Miracle-dz/UniFAR-FTUs huggingface dataset:
Amperometric Biosensor Approaches for Quantification of Indole 3-Acetic Acid in Plant Stress Responses

Amperometric biosensors are known to be sensitive, reliable, and inexpensive instruments for biomolecular detection. Recently, self-referencing amperometric biosensors have been utilized to quantify the endogenous apoplastic flux of plant hormone indole 3-acetic acid (IAA) in vivo. There is still a significant need for sampling and testing methods to measure IAA concentrations in whole tissue samples. In the present study we used nanomaterial-modified platinum microelectrodes for the detection of IAA extracted from whole plant tissues. The key to the use of the nanomaterials was to enhance the surface area and thus the limit of detection for IAA. The nanoscale electrochemical interface was modified by the application of a layer of platinum black, followed by silanization overnight and a coating of multiwalled carbon nanotubes. Electroanalytical characterization of sensor performance was evaluated using both IAA standards and IAA extracted from corn plant samples, using a three-electrode scheme (reference, sensing, and auxillary). We compared tissue concentrations in water- and salt-stressed corn seedlings and compared these results to values measured using established enzyme-linked immunosorbent assay (ELISA) protocols. We found that the values obtained from both methods were comparable. The data obtained from the IAA sensor suggested that the electroanalytical biosensor approach was slightly more reliable and sensitive. Our results demonstrate a novel nanomaterial biosensor approach for IAA quantification in plant tissue extracts. The results of the stress study clearly indicated that root and shoot elongation and growth had significant positive correlation with IAA content in the raw plant tissue extracts. Water and salt stress both reduced root and shoot growth, which may be due to overaccumulation of IAA, resulting in inhibition of elongation. In comparison to established methods, such as high-performance liquid chromatography (HPLC) or ELISA, our approach is simple and inexpensive. Unlike HPLC, our approach does not require any elaborate sample purification, nor does it require antibody development as needed for ELISA. In summary, this electroanalytical biosensor method can be effectively utilized for simple, cheap, and reliable detection of IAA extracted from plant samples.
Application of Ion Chromatography with ICP-MS or MS Detection to the Determination of Selected Halides and Metal/Metalloids Species

Speciation analytics as determination of various forms of elements is becoming more and more popular and valuable in areas such as environmental protection, biochemistry, geology, medicine, pharmaceuticals, and food product quality control. Its growing importance is related to the fact that what often decides the toxicological properties of a given element or compound is not its total content but the presence of its various forms, especially ionic. Determination of low concentrations of analytes requires complex and sophisticated analytical methods and techniques. The latest trends in this area concern the so-called “hyphenated techniques,” in which the separation and various detection methods are combined. This article is a review of possible uses of ion chromatography in combination with ICP-MS and MS detectors for the speciation analytics of selected halides as well as metals and metalloids species.
Multiepoch VLT–FORS spectropolarimetric observations of supernova 2012aw reveal an asymmetric explosion

We present VLT–FORS spectropolarimetric observations of the type II supernova (SN) 2012aw taken at seven epochs during the photospheric phase, from 16 to 120 d after explosion. We corrected for interstellar polarization by postulating that the SN polarization is naught near the rest wavelength of the strongest lines – this is later confirmed by our modeling. SN 2012aw exhibits intrinsic polarization, with strong variations across lines, and with a magnitude that grows in the 7000 Å line-free region from 0.1% at 16 d up to 1.2% at 120 d. This behavior is qualitatively similar to observations gathered for other type II SNe. A suitable rotation of Stokes vectors places the bulk of the polarization in q, suggesting the ejecta of SN 2012aw is predominantly axisymmetric. Using an upgraded version of our 2D polarized radiative transfer code, we modeled the wavelength- and time-dependent polarization of SN 2012aw. The key observables may be explained by the presence of a confined region of enhanced 56Ni at ~4000 km s−1, which boosts the electron density in a cone having an opening angle of ~50 deg and an observer’s inclination of ~70 deg to the axis of symmetry. With this fixed asymmetry in time, the observed evolution of the SN 2012aw polarization arises from the evolution of the ejecta optical depth, ionization, and the relative importance of multiple versus single scattering. However, the polarization signatures exhibit numerous degeneracies. Cancellation effects at early times imply that low polarization may even occur for ejecta with a large asymmetry. An axisymmetric ejecta with a latitudinal-dependent explosion energy can also yield similar polarization signatures as asymmetry in the 56Ni distribution. In spite of these uncertainties, SN 2012aw provides additional evidence for the generic asymmetry of type II SN ejecta, of which VLT–FORS spectropolarimetric observations are a decisive and exquisite probe.
Transforming Stäckel Hamiltonians of Benenti type to polynomial form

In this paper we discuss two canonical transformations that turn St\"{a}ckel separable Hamiltonians of Benenti type into polynomial form: transformation to Vi\`ete coordinates and transformation to Newton coordinates. Transformation to Newton coordinates has been applied to these systems only very recently and in this paper we present a new proof that this transformation indeed leads to polynomial form of St\"{a}ckel Hamiltonians of Benenti type. Moreover we present all geometric ingredients of these Hamiltonians in both Vi\`ete and Newton coordinates.
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization

The low-rank stochastic semidefinite optimization has attracted rising attention due to its wide range of applications. The nonconvex reformulation based on the low-rank factorization, significantly improves the computational efficiency but brings some new challenge to the analysis. The stochastic variance reduced gradient (SVRG) method has been regarded as one of the most effective methods. SVRG in general consists of two loops, where a reference full gradient is first evaluated in the outer loop and then used to yield a variance reduced estimate of the current gradient in the inner loop. Two options have been suggested to yield the output of the inner loop, where Option I sets the output as its last iterate, and Option II yields the output via random sampling from all the iterates in the inner loop. However, there is a significant gap between the theory and practice of SVRG when adapted to the stochastic semidefinite programming (SDP). SVRG practically works better with Option I, while most of existing theoretical results focus on Option II. In this paper, we fill this gap via exploiting a new semi-stochastic variant of the original SVRG with Option I adapted to the semidefinite optimization. Equipped with this, we establish the global linear submanifold convergence (i.e., converging exponentially fast to a submanifold of a global minimum under the orthogonal group action) of the proposed SVRG method, given a provable initialization scheme and under certain smoothness and restricted strongly convex assumptions. Our analysis includes the effects of the mini-batch size and update frequency in the inner loop as well as two practical step size strategies, the fixed and stabilized Barzilai-Borwein step sizes. Some numerical results in matrix sensing demonstrate the efficiency of proposed SVRG method outperforming Option II counterpart as well as others.
A microscopic view on the large scale chain dynamics in nanocomposites with attractive interactions

We use neutron spin-echo spectroscopy to investigate the large scale chain dynamics in unentangled polymer nanocomposites where stable polymer layers around nanoparticles are dynamically formed due to attractive segment–surface interactions. The work here focuses on the detailed microscopic characterization of the dynamics within these layers of bound poly(ethylene glycol) (PEO) and poly(butylene oxide) (PBO) chains at a fixed silica nanoparticle fraction of 15%. The substitution of hydroxy by methoxy terminated chains thereby clearly evidences the importance of the chain end chemistry in these systems as the layer structure and dynamics therein significantly depend on the specific interaction mechanism. The experimental data reveal a densely packed thick shell of end-attached chains in the case of hydroxy ends contrasted by a thin shell of laterally adsorbed chains with multiple attachments in the methoxy case. In all cases a consistent quantitative modeling is presented that evidences unchanged segmental dynamics within the bound layers. The obtained picture is further validated on an independent model system based on PBO polymers which shows surprisingly similar chain dynamics as for PEG in the nanocomposite pointing to a very generic dynamic scenario.
Bottlenecks to interstellar sulfur chemistry

Hydride molecules lie at the base of interstellar chemistry, but the synthesis of sulfuretted hydrides is poorly understood. Motivated by new observations of the Orion Bar PDR - 1'' resolution ALMA images of SH+; IRAM 30m detections of H2S, H2S34, and H2S33; H3S+ (upper limits); and SOFIA observations of SH - we perform a systematic study of the chemistry of S-bearing hydrides. We determine their column densities using coupled excitation, radiative transfer as well as chemical formation and destruction models. We revise some of the key gas-phase reactions that lead to their chemical synthesis. This includes ab initio quantum calculations of the vibrational-state-dependent reactions SH+ + H2 H2S+ + H and S + H2 SH + H. We find that reactions of UV-pumped H2 (v>1) with S+ explain the presence of SH+ in a high thermal-pressure gas component, P_th~10^8 cm^-3 K, close to the H2 dissociation front. However, subsequent hydrogen abstraction reactions of SH+, H2S+, and S with vibrationally excited H2, fail to ultimately explain the observed H2S column density (~2.5x10^14 cm^-2, with an ortho-to-para ratio of 2.9+/-0.3). To overcome these bottlenecks, we build PDR models that include a simple network of grain surface reactions leading to the formation of solid H2S (s-H2S). The higher adsorption binding energies of S and SH suggested by recent studies imply that S atoms adsorb on grains (and form s-H2S) at warmer dust temperatures and closer to the UV-illuminated edges of molecular clouds. Photodesorption and, to a lesser extent, chemical desorption, produce roughly the same H2S column density (a few 10^14 cm-^2) and abundance peak (a few 10^-8) nearly independently of n_H and G_0. This agrees with the observed H2S column density in the Orion Bar as well as at the edges of dark clouds without invoking substantial depletion of elemental sulfur abundances.
A Method for a Pseudo-local Measurement of the Galactic Magnetic Field

Much of the information about the magnetic field in the Milky Way and other galaxies comes from measurements which are path integrals, such as Faraday rotation and the polarization of synchrotron radiation of cosmic ray electrons. The measurement made at the radio telescope results from contributions of volume elements along a long line of sight (LOS). A magnetic field measurement at a given spatial location is of much more physical significance. In this paper, I point out that H ii regions fortuitously offer such a “point” measurement, albeit of one component of the magnetic field, and averaged over the sightline through the H ii region. However, the LOS through an H ii region is much smaller (e.g., 30–50 pc) than one through the entire Galactic disk, and thus constitutes a “pseudo-local” measurement. I use published H ii region Faraday rotation measurements to provide a new constraint on the magnitude of magnetohydrodynamic turbulence in the Galaxy.
Formation and evolution of protostellar accretion discs – I. Angular-momentum budget, gravitational self-regulation, and numerical convergence

We investigate the formation and early evolution of a protostellar disc from a magnetized pre-stellar core using non-ideal magnetohydrodynamic (MHD) simulations including ambipolar diffusion and Ohmic dissipation. The dynamical contraction of the pre-stellar core ultimately leads to the formation of a first hydrostatic core, after ambipolar diffusion decouples the magnetic field from the predominantly neutral gas. The hydrostatic core accumulates angular momentum from the infalling material, evolving into a rotationally supported torus; this `first hydrostatic torus' then forms an accreting protostar and a rotationally supported disc. The disc spreads out by gravitational instability, reaching $\sim$30 au in diameter at $\sim$3 kyr after protostar formation. The total mass and angular momentum of the protostar-disc system are determined mainly by accretion of gas from an infalling pseudo-disc, which has low specific angular momentum because of magnetic braking; their removal from the protostar-disc system by outflow and disc magnetic braking are negligible, in part because the magnetic field is poorly coupled there. The redistribution of angular momentum within the protostar-disc system is facilitated mainly by gravitational instability; this allows formation of relatively large discs even when the specific angular momentum of infalling material is low. We argue that such discs should remain marginally unstable as they grow (with Toomre $Q\sim 1$-$2$), an idea that is broadly consistent with recent observational estimates for Class 0/I discs. We discuss the numerical convergence of our results, and show that properly treating the inner boundary condition is crucial for achieving convergence at an acceptable computational cost.
In-Ear SpO₂ for Classification of Cognitive Workload

The brain is the most metabolically active organ in the body, which increases its metabolic activity and, thus, oxygen consumption, with increasing cognitive demand. This motivates us to question whether the increased cognitive workload may be measurable through changes in blood oxygen saturation. To this end, we explore the feasibility of cognitive workload tracking based on in-ear SpO2 measurements, which are known to be both robust and exhibit minimal delay. We consider a cognitive workload assessment based on an $N$ -back task with a randomized order. It is shown that the 2-back and 3-back tasks (high cognitive workload) yield either the lowest median absolute SpO2 or largest median decrease in SpO2 in all of the subjects, indicating a measurable and statistically significant decrease in blood oxygen in response to the increased cognitive workload. This makes it possible to classify the four $N$ -back task categories, over 5-s epochs, with a mean accuracy of 90.6%, using features derived from in-ear pulse oximetry, including SpO2, pulse rate, and respiration rate. These findings suggest that in-ear SpO2 measurements provide sufficient information for the reliable classification of cognitive workload over short time windows, which promises a new avenue for real-time cognitive workload tracking.
Semantics for Robotic Mapping, Perception and Interaction: A Survey

For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world"mean"to a robot, and is strongly tied to the question of how to represent that meaning. With humans and robots increasingly operating in the same world, the prospects of human-robot interaction also bring semantics and ontology of natural language into the picture. Driven by need, as well as by enablers like increasing availability of training data and computational resources, semantics is a rapidly growing research area in robotics. The field has received significant attention in the research literature to date, but most reviews and surveys have focused on particular aspects of the topic: the technical research issues regarding its use in specific robotic topics like mapping or segmentation, or its relevance to one particular application domain like autonomous driving. A new treatment is therefore required, and is also timely because so much relevant research has occurred since many of the key surveys were published. This survey therefore provides an overarching snapshot of where semantics in robotics stands today. We establish a taxonomy for semantics research in or relevant to robotics, split into four broad categories of activity, in which semantics are extracted, used, or both. Within these broad categories we survey dozens of major topics including fundamentals from the computer vision field and key robotics research areas utilizing semantics, including mapping, navigation and interaction with the world. The survey also covers key practical considerations, including enablers like increased data availability and improved computational hardware, and major application areas where.
Underwater Image Enhancement based on Deep Learning and Image Formation Model

Underwater robots play an important role in oceanic geological exploration, resource exploitation, ecological research, and other fields. However, the visual perception of underwater robots is affected by various environmental factors. The main challenge now is that images captured by underwater robots are color-distorted. The hue of underwater images tends to be close to green and blue. In addition, the contrast is low and the details are fuzzy. In this paper, a new underwater image enhancement algorithm based on deep learning and image formation model is proposed. Experimental results show that the advantages of the proposed method are that it eliminates the influence of underwater environmental factors, enriches the color, enhances details, achieves higher scores in PSNR and SSIM metrics, and helps feature key-point point matching get better results. Another significant advantage is that its computation speed is much faster than other methods.
Effect of Sol-Gel-Derived Nano-silica on the Properties of Natural Rubber-Poly Butadiene Rubber-Reclaim Rubber Ternary Blends/Silica Nanocomposites

Silica incorporation into natural rubber (NR)-polybutadiene rubber (PBR)-reclaim rubber (RR) ternary blend system was carried out by sol-gel technique at different temperatures. The effect of RR on silica reinforcement was studied for NR-PBR-RR blend systems. The physicochemical properties of sol-gel vulcanizates indicates that the reinforcing efficiency of the nanocomposites increases with increasing RR content. Sol-gel vulcanizates prepared at 50°C shows superior mechanical properties than others. The amount of silica incorporated by sol-gel technique was determined through thermogravimetry analysis, which indicates the increasing trend of thermal stability with silica content. SEM studies indicate the coherency and homogeneity in the NR-PBR-RR/SiO 2 nanocomposites.
A Reduced Codebook and Re-Interpolation Approach for Enhancing Quality in Chroma Subsampling

Prior to encoding RGB full-color images or Bayer color filter array (CFA) images, chroma subsampling is a necessary and crucial step at the server side. In this paper, we first propose a flow diagram approach to analyze the coordinate-inconsistency (CI) problem and the upsampling process-inconsistency (UPI) problem existing in the traditional and state-of-the-art chroma subsampling methods under the current coding environment. In addition, we explain why the two problems degrade the quality of the reconstructed images. Next, we propose a reduced codebook and re-interpolation (RCRI) approach to solve the two problems for enhancing the quality of the reconstructed images. Based on the testing RGB full-color images and Bayer CFA images, the comprehensive experimental results demonstrated at least 1.4 dB and 2.4 dB quality improvement effects, respectively, of our RCRI approach against the CI and UPI problems for the traditional and stateof-the-art chroma subsampling methods.
An Ontology Design Pattern for representing Recurrent Situations

In this paper, we present an Ontology Design Pattern for representing situations that recur at regular periods and share some invariant factors, which unify them conceptually: we refer to this set of recurring situations as recurrent situation series. The proposed pattern appears to be foundational, since it can be generalised for modelling the top-level domain-independent concept of recurrence, which is strictly associated with invariance. The pattern reuses other foundational patterns such as Collection, Description and Situation, Classification, Sequence. Indeed, a recurrent situation series is formalised as both a collection of situations occurring regularly over time and unified according to some properties that are common to all the members, and a situation itself, which provides a relational context to its members that satisfy a reference description. Besides including some exemplifying instances of this pattern, we show how it has been implemented and specialised to model recurrent cultural events and ceremonies in ArCo, the Knowledge Graph of Italian cultural heritage.
A Hybrid Learner for Simultaneous Localization and Mapping

Simultaneous localization and mapping (SLAM) is used to predict the dynamic motion path of a moving platform based on the location coordinates and the precise mapping of the physical environment. SLAM has great potential in augmented reality (AR), autonomous vehicles, viz. self-driving cars, drones, Autonomous navigation robots (ANR). This work introduces a hybrid learning model that explores beyond feature fusion and conducts a multimodal weight sewing strategy towards improving the performance of a baseline SLAM algorithm. It carries out weight enhancement of the front end feature extractor of the SLAM via mutation of different deep networks' top layers. At the same time, the trajectory predictions from independently trained models are amalgamated to refine the location detail. Thus, the integration of the aforesaid early and late fusion techniques under a hybrid learning framework minimizes the translation and rotation errors of the SLAM model. This study exploits some well-known deep learning (DL) architectures, including ResNet18, ResNet34, ResNet50, ResNet101, VGG16, VGG19, and AlexNet for experimental analysis. An extensive experimental analysis proves that hybrid learner (HL) achieves significantly better results than the unimodal approaches and multimodal approaches with early or late fusion strategies. Hence, it is found that the Apolloscape dataset taken in this work has never been used in the literature under SLAM with fusion techniques, which makes this work unique and insightful.