Miracle-dz/UniFAR-FTUs huggingface dataset:
Conformational fluctuations in a green fluorescent protein-like Akane family protein: a high-pressure fluorescence study at 0.1–700 MPa

ABSTRACT We have investigated conformational fluctuations in a new green fluorescent protein(GFP)-like protein rb-Akane found in a red-brown-colored octocoral, Scleronephthya gracillima (Kuekenthal)), with high pressure fluorescence spectroscopy at 0.1–700 MPa. Besides the green fluorescence at 510 nm, two red fluorescence peaks are observed at 590 and 629 nm, the relative intensity of which varies reversibly with pressure. The phenomenon is interpreted as representing the cis–trans isomerization of the chromophore accompanied by the conformational transition between two sub-states of the red fluorescence form of rb-Akane. The two sub-states are separated only marginally in free energy (ΔG 0 = 1.9 ± 0.4 kJ mol−1), but significantly in partial molar volume (ΔV 0 = −19.8 ± 1.4 ml mol−1) at 0.1 MPa (pH 7.5, 25°C). Above 500 MPa, the fluorescence at λ max 629 nm undergoes another reversible change with pressure, showing the onset of unfolding.
Bivalirudin versus heparin in PCI: Is the pendulum swinging again in favor of heparin?

• The optimal antithrombotic strategy in patients with left main disease (LMD) and undergoing percutaneous coronary intervention (PCI) remains unknown due to the underrepresentation of these high-risk patients in clinical trials. • Bivalirudin does not seem to offer any additional benefits when compared with heparin, even in high-risk patients such as those with LMD. • Additional randomized clinical trials are needed to establish the optimal antithrombotic therapy regimen among LMD patients undergoing PCI.
Correlating the interplanetary factors to distinguish extreme and major geomagnetic storms

We investigate the correlation between Disturbance Storm Time (Dst) characteristics and solar wind conditions for the main phase of geomagnetic storms, seeking possible factors that distinguish extreme storms (minimum Dst <−250 nT) and major storms (minimum Dst <−100 nT). In our analysis of 170 storms, there is a marked correlation between the average rate of change of Dst during a storm's main phase (ΔDst/Δt) and the storm's minimum Dst, indicating a faster ΔDst/Δt as storm intensity increases. Extreme events add a new regime to ΔDst/Δt, the hourly time derivative of Dst (dDst/dt), and sustained periods of large amplitudes for southward interplanetary magnetic field Bz and solar wind convection electric field Ey. We find that Ey is a less efficient driver of dDst/dt for extreme storms compared to major storms, even after incorporating the effects of solar wind pressure and ring current decay. When minimum Dst is correlated with minimum Bz, we observe a similar divergence, with extreme storms tending to have more negative Dst than the trend predicted on the basis of major storms. Our results enable further improvements in existing models for storm predictions, including extreme events, based on interplanetary measurements.
Matching With Contracts-Based Resource Trading and Price Negotiation in Multi-Access Edge Computing

In order to provide high quality services for Internet of Things (IoT) users with ever-changing requirements, a novel multi-access edge computing (MEC) based network paradigm is proposed in this letter. In the proposed framework, the service providers rent resources from resource providers (RPs), and then deploy them into MEC nodes (MNs). In this way, the MNs are labeled as temporal service points, which is flexible and cost-effective. Aiming to address the resource trading and price negotiation problem between the RPs and MNs, a matching with contracts model is leveraged in our proposed paradigm. Next, we further propose a distributed price negotiation algorithm to solve the problem. Finally, the simulations are presented to demonstrate the superiority of our proposed algorithm.
Robust Cooperative Communication Optimization for Multi-UAV-Aided Vehicular Networks

Aerial-ground cooperative vehicular networks are envisioned as a novel paradigm in B5G/6G visions. In this letter, the challenge of optimizing the global energy-efficiency (EE) of multi-UAV-aided vehicular networks in the presence of uncertain air-to-ground (A2G) channels is addressed. Specifically, we propose a maximin paradigm to characterize the system, which aims to maximize its global EE meanwhile satisfying Quality-of-Service (QoS)-oriented data rate requirements in the worst-case situation. We theoretically derive a closed-form optimal solution for an embedded minimization subproblem under a parametric channel uncertainty set and thus develop a computationally tractable robust counterpart, which leads to a robust EE optimization design. Simulation results show that the proposed method significantly outperforms conventional EE schemes in terms of achieving higher global system performance and better robustness under random uncertain environments.
Stochastic Optimization of URLLC-eMBB Joint Scheduling With Queuing Mechanism

This letter proposes a dynamic joint scheduling for the ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) traffic at a sub-frame level with a queuing mechanism, which monitors and controls the latency of each URLLC packet in real time to ensure its strict requirements. We analytically derive the outage probability (i.e., the probability of any URLLC packet drop over all transmission channels) and URLLC expected throughput in addition to the expected value of served URLLC packets. Then, a stochastic optimization problem is formulated to maximize the total throughput for the eMBB services, constraining the URLLC outage probability. Numerical results confirm effectiveness of our queuing policy to significantly reduce the URLLC loss rate while ensuring that the total eMBB throughput is not affected.
Tensor Recovery via <inline-formula><tex-math notation="LaTeX">$*_L$</tex-math></inline-formula>-Spectral <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-Support Norm

Unlike traditional tensor decompositions which model low-rankness in the original domain, the recently proposed tensor <inline-formula><tex-math notation="LaTeX">$*_L$</tex-math></inline-formula>-Singular Value Decomposition (<inline-formula><tex-math notation="LaTeX">$*_L$</tex-math></inline-formula>–SVD) casts a new light on tensor analysis by exploiting low-rankness in the spectral domain. For convexized rank minimization within the framework of <inline-formula><tex-math notation="LaTeX">$*_L$</tex-math></inline-formula>–SVD, the tensor <inline-formula><tex-math notation="LaTeX">$*_L$</tex-math></inline-formula>–Tubal Nuclear Norm (<inline-formula><tex-math notation="LaTeX">$*_L$</tex-math></inline-formula>–TNN) outperforms classical tensorial extensions of matrix nuclear norm in many applications. In this paper, we first generalize <inline-formula><tex-math notation="LaTeX">$*_L$</tex-math></inline-formula>–TNN to the <inline-formula><tex-math notation="LaTeX">$*_L$</tex-math></inline-formula>-Spectral <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-Support Norm (<inline-formula><tex-math notation="LaTeX">$*_L$</tex-math></inline-formula>-SpSN-<inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>) as a new low-rank regularizer, and then adopt it to formulate two estimators for tensor recovery from noisy linear observations. Further, statistical performance of the proposed estimators is analyzed by establishing both deterministic and non-asymptotic upper bounds on the estimation error, which indicates that the proposed tensor Dantzig selector enjoys near-optimal estimation error for tensor compressive sensing and tensor completion. Moreover, proximal operator of the proposed norm is derived and embeded in an efficient algorithm to compute the estimators. Experiments on synthetic datasets verify correctness of the proposed error bounds and image inpainting results demonstrate superiority of the proposed estimators.
Feature Extraction Algorithms for Automatic Craters Identification

: Recently the feature selection algorithms are extensively studied. Using 3D data, the features are drawn for automatic classification and identify craters. This will also help to text the performance of the classifiers. Our intention in this work is to observe the discriminative power of the original values, hereafter called “pure” values, of a minimal curvature by only converting them in the range of grey scale. We have tested the system and found that the five different classifiers show that better accuracy results are obtained over the features selected from the grey scale image. We also found that the method from computer vision is applied for the crater detection.
Hybridization Biosensors Relying on Electrical Properties of Nucleic Acids

Electrochemical nucleic acids (NA) technologies allow development of sensitive and accurate, yet simple, inexpensive and robust analytical platforms, which can successfully compete with other approaches. In this Review, I critically overview the basic trends in construction of label-free DNA hybridization genosensors exploiting variations in electrical properties of the electrode-immobilized NA. It will be discussed how interfacial and electronic properties of NA can be modulated to allow efficient biosensor applications, including the choice of the redox probe and use of alternative linkers and immobilization strategies These results contribute to understanding of interfacial ET reactions in DNA and advanced design of fast, reliable and cost-effective diagnostic devices.
Unveiling the Unique Phase Transformation Behavior and Sodiation Kinetics of 1D van der Waals Sb2S3 Anodes for Sodium Ion Batteries

Carbon‐coated van der Waals stacked Sb2S3 nanorods (SSNR/C) are synthesized by facile hydrothermal growth as anodes for sodium ion batteries (SIBs). The sodiation kinetics and phase evolution behavior of the SSNR/C anode during the first and subsequent cycles are unraveled by coupling in situ transmission electron microscopy analysis with first‐principles calculations. During the first sodiation process, Na+ ions intercalate into the Sb2S3 crystals with an ultrafast speed of 146 nm s−1. The resulting amorphous NaxSb2S3 intermediate phases undergo sequential conversion and alloying reactions to form crystalline Na2S, Na3Sb, and minor metallic Sb. Upon desodiation, Na+ ions extract from the nanocrystalline phases to leave behind the fully desodiated Sb2S3 in an amorphous state. Such unique phase evolution behavior gives rise to superb electrochemical performance and leads to an unexpectedly small volume expansion of ≈54%. The first‐principles calculations reveal distinctive phase evolution arising from the synergy between the extremely low Na+ ion diffusion barrier of 190 meV and the sharply increased electronic conductivity upon the formation of amorphous NaxSb2S3 intermediate phases. These findings highlight an anomalous Na+ ion storage mechanism and shed new light on the development of high performance SIB anodes based on van der Waals crystals.
Crystal and molecular structure of (9aS,10S)-6-Oxo-6,7,8,9,9a,10-hexahydro-4H-thieno-[2,3-b]quinolizin-10-yl acetate

Abstract The title compound, C13H15NO3S, is a chiral molecule with two stereogenic centres. Its absolute configuration was derrived from the synthesis and confirmed by structure determination. The expected stereochemistry of atoms C5 and C6 was confirmed to be S. The central N-heterocyclic rings are not planar and adopt a half-chair conformation. A calculation of least-squares planes showed that these rings are puckered in such a manner that the five atoms C1, C2, C3, C5 and N1 (second ring: N1, C6, C7, C10 and C11) are planar, while atoms C4 (C5) are displaced from these planes with the out-of-plane displacement of 0.582 (3) Å and 0,666 (2) Å in the second ring, respectively. Dihedral angle between the planes of the central N-heterocyclic rings is 40.0 (1)°. Crystal structure is stabilized by C—H···O hydrogen interactions.
Characterizing mechanical properties of living C2C12 myoblasts with single cell indentation experiments : application to Duchenne muscular dystrophy

This interdisciplinary thesis was dedicated to the atomic force microscopy (AFM) characterization of the mechanical properties of myoblasts (murine and human) and myotubes (murine). We reported that the mechanical properties of these cells were modified when their actin cytoskeleton (CSK) dynamics was inhibited or altered. Recording single AFM force indentation curves, we showed that adherent layers of myoblasts and myotubes introduced on the AFM cantilever an extra hydrodynamic drag as compared to a solid wall. This phenomenon was dependent on the cantilever scan speed and not negligible even at low scan velocities (1μm/s). We observed that the mechanical properties of the muscle precursor cells became non-linear (plastic behaviour) for large local deformations (>1μm) and that they varied depending on the state, type and environment of the cells. Combining AFM experiments, viscoelastic modeling and multi-scale analyzing methods based on the wavelet transform, we illustrated the variability of the mechanical responses of these cells (from viscoelastic to viscoplastic). Through AFM force indentation curves analysis, morpho-structural imaging (DIC, fluorescence microscopy) and pharmacological treatments, we enlightened the important role of active (ATP-dependent) processes in myoblast mechanics, focusing especially on those related to the molecular motors (myosin II) coupled to the actin filaments. In particular, we showed that the perinuclear actin stress fibers could exhibit some abrupt remodelling events (ruptures), which are characteristic of the ability of these cells to tense their CSK. Finally, we showed that this approach can be generalized to some human clinical cases, namely primary human myoblasts from healthy donors and patients affected by Duchenne muscular dystrophy, paving the way for broader studies on different cell types and diseases.
Ways to Slow Down the Degradation and Enhance the Stability of Perovskite Solar Cells

The issues of the perovskite degradation and the stability of perovskite solar cells are studied in this article. The method of vacuum thermal deposition, spray pyrolysis, and spin-coating methods are adopted for the fabrication of different constituents of solar cells to achieve good reproducibility and long lifetime for samples with high efficiency. The applied one-step coating method for creation of perovskite layer is described in detail. It is investigated what exactly leads to the degradation of perovskite solar cells under constant sunlight illumination, because this seems to be one of the biggest obstacles to their commercialization.
Design of Bi-Static Scatterometer for Soil Moisture Estimation

The science of accessing data about the crust of the Ground without actually to be in touch with it is remote sensing. By detecting and verifying reflected or emitted energy, this is done. The scatterometer is a type of spatial data radar which can distinguish various external electromagnetic characteristics depending on the magnitude of electromagnetic microwave pulses. Basically, this paper is aimed at designing and developing bistatic scatterometer to estimate soil moisture. The scattering coefficient behavior with regard to angle θ for different roughness and moisture conditions for the bi-static scatterometer is calculated in this paper. This scattered signal describes the normalized radar specular scattering cross section or specular scattering coefficient σ°. The scattering coefficient of a surface is governed by the roughness parameter, moisture content, conductivity and dielectric constant. Frequency of operation is 10GHz.
An Open-IoT Approach on Elevator for Enabling Autonomous Robotic Vertical Mobility

Manipulating elevator is essential to service a robot to move in multiple floors inside the building. A well-constructed robotic navigation system in elevator environment is required to insure the safety, trust and robustness. So far, earlier works have concentrated on the optimization of the stand-alone robots, which causing heavy computation overhead, limited applicability and so on. Our proposal is a lightweight open-IoT solution aiming to ensure the applicability of elevator-using for various robots. Further, it reduces the computation complexity of the robot side as well. In this paper, the IoT-enabling development on elevator, communication flow definitions and implementation of this proposal are introduced.
Time-frequency analysis of acoustic modal intensity fluctuations induced by internal solitary wave packets

Modal intensity fluctuations caused by a moving nonlinear internal wave packet in a shallow water waveguide are investigated in this paper. An expression of the modal intensity is derived based on the coupled mode theory in a simple waveguide model. Theoretical calculations indicate that the time-varying modal interferences induced by mode coupling effects and mode stripping (different attenuation rates between different modes) give rise to the modal intensity fluctuations. The temporal variation of the oscillatory amplitude of an individual modal interference component is found to be dependent on the relationship between the attenuation rate of the observed mode arriving at receivers and that of the incident modes entering the internal solitary wave packet. The modal intensity fluctuations, characterized by the modal interferences with different frequency components and time-varying oscillatory amplitudes, are expected to have visible patterns in the time-frequency plane. The reassigned spectrogram with high time-frequency concentration is proved to be a desirable representation of the features of modal intensity fluctuations.
A Comparative Study of MPPT for PV System Based on Modified Perturbation & Observation Method

The Perturbation and Observation (P&O) algorithm is one of the best tracking technologies for maximum power practically possible for its flexibility and ease of execution in low-fee devices. The major challenges of the traditional P&O algorithm are energy waste in steady-state conditions and the dynamic performances are poor when the solar irradiance or temperature is changing suddenly. In the paper, a distinction between the variable-step scale, the two-step size and the traditional P&O is performed in order to play an important reference role for potential users of MPPT in PV systems. The results show how the two-step and variable-step have a better dynamical reaction and steady-state precision than the conventional P&O MPPT algorithm.
A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

Since the rise of deep learning in the past few years, convolutional neural networks (CNNs) have quickly found their place within the remote sensing (RS) community. As a result, they have transitioned away from other machine learning techniques, achieving unprecedented improvements in many specific RS applications. This article presents a meta-analysis of 416 peer-reviewed journal articles, summarizes CNN advancements, and its current status under RS applications. The review process includes a statistical and descriptive analysis of a database comprised of 23 fields, including: 1) general characteristics, such as various applications, study objectives, sensors, and data types, and 2) algorithm specifications, such as different types of CNN models, parameter settings, and reported accuracies. This review begins with a comprehensive survey of the relevant articles without considering any specific criteria to give readers an idea of general trends, and then investigates CNNs within different RS applications to provide specific directions for the researchers. Finally, a conclusion summarizes potentialities, critical issues, and challenges related to the observed trends.
Residual-Network -Based Deep Learning for Parkinson's Disease Classification using Vocal Datasets

For the diagnosis and early detection of Parkinson's disease, a noninvasive method based on observed abnormal motor signs is desired. Therefore, in this paper, a 10-layered 1-d convolutional neural network (CNN) and novel-residual-network-type 1-d CNN were introduced for Parkinson's disease classification using vocal feature datasets. The resulting residual network provided a good classification result with an accuracy of 0.888, F-measure of 0.928, and MCC of 0.692.
Towards Assisting the Visually Impaired: A Review on Techniques for Decoding the Visual Data From Chart Images

The textual data of a document is supplemented by the graphical information in it. To make communication easier, they contain tables, charts and images. However, it excludes a section of our population - the visually impaired. With technological advancements, the blind can access the documents through text to speech software solutions. In this method, even images can be conveyed by reading out the figure captions. However, charts and other statistical comparisons which involve critical information are difficult to be “read” out this way. Aim of this paper is to analyse various methods available to solve this vexatious issue. We survey the state-of-the-art works that do the exact opposite of graphing tools. In this paper, we explore the existing literature in understanding the graphs and extracting the visual encoding from them. We classify these approaches into modality-based approaches, conventional and deep-learning based methods. The survey also contains comparisons and analyses relevant study datasets. As an outcome of this survey, we observe that: (i) All existing works under each category need decoding in a variety of graphs. (ii) Among the approaches, deep learning performs remarkably well in localisation and classification. However, it needs further improvements in reasoning from chart images. (iii) Research works are still in progress to access data from vector images. Recreating data from the raster images has unresolved issues. Based on this study, the various applications of decoding the graphs, challenges and future possibilities are also discussed. This paper explores current works in the extraction of chart data, which seek to enable researchers in Human Computer Interaction to achieve human-level perception of visual data by machines. In this era of visual summarisation of data, the AI approaches can automate the underlying data extraction and hence provide the natural language descriptions to support visually disabled users.