Range Of Fields Research Articles

Imaging inter-valley coherent order in magic-angle twisted trilayer graphene.

Magic-angle twisted trilayer graphene (MATTG) exhibits a range of strongly correlated electronic phases that spontaneously break its underlying symmetries1,2. Here we investigate the correlated phases of MATTG using scanning tunnelling microscopy and identify marked signatures of interaction-driven spatial symmetry breaking. In low-strain samples, over a filling range of about two to three electrons or holes per moiré unit cell, we observe atomic-scale reconstruction of the graphene lattice that accompanies a correlated gap in the tunnelling spectrum. This short-scale restructuring appears as a Kekulé supercell-implying spontaneous inter-valley coherence between electrons-and persists in a wide range of magnetic fields and temperatures that coincide with the development of the gap. Large-scale maps covering several moiré unit cells further reveal a slow evolution of the Kekulé pattern, indicating that atomic-scale reconstruction coexists with translation symmetry breaking at a much longer moiré scale. We use auto-correlation and Fourier analyses to extract the intrinsic periodicity of these phases and find that they are consistent with the theoretically proposed incommensurate Kekulé spiral order3,4. Moreover, we find that the wavelength characterizing moiré-scale modulations monotonically decreases with hole doping away from half-filling of the bands and depends weakly on the magnetic field. Our results provide essential insights into the nature of the correlated phases of MATTG in the presence of strain and indicate that superconductivity can emerge from an inter-valley coherent parent state.

Optical bistability modulation based on graphene sandwich structure with topological interface modes.

In this paper, we have investigated optical bistability modulation of transmitted beam that can be achieved by graphene sandwich structure with topological interface modes at terahertz frequency. Graphene with strong nonlinear optical effect was combined with sandwich photonic crystal to form a new sandwich structure with topological interface modes. The light-limiting properties of the topological interface modes, as well as its high unidirectionality and high transmission efficiency, all contribute positively to the reduction of the optical bistability threshold. In addition, the topological interface modes can effectively ensure the stability of the two steady state switching in the case of external interference. Moreover, optical bistability is closely related to the incident angle, the Fermi energy, the relaxation time, and the number of layers of graphene. Through parameter optimization, optical bistability with threshold of 105 V/m can be obtained, which has reached or is close to the range of the weak field.

Hermes-3: Multi-component plasma simulations with BOUT++

A new open source tool for fluid simulation of multi-component plasmas is presented, based on a flexible software design that is applicable to scientific simulations in a wide range of fields. Hermes-3 is built on plasma simulation framework BOUT++, consolidating earlier SD1D and Hermes models into a single code that can be configured at run-time to solve plasma models in 1D, 2D or 3D, either for transport (steady-state) or turbulent (time-evolving) problems, with an arbitrary number of ion and neutral species. We describe the improved numerical algorithms and software design that have been implemented in Hermes-3.To demonstrate the capabilities of this tool, applications relevant to the boundary of tokamak plasmas are presented: 1D simulations of diveror plasmas evolving equations for all charge states of neon and deuterium; 2D transport simulations of tokamak equilibria in single-null X-point geometry with plasma ion and neutral atom species; and simulations of the time-dependent propagation of plasma filaments (blobs).Hermes-3 is publicly available on Github under the GPL-3 open source license. The repository includes documentation and a suite of unit, integrated and convergence tests. Program summaryProgram Title: Hermes-3CPC Library link to program files:https://doi.org/10.17632/rtkvc3r743.1Developer's repository link:https://github.com/bendudson/hermes-3Licensing provisions: GPLv3Programming language: C++14Nature of problem: Simulation of dynamics and steady-state solutions of multiple ion and neutral species in magnetised plasmas using drift-reduced fluid plasma models in 1 to 3 spatial dimensions. Focused on but not restricted to the simulation of turbulence in the boundary of tokamaks.Solution method: Hermes-3 implements a system of flexible software components, that are configured at run-time and coupled by passing a state object between them (the Encapsulate State and Command patterns). Hermes-3 builds on BOUT++ [1], employing the Method of Lines with implicit and explicit time-integration methods in curvilinear coordinates on block-structured meshes, making use of libraries including SUNDIALS, PETSc and Hypre. Hermes-3 implements drift-reduced plasma fluid equations using conservative finite difference methods, and atomic processes that couple plasma and neutral fluids.

An IoT-based intelligent smart energy monitoring system for solar PV power generation

Abstract As the world’s attention turns to cleaner, more dependable, and sustainable resources, the renewable energy sector is rising quickly. The decline in world energy use and climate change are the two most significant factors nowadays. PV forecasting was essential to enhancing the efficiency of the real-time control system and preventing any undesirable effects. The smart energy management systems of distributed energy resources, the forecasting model of irradiation received from the sun, and therefore PV energy production might mitigate the impact of uncertainty on PV energy generation, improve system dependability, and increase the incursion level of solar power generation. Smart sensors and Internet of Things technologies are essential for monitoring and controlling applications in a broad range of fields. As a result, solar power generation forecasting was essential for microgrid stability and security, as well as solar photovoltaic integration in a strategic approach. This paper examines how to use IoT, a solar photovoltaic system being monitored, and shows the proposed monitoring system is a potentially viable option for smart remote and in-person monitoring of a solar PV system.

Resistivity Tensor of Vortex-Lattice States in Josephson Junction Arrays.

Two-dimensional Josephson junction arrays frustrated by a perpendicular magnetic field are predicted to form a cascade of distinct vortex lattice states. Here, we show that the resistivity tensor provides both structural and dynamical information on the vortex-lattice states and intervening phase transitions, which allows for experimental identification of these symmetry-breaking ground states. We illustrate our general approach by a microscopic theory of the resistivity tensor for a range of magnetic fields exhibiting a rich set of vortex lattices as well as transitions to liquid-crystalline vortex states.

Ligand 1 H NMR Chemical Shifts as Accurate Reporters for Protein-Ligand Binding Interfaces in Solution.

The availability of high-resolution 3D structural information is crucial for investigating guest-host systems across a wide range of fields. In the context of drug discovery, the information is routinely used to establish and validate structure-activity relationships, grow initial hits from screening campaigns, and to guide molecular docking. For the generation of protein-ligand complex structural information, X-ray crystallography is the experimental method of choice, however, with limited information on protein flexibility. An experimentally verified structural model of the binding interface in the native solution-state would support medicinal chemists in their molecular design decisions. Here we demonstrate that protein-bound ligand 1 H NMR chemical shifts are highly sensitive and accurate probes for the immediate chemical environment of protein-ligand interfaces. By comparing the experimental ligand 1 H chemical shift values with those computed from the X-ray structure using quantum mechanics methodology, we identify significant disagreements for parts of the ligand between the two experimental techniques. We show that quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) ensembles can be used to refine initial X-ray co-crystal structures resulting in a better agreement with experimental 1 H ligand chemical shift values. Overall, our findings highlight the usefulness of ligand 1 H NMR chemical shift information in combination with a QM/MM MD workflow for generating protein-ligand ensembles that accurately reproduce solution structural data.

Vertical heterojunction photodetector with self-powered broadband response and high performance

The development trend of modern electronics and optoelectronics is constantly moving towards highly integrated, comprehensive, and miniaturized devices. Vertical configuration provides an easy means to achieve higher integration density than traditional planar configuration, and possess higher carrier generation capabilities and transfer speed, which can further enhance the performance of optoelectronic devices. This technology holds tremendous potential for designing the next generation of electronic/optoelectronic devices. In this work, we developed a vertical heterojunction photodetector, which combines narrow-bandgap 2D topological insulator Bi2Se3, ultra-wide-bandgap amorphous Ga2O3, and high-performance p-Si substrate, achieving broad-spectrum detection from 254 to 1000 nm and significantly shortened response time due to the advantage of carrier vertical transmission. The response time of the device is less than 50 ms under illumination with different wavelengths. Moreover, the device exhibits self-powered characteristics, with maximum responsivity and detectivity of 0.175 mA/W and 1.58 × 1010 Jones, respectively, under 365 nm light irradiation at zero bias. A switch ratio as high as 103 is demonstrated under 365 and 470 nm illumination, highlighting the high performance of device. Additionally, the device displays excellent stability and imaging capability. Our research provides a promising and instructive prospect for the practical application of vertical broadband self-powered heterojunction PDs in a wider range of fields.

Preconditioned weighted full orothogonalization method for solving singular linear systems from PageRank problems

AbstractThe PageRank model, which was first proposed by Google for its web search engine application, has since become a popular computational tool in a wide range of scientific fields, including chemistry, bioinformatics, neuroscience, bibliometrics, social networks, and others. PageRank calculations necessitate the use of fast computational techniques with low algorithmic and memory complexity. In recent years, much attention has been paid to Krylov subspace algorithms for solving difficult PageRank linear systems, such as those with large damping parameters close to one. In this article, we examine the full orthogonalization method (FOM). We present a convergence study of the method that extends and clarifies part of the conclusions reached in Zhang et al. (J Comput Appl Math. 2016; 296:397–409.). Furthermore, we demonstrate that FOM is breakdown free when solving singular PageRank linear systems with index one and we investigate the effect of using weighted inner‐products instead of conventional inner‐products in the orthonormalization procedure on FOM convergence. Finally, we develop a shifted polynomial preconditioner that takes advantage of the special structure of the PageRank linear system and has a good ability to cluster most of the eigenvalues, making it a good choice for an iterative method like FOM or GMRES. Numerical experiments are presented to support the theoretical findings and to evaluate the performance of the new weighted preconditioned FOM PageRank solver in comparison to other established solvers for this class of problem, including conventional stationary methods, hybrid combinations of stationary and Krylov subspace methods, and multi‐step splitting strategies.

AI and Search Give New Impetus to Tencent: The Perspectives Based on Tencent's Acquisition of SOGO

The research background is that the search field is gradually becoming a hot object on the internet and can be combined with a very wide range of fields to produce marginal effects. The research optional of the article is analyzing the disadvantage of SOGO and the advantage of Tencent. The study content is that what trouble are SOGO face and how SOGO cope with the dilemma, how Tencent solves the single revenue structure and develops the search area. The purpose of the article is that explaining Tencent's motives for acquiring SOGO and what benefit SOGO can take to Tencent. The key findings of the article is that Tencent acquires SOGO can help Tencent develop AI area. The research significance is that inspiring people to think that Tencent's acquisition of SOGO should not only be limited to search area, but also expand horizons into the field of AI. Investors can keep a positive position of the future of Tencent.

Thermodynamic and magnetotransport properties of EuAg2Ge2 single crystals

Here, we report the crystallographic, thermodynamic, and magnetotransport properties of flux grown EuAg2Ge2 single crystals using X-ray diffraction, anisotropic magnetic susceptibility, and magnetization, heat capacity, electrical resistivity, and magnetoresistance measurements. The room temperature powder X-ray diffraction pattern confirms that EuAg2Ge2 crystallizes in a body-centered tetragonal ThCr2Si2–type structure (space group I4/mmm) with lattice parameters, a = 4.401 Å and c = 10.845 Å. The magnetic susceptibility data reveal two subsequent antiferromagnetic transitions at TN1 ∼ 8.3 K, and TN2 ∼ 6.2 K. For H‖c at T = 5 K, the magnetization data show the spin-flop like metamagnetic transition around the critical field of 1.4 T. The existence of antiferromagnetic transitions is further confirmed by the heat capacity measurements, which display two anomalies around TN1 = 8.3 K and TN2 = 6.1 K. The electrical resistivity data show the metallic behavior of the compound, which is best described by the Bloch-Grüneisen model above 30 K. At low temperatures, the antiferromagnetic transition manifests through a slight upturn around 8 K, followed by a sharp drop in the resistivity data. The transverse magnetoresistance (MR) measured for H‖c below TN first increases, showing a broad maximum at critical field strength that shifts towards lower fields with increasing temperature. With further increasing fields beyond the maximum, MR decreases, and changes its value from positive to negative. The evolution of MR from positive to negative could be associated with the metamagnetic transition in EuAg2Ge2. In the paramagnetic region, MR remains negative across the entire field range.

In-vitro Pharmacological Investigation of Ludwigia adscendens

Aims: This research objective was to investigate some in-vitro properties of methanol-extracted plant extracts of Ludwigia adscendens (the water primrose). The primary method of assessment for the methanolic extract of Ludwigia adscendens (MELA) was investigated for phytochemical screening. It was determined to look into the plant's potential for in vitro activities, such as antioxidant, antimicrobial, anti-inflammatory anti-arthritis, cytotoxic, thrombolytic and membrane stabilizing activity due to the pharmaceutical interest in its component parts.
 Methodology: Phytochemical screening was performed utilizing a battery of test reagents. Antioxidant, antimicrobial, anti-inflammatory, anti-arthritic, cytotoxic, thrombolytic and membrane stabilizing activities were measured at various doses using the DPPH Free Radical Scavenging Assay, the Disk Diffusion Method, Egg Albumin Protein Denaturation Assay, the Inhibition of Protein Denaturation Assay, the Brine Shrimp Lethality Bioassay, Clot lysis assay and heat induced and hypotonic induced haemolysis method.
 Results: The findings of research on MELA's phytochemical screening showed the presence of a wide range of various phytochemical components. The higher antioxidant activity of MELA is shown by its IC50 value of 85.76 µg/mL. Significant antibacterial action was shown by MELA. As compared to the standard ascorbic acid, this extract showed significantly greater anti-arthritic and anti-inflammatory effects. MELA exhibited much higher LC50 value which is 1.147 µg/mL than the typical vincristine-sulphate in a lethality test with brine shrimp. Comparatively speaking, this herb has quite powerful thrombolytic action. Significant membrane stabilizing action has also been shown by MELA.
 Conclusion: To wrap it up, it is clear that this plants phytochemical can be used for wide range of drug discovery field due to its potent pharmacological actions.

Predicting 3D particles shapes based on 2D images by using convolutional neural network

In order to accurately determine the three-dimensional (3D) shape of granular particles, one laborious approach is to employ X-ray microtomography and image processing techniques. As an alternative, in this study a 3D convolutional neural network (3D-CNN) methodology is presented to generate realistic 3D models of particles from a number of distinct two-dimensional (2D) projections. Initially, 3D synthetic particles with the targeted statistical distributions of the relevant geometric parameters, such as sphericity, elongation, aspect ratio, and flatness, were randomly generated using Fourier shape descriptors (FSDs). The database based on the statistical distributions of geometric parameters of Avicel® PH 200, obtained through dynamic image analysis. Following that, projections from multiple angles of 2D particle images were extracted for model training. By using a convolutional neural network (CNN) the shape of the particles was predicted, which involves the following sequential steps: a) model training on 2D images; b) a decoder is built to generate the 3D voxel model after an encoder extracts the properties of 2D images; c) a marching cube algorithm that converts the particle 3D voxel model into a mesh model; d) the accuracy of the prediction is evaluated by comparing the geometric parameters of the prediction models that were generated by using FSDs. The methodology was validated using a database of 3D synthetic particles with targeted statistical distributions of geometric parameters, such as sphericity, elongation, aspect ratio, and flatness. The mean absolute percentage error (MAPE) of the AI model for these parameters was 2.24%, 7.01%, 10.93%, and 7.67%, respectively. The 3D-CNN methodology has the potential for broad applications in various fields, such as rock and mineral analysis, battery materials, pharmaceuticals, and space exploration, offering a practical and affordable solution for shape analysis of relevant materials. It can significantly improve the efficiency and accuracy of numerical simulations and analyses, leading to new insights and innovations in a wide range of fields.

A 13‐step framework for better integration of streamlined conservation research

AbstractThe rise of integrated conservation research underscores its pivotal role in raising global environmental concerns, making it an attractive research subject from numerous perspectives. This general interest in integrated research is positive in that it provides an interdisciplinary approach with important innovations, encompassing a variety of fields, in opposition to considering conservation as an “afterthought” of ecological results. The current challenge is to organize research findings in a consistent, streamlined process that effectively supports and influences world biodiversity conservation. Disciplines once deemed unrelated to conservation science, such as social sciences, are now being incorporated into conservation research, and there is a pressing need to unify these diverse areas into a cohesive process to enhance the coverage of this growing field. It is also vital to acknowledge that each focal population unit (e.g., species, taxa, clade, etc.) may have specific requirements. Here, I propose a 13‐step framework that aims to integrate a comprehensive range of conservation research fields, from traditional ecology and behavior to inclusive mitigation plans, policy recommendations, and monitoring processes, ensuring that conservation research results are translated into tangible conservation actions. Integrating research‐based conservation results will facilitate the required coordinated effort from scientists, government agencies, the general public, and relevant sections of the private sector, thereby reinforcing the foundations to support conservation initiatives. This manuscript defines a procedure to address the conservation research requirements of specific focal population units (e.g., species, taxa, clade, population, etc.). It emphasizes the need for a consistent and streamlined process integrating all research fields, aiming to increase the breadth of knowledge essential for generating actionable conservation research results. The process supports clearly defined goal‐driven objectives that can be readily adopted by organizations interested in actionable conservation research results at varying levels. Nevertheless, conservation measures can be taken at any stage within the process, recognizing that achieving all research outcomes is a resource‐intensive optimum.

Design of Nupix-A2, a Monolithic Active Pixel Sensor for heavy-ion physics

The High Intensity heavy-ion Accelerator Facility (HIAF) is being constructed to generate intense beams of primary and radioactive ion for a wide range of research fields. Hence, a Monolithic Active Pixel Sensor (MAPS) named Nupix-A2 has been developed in a 130-nm High Resistivity CMOS process. The Nupix-A2 can simultaneously measure the particle hit' energy, arrival time, and position. It consists of a 128 × 128 pixel array, a digital-to-analog converter array, and a digital control module. The Nupix-A2 can measure energy deposition from 300 e- to over 50 ke- and time duration from 13 μs to 140 μs. This sensor also offers full readout mode and fast readout mode. In full-readout mode, all pixels measure arrival time and energy, suitable for real-time beam monitoring and image reconstruction. In fast-readout mode, only particle hit's positions are detected and read out. This paper presents the design of the Nupix-A2.

Optimization of high-temperature superconducting multilayer films using artificial intelligence

We have studied the possibility of utilizing artificial intelligence (AI) models to optimize high-temperature superconducting (HTS) multilayer structures for applications working in a specific field and temperature range. For this, we propose a new vortex dynamics simulation method that enables unprecedented efficiency in the sampling of training data required by the AI models. The performance of several different types of AI models has been studied, including kernel ridge regression (KRR), gradient-boosted decision tree (GBDT) and neural network. From these, the GBDT based model was observed to be clearly the best fitted for the associated problem. We have demonstrated the use of GBDT for finding optimal multilayer structure at 10 K temperature under 1 T field. The GBDT model predicts that simple doped-undoped bilayer structures, where the vast majority of the film is undoped superconductor, provide the best performance under the given environment. The obtained results coincide well with our previous studies providing further validation for the use of AI in the associated problem. We generally consider the AI models as highly efficient tools for the broad-scale optimization of HTS multilayer structures and suggest them to be used as the foremost method to further push the limits of HTS films for specific applications.

A User’s guide for inference in models defined by moment inequalities

Models defined by moment inequalities have become a standard modeling framework for empirical economists, spreading over a wide range of fields within economics. From the point of view of an empirical researcher, the literature on inference in moment inequality models is large and complex, including multiple survey papers that document the non-standard features these models possess, the main novel concepts behind inference in these models, and the most recent developments that bring advances in accuracy and computational tractability. In this paper we present a guide to empirical practice intended to help applied researchers navigate all the decisions required to frame a model as a moment inequality model and then to construct confidence intervals for the parameters of interest. We divide our template into four main steps: (a) a behavioral decision model, (b) moving from the decision model to a moment inequality model, (c) choosing a test statistic and critical value, and (d) accounting for computational challenges. We split each of these steps into a discussion of the “how” and the “why”, and then illustrate how to take these steps to practice in an empirical application that studies identification of expected sunk costs of offering a product in a market.

Approaches to NMR sensitivity enhancement based on theoretical analysis

Nuclear magnetic resonance (NMR) has been broadly investigated and applied in many industries (e.g., medical treatment, food safety and materials) as a powerful approach to explore the internal structure and composition of substances. However, the need for more sensitivity has been one of the most severe problems hindering the practical application of NMR. This paper suggests various approaches to deal with this defect, from adjusting factors based on the theoretical principle to the progressive hyperpolarization method. Classified by polarization sources, several representative sub-fields of hyperpolarization are further interpreted and analyzed, including Dynamic Nuclear Polarization (DNP), Optical Pumping (OP) and Parahydrogen Induced Polarization (PHIP). Based on the analysis and prospect of the aforementioned feasible methods, the main factors that restrict the sensitivity of NMR will be identified. Moreover, the prospects and new technologies brought by the methods based on different theoretical foundations have been considered to improve these factors and make NMR apply to a broader range of fields. Overall, these results not only indicate the current research status but also shed light on guiding further exploration of methods of enhancing NMR sensitivity and usability.

A new network representation for time series analysis from the perspective of combinatorial property of ordinal patterns

Revealing system behavior from observed time series is a fundamental problem worthy of in-depth study and exploration, and has attracted extensive attention in a wide range of fields due to its wide application values. In this paper, we propose a novel network construction method for time series analysis, which is different from the existing ordinal network method concerning the transition probability of ordinal patterns in transition networks. The proposed network representation is based on the combinatorial property concerning the inversion number of ordinal patterns from the ordinal partitions of time series. For the proposed network construction method, the network nodes are represented by each ordinal partition of time series and the edge weight between network nodes is determined by a novel proximity relationship of ordinal patterns which is a newly defined metric based on the inversion number of ordinal patterns. Using random signals and chaotic signals as examples, we demonstrate the potential of the proposed network construction method for the network representation of time series. We also employ the proposed network construction method in quantitative EEG for the identification of three different physiological and pathological brain states. According to the results of AUC values, one can observe that the discriminating power of the AND of the proposed network construction method is slightly stronger than that of the available ordinal network. The experimental results illustrate that our proposed network construction method opens up a new pathway for network representation of time series, which is capable of quantifying time series for feature extraction and pattern learning for time series analysis.

Magnetic field and gas pressure sensor based on Mach–Zehnder interferometer with micro-groove fabricated by femtosecond laser pulse

A microgroove type Mach–Zehnder interferometer (MZI) for gas pressure and magnetic field measurements was prepared using a femtosecond laser pulse in a single mode fiber. Due to the interference light passing through the microgroove, changing the refractive index (RI) of the medium in the microgroove will change the optical path difference between the two channels, leading to significant drift of the MZI transmission spectrum, thus achieving the measurement of environmental parameters. When the gas pressure inside the microgroove changes, the RI of the air changes, achieving the measurement of environmental gas pressure. When the microgroove is filled with magnetic fluid, changes in the environmental magnetic field will cause changes in the RI of the magnetic fluid, thus achieving measurement of the magnetic field. The experimental results show that the gas pressure sensitivity of the sensor is −5.03 nm/MPa, and the temperature cross-sensitivity is only 1.31 kPa/°C. This sensor achieves a magnetic field sensitivity of −0.3505 nm/mT in the magnetic field range of 6.4–13.6 mT. The sensor is small in size and easy to manufacture, making it a good choice for measuring air pressure and magnetic field.

Two-scale data-driven design for heat manipulation

Data-driven methods have gained increasing attention in computational mechanics and design. This study investigates a two-scale data-driven design for thermal metamaterials with various functionalities. To address the complexity of multiscale design, the design variables are chosen as the components of the homogenized thermal conductivity matrix originating from the lower scale unit cells. Multiple macroscopic functionalities including thermal cloak, thermal concentrator, thermal rotator/inverter, and their combinations, are achieved using the developed approach. Sensitivity analysis is performed to determine the effect of each design variable on the desired functionalities, which is then incorporated into topology optimization. Geometric extraction demonstrates an excellent matching between the optimized homogenized conductivity and the extraction from the constructed database containing both architecture and property information. The designed heterostructures exhibit multiple thermal meta-functionalities that can be applied to a wide range of heat transfer fields from personal computers to aerospace engineering.