Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface covalent encapsulation of multiwalled carbon nanotubes with poly(acryloyl chloride) grafted poly(ethylene glycol)

Multiwall carbon nanotube (MWNT) was grafted with polyacrylate-g-poly (ethylene glycol) via the following two steps. First, hydroxyl groups on the surface of acid-treated MWNT reacted with linear poly(acryloyl chloride) to generate graft on MWNT; secondly, the remaining acryloyl chloride groups were subjected to esterification with poly(ethylene glycol) leading the grafted chains on the surface of MWNTs. Thus obtained grafted MWNT was characterized using Fourier transform infrared spectrometer, transmission electron microscopy, and X-ray photoelectron spectroscopy. Thermogravimetric analysis showed that the weight fraction of grafted polymers amounted to 80% of the modified MWNT. © 2006 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 44: 6880–6887, 2006
In Situ Evidence of Ion Acceleration between Consecutive Reconnection Jet Fronts

Processes driven by unsteady reconnection can efficiently accelerate particles in many astrophysical plasmas. An example is the reconnection jet fronts in an outflow region. We present evidence of suprathermal ion acceleration between two consecutive reconnection jet fronts observed by the Magnetospheric Multiscale mission in the terrestrial magnetotail. An earthward propagating jet is approached by a second faster jet. Between the jets, the thermal ions are mostly perpendicular to magnetic field, are trapped, and are gradually accelerated in the parallel direction up to 150 keV. Observations suggest that ions are predominantly accelerated by a Fermi-like mechanism in the contracting magnetic bottle formed between the two jet fronts. The ion acceleration mechanism is presumably efficient in other environments where jet fronts produced by variable rates of reconnection are common and where the interaction of multiple jet fronts can also develop a turbulent environment, e.g., in stellar and solar eruptions.
Multimodal Privacy-preserving Mood Prediction from Mobile Data: A Preliminary Study

Mental health conditions remain under-diagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications towards the early detection and intervention of mental health disorders. One promising data source to help monitor human behavior is from daily smartphone usage. However, care must be taken to summarize behaviors without identifying the user through personal (e.g., personally identifiable information) or protected attributes (e.g., race, gender). In this paper, we study behavioral markers or daily mood using a recent dataset of mobile behaviors from high-risk adolescent populations. Using computational models, we find that multimodal modeling of both text and app usage features is highly predictive of daily mood over each modality alone. Furthermore, we evaluate approaches that reliably obfuscate user identity while remaining predictive of daily mood. By combining multimodal representations with privacy-preserving learning, we are able to push forward the performance-privacy frontier as compared to unimodal approaches.
On the flaring of thick discs of galaxies: insights from simulations

Using simulated galaxies in their cosmological context, we analyse how the flaring of mono-age populations (MAPs) influences the flaring and the age structure of geometrically defined thick discs. We also explore under which circumstances the geometric thin and thick discs are meaningfully distinct components, or are part of a single continuous structure as in the Milky Way. We find that flat thick discs are created when MAPs barely flare or have low surface density at the radius where they start flaring. When looking at the vertical distribution of MAPs, these galaxies show a continuous thin/thick structure. They also have radial age gradients and tend to have quiescent merger histories. Those characteristics are consistent with what is observed in the Milky Way. Flared thick discs, on the other hand, are created when the MAPs that flare have a high surface density at the radius where they start flaring. The thick discs’ scale heights can either be dominated by multiple MAPs or just a few, depending on the mass and scale height distribution of the MAPs. In a large fraction of these galaxies, thin and thick discs are clearly distinct structures. Finally, flared thick discs have diverse radial age gradients and merger histories, with galaxies that are more massive or that have undergone massive mergers showing flatter age radial gradients in their thick disc.
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data

The last few years have witnessed an increased interest in incorporating physics-informed inductive bias in deep learning frameworks. In particular, a growing volume of literature has been exploring ways to enforce energy conservation while using neural networks for learning dynamics from observed time-series data. In this work, we present a comparative analysis of the energy-conserving neural networks - for example, deep Lagrangian network, Hamiltonian neural network, etc. - wherein the underlying physics is encoded in their computation graph. We focus on ten neural network models and explain the similarities and differences between the models. We compare their performance in 4 different physical systems. Our result highlights that using a high-dimensional coordinate system and then imposing restrictions via explicit constraints can lead to higher accuracy in the learned dynamics. We also point out the possibility of leveraging some of these energy-conserving models to design energy-based controllers.
A Fast Graph Program for Computing Minimum Spanning Trees

When using graph transformation rules to implement graph algorithms, a challenge is to match the efficiency of programs in conventional languages. To help overcome that challenge, the graph programming language GP 2 features rooted rules which, under mild conditions, can match in constant time on bounded degree graphs. In this paper, we present an efficient GP 2 program for computing minimum spanning trees. We provide empirical performance results as evidence for the program's subquadratic complexity on bounded degree graphs. This is achieved using depth-first search as well as rooted graph transformation. The program is based on Boruvka's algorithm for minimum spanning trees. Our performance results show that the program's time complexity is consistent with that of classical implementations of Boruvka's algorithm, namely O(m log n), where m is the number of edges and n the number of nodes.
Optimal local approximation spaces for parabolic problems

We propose local space-time approximation spaces for parabolic problems that are optimal in the sense of Kolmogorov and may be employed in multiscale and domain decomposition methods. The diffusion coefficient can be arbitrarily rough in space and time. To construct local approximation spaces we consider a compact transfer operator that acts on the space of local solutions and covers the full time dimension. The optimal local spaces are then given by the left singular vectors of the transfer operator. To proof compactness of the latter we combine a suitable parabolic Caccioppoli inequality with the compactness theorem of Aubin-Lions. In contrast to the elliptic setting [I. Babu\v{s}ka and R. Lipton, Multiscale Model. Simul. , 9 (2011), pp. 373-406] we need an additional regularity result to combine the two results. Furthermore, we employ the generalized finite element method to couple local spaces and construct an approximation of the global solution. Since our approach yields reduced space-time bases, the computation of the global approximation does not require a time stepping method and is thus computationally efficient. Moreover, we derive rigorous local and global a priori error bounds. In detail, we bound the global approximation error in a graph norm by the local errors in the $L^2(H^1)$-norm, noting that the space the transfer operator maps to is equipped with this norm. Numerical experiments demonstrate an exponential decay of the singular values of the transfer operator and the local and global approximation errors for problems with high contrast or multiscale structure regarding space and time.
Proximal Policy Optimization Smoothed Algorithm

Proximal policy optimization (PPO) has yielded state-of-the-art results in policy search, a subfield of reinforcement learning, with one of its key points being the use of a surrogate objective function to restrict the step size at each policy update. Although such restriction is helpful, the algorithm still suffers from performance instability and optimization inefficiency from the sudden flattening of the curve. To address this issue we present a PPO variant, named Proximal Policy Optimization Smooth Algorithm (PPOS), and its critical improvement is the use of a functional clipping method instead of a flat clipping method. We compare our method with PPO and PPORB, which adopts a rollback clipping method, and prove that our method can conduct more accurate updates at each time step than other PPO methods. Moreover, we show that it outperforms the latest PPO variants on both performance and stability in challenging continuous control tasks.
Nutation in antiferromagnetic resonance

The effect of inertial spin dynamics is compared between ferromagnetic, antiferromagnetic and ferrimagnetic systems. The linear response to an oscillating external magnetic field is calculated within the framework of the inertial Landau--Lifshitz--Gilbert equation using analytical theory and computer simulations. Precession and nutation resonance peaks are identified, and it is demonstrated that the precession frequencies are reduced by the spin inertia, while the lifetime of the excitations is enhanced. The interplay between precession and nutation is found to be the most prominent in antiferromagnets, where the timescale of the exchange-driven sublattice dynamics is comparable to inertial relaxation times. Consequently, antiferromagnetic resonance techniques should be better suited for the search for intrinsical inertial spin dynamics on ultrafast timescales than ferromagnetic resonance.
Relational Pretrained Transformers towards Democratizing Data Preparation [Vision]

Can AI help automate human-easy but computer-hard data preparation tasks (for example, data cleaning, data integration, and information extraction), which currently heavily involve data scientists, practitioners, and crowd workers? We envision that human-easy data preparation for relational data can be automated. To this end, we first identify the desiderata for computers to achieve near-human intelligence for data preparation: computers need a deep-learning architecture (or model) that can read and understand millions of tables; computers require unsupervised learning to perform self-learning without labeled data, and can gain knowledge from existing tasks and previous experience; and computers desire few-shot learn-ing that can adjust to new tasks with a few examples. Our proposal is called Relational Pretrained Transformers (RPTs), a general frame-work for various data preparation tasks, which typically consists of the following models/methods: (1) transformer, a general and powerful deep-learning model, that can read tables/texts/images;(2) masked language model for self-learning and collaborative train-ing for transferring knowledge and experience; and (3) pattern-exploiting training that better interprets a task from a few examples.We further present concrete RPT architectures for three classical data preparation tasks, namely data cleaning, entity resolution, and information extraction. We demonstrate RPTs with some initial yet promising results. Last but not least, we identify activities that will unleash a series of research opportunities to push forward the field of data preparation.
Understanding Attention: In Minds and Machines

Attention is a complex and broad concept, studied across multiple disciplines spanning artificial intelligence, cognitive science, psychology, neuroscience, and related fields. Although many of the ideas regarding attention do not significantly overlap among these fields, there is a common theme of adaptive control of limited resources. In this work, we review the concept and variants of attention in artificial neural networks (ANNs). We also discuss the origin of attention from the neuroscience point of view parallel to that of ANNs. Instead of having seemingly disconnected dialogues between varied disciplines, we suggest grounding the ideas on common conceptual frameworks for a systematic analysis of attention and towards possible unification of ideas in AI and Neuroscience.
Hollow fiber-based liquid phase microextraction combined with HPLC for the simultaneous determination of two pyrimidines in Alysicarpus vaginalis (L.) DC.

A simple, inexpensive and efficient three phase hollow fiber liquid phase microextraction technique combined with HPLC was used for the simultaneous determination of uracil and thymine in Alysicarpus vaginalis (L.) DC. Different factors affecting the hollow fiber liquid phase microextraction procedure were investigated and optimized. The optimized extraction conditions were as follows: 1-octanol as the organic solvent, pHdonor = 9.5, pHacceptor = 3.0, 600 rpm stirring speed, 50 min extraction time, and without addition of salt. Under these conditions, the value of the enrichment factor for uracil and thymine was 48.2 and 43.6, respectively. Calibration curves showed good linearity (r > 0.999) within the test ranges. The limit of detection was 8.2 ng mL−1 and 10.7 ng mL−1, respectively and the intra- and inter-day RSDs were no more than 9.6%. The content of uracil and thymine in Alysicarpus vaginalis (L.) DC. was 0.106 μg g−1 and 0.132 μg g−1, respectively. The method was applied successfully for the simultaneous determination of uracil and thymine in Alysicarpus vaginalis (L.) DC.
Hybrid CMOS/Memristor Circuit Design Methodology

RRAM technology has experienced explosive growth in the last decade, with multiple device structures being developed for a wide range of applications. However, transitioning the technology from the lab into the marketplace requires the development of an accessible and user-friendly design flow, supported by an industry-grade toolchain. In this work, we demonstrate with examples an end-to-end design flow for RRAM-based electronics, from the introduction of a custom RRAM model into our chosen CAD tool to performing layout-versus-schematic and post-layout checks including the RRAM device. We envisage that this step-by-step guide to introducing RRAM into the standard integrated circuit design flow will be a useful reference document for both device developers who wish to benchmark their technologies and circuit designers who wish to experiment with RRAM-enhanced systems.
VELOCITY FIELD UNDER PLUNGING WAVES

In order to clarify the characteristics of the velocity field in the surf zone, three sets of detailed and precise two dimensional laboratory experiments were performed. Spatial distributions and time histories of velocity were measured by using a hot film velocimeter with a split type probe or a two components laser doppler velocimeter for regular wave conditions. Typical plunging breakers were formed during the experiments. Based on the experimental results, a model was investigated in order to estimate the two dimensional distribution of the on-offshore steady current below the trough level.
Shell-thickness dependent electron transfer and relaxation in type-II core–shell CdS/TiO2 structures with optimized photoelectrochemical performance

Core–shell CdS/TiO2 structures are promising for solar-to-fuel conversion applications because their ideal type-II band alignment helps effective charge transfer to form the CdS+/TiO2− system. A better understanding of the charge carrier dynamics is critical to provide guiding principles for designing photoelectrochemical (PEC) devices. Hence, TiO2 shell-thickness dependent charge carrier dynamics and competition between electron relaxation in CdS (e.g. recombination and trapping) and electron transfer from CdS to TiO2 were investigated using ultrafast transient absorption (TA) spectroscopy. The results indicate that the CdS/TiO2 nanocomposite with a molar ratio of 2 : 1 exhibits the highest electron transfer rate constant of ET = 2.71 × 1010 s−1, along with an electron relaxation rate of CdS/TiO2 = 3.43 × 1010 s−1, resulting in an electron transfer quantum efficiency of QET = 79%, which also corresponds to the best PEC hydrogen generation in the CdS/TiO2 core–shell composites. However, the electron transfer rate decreases with increasing thickness of the TiO2 shell consisting of aggregated nanoparticles. One possible explanation is that the CdS and TiO2 form relatively larger, separate particles, or less conforming small particles, with poor interfaces with increasing TiO2, thereby reducing electron transfer from CdS to TiO2, which is supported by SEM, and TEM data and consistent with PEC results. The thickness and morphology dependence of electron transfer and relaxation provides new insight into the charge carrier dynamics in such composite structures, which is important for optimizing the efficiency of PEC for solar fuel generation applications.
Adjustment of the SIBFA Method for Potential Maps to Study Hydrogen Bonding Vibrational Frequencies

Calculation of Intermolecular interaction energy using perturbative methods allows to split up total interaction energy into its components. Each contribution can thus be individually analyzed. Hydrogen and water dimers, in several geometries and orientations, were fully studied using Symmetry Adapted Perturbative Theory (SAPT) and empirical SIBFA potential is fitted from these results.
A simplified model for analyzing the flow behavior of electrorheological fluids containing silica nanoparticle-decorated polyaniline nanofibers

A new rheological model was applied to the analysis of the electrorheological behavior of a fluid containing silica nanoparticle-decorated polyaniline nanofibers. The model's predictions were compared with the experimental data, revealing that the proposed model correctly predicted the shear stress behavior both quantitatively and qualitatively. The shear stress data of the electrorheological fluid showing aligned fibers' structural reformation as a function of the shear rate agreed well with the new model which required fewer parameters than the CCJ (Cho–Choi–Jhon) model. The static yield stress was found to be quadratically dependent on the field strength, in agreement with the predictions of the polarization model. A scaling function was used to model the yield stress behavior of the electrorheological fluid over a range of electric fields, and it correctly predicted the static yield stress behavior both quantitatively and qualitatively.
Development of a method for the determination of phthalate esters in sewage sludge including chromatographic separation from polychlorinated biphenyls, pesticides and polyaromatic hydrocarbons

A method was developed that allows phthalate esters (PEs) to be determined in sewage sludge samples by use of gas chromatography (GC) with electron-capture detection. Discrimination of the PEs in the split injector of the GC system was detected and the resulting problems are discussed. The ultrasonic technique was optimised for extraction time and number of extractions and compared with the Soxhlet extraction technique. It was shown that extraction efficiency is slightly better with ultrasonication than using Soxhlet extraction. The Soxhlet extraction also yielded much higher blank levels. The extract clean-up was carried out by dual column liquid chromatography with neutral alumina and Florisil. In this way separation of polychlorinated biphenyls, pesticides and polyaromatic hydrocarbons from the PEs was attained. This procedure was used successfully to determine PEs in several sewage sludge samples.
EMULGEL: A COMBINATION OF EMULSION AND GEL

Dermatological products applied to skin are diverse in formulation and range in consistency from liquid to powder but the most popular products are semisolid preparation. Emulgels have emerged as one of the most interesting topical delivery system as it has dual release control system i.e. gel and emulsion. When gel and emulsion are used in combined form the dosage form are referred as emulgel. Emulgels are having major advantages on novel vesicular systems as well as on conventional systems in various aspects. The emulgel for dermatological use has several favorable properties such as being thixotropic, greaseless, easily spreadable, easily removable, emollient, non-staining, water-soluble, longer shelf life, bio-friendly, transparent & pleasing appearance. Various permeation enhancers can potentiate the effect, so emulgels can be used as better topical drug delivery systems over present systems. The use of emulgels can be extended in analgesics and antifungal drugs. KEY WORDS: Emulgel, Skin, Gel, Emulsion, Hydrophobic Drugs
A copper(II) complex of the fungicide tebuconazole

In the title copper(II) complex, di­chloro­[(RS)-1-(4-chloro­phenyl)-4,4-di­methyl-3-(1H-1,2,4-triazol-1-yl­methyl-κN4)­pentan-3-ol]copper(II) ethanol tetrasolvate, [CuCl2(C16H22ClN3O)4]·4C2H5OH, formed with the triazole fungicide tebuconazole, the Cu atom lies on an inversion center and has an elongated octahedral coordination. The mol­ecules are linked to form a network via O—H⋯Cl and O—H⋯O hydrogen bonds.
In Situ Rhamnolipid Production at an Abandoned Petroleum Refinery

A simple screening method was developed to detect in situ biosurfactant production by exploiting the relationship between surface tension (ST) and surfactant concentration. Filtered groundwater from contaminated wells with ST values of 60 to 70 dynes/cm decreased to 29 dynes/cm after being concentrated 10 to 15 times in a rotary evaporator, indicating that biosurfactants in the sample reached the critical micelle concentration (CMC). Samples from uncon-taminated groundwater concentrated 25 times showed no decrease in ST below 72 dynes/cm, suggesting that biosurfactants were not present. Microorganisms from soil cores were cultured on diesel fuel and identified using fatty acid methyl ester (FAME) analysis. Pseudomonas aeruginosa was found at very low numbers in uncontami-nated soil but was the dominant species in contaminated soil, indicating that hydrocarbon release impacted microbial diversity significantly. High-performance liquid chromatography (HPLC) was used to quantify rhamnolipids, biosurfactants produced by P. aeruginosa, in concentrated ground-water samples. Rhamnolipid concentrations in samples from contaminated soil were observed equal to their CMC (50 mg/L), but were not detected in samples from un-contaminated wells. We conclude that biosurfactant production may be an indicator of intrinsic bioremediation.
Lithium Phosphorus Oxynitride Thin Film Fabricated by a Nitrogen Plasma-Assisted Deposition of E-beam Reaction Evaporation

Lithium phosphorus oxynitride (Lipon) thin films have been fabricated successfully by nitrogen plasma-assisted deposition of E-beam reactive evaporated Li 3 PO 4 for the first time. The effect of inductively coupled plasma (ICP) powers on the electrical and optical properties of Lipon thin film was investigated. X-ray photoelectron spectra confirmed that the insertion of N into Li 3 PO 4 and N concentration were dependent on ICP powers. Infrared and UV-vis spectrophotometry were used to characterize their optical properties. The electrical properties of the as-deposited Lipon thin films were investigated by impedance spectroscopy and isothermal transient ionic current measurements.