotionally expressive music and dance occur together across the world. This may be because features shared across the senses are represented the same way even in different sensory brain areas, putting music and movement in directly comparable terms. These shared representations may arise from a general need to identify environmentally relevant combinations of sensory features, particularly those that communicate emotion. To test the hypothesis that visual and auditory brain areas share a representational structure, we created music and animation stimuli with crossmodally matched features expressing a range of emotions. Participants confirmed that each emotion corresponded to a set of features shared across music and movement. A subset of participants viewed both music and animation during brain scanning, revealing that representations in auditory and visual brain areas were similar to one another. This shared representation captured not only simple stimulus features, but also combinations of features associated with emotion judgments. The posterior superior temporal cortex represented both music and movement using this same structure, suggesting supramodal abstraction of sensory content. Further exploratory analysis revealed that early visual cortex used this shared representational structure even when stimuli were presented auditorily. We propose that crossmodally shared representations support mutually reinforcing dynamics across auditory and visual brain areas, facilitating crossmodal comparison. These shared representations may help explain why emotions are so readily perceived and why some dynamic emotional expressions can generalize across cultural contexts.
Two levels of selection of rhythmicity in gene expression: energy saving for rhythmic proteins and noise optimization for rhythmic transcripts

Many genes have nycthemeral rhythms of expression, i.e. a 24-hours periodic variation, at either mRNA or protein level or both, and, most rhythmic genes are tissue-specific. Here, we investigate and discuss the evolutionary origins of rhythms in gene expression. Our results suggest that thythmicity of protein expression has been favored by selection for low cost. Trends are consistent in bacteria, plants and animals, and are also supported in tissue-specific patterns in mouse. Cost cannot explain rhythm at the RNA level, and we suggest instead it allows to periodically and drastically reduce expression noise. Noise control had strongest support in mouse, with limited power in other species. Genes under stronger purifying selection are rhythmically expressed at the mRNA level, probably because they are noise sensitive genes. We also suggest that mRNA rhythmicity allows to switch between optimal precision and higher stochasticity. Higher precision allows to maximize the robustness of gene expression when the function is most needed, while higher stochasticity allows to maintain oscillations and to exhibit diverse molecular phenotypes, i.e. “blind anticipation” of cells. The ability to alternate between these two states, enabled by rhythmicity at the mRNA level, might be adaptive in fluctuating environments. Finally, the adaptive role of rhythmic expression is also supported by rhythmic genes being highly expressed yet tissue-specific genes. This provides a good evolutionary explanation for the observation that nycthemeral rhythms are often tissue-specific.
Indonesian Millennial Segmentation to Increase Customer Retention in Using Online Grocery Apps

As Indonesian millennials become more familiar in purchasing products online, it also encourages the purchase of groceries to be done online. Although most millennials have used online grocery apps, but the retention is still low. The purpose of this research is to confirm the influence of customer retention antecedents to the customer retention in using online grocery apps, to identify millennial segmentation based on customer retention antecedents in using online grocery apps, and to test whether there are differences among the segments. To answer the research objectives, questionnaire is distributed to 200 millennials using simple random sampling technique. Linear regression, cluster analysis, cross-tabulation, and multiple discriminant analysis are used as analysis methods in this research. The results show that all independent variables significantly affect customer retention; with all those customer retention driving factors, four millennial segments in online grocery apps can be identified, namely Youngster Millennial, Trust Oriented Mid-Millennial, Productive Millennial, and Tech Savvy Millennial; and a significant difference is proven among those four segments with Perceived Security, Perceived Privacy, and Convenience as the most dominant differentiating factors.
Fast CU Partition Decision Strategy Based on Human Visual System Perceptual Quality

A fast Coding Unit (CU) partition decision strategy based on Human Visual System (HVS) perception quality is proposed in this paper. Considering that it is difficult for existing fast algorithms to further improve compression efficiency, perceptual coding technology has been tried to remove visual redundancy for achieving the purpose of reducing the bit rate on the basis of maintaining subjective visual quality. However, the existing perceptual coding model is still insufficient to reflect the characteristics of the HVS, which has limited improvement in coding efficiency, especially in fast algorithms. In this method, the characteristics of the limited human capacity for spatial-temporal resolution and the visual sensory memory are used to improve coding performance. First, the color complexity is used as a control factor to optimize the Just Noticeable Difference (JND) model to remove visual redundancy in a way that is more in line with the characteristics of visual perception. Second, a classification model of motion patterns based on human visual saliency is designed to provide a basis for CU classification, which effectively improves the coding accuracy. Finally, an offline Decision Tree (DT) classifier is designed based on the above model, and texture features are incorporated into the classifier as another key attribute to further reduce the computational complexity. The results of performance evaluation confirm that the proposed method achieves significantly improved coding performance compared with original Versatile Test Model (VTM). Compared with existing algorithms, our method not only improves coding efficiency, but also improves subjective visual quality.
Quasi-Static Magnetic Localization of Capsule Endoscopes with an Active Integrated Coil

The reliable localization for capsule endoscopes is an open research topic. In this study, a low-frequency magnetic localization method for capsule endoscopes with an integrated active coil is proposed. The spatial constraint, the limited battery capacity and the ferromagnetic battery shell of commercial capsules were considered. The generated magnetic flux density was evaluated depending on the distance to the coil and the maximal detectable range was determined. Twelve sensors were arranged in rings and by comparing the measured magnetic flux density with the analytic dipole model, the position and orientation of the coil were reconstructed. The results revealed that the ferromagnetic shell increases the magnetic moment of the coil by approximately a factor of 2.4. Moreover, the mean position and orientation errors were 0.5 mm and 0.3°. Furthermore, by using only the three closest sensors to the coil, a similar localization performance was achieved. Therefore, it was concluded that it is a feasible approach to choose the sensors, which measure the strongest signal to address the problem of the maximal detectable range of magnetic sensors. Moreover, by considering the limited battery capacity, the localization with the proposed coil must be conducted in short time intervals instead of continuously.
Storing Blockchain Data in Public Storage

The idea of blockchain has emerged in many applications, including IoT data processing, over the last few years. Because of the guaranteed security, the blockchain is believed to create the backbone of future technologies as a P2P network. However, applying the blockchain protocol in IoT data computing faces a severe challenge related to extensive data management. This paper presents a BlockStore, the method for storing blockchain data in single public storage while maintaining the security features. The BlockStore also resolves the access management and recovery issues among the blockchain nodes. The preliminary experiments prove that the expected research brings a considerable advantage to IoT data management using blockchain.1
Deep Learning-based Power Allocation in Massive MIMO Systems with SLNR and SINR Criterions

In this paper, we design a deep learning framework for the power allocation problems in massive MIMO networks. In particular, we formulate the max-min and max-product power allocation problems by using signal-to-interference-plus-noise ratio (SINR) and signal-to-leak-plus-noise ratio (SLNR) criteria for linear precoder design. Multiple base stations are deployed to serve multiple user equipments, the power allocation process to each user equipment takes long processing time to converge, which is inefficient approach. We tackle this problem by designing a framework based on deep neural network, where the user equipment position is used to train the deep model, and then it is used to predict the optimal power allocation according to the user's locations. The resulting deep learning helps to reduce the processing time of the system in determining the optimal power allocation for the user equipment. Compared to the standard optimization approach, the deep learning design helps to obtain the optimal solution of the power allocation problem within a short time via a quick-inference process. Simulation results show that the SINR criterion outperforms the SLNR one. Meanwhile, deep learning performance in predicting power allocation gets excellent results with an accuracy of 85% for the max-min strategy and 99% for the max-product strategy.