Miracle-dz/UniFAR-FTUs huggingface dataset:
Observation of cold, high-density plasma near the Doublet III limiter

Interferometer measurements on Doublet III indicate that a region of high-density cold plasma exists near the inside limiter. This cold plasma region appears above a certain threshold density in the main plasma and can be more than five times the average plasma density. The formation of the high-density cold region occurs somewhat before the maximum achievable plasma density.
Poloxamer sols endowed with in-situ gelability and mucoadhesion by adding hypromellose and hyaluronan for prolonging corneal retention and drug delivery

Abstract The purpose of this study was to develop poloxamer (P407)-based in-situ thermogellable hydrogels with reducing concentration of P407 by adding hypromellose (HPMC) and with enhancing mucoadhesion of resulting hydrogels by adding hyaluronic acid (HA) for prolonging ocular delivery of hydroxypropyl-β-cyclodextrin (HPβCD)-solubilized testosterone (TES). Results demonstrated that 0.5% TES solution was successfully solubilized with adding 10% HPβCD. Non-gellable 13% P407 sol became in-situ gellable with adding 2.0-2.5% HPMC and mucoadhesibility was further imporved with adding 0.3% HA-L (low MW) or HA-H (high MW). Optimized 0.5% HPβCD-solubilized TES P407-based thermogellable hydrogels with enhancement of mucoadhesion for prolonging ocular delivery comprised 13% P407, 2.5% HPMC, and 0.3% HA-L or HA-H. Furthermore, rheological measurements under simulated eye blinking confirmed that non-thixotropic properties of optimized hydrogels could be spreaded evenly and retain a greater amount of drug-loaded hydrogels on the ocular surface for a longer period to prolong drug delivery. Compared with conventional eye drops, the prolonged residence time of optimized hydrogels from ex vivo and in vivo studies were observed, indicating relationships between rheological properties and in vivo performances. It was concluded that P407-based thermosensitive hydrogels with reducing concentration of P407 and enhancing mucoadhesion was successfully formulated by adding 2.5% HPMC and 0.3% HA in 13% P407 for potentially accomplishing effective clinical treatment of DED.
Adaptive robust Kalman filtering for precise point positioning

The optimality of precise point postioning (PPP) solution using a Kalman filter is closely connected to the quality of the a priori information about the process noise and the updated mesurement noise, which are sometimes difficult to obtain. Also, the estimation enviroment in the case of dynamic or kinematic applications is not always fixed but is subject to change. To overcome these problems, an adaptive robust Kalman filtering algorithm, the main feature of which introduces an equivalent covariance matrix to resist the unexpected outliers and an adaptive factor to balance the contribution of observational information and predicted information from the system dynamic model, is applied for PPP processing. The basic models of PPP including the observation model, dynamic model and stochastic model are provided first. Then an adaptive robust Kalmam filter is developed for PPP. Compared with the conventional robust estimator, only the observation with largest standardized residual will be operated by the IGG III function in each iteration to avoid reducing the contribution of the normal observations or even filter divergence. Finally, tests carried out in both static and kinematic modes have confirmed that the adaptive robust Kalman filter outperforms the classic Kalman filter by turning either the equivalent variance matrix or the adaptive factor or both of them. This becomes evident when analyzing the positioning errors in flight tests at the turns due to the target maneuvering and unknown process/measurement noises.
Feasibility Analysis of Ambient RF Energy Harvesting for Low-Power IoT Devices

This paper presents a feasibility analysis of ambient RF energy harvester for low-power IoT Devices. The RF harvester consists of a half-triangular monopole antenna, a rectifier with a matching network, and a power management unit (PMU) with a battery. In this paper, The required charging time for RF harvesting for each application is analyzed with considering the feasibility for commercialization. The Analyzed result is proposed in the guideline. Feasibility analysis of IoT device application based on proposed Ambient RF energy harvester is verified by experiments.
High-resolution sample size enrichment of single-cell multi-modal low-throughput Patch-seq datasets

Single-cell multimodal technologies are becoming the hot topic of single-cell heterogeneity and function studies, promising to unravel the hidden relationship and functionalities of different aspects of the cells. Among the plethora of single-cell technologies, interesting is the patch-seq technology, which simultaneously performs Patch clamp measures and scRNA-seq on the same cells. However, given the experimental limitations of throughput of Patch clamp, the scRNA-seq analysis is challenging because it requires more samples to investigate cellular heterogeneity. Usually, the solution is associating the cells with the cell types in an existing scRNA-seq dataset. However, doing so loses part of the single cell resolution of the multimodal technique. Therefore, this work proposes a procedure leveraging the Seurat Integration process to find from a reference dataset t he most similar cells to the ones from the patch-seq. The similarity is how much gene expression profiles are identical, and to evaluate that, this work defines various etrics based on R and Index. In this way, one obtains a selection of suitable Reference cells to enrich the number of cells on which to perform multimodal investigation.
The economic valuation of ecosystem services: bibliometric analysis

Research background: The services provided by ecosystems are the main support for human populations and for the development of any type of activity. Today, the provision of these services is under threat. The economic valuation of ecosystem services is vital to design appropriate policies, define strategies and manage ecosystems. Purpose of the article: The objective of this study is to analyse the evolution of research on the economic valuation of ecosystem services over the last two decades. More specifically, it aims firstly to identify the main agents driving research and, secondly, it seeks to synthesize in a single document the relevant information on the main economic valuation methods, relating them to the categories of services, ecosystems and regions where they have been employed. Methods: A quantitative review was first carried out through a bibliometric analysis to identify the main drivers of this line of research and its development trends. Secondly, a qualitative review was conducted through a systematic review focusing on the most commonly used valuation techniques in relation to the characteristics of the service, the geographical scope and the ecosystem analysed. Findings & value added: The main novelty of this work, compared to previous literature, is that the relationship between the study area, the type of ecosystem, the category of service and the economic valuation methodology are analysed for the first time. The results highlight the need to continue expanding knowledge in relation to the temporal and spatial scale in the economic value of ecosystem services, the subjective nature of the estimates and the heterogeneity between the different social sectors with respect to the benefit obtained.
Linearized Boundary Value Problem for a Gas and Sound Propagation

A complete set of eigenfunctions of the linearized modeled kinetic theory Boltzmann equation appropriate to the boundary value problem is constructed. These functions are then used to solve a boundary value problem which contains the essential features of sound propagation experiments. The calculated pressure field correctly gives the hydrodynamic solution as its high‐density limit and modified free flow behavior as its low‐density limit. Even for very simple models the results are in remarkably good agreement with experiments.
Redshift drift and inhomogeneities

We derive the redshift drift formula for the spherically symmetric Stephani cosmological models. We show that in the considered class of the Stephani models with a centrally placed observer the redshift drift exhibits the behavior similar to the behavior of the redshift drift in the void LTB cosmological models at high redshifts. However, unlike in the void models where the redshift drift is always negative, the redshift drift in the considered class of Stephani models becomes slightly positive for some initial values of the redshift. This property may have the potential application for discriminating between the Stephani and the void LTB cosmological models.
Stabilization of Explicit Flow Solvers Using a Proper-Orthogonal-Decomposition Technique

A new numerical technique for the stabilization of explicit computational-fluid-dynamic solvers is presented. When using computational-fluid-dynamic codes to solve practical problems one sometimes finds the solution fails to converge due to the presence of a small number of eigenvalues of the flow solver outside the unit circle. In this paper we use the proper orthogonal decomposition technique to estimate the unstable eigenvalues and eigenmodes of the flow solver as the solution diverges for linear problems or exhibits limit-cycle oscillations for nonlinear problems. We use the resulting eigeninformation to construct a preconditioner that repositions the unstable eigenvalues from outside the unit circle to a point inside the unit circle close to the origin resulting in a stable flow solver. In this work the proposed method is applied to a nonlinear steady cascade solver. However, the technique can be easily applied to external flow solvers and to unsteady frequency-domain (time-linearized and harmonic-ba.
Some Recent Developments in Applied Functional Analysis

From its early stages, the intensive development of functional analysis and the remarkable advances of its methods cannot be explained without its link with other areas of mathematics and, above all, its role as an essential framework for numerical analysis and computer simulation, PDEs, modeling realworld phenomena, variational inequalities, or optimization, just to name a few. In this special issue we highlight some aspects of functional analysis which are used in connection with other branches of mathematics or science, either as a direct application or as a theoretical result which is essential for such an application. Although it is not possible to collect here the huge production of the research activity on this vast field of modern mathematics, the selected works gather together a range of topics which reflect some of the current research on applied functional analysis: bases in Banach spaces, wavelet transforms, fixed point theory, and applications to ODEs, electronic circuit simulation, or numerical solution of PDEs, integral equations, or problems on option pricing in mathematical finance. In this way, we have achieved one of our purposes, which is the exchange of ideas among researchers working both in abstract and applied functional analysis.
Intermittent dynamics of turbulence hibernation in Newtonian and viscoelastic minimal channel flows

Abstract Maximum drag reduction (MDR), the asymptotic upper limit of reduction in turbulent friction drag by polymer additives, is the most important unsolved problem in viscoelastic turbulence. Recent studies of turbulence in minimal flow units have identified time intervals showing key features of MDR. These intervals, denoted ‘hibernating turbulence’ are found in both Newtonian and viscoelastic flows. The present study provides a comprehensive examination of this turbulence hibernation phenomenon in the minimal channel geometry, and discusses its impact on the turbulent dynamics and drag reduction. Similarities between hibernating turbulence and MDR are established in terms of both flow statistics (an intermittency factor, mean and fluctuating components of velocity) and flow structure (weak vortices and nearly streamwise-invariant kinematics). Hibernation occurs more frequently at high levels of viscoelasticity, leading to flows that increasingly resemble MDR. Viscoelasticity facilitates the occurrence of hibernation by suppressing the conventional ‘active’ turbulence, but has little influence on hibernation itself. At low Weissenberg number $\mathit{Wi}$, the average duration of active turbulence intervals is constant, but above a critical value of $\mathit{Wi}$, the duration decreases dramatically, and accordingly, the fraction of time spent in hibernation increases. This observation can be explained with a simple mathematical model that posits that the lifetime of an active turbulence interval is the time that it takes for the turbulence to stretch polymer molecules to a certain threshold value; once the molecules exceed this threshold, they exert a large enough stress on the flow to suppress the active turbulence. This model predicts an explicit form for the duration as a function of $\mathit{Wi}$ and the simulation results match this prediction very closely. The critical point where hibernation frequency becomes substantially increased coincides with the point where qualitative changes are observed in overall flow statistics – the transition between ‘low-drag-reduction’ and ‘high-drag-reduction’ regimes. Probability density functions of important variables reveal a much higher level of intermittency in the turbulent dynamics after this transition. It is further confirmed that hibernating turbulence is a Newtonian structure during which polymer extension is small. Based on these results, a framework is proposed that explains key transitions in viscoelastic turbulence, especially the convergence toward MDR.
Singularity Avoidance and the Control of an Eight-Revolute-Joint Manipulator

The "optimum" 6R manipulator's singular configurations are used to develop the "best" 4R orientation and 4R regional structures for singularity avoidance. For comparison with the "best" 4R regional structure, the kinematics of a second 4R regional structure is explored. Conceptually, the control algo rithms for the 4R orientation and two 4R regional structures begin in primary mode with three actuator screws apiece. In primary mode the traditional position kinematics approach is used. However, when either the orientation or regional struc ture has difficulty following its Cartesian trajectory, control for that structure goes into secondary mode, where the redundant actuator is called on. The algorithms maintain a sixth-order screw system for an 8R manipulator with bounded actuator rates and well-behaved actuator values. The numerical com putations for an 8R manipulator are comparable to those for the "optimum" 6R manipulator and can be implemented in real time.
The Chinese Medicines Reduce Osteoporosis Caused by Therapy of Glucocorticoids and Tafacitinib Against Rheumatoid Arthritis

In clinical fights against rheumatoid arthritis (RA), tofacitinib and glucocorticoids are usually combined with Chinese medicines in China for better therapeutic effects against inflammation (9.6 days of average hospital stay, 73% reduced C-reactive protein, 69% decreased erythrocyte sedimentation rate) with fewer side effects of osteoporosis (16.6% RA patients were diagnosed with osteoporosis compared with around 30% onset ratio without Chinese medicines). To discover the candidate mechanisms of Chinese medicines against RA and osteoporosis, bioinformatics analysis was launched with hospitalized data, blood tests, and microarray. As a result, six top Chinese medicines were identified to deliver significant coordinative regulation networks against RA inflammation while reducing osteoporosis with their bioactive compounds, targeted genes/proteins, and genes/proteins associated with RA and osteoporosis.
On the generalization of true proportional navigation

A simple framework to define generalized true proportional navigation (GTPN) guidance laws is presented. It is shown that this framework subsumes many of the generalizations presented in the earlier literature. The capture regions of a number of GTPN guidance laws are obtained through a rigorous qualitative analysis. The method of analysis is simpler and lends itself directly to an easy geometrical interpretation. A considerable amount of misinterpretation in the previous results, arising out of certain basic misconceptions, are corrected here. It is shown that a logical application of the guidance philosophy, through a minor modification of GTPN to take into account the direction of rotation of the line-of-sight (LOS), contributes substantially to the expansion of the capture region in the relative velocity space. In particular, it is shown that the capture region also extends to the negative closing velocity region, thus making the modified GTPN almost comparable, so far as the domain of capturability of guidance laws is concerned, to the pure proportional navigation (PPN) guidance law. A number of new results on the exact bounds on the capture region are derived and illustrated through examples. >
Interatomic potential for the Cu-Ta system and its application to surface wetting and dewetting

An angle-dependent interatomic potential has been developed for the Cu-Ta system by crossing two existing potentials for pure Cu and Ta. The cross-interaction functions have been ﬁtted to ﬁrst-principles data generated in this work. The potential has been extensively tested against ﬁrst-principles energies not included in the ﬁtting database and applied to molecular dynamics simulations of wetting and dewetting of Cu on Ta. We ﬁnd that a Cu ﬁlm placed on a Ta (110) surface dewets from it, forming a Cu droplet on top of a stable Cu monolayer. We also observe that a drop of liquid Cu placed on a clean Ta (110) surface spreads over it as a stable monolayer while the extra Cu atoms remain in the drop. The stability of a Cu monolayer and instability of thicker Cu ﬁlms are consistent with recent experiments and ﬁrst-principles calculations. This agreement demonstrates the utility of the new potential for atomistic simulations of Cu-Ta interfaces
Hydromagnetic theory of geomagnetic storms

A hydromagnetic theory is presented which explains the average characteristics of geomagnetic storms. The magnetic storm is caused by a sudden increase in the intensity of the solar wind. Stresses are then set up in the geomagnetic field by the solar plasma impinging upon the geomagnetic field and becoming trapped in it. These stresses, which are propagated to the earth as hydromagnetic waves, account for the observed average magnetic storm variations. The sudden commencement of the magnetic storm is due to a hydromagnetic wave generated by the impact of the solar plasma on the geomagnetic field. The initial phase of the magnetic storm, during which the magnetic field is above average intensity, is due to the increased solar wind pressure. During the initial phase, instability causes small plasma clouds to become imbedded in the magnetic field. They break up and diffuse into the magnetic field to form a belt of trapped particles from the sun (principally protons and electrons). The trapped protons set up stresses, mainly due to centrifugal force, which account for the main phase of the magnetic storm. The recovery from the main phase is attributed to the relief of the stress on the geomagnetic field by the transfer of the energy of the trapped protons to neutral hydrogen by means of ion-atom charge exchange. The correct recovery time for the magnetic storm is predicted from the measured cross section of the ion-atom charge-exchange process and the hydrogen density values around the earth deduced from the scattering of solar Lyman-α radiation.
Towards Generalized ML Model in Automated Physiological Arousal Computing: A Transfer Learning-Based Domain Generalization Approach

Physiological signal-based pattern recognition has progressed significantly, such as automated pain assessment and stress detection. Public datasets provide a research platform to conduct machine learning studies. However, models trained from public datasets easily overfit that specific dataset and do not apply to unseen data collected in real-life scenarios. This paper proposes to use the transfer learning-based domain generalization technique to generalize the models to solve this issue. Data from different training domains are generalized, i.e., the dissimilarity is minimized by the proposed approach such that the model trained is generalized. We proved that the generalized model is more adaptive to new unseen data. Experiments have been done on the BioVid heat pain dataset and WESAD stress dataset, and results showed that our proposed methods significantly improve the model performance on new unseen data.
Unseen Epitope-TCR Interaction Prediction based on Amino Acid Physicochemical Properties

Successful prediction of epitope-T cell receptor (TCR) interactions can help with effective vaccination and personalized healthcare. Unseen epitope-TCR interaction prediction is based on independent sets of training and testing data, which is good to find their corresponding epitopes for novel, unseen diseases. In this study, we present a framework for predicting the unseen epitope-TCR interactions based on physicochemical properties of constituent amino acids of epitope and TCR CDR3 sequences. Sequence based models for epitope-TCR interaction prediction generally extract features individually from each sequence and then combine them together. However, in this study, the features for the unseen epitope-TCR interaction model have been generated as images from both sequences simultaneously by computing the absolute difference and outer product of two vectors consisting of the physicochemical property values of amino acids. The performances based on nine different physicochemical properties of amino acids have been compared and the best performing properties are selected. Some properties are combined together to achieve the highest performance. The model exhibits much higher performance in comparison with the existing unseen epitope prediction models. The model produces the AUC of 0.64 for absolute difference based features with only two best performing properties, and the AUC of 0.60 for vector outer product with the same two properties. Furthermore, our model achieves the AUC of 0.82 by combining both types of features while the best existing model achieves the AUC of only 0.55 in the setting of unseen epitope-TCR interaction prediction.