Miracle-dz/UniFAR-FTUs huggingface dataset:
Langmuir waves across the heliosphere

All the bodies of the solar system are embedded in the supersonic flux of energetic particles emitted by the Sun. Since the advent of the space age, the models to describe the interaction of this plasma flow with the planets, asteroids, comets etc. have drastically progressed. The possibilities of in situ measurements of the particle distributions and electromagnetic fields have enabled the plasma theories to be tested under astrophysical conditions. Energy transfer from the Sun to the outermost regions of the heliosphere as well as the processes leading to the dissipation of this energy are central questions for heliophysicists. Understanding the dynamics of the particles is thus critical. It is a particularly complicated subject since the medium is (almost) non-collisional. Thus, next to the description of the particles, the development of waves must be considered. Indeed, they participate to the exchange of energy between different species that would not interact otherwise. In other words, waves may play the role of collisions. This paper concentrates on Langmuir waves for their strong links with the electron dynamics. The basic processes of growth and saturation of the Langmuir waves are reviewed to stress their diagnostic capabilities. Then, the characteristics of the waves are described in the several heliophysical contexts: the planetary environments (in particular the ionosphere, the magnetotail and the foreshock) and in the interplanetary medium (in quiescent conditions of the solar wind or during transient events). A particular emphasis is given to results obtained in the last 15 years.
A Neural Bag-of-Words Point Process Model for User Return Time Prediction in E-commerce

Monitoring and predicting user engagement is essential to gauge the overall health of an E-commerce platform. A healthy active user base indicates that the platform is able to retain users and is performing well on the user satisfaction metric. To measure long-term user satisfaction, predicting the return rate of a user is essential. The frequent return of the user indicates that they are overall satisfied with the platform. To this end, we consider the problem of predicting users’ return time on the platform given their historical interactions. The current state-of-the-art models for user return time prediction are based on recurrent neural network, which models the sequence of user interactions and predicts the return time using a Temporal Point Process based formulation. However, it is well-known that the inference time for these models grows as the sequence length increases, due to the complex recurrent and gating mechanisms, which deems them unfit to be used in a real-time prediction setting. Towards this end, we propose a lightweight and simple neural bag-of-words-based model for user return time prediction, which considers the user activity trail as a bag-of-words embedding model and performs a simple aggregation operation, used for the final prediction. We perform experiments on interaction log data from a major e-commerce company, and our proposed bag-of-words model outperforms the complex recurrence-based neural network by 6.14% and 4.81% on average, in terms of the Root-Mean-Squared-Error and Mean-Absolute-Error, respectively. We also compare the inference time of our method to the recurrent neural network-based method, with an overall reduction of 78.5% in terms of the wall-clock time.
All exons are not created equal - Exon vulnerability determines the effect of exonic mutations on splicing

It is now widely accepted that aberrant splicing of constitutive exons is often caused by mutations affecting cis-acting splicing regulatory elements (SREs), but there is a misconception that all exons have an equal dependency on SREs and thus a similar vulnerability to aberrant splicing. We demonstrate that some exons are more likely to be affected by exonic splicing mutations (ESM) due to an inherent vulnerability, which is context-dependent and influenced by the strength of exon definition. We have developed VulExMap, a tool which based on empirical data that can designate whether a constitutive exon is vulnerable. Using VulExMap, we find that only 27% of all exons can be categorized as vulnerable whereas two-thirds of 332 previously reported ESMs in 71 disease genes are located in vulnerable exons. Because VulExMap analysis is based on empirical data on splicing of exons in their endogenous context, it includes all features important in determining the vulnerability. We believe that VulExMap will be an important tool when assessing the effect of exonic mutations by pinpointing whether they are located in exons vulnerable to ESMs.
Stimulation of VTA dopamine inputs to LH upregulates orexin neuronal activity in a DRD2-dependent manner

Dopamine and orexins (hypocretins) play important roles in regulating reward-seeking behaviors. It is known that hypothalamic orexinergic neurons project to dopamine neurons in the ventral tegmental area (VTA), where they can stimulate dopaminergic neuronal activity. Although there are reciprocal connections between dopaminergic and orexinergic systems, whether and how dopamine regulates the activity of orexin neurons is currently not known. Here we implemented an opto-Pavlovian task in which mice learn to associate a sensory cue with optogenetic dopamine neuron stimulation to investigate the relationship between dopamine release and orexin neuron activity in the LH. We found that dopamine release can be evoked in LH upon optogenetic stimulation of VTA dopamine neurons, and is also naturally evoked by cue presentation after opto-Pavlovian learning. Furthermore, orexin neuron activity could also be upregulated by local stimulation of dopaminergic terminals in the LH in a way that is partially dependent on dopamine D2 receptors (DRD2). Our results reveal previously unknown orexinergic coding of reward expectation and unveil an orexin-regulatory axis mediated by local dopamine inputs in the LH. Highlights Optical VTA DA neuron stimulation is sufficient to elicit a Pavlovian-like dopamine transient in the NAc Dopamine in the LH encodes both negative and positive reward prediction errors Dopamine in the LH positively modulates orexin neuronal activity locally in a D2R dependent way
CRISPR-mediated germline mutagenesis for genetic sterilization of Anopheles gambiae males

Rapid spread of insecticide resistance among anopheline mosquitoes threatens malaria elimination efforts, necessitating development of alternative vector control technologies. Sterile Insect Technique (SIT) has been successfully implemented in multiple insect pests to suppress field populations by the release of large numbers of sterile males, yet it has proven difficult to adapt to Anopheles vectors. Here we outline adaptation of a CRISPR-based genetic sterilization system to selectively ablate male sperm cells in the malaria mosquito Anopheles gambiae. We achieve robust mosaic biallelic mutagenesis of zero population growth (zpg, a gene essential for differentiation of germ cells) in F1 individuals after intercrossing a germline-expressing Cas9 transgenic line to a line expressing zpg-targeting gRNAs. Approximately 95% of mutagenized males display complete genetic sterilization, and cause similarly high levels of infertility in their female mates. Using a fluorescence reporter that allows detection of the germline leads to a 100% accurate selection of spermless males, improving the system. These males cause a striking reduction in mosquito population size when released at field-like frequencies in competition cages against wild type males. These findings demonstrate that such a genetic system could be adopted for SIT against important malaria vectors.
Analysis of vibrations in a nonhomogeneous thermoelastic thin annular disk under dynamic pressure

ABSTRACT The forced vibration analysis of nonhomogeneous thermoelastic, isotropic, thin annular disk under periodic and exponential types of axisymmetric dynamic pressures applied on its inner boundary has been performed and analytical benchmark solution has been obtained. The material has been assumed to have inhomogeneity according to a simple power law in the radial coordinate. The present analysis has been worked out in the context of generalized theory of thermoelasticity with one relaxation time. The two coupled partial differential equations have been clubbed and solved by employing Laplace transform technique to obtain the solution for radial displacement and temperature change in the space domain. In order to obtain the solution in physical domain, the inversion of the transform has been carried out by using residue calculus. The radial displacement, radial stress, circumferential stress, and temperature change have been computed numerically for copper material annular disk. The numerically computed results have been presented graphically to demonstrate the effect of two different types of dynamic pressure in reference to homogeneous and nonhomogeneous material disk. The results for homogeneous material disk have been deduced and validated with that available in literature. The closed-form solution obtained here is interesting and allows further parametric studies of nonhomogeneous structures.
Thermal Simulations of the LMJ Cryogenic Target

Abstract In this paper, we report the results of thermal simulations made for the cryogenic target of Laser MegaJoule (LMJ). Thermal simulations are done with FLUENT for a new 3-D model which takes in account the fill tubes of the hohlraum and the membranes geometry inside the hohlraum. Firstly, a 1-D simple analytical model was developed in order to help us to limit the number of simulations and time calculations. Then, with the 3-D model, we have studied the effect of external radiation when shield is disconnected from the cryostat and we have determined the maximal height of shield removal before the shot. Finally, we have studied the sensitivity of the target to thermal contact resistances.
An Objective Climatology, Classification Scheme, and Assessment of Sensible Weather Impacts for Appalachian Cold-Air Damming

Abstract The geostrophic adjustment process for a rotating, stratified atmosphere in the presence of an orographic barrier may be manifest as a phenomenon known as “cold-air damming” (CAD). The degree of blocking by an orographic barrier, and therefore CAD intensity, is related to the static stability of the upstream air mass. When precipitation falls into dry near-surface air, differential evaporational cooling can increase static stability, and strengthen or initiate CAD. The sheltering effect of clouds can also maintain surface-based stability. Therefore, the ability of numerical forecast models to accurately predict CAD requires adequate representation of cloud and precipitation processes. Operational forecasters in the Appalachian damming region have previously developed a subjective classification scheme that distinguishes those CAD events that are heavily influenced by diabatic processes from those that are dominated by synoptic-scale forcing. In this study the subjective scheme is formalized in or.
The Effect of Viscous Dissipation and Variable Properties on Nanofluids Flow in Two Dimensional Microchannels

Laminar two dimensional forced convective heat transfer of Al2O3 -water nanofluid in a horizontal microchannel has been studied numerically, considering axial conduction, viscous dissipation and variable properties effects. The existing criteria in the literature for considering viscous dissipation in energy equation are compared for different cases and the most proper one is applied for the rest of the paper. The results showed that nanoparticles enhance heat transfer characteristics of the channel and on contrast, viscous dissipation causes the Nusselt number and friction factor to decrease. The viscous dissipation effect may be emphasized by increasing Reynolds number and decreased by raising the exerted heat flux. Also, it was found that there is a critical Reynolds number below which the average Nusselt number of the nanofluid changes unusually with Reynolds number as a result of variable properties effect.
Coupled Tilt and Translational Ground Motion Response Spectra

Dynamic response of structures subjected to earthquake-induced base excitations are often simplified by ignoring the tilt components of ground motion. However, close to the earthquake source, tilting of the ground surface may become significant. Based on strong-motion records at the Pacoima Dam-upper left abutment obtained during the 1994 Northridge Earthquake, residual tilt reached 3.1° in the N45°E direction while the dynamic tilt remained higher. This study investigates the consequences of neglecting the effects of the tilt component in ground motions on elastic and inelastic spectral ordinates. A complete equation of motion for a single-degree-of-freedom SDOF oscillator is developed which includes the effects of tilt as the secondary P- effect in addition to inertial force effects due to angular and translation accelerations. The expected values of the largest response peaks are computed for the translational and tilting excitations to investigate the relative contribution of each forcing function. The coupled tilt and translational ground motion response spectrum CTT spectrum is generated considering elastic and inelastic response of SDOF oscillator. The CTT spectrum reflects kinematic characteristics of the ground motion that are not identifiable by the translational ground motion response spectrum alone and therefore emerges as a distinct intensity measure of translational ground motion when it is coupled with dynamic tilting of the ground surface on the order of few degrees.
A Reliable Method for Establishing a Common Time synchronization in Mobile Ad Hoc Networks in VANET.

Recently, new networks have been developed that integrate mobile ad hoc networks with fog computing. These networks, often referred to as vehicular ad-hoc networks (VANETs), may be used for several purposes, such as traffic monitoring, intelligent driving, and safe driving. of a heterogeneous network is the fog-based VANET. which consists of both static and dynamic nodes in The network heterogeneity causes a significant clock rate difference across the nodes in a fog-based VANET. nodes’ clocks losing their accuracy and cause problems like inaccurate duty cycle scheduling and erroneous event sequencing.Several message exchanges must occur between the nodes to synchronise the clocks. In this work, this paper propose a method to synchronise the time. In this method, a group of nodes is selected to communicate to disseminate temporal information over the whole network, as opposed to having each node contact with the nodesclose to it. The time synchronisation protocol proposed can adapt to the high nodal velocity and random changes in the network topology while maintaining high accuracy and resistance to node failure and packet loss. The combination of NS2 simulators used to carry out the protocol simulation to assess the system performance has produced persuasive findings, exceeding those shown by the basic protocols.
Stolarsky-Tobey mean in n variables

In this paper, an n –dimensional weighted Stolarsky–Tobey mean is defined via measure. This mean includes as special cases various generalizations of the logarithmic mean. Some elementary properties are listed and various inequalities derived. Attention is given to the case when the mean is specialized to Dirichlet measure. Relations to hypergeometric function are exhibited. An explicit form is given for the mean in the special case when all variables have equal weights. Mathematics subject classification (1991): 26D15.
CODet: Component Object Detector Extracting Structural Features Based on Target Characteristics

Deep learning technology has promoted the object detection task in the remote sensing (RS) field to move toward better performance and more demanding requirements. Except for rigid body objects, component objects (COs) with more complex characteristics remain a detection challenge. Its “partial rules and overall disorder” characteristic limits the model learning ability to the structural features. And the internal noise and relatively sparse arrangement are not conducive to optimizing the model by the existing sample assignment strategies. We propose CODet to detect COs in RS scenes. It consists of a cross-hierarchy feature fusion module (CFM) and a noise-sparse sample assignment (NSA) strategy. CFM learns the potential representation and relative position relationship of components by fusing different level features. NSA redefines the optimization process of sample assignment. It aims to alleviate the problems of classification–localization misalignment (CLM) and the positive–negative sample imbalance (PNI) caused by the object’s internal noise and sparse arrangement. The method is verified on the proposed COD dataset of six categories of COs, reaching an average mAP/mAP50 of 54.3/86.0. To be closer to the task requirements of the practical RS scene, we also propose a RS large-scale images inference framework. It includes a dataset (APRoI, labeled with COs and rigid body objects), a large-scale image inference strategy, and a set of evaluation metrics. With CODet as the core, the framework can effectively reduce the inference time by three to four times on images with an average of more than 100 million pixels.
Exploring Gamification for Live-Streaming Shopping—Influence of Reward, Competition, Presence and Immersion on Purchase Intention

As a new form of social commerce, live-streaming shopping (LSS) provides customers with vivid real-time communication/interaction. However, there is limited research investigating the impacts of gamification in LSS. Thus, this study explores the relationships between gamification, customers’ engagement and purchase intention in LSS. A research model is formulated, and a questionnaire-based online survey is administrated to LSS viewers for empirically validating the model. Through the analysis of 272 collected questionnaires by using SPSS and AMOS, this study shows that gamification elements (including <italic>reward</italic> and <italic>competition</italic>) can enhance customers’ engagement (comprising <italic>sense of presence</italic> and <italic>immersion</italic>) and <italic>purchase intention</italic>. Moreover, <italic>sense of presence</italic> significantly mediates the indirect effects of <italic>reward</italic> and <italic>competition</italic> on <italic>purchase intention</italic>, but <italic>immersion</italic> does not mediate such effects. Stores and brands can design LSS programs with <italic>reward</italic> and <italic>competition</italic> to enhance customers’ <italic>sense of presence</italic>, <italic>immersion</italic> and <italic>purchase intention</italic>. Strategically, they should focus more on presence than immersion to better achieve their goals of LSS.
AN EFFICIENT COMPUTATIONAL METHOD FOR BOUNDS OF EIGENVALUES OF AN INTERVAL SYSTEM USING A CONVEX HULL ALGORITHM

صلاخلا ـ ة : تاريغتملا تاذ تافوفصملا روذجل دودحلا ديدحت ىلع لوصحلل ةلاعفو ةبسوحم ةقيرط ةقرولا هذه مدقت ، تايمزراوخ مادختساب كلذو تاذ ةلهس ةيلاع ةيلاعف . تمدختسا دقو دودحلا ددعتمل ةيفصولا طاقنلا عيمج ىلع يوتحي بدحم علضم رغصأ داجيلإ بدحملا فلاغلا ةقيرط . متو اريخأ رابتخا رصانعلا ةريغتم ةمظنأ ةدع ىلع اهتاقيبطت للاخ نم ةحرتقملا تايمزراوخلا ، للاخ نم اهرابتخا كلذآو لماكتم يئابرهآ ماظن ، ةءافآ نم دآأتلا مث نم و ةحرتقملا ةقيرطلا .
Parametric Delayed Output Feedback Control for Versatile Human-Exoskeleton Interactions During Walking and Running

This letter presents a parametric delayed output feedback controller for various types of human-exoskeleton interactions during walking or running gait. The parametric delayed-feedback controller operates reliably during various gait pattern changes, utilizing three adjustable control parameters: smoothing factor, time delay, and feedback gain. We show that various human-exoskeleton interaction torques can be generated using these three parameters, such as assistance with running or walking and different types of resistances. Feasibility tests were performed to demonstrate the efficacy of the proposed interaction controller. Hip exoskeleton assistance reduces the metabolic cost by 12.3% during running at 7 km/h compared with running without the exoskeleton. The generalizability of the controller performance was also demonstrated through various gait speed tests. Furthermore, we measured the electromyography signal data and the human lower limb joint kinematics to examine the feasibility of providing different gait resistances using different time delays. The results show that resistance with a delay of 0.3 s (Late mode) instead of a delay of 0.1 s (Early mode) is possible for walking resistance training, focusing on the hip flexors compared to the zero-torque mode.
Inverse probability weighted estimators for single-index models with missing covariates

Abstract In this article, we consider the inverse probability weighted estimators for a single-index model with missing covariates when the selection probabilities are known or unknown. It is shown that the estimator for the index parameter by using estimated selection probabilities has a smaller asymptotic variance than that with true selection probabilities, thus is more efficient. Therefore, the important Horvitz-Thompson property is verified for the index parameter in single index model. However, this difference disappears for the estimators of the link function. Some numerical examples and a real data application are also conducted to illustrate the performances of the estimators.
Polytopes with Centrally Symmetric Faces

If a convex polytope P is centrally symmetric, and has the property that all its faces (of every dimension) are centrally symmetric, then P is called a zonotope. Zonotopes have many interesting properties which have been investigated by Coxeter and other authors (see (4, §2.8 and §13.8) and (5) which contains a useful bibliography). In particular, it is known (5, §3) that a zonotope is completely characterized by the fact that all its two-dimensional faces are centrally symmetric.
On the Automatic Detection of Comorbidity of Mental Disorders Using Audio-Visual Data

It is reported that a patient with a mental disorder may develop other mental disorders over time. The condition of the simultaneous existence of multiple disorders is referred to as comorbidity. The treatment for a mental disorder is required to be modulated in the case of comorbidity. Thus, the detection of comorbidity is necessary. The comorbidity between major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) is well investigated through several modalities, which are either intrusive or costly. Recently, the audio-video modality has been explored to detect several mental disorders for being non-intrusive and cost-effective. The comorbidity detection between MDD and PTSD on audio-video modality is yet to be reported. In this study, we present initial work on the detection of comorbidity between MDD and PTSD on a publicly available audio-video dataset. To detect MDD/PTSD comorbidity separately, we created two overlapping subsets of the dataset comprising participants diagnosed with MDD/PTSD. The overlapping part in the two subsets comprises data of participants diagnosed with both MDD and PTSD. The best performance for MDD comorbidity detection is found to be 0.789 in terms of macro-averaging F1score. In contrast, it is found to be 0.647 for PTSD comorbidity detection.