Miracle-dz/UniFAR-FTUs huggingface dataset:
Sodium Alginate—Natural Microencapsulation Material of Polymeric Microparticles

From the multitude of materials currently available on the market that can be used in the development of microparticles, sodium alginate has become one of the most studied natural anionic polymers that can be included in controlled-release pharmaceutical systems alongside other polymers due to its low cost, low toxicity, biocompatibility, biodegradability and gelatinous die-forming capacity in the presence of Ca2+ ions. In this review, we have shown that through coacervation, the particulate systems for the dispensing of drugs consisting of natural polymers are nontoxic, allowing the repeated administration of medicinal substances and the protection of better the medicinal substances from degradation, which can increase the capture capacity of the drug and extend its release from the pharmaceutical form.
Ice Thickness From Deep Learning and Conditional Random Fields: Application to Ice-Penetrating Radar Data With Radiometric Validation

Identifying the location of the ice–bedrock interface of glaciers and ice sheets is crucial for a wide range of geophysical applications, such as searching for liquid water in basal regions and computing ice thickness to quantify ice sheet and glacier mass balance. Simple, record-by-record, approaches to detecting the bottom of the ice echo may be affected by spurious off-nadir noise that requires significant manual interaction to correct. In this article, we propose a deep learning model based on convolutional neural networks (CNNs) and continuous conditional random fields (CCRFs) to automate ice bed identification and better capture fine-grained basal detail. We deploy this approach on high-capability radar sounder (HiCARS) radargrams, and this is the first time deep learning methods have been applied to this dataset. Intuitively, our CNN captures the global geometry of the ice bed, while the CCRF adjusts the initial CNN outputs to better incorporate fine-scale spatial information into the final prediction. We also develop a coherent geophysical framework using three echo characters (along-track continuity, relative delay, and signal coherency) to compare our model’s outputs with those of a manually targeted approach. Our analysis suggests that our CNN + CCRF model is as suitable as the manual approach for radiometric applications, and it outperforms the manual technique in identifying the first continuous return, which is most often the near-nadir reflection. Thus, our approach is more universal than the current manual labeling methodology in the range of geophysical applications where it can be used, and it provides better confidence regarding the source of the basal return detected.
Hi-C OBSERVATIONS OF SUNSPOT PENUMBRAL BRIGHT DOTS

We report observations of bright dots (BDs) in a sunspot penumbra using High Resolution Coronal Imager (Hi-C) data in 193 Å and examine their sizes, lifetimes, speeds, and intensities. The sizes of the BDs are on the order of 1″ and are therefore hard to identify in the Atmospheric Imaging Assembly (AIA) 193 Å images, which have a 1. ″2 spatial resolution, but become readily apparent with Hi-C's spatial resolution, which is five times better. We supplement Hi-C data with data from AIA's 193 Å passband to see the complete lifetime of the BDs that appeared before and/or lasted longer than Hi-C's three-minute observation period. Most Hi-C BDs show clear lateral movement along penumbral striations, either toward or away from the sunspot umbra. Single BDs often interact with other BDs, combining to fade away or brighten. The BDs that do not interact with other BDs tend to have smaller displacements. These BDs are about as numerous but move slower on average than Interface Region Imaging Spectrograph (IRIS) BDs, which was recently reported by Tian et al., and the sizes and lifetimes are on the higher end of the distribution of IRIS BDs. Using additional AIA passbands, we compare the light curves of the BDs to test whether the Hi-C BDs have transition region (TR) temperatures like those of the IRIS BDs. The light curves of most Hi-C BDs peak together in different AIA channels, indicating that their temperatures are likely in the range of the cooler TR (1−4 × 105 K).
A -site-driven ferroelectricity in strained ferromagnetic La 2 NiMnO 6 thin films

We report on theoretical and experimental investigation of A-site driven ferroelectricity in ferromagnetic La2NiMnO6 thin films grown on SrTiO3 substrates. Structural analysis and density functional theory calculations show that epitaxial strain stretches the rhombohedral La2NiMnO6 crystal lattice along the [111]cubic direction, triggering a displacement of the A-site La ions in the double perovskite lattice. The lattice distortion and the A-site displacements stabilize a ferroelectric polar state in ferromagnetic La2NiMnO6 crystals. The ferroelectric state only appears in the rhombohedral La2NiMnO6 phase, where MnO6 and NiO6 octahedral tilting is inhibited by the 3-fold crystal symmetry. Electron localization mapping showed that covalent bonding with oxygen and 6s orbital lone pair formation are negligible in this material.
Spin noise spectroscopy of a single-quantum-well microcavity

We report on an experimental observation of spin noise in a single semiconductor quantum well embedded into a microcavity. The great cavity-enhanced sensitivity to fluctuations of optical anisotropy has allowed us to measure the Kerr rotation and ellipticity noise spectra in the strong-coupling regime. The spin noise spectra clearly show two resonant features: a conventional magnetoresonant component shifting towards higher frequencies with a magnetic field and an unusual “nonmagnetic” component centered at zero frequency and getting suppressed with an increasing magnetic field. We attribute the first of them to the Larmor precession of free electron spins, whereas, the second one is presumably due to hyperfine electron-nuclei spin interactions.
Ballistic Studies of the Rotational Motion of the Artillery Projectile into Account the Equatorial Damping Moment

The problem of calculating the equatorial damping moment during trajectory flight is an actual problem inballistic studies of the rotational motion of artillery projectiles. The practice of ballistic research needs short algorithmsthat make it possible to calculate the damping moment together with the calculations of the trajectory parametersunder conditions of continuously changing characteristics of the oncoming flow. In this regard, a simplified methodfor calculating the equatorial damping moment of artillery projectiles in the oncoming flow is proposed, based onthe differentiation of the dependence for the overturning aerodynamic moment by the angle of attack and the Machnumber of the oncoming flow. Calculations of the parameters of the rotational motion of the 155-mm artilleryprojectile on the flight trajectory have been carried out. The influence of the equatorial damping moment on theperiodic components of the angular displacements of the projectile is revealed. The results of ballistic calculationswith the loss of stability of the rotational motion of the projectile showed the destabilizing effect of the equatorialdamping moment on the boundary parameters in terms of stability in the case of opposite directions of rotation ofthe projectile and crosswind.
The Use of Bayesian Statistics in Image Estimation from Interferometer Data

The problem of estimating radio sky brightness distributions from incomplete, and noisy, visibility measurements, such as those collected by a long baseline interferometer, has recently been attacked using non-linear data-adaptive techniques such as the Maximum Entropy method (Abies (1974), Wernecte (1976), Wernecke and D’Addario (1977), Gull and Daniell (1978)) and the Maximum Likelihood method (Papadopoulos (1975)).
Assessing the accuracy of the surface gravity determination in late-type stars with solar-like pulsators

The frequency of maximum oscillation power measured in dwarfs and giants exhibiting solar-like pulsations provides a precise, and potentially accurate, inference of the stellar surface gravity. An extensive comparison for about 40 well-studied pulsating stars with gravities derived using classical methods (ionization balance, pressure-sensitive spectral features or location with respect to evolutionary tracks) supports the validity of this technique and reveals an overall remarkable agreement with mean differences not exceeding 0.05 dex (although with a dispersion of up to ∼0.2 dex). It is argued that interpolation in theoretical isochrones may be the most precise way of estimating the gravity by traditional means in nearby dwarfs. Attention is drawn to the usefulness of seismic targets as benchmarks in the context of large-scale surveys.
Trap-assisted tunneling contributions to subthreshold forward current in InGaN/GaN light-emitting diodes

We present results from a combined experimental and numerical investigation of trap-assisted tunneling contributions to subthreshold forward current in InGaN/GaN light-emitting diodes. We show that the excess forward leakage current in single-quantum-well InGaN/GaN light-emitting diodes can be explained by non-local tunneling-into-traps processes and subsequent non-radiative recombination with free carriers.
Aktualisierte S2k-Leitlinie chronische Obstipation der Deutschen Gesellschaft für Gastroenterologie, Verdauungs- und Stoffwechselkrankheiten (DGVS) und der Deutschen Gesellschaft für Neurogastroenterologie & Motilität (DGNM) – April 2022 – AWMF-Registriernummer: 021–019

Autoren V. Andresen1*, G. Becker2, T. Frieling3, M. Goebel-Stengel4, F. Gundling5, A. Herold6, M. Karaus7, J. Keller1, M. Kim8, P. Klose9, H. Krammer6, M. E. Kreis10, R. Kuhlbusch-Zicklam3, J. Langhorst11, P. Layer1, R. Lenzen-Großimlinghaus12, A. Madisch13, H. Mönnikes14, S. Müller-Lissner15, D. Rubin16, 17, M. Schemann18, J. Schwille-Kiuntke19, 20, A. Stengel21, M. Storr22, I. van der Voort23, W. Voderholzer24, T. Wedel25, S. Wirz26, H. Witzigmann27, C. Pehl28*
Atmospheric parameters and rotational velocities for a sample of Galactic B‐type supergiants

High-resolution optical spectra of 57 Galactic B-type supergiant stars have been analysed to determine their rotational and macroturbulent velocities. In addition, their atmospheric parameters (effective temperature, surface gravity and microturbulent velocity) and surface nitrogen abundances have been estimated using a non-local thermodynamic equilibrium grid of model atmospheres. Comparisons of the projected rotational velocities have been made with the predictions of stellar evolutionary models and in general good agreement was found. However, for a small number of targets, their observed rotational velocities were significantly larger than predicted, although their nitrogen abundances were consistent with the rest of the sample. We conclude that binarity may have played a role in generating their large rotational velocities. No correlation was found between nitrogen abundances and the current projected rotational velocities. However, a correlation was found with the inferred projected rotational velocities of the main-sequence precursors of our supergiant sample. This correlation is again in agreement with the predictions of single star evolutionary models that incorporate rotational mixing. The origin of the macroturbulence and microturbulent velocity fields is discussed and our results support previous theoretical studies that link the former to subphotospheric convection and the latter to non-radial gravity mode oscillations. In addition, we have attempted to identify differential rotation in our most rapidly rotating targets.
Control of Problematic Weeds in Mediterranean Vineyards with the Bioherbicide Pelargonic Acid

Pelargonic acid (PA) is the only natural herbicide authorized for professional use in Spain. Incorporating PA into an integrated weed management strategy in vineyards may enable a more sustainable production method for grapes. In this work, PA of 55% concentration, formulated by a commercial company (PSEI), was evaluated and applied at 8, 10, 12, and 15 L/ha for weed control in Mediterranean vineyards during 2020 and 2021. A total of 22 different weed species, 16 dicotyledonous and 6 monocotyledonous, were identified in the experimental areas. Previously, greenhouse assays were performed against Avena fatua L. and Chenopodium album L. to determine the dose/response curves. PSEI proved to be a viable post-emergence herbicide with an efficacy of 40.79–80.90%, depending on the applied dose (higher doses were the most effective). Broader herbicidal activity (20% or more) was obtained against dicotyledonous weeds compared with monocotyledonous. The PA formulation was remarkable in achieving PSEI-similar effects as compared to the market reference but at lower concentrations (around 13% less PA) and doses (1–8 less L/ha). PA has proved to be a good candidate to control weeds in Mediterranean vineyards when used as a post-emergence broad-spectrum herbicide in the first stages of weed development.
Early Stage DRC Prediction Using Ensemble Machine Learning Algorithms

At leading technology nodes, the industry is facing a stiff challenge to make profitable integrated circuits (ICs). One of the primary issues is the design rule checking (DRC) violation. This research cohort with the DARPA IDEA program aims for “no-human-in-the-loop” and 24-h turnaround time to implement an IC from design specifications. In order to reduce human effort, this work introduces the ensemble random forest, gradient boosting, and Adaboost algorithms to predict DRC violations before detailed routing, which is considered the most time-consuming step in an IC design flow. In addition, this work identifies the features that critically impact DRC violations. The proposed algorithm has a 2% better F1-score compared to the existing support-vector machine (SVM) classifiers. The proposed ensemble approach has up to an area-under-the-curve–receiver operating characteristics (AUC–ROC) curve mean of 0.940 with ± 0.011 standard deviation compared to the state-of-the-art SVM classifier with an AUC–ROC curve mean of 0.854 with ± 0.01 standard deviation. The proposed ensemble approach exhibits up to 28.7% better DRC violation prediction rate compared to those using SVM algorithms on the test data. In addition, the proposed gradient boosting algorithm requires $37.5\times $ lower average training time and $50\times $ lower average testing time compared to the existing SVM methodologies.
External arguments and gerunds

Certain English nominalizations present evidence that the absence of accusative case assignment does not necessarily correlate with the absence of an external argument (one half of Burzio's generalization). Nominal gerunds (NGs), or ing-of constructions, contain an external argument position even though accusative case is not observed on the object. The hypothesis posits a defective light v, which introduces external arguments but does not value accusative case. We then make the following testable predications about the structure of nominalizations of this type: (i) Spec,D hosts both moved and merged arguments, although it remains a theta-assigning position (cf. Alexiadou 2001); (ii) English gerunds are limited to a nominalizing head selecting fully saturated predicates (vP, vPdefective, TP) or forming a nominal in the lexicon. The behaviour of NGs in contrast to unaccusatives and passives and the variability of the semantic interpretation of the external argument of NGs bear out the predictions.
A collaborative spectrum sensing algorithm for cognitive radio based on related vector machine

Due to the presence of tall buildings, mountains and other high occlusions in mountainous cities, this will produce fading phenomena, which will result in weak or even unrecognizable signals from the main users. To address this problem, a Related Vector Machine (RVM) based spectrum sensing method is proposed in this paper. First, the cognitive radio users (CR users) selection mechanism based on location correlation is designed, and some CR users with the best sensing performance are selected to participate in the sensing of the primary user (PU). Second, some parameters that reflect the characteristics of the PU signal are selected as the sample parameters. Finally, the signal samples received for both the presence and absence of the PU are sensed by using RVM. The experimental results show that the proposed algorithm has high classification detection performance in each low signal-to-noise ratio case, and effectively realizes the perception of the PU signal.
Disorder from disorder and confinement in the quantum Ising model in the pyrochlore lattice

At zero temperature, the classical antiferromagnetic Ising model on the pyrochlore lattice is a spin disorder phase of the critical spin correlation. It is a deconned phase in that the binding energy of the monopole-anti-monopole pair is independent of their distance of separation. We show that turning on a transverse magnetic eld turns it into the cooperative paramagnet, and the spin correlation becomes exponential decay. Furthermore, it introduces the quantum connement (of magnetic monopoles), where the binding energy of the pair is proportional to their distance of separation. This disorder state undergoes adiabatic transition to the paramagnetic state in the large eld limit. The eective Hamiltonian (without magnetic monopoles) in small eld is the Ising Hamiltonian plus ring exchange interaction.
Neuro-adaptive cooperative tracking control with prescribed performance of unknown higher-order nonlinear multi-agent systems

ABSTRACT This paper is concerned with the design of a distributed cooperative synchronisation controller for a class of higher-order nonlinear multi-agent systems. The objective is to achieve synchronisation and satisfy a predefined time-based performance. Dynamics of the agents (also called the nodes) are assumed to be unknown to the controller and are estimated using neural networks. The proposed robust neuro-adaptive controller drives different states of nodes systematically to synchronise with the state of the leader node within the constraints of the prescribed performance. The nodes are connected through a weighted directed graph with a time-invariant topology. Only few nodes have access to the leader. Lyapunov-based stability proofs demonstrate that the multi-agent system is uniformly ultimately bounded stable. Highly nonlinear heterogeneous networked systems with uncertain parameters and external disturbances were used to validate the robustness and performance of the new novel approach. Simulation results considered two different examples: single-input single-output and multi-input multi-output, which demonstrate the effectiveness of the proposed controller.
Ability and Context Based Adaptive System: A Proposal for Machine Learning Approach

When we interact with small screen devices, sometimes we make errors, due to our abilities/disabilities, contextual factors that distract our attention or problems related to the interface. Recovering from these errors may be time consuming or cause frustration. Predicting and learning these errors based on the previous user interaction and contextual factors, and adapting user interface to prevent from these errors can improve user performance and satisfaction. In this paper, we propose a system that aims to monitor user performance and contextual changes and do adaptations based on the user performance by using machine learning techniques. Here, we briefly present our systematic literature review findings and discuss our research questions towards developing such an adaptive system.
Variational Bayes for High-Dimensional Linear Regression With Sparse Priors

Abstract We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian model selection priors in sparse high-dimensional linear regression. Under compatibility conditions on the design matrix, oracle inequalities are derived for the mean-field VB approximation, implying that it converges to the sparse truth at the optimal rate and gives optimal prediction of the response vector. The empirical performance of our algorithm is studied, showing that it works comparably well as other state-of-the-art Bayesian variable selection methods. We also numerically demonstrate that the widely used coordinate-ascent variational inference algorithm can be highly sensitive to the parameter updating order, leading to potentially poor performance. To mitigate this, we propose a novel prioritized updating scheme that uses a data-driven updating order and performs better in simulations. The variational algorithm is implemented in the R package sparsevb. Supplementary materials for this article are available online.
Nonsingular Fixed-Time Control of Nonstrict Feedback MIMO Nonlinear System With Asymptotically Convergent Tracking Error

In this article, the research on fixed-time tracking control problem of multiple-input multiple-output nonlinear systems with nonstrict feedback control structure is carried out. By means of the fuzzy logic systems, the unknown system nonlinear dynamics are identified, and the “algebraic loop problem” is solved. Then, through the combination of adaptive backstepping recursive technology and adding power integration technology, a nonsingular fixed-time adaptive tracking control scheme is presented. Under the action of the presented control scheme, the system can track the specified reference signal within a fixed time independent of the initial state of the system, and the tracking control error can converge to zero asymptotically. Next, on the basis of the fixed-time Lyapunov stability theory, the practical fixed-time stability of the closed-loop system is theoretically demonstrated. Finally, two simulations verify the validity and practicability of the proposed control scheme.