Fast Evaluation Research Articles

Sensing fear: fast and precise threat evaluation in human sensory cortex.

Animal models of threat processing have evolved beyond the amygdala to incorporate a distributed neural network. In human research, evidence has intensified in recent years to challenge the canonical threat circuitry centered on the amygdala, urging revision of threat conceptualization. A strong surge of research into threat processing in the sensory cortex in the past decade has generated particularly useful insights to inform the reconceptualization. Here, synthesizing findings from both animal and human research, we highlight sensitive, specific, and adaptable threat representations in the sensory cortex, arising from experience-based sculpting of sensory coding networks. We thus propose that the human sensory cortex can drive smart (fast and precise) threat evaluation, producing threat-imbued sensory afferents to elicit network-wide threat responses.

Real-time and accurate estimation ex vivo of four basic optical properties from thin tissue based on a cascade forward neural network.

Double integrating sphere measurements obtained from thin ex vivo tissues provides more spectral information and hence allows full estimation of all basic optical properties (OPs) theoretically. However, the ill-conditioned nature of the OP determination increases excessively with the reduction in tissue thickness. Therefore, it is crucial to develop a model for thin ex vivo tissues that is robust to noise. Herein, we present a deep learning solution to precisely extract four basic OPs in real-time from thin ex vivo tissues, leveraging a dedicated cascade forward neural network (CFNN) for each OP with an additional introduced input of the refractive index of the cuvette holder. The results show that the CFNN-based model enables accurate and fast evaluation of OPs, as well as robustness to noise. Our proposed method overcomes the highly ill-conditioned restriction of OP evaluation and can distinguish the effects of slight changes in measurable quantities without any a priori knowledge.

Enhanced WPT structures via EC-SRR-based metasurfaces

PurposeThe purpose of this paper is to present a family of robust metasurface-oriented wireless power transfer systems with improved efficiency and size compactness. The effect of geometric and structural features on the overall efficiency and miniaturisation is elaborately studied, while the presence of substrate losses is, also, considered. Moreover, to further enhance the performance, possible means for reducing the operating frequency, without comprising the unit-cell size, are proposed.Design/methodology/approachThe key element of the design technique is the edge-coupled split-ring resonators patterned in various metasurface configurations and optimally placed to increase the total efficiency. To this goal, a rigorous three-dimensional algorithm, launching a new high-order prism macroelement, is developed in this paper for the fast evaluation of the required quantities. The featured scheme can host diverse approximation orders, while it is drastically more economical than existing methods. Hence, the demanding wireless power transfer systems are precisely modelled via reduced degrees of freedom, without the need to conduct large-scale simulations.FindingsNumerical results, compared with measured data from fabricated prototypes, validate the design methodology and prove its competence to provide enhanced metasurface wireless power transfer systems. An assortment of optimized 3 x 3 and 5 x 5 metamaterial setups is investigated, and interesting deductions, regarding the impact of the inter-element gaps, the distance between the transmitting and receiving components and the substrate losses, are derived. Also, the proposed vector macroelement technique overwhelms typical implementations in terms of computational burden, particularly when combined with the relevant commercial software packages.Originality/valueSystematic design of advanced real-world wireless power transfer structures through optimally selected metasurfaces with fully controllable electromagnetic properties is presented. The analysis is performed by means of a rapid prism macroelement methodology, which leads to very confined meshes, accurate results and significantly reduced overhead. The selected metamaterial resonators are found to be very flexible and reconfigurable, even in the case of large substrate conductivity losses, whereas their contribution to the system’s total efficiency is decisive.

Renormalized perturbation theory for fast evaluation of Feynman diagrams on the real frequency axis

We present a method to accelerate the numerical evaluation of spatial integrals of Feynman diagrams when expressed on the real frequency axis. This can be realized through use of a renormalized perturbation expansion with a constant but complex renormalization shift. The complex shift acts as a regularization parameter for the numerical integration of otherwise sharp functions. This results in an exponential speed up of stochastic numerical integration at the expense of evaluating additional counter-term diagrams. We provide proof of concept calculations within a difficult limit of the half-filled 2D Hubbard model on a square lattice.

Z′-explorer 2.0: Reconnoitering the dark matter landscape

We introduce version 2.0 of Z′-explorer, a software tool that provides a simple, fast, and user-friendly test of models with an extra U(1) gauge boson (Z′) against experimental LHC results. The main novelty of the second version is the inclusion of missing energy searches, as the first version only included final states into SM particles. Hence Z′-explorer 2.0 is able to test dark matter models where the Z′ acts as an s-channel mediator between the Standard Model and the dark sector, a widespread benchmark employed by the ATLAS and CMS experimental collaborations. To this end, we perform here the first public reinterpretation of the most recent ATLAS mono-jet search with 139 fb−1. In addition, the corresponding searches in the visible final states have also been updated. We illustrate the power of our code by re-obtaining public plots and also showing novel results. In particular, we study the cases where the Z′ couples strongly to top quarks (top-philic), where dark matter couples with a mixture of vector and axial-vector couplings, and also perform a scan in the parameter space of a string inspired Stückelberg model. Z′-explorer 2.0 is publicly available on GitHub. Program summaryProgram Title:Z′-explorer 2.0CPC Library link to program files:https://doi.org/10.17632/k7tdp8kwgf.2Developer's repository link:https://github.com/ro-sanda/Z--explorer-2.0Licensing provisions: GPLv3Programming language: C++ and bashNature of problem: New SM neutral gauge bosons, Z′, are ubiquitously present in models of New Physics. In order to confront these models versus a large and ever-growing library of LHC searches, Z′-explorer 1.0 had already included all final states including Standard Model particles. Notably, the previous version of this tool lacked the so-called invisible final states manifested as a momentum imbalance in the transverse plane (“missing energy”). These searches help to probe mediators into a dark sector, where a dark matter candidate resides.Solution method:Z′-explorer encodes the production cross sections for Z′ bosons at the LHC as a function of their mass, allowing for a fast evaluation of the exclusion limits. This version of Z′-explorer includes a careful validation of the latest search with one energetic jet (mono-jet) performed by the ATLAS collaboration. Hence one can now test if a given point in parameter space is excluded by both visible and invisible searches. The modular structure of the code has been kept, which allows for potential additions (low-energy constraints, flavor, extrapolation to future colliders).

Fast Disintegration Tablets Problems and Evaluation

Over the past ten years, the demand for fast disintegrating tablets (FDTs) has grown steadily, and the field is now one of the fastest-growing segments of the pharmaceutical industry. For many medications, oral drug delivery is still the preferred method of administration. Scientists have created FDTs that are more patient-friendly and convenient as a result of recent technological advancements. These tablets dissolve or disintegrate in the mouth when placed there without the need for additional water, making it simple to administer the active pharmaceutical ingredients. The formulation’s acceptance and usefulness led to the creation of a number of FDT technologies. Solid unit dose forms known as FDTs dissolve or disintegrate quickly in the mouth without chewing or drinking. FDTs, orally disintegrating tablets, are advantageous for elderly and paediatric patients

Gelatin‐Coated ZnO Array Nanoplatform for Capture, Drug Resistance Identification, and Recovery of Circulating Tumor Cells

AbstractDrug resistance causes failure of cancer treatment. Identifying drug sensitivity of tumors can guide therapeutic administration for cancer patients, however this approach lacks a facile, fast, and noninvasive evaluation platform. Herein, a gelatin‐coated ZnO array nanoplatform to isolate circulating tumor cells (CTCs) is developed, and their drug resistance for inspecting tumor sensitivity is assessed. The nanoplatform is comprised of a nanosubstrate of ZnO submicropillars (ZnO SMPs) for efficiently isolating cancer cells, and Au nanoparticles (AuNPs) based electrochemical probes for evaluating drug resistance of the captured cells. Gelatin, an extracellular matrix‐like and enzyme‐degradable material, is used to coat ZnO SMPs and AuNPs, endowing the nanoplatform with the ability to maintain cancer cell viability and realize enzyme‐responsive cell recovery for downstream bioanalysis. This nanoplatform is expected to noninvasively evaluate drug sensitivity of cancer patients through simple liquid biopsy of CTCs in peripheral blood and benefit the treatment.

Visualizing Efficacy of Antineoplastic Drug via Ratiometric Near-Infrared Fluorescence Imaging of Tumor-Associated Macrophage-Specific Nitric Oxide.

Elevated nitric oxide (NO) within tumor-associated macrophages (TAMs) suggests a reduction of TAM-mediated tumoral immune tolerance. This cellular event could be a reliable indicator for efficacy evaluation of antineoplastic drugs. However, a suitable method for TAM-specific NO measurement is still lacking. In this work, a simple and fast efficacy evaluation method for antineoplastic drugs is established based on a ratiometric TAM-specific NO near-infrared (NIR) fluorescence probe TAM-Cy-NO. Molecular fluorescence probe Cy-NO for NO response was encapsulated in the TAM-targeting peptide (M2pep)-functionalized liposome to construct TAM-Cy-NO. After TAM enters through M2pep, Cy-NO reacts with NO specifically, resulting in a dose-dependent ratiometric fluorescence signal (I 610/I 815) change manner. Utilizing this strategy, we observed that PLX-3397, metformin, and ibrutinib triggered NO generation within TAM greater than that with sorafenib. Notably, metformin and ibrutinib promoted TNF-α and reduced PD-L1 expressions, which suggest reductions of TAM-mediated immunosuppression. As expected, these drugs delayed tumor progression in mice. This method provides a promising efficacy evaluation strategy for rapid screening of antineoplastic drugs.

A fast implicit difference scheme for solving high-dimensional time-space fractional nonlinear Schrödinger equation

In this work, an efficient implicit difference scheme is developed for solving the high-dimensional time-space fractional nonlinear Schrödinger equation. The derived scheme is constructed by utilizing a fast evaluation of Caputo fractional derivative based on the - formula; meanwhile, the compact finite difference with matrix transfer technique is adopted for the spatial discretization. Moreover, a linearized iteration method based on the fast discrete sine transform technique is considered to solve the nonlinear system effectively. Because the resultant algorithm does not use matrix inversion, it is computationally efficient in long-time calculations. The stability, truncation error and convergence analysis of the discrete scheme are discussed in detail. Furthermore, a fast iterative algorithm is provided. Finally, several numerical examples are given to verify the efficiency and accuracy of the derived scheme, and a comparison with similar work is presented.

Machine learning exploration of the mobility and environmental assessment of toxic elements in mining-associated solid wastes

Continuous development of the mining industry has led to the production of large volumes of solid wastes. Toxic elements (TEs) have been identified in mining-associated solid wastes, whose leaching potential poses a significant environmental risk. In this study, we introduce a synthesis framework titled Fast mobIlity Evaluation and environmentaL inDex (FIELD). The physicochemical properties of solid waste, element properties, and total TEs concentrations were used as input variables to construct the machine learning model and predict the TEs fractions. Novel environmental indexes were then proposed based on both the total TEs concentrations and corresponding chemical speciation. Applying the framework to a case study of coal fly ash (CFA) demonstrated robust performance of the deep neural network model with good generalization capabilities (R2 = 0.83 on the testing set), indicating it can rapidly and accurately predict TEs fractions. The TEs fractions were found to be primarily affected by the element properties. The results also demonstrate that the proposed environmental indexes are superior to current indexes for identifying the most environmentally hazardous TEs within a specific CFA sample, as well as for identifying the most dangerous CFA sample based on TEs fractions. Compared with traditional laboratory analysis and environmental risk assessment methods, the FIELD framework proposed in this study is more efficient and achieves robust results, with important reference significance for environmental pollution control.

Fast forward approximation and multitask inversion of gravity anomaly based on UNet3+

SUMMARYGravity inversion is a process that obtains the spatial structure and physical properties of underground anomalies using surface collected gravity anomaly data. In recent years, the rapid development of deep learning (DL) has enabled the achievement of good results for gravity inversion methods based on DL. These methods aim to learn the mapping between geological models and gravity anomaly data by training a neural network with geological models as labels. However, using DL inversion requires generating a large amount of training data for each geological target and involves the forward calculation of the generated models, which inevitably consumes a large amount of time and storage space. To address this issue, we propose using a neural network to approximate the expensive forward computation with a fast evaluation alternative. After training, the network can reproduce gravity anomalies at any observation point. To evaluate the effectiveness of the forward model, we use the gravity anomalies predicted by the forward network for inversion network training. Additionally, to mitigate the problem of poor generalization of existing DL inversions, we propose using multitask learning. By learning multiple related tasks simultaneously, the generalization ability of the model improves, thus enhancing the performance of the main task. In this paper, a multitask UNet3+ network is proposed to realize anomaly bodies localization and density contrasts reconstruction simultaneously. Test results on the synthetic data set demonstrate that the gravity anomalies predicted by the forward network can be successfully inverted, and the multitask approach can predict subsurface geology more accurately than the single-task. To further illustrate the effectiveness of the algorithm, we apply this method to the inversion of the San Nicolas deposit in central Mexico.

NON-COMMUNICABLE DISEASE SYNDEMICS AMONG LOW-INCOME POPULATIONS: PAKISTAN AND WAY FORWARD

Non-communicable diseases (NCDs) have substantially contributed to morbidity and mortality of populations. The emergence of the novel coronavirus SARS-CoV2 pandemic took centre stage toppling over the global health and economies leading to high morbidity and mortality rates. Presently as many as one in five with underlying NCDs are at risk of severe Covid-19[3].In May 2020, the World Health Organization (WHO) undertook a fast evaluation survey, which revealed that 75% of nations have experienced service outages related to NCDs. Public health campaigns and NCD surveillance programmes were among the hardest damaged. Gains against COVID-19 are hollow wins when the risky interactions between NCDs and virus infection are not perceived as syndemics along with social and economic disparities.

Fast Barrier Option Pricing by the COS BEM Method in Heston Model (with Matlab Code)

Abstract In this work, the Fourier-cosine series (COS) method has been combined with the Boundary Element Method (BEM) for a fast evaluation of barrier option prices. After a description of its use in the Black and Scholes (BS) model, the focus of the paper is on the application of the proposed methodology to the barrier option evaluation in the Heston model, where its contribution is fundamental to improve computational efficiency and to make BEM appealing among finance practitioners as a valid alternative to Monte Carlo (MC) or other more traditional approaches. An error analysis is provided on the number of terms used in the Fourier-cosine series expansion, where the error bound estimation is based on the characteristic function of the log-asset price process. A Matlab code implementing this technique is attached at the end of the paper.

Research on Fast CFD Simulation of Automobile Flow Field Based on Artificial Intelligence

Fast automotive aerodynamic evaluation can extremely reduce the design cycles of automotive. This paper establishes a rapid simulation model of the automobile flow field based on the artificial intelligence surrogate model. The end-to-end network is used to map the geometric model, incoming flow conditions, and result. The decoder is used to splice high-dimensional and low-dimensional features to achieve feature sharing. The average MAE error of the optimal model is 5.249%. The average calculation time of a single example is 1.2968s, which is about 0.62% of CFD solver. The simulation result demonstrate that the deep learning method can not only accelerate the calculation, but also can improve the design efficiency of automobile aerodynamic profile with high accuracy.

When Database Meets New Storage Devices: Understanding and Exposing Performance Mismatches via Configurations

NVMe SSD hugely boosts the I/O speed, with up to GB/s throughput and microsecond-level latency. Unfortunately, DBMS users can often find their high-performanced storage devices tend to deliver less-than-expected or even worse performance when compared to their traditional peers. While many works focus on proposing new DBMS designs to fully exploit NVMe SSDs, few systematically study the symptoms, root causes and possible detection methods of such performance mismatches on existing databases. In this paper, we start with an empirical study where we systematically expose and analyze the performance mismatches on six popular databases via controlled configuration tuning. From the study, we find that all six databases can suffer from performance mismatches. Moreover, we conclude that the root causes can be categorized as databases' unawareness of new storage devices characteristics in I/O size, I/O parallelism and I/O sequentiality. We report 17 mismatches to developers and 15 are confirmed. Additionally, we realize testing all configuration knobs yields low efficiency. Therefore, we propose a fast performance mismatch detection framework and evaluation shows that our framework brings two orders of magnitude speedup than baseline without sacrificing effectiveness.

Spectral-Element Spectral-Integral (SESI) Method for Doubly Periodic Problems With 3-D Scatterers Embedded in Multiple Regions of Layered Media

In this work, a hybrid 3-D spectral-element spectral-integral (SESI) method is developed by combining the spectral element method (SEM) and spectral integral method (SIM) for the fast evaluation of electromagnetic (EM) scattering from double Bloch (Floquet) periodic problems with 3-D scatterers embedded in multiple regions of layered media. This 3-D SESI method is a further extension of the previously developed 2-D SESI method. In the proposed method, the computational domain is divided into several SEM and SIM subdomains, where the subdomains are coupled by Riemann transmission conditions (RTCs) to allow the use of nonconforming meshes for these subdomains. Meanwhile, the 3-D periodic layered medium Green’s function (PLMGF) is derived and employed to simulate 3-D structures in layered media without their volumetric discretization. Four numerical experiments show that the developed SIM method is highly efficient and accurate; the SESI method is more efficient than the traditional finite element method (FEM), thus is a promising solver for designing and optimizing frequency selective surfaces (FSSs), metamaterials, and metasurfaces.

Performance-Enhanced Static Modulated Fourier Transform Spectrometer with a Spectral Reconstruction

A static modulated Fourier transform spectrometer has been noted to be a compact and fast evaluation tool for spectroscopic inspection, and many novel structures have been reported to support its performance. However, it still suffers from poor spectral resolution due to the limited sampling data points, which marks its intrinsic drawback. In this paper, we outline the enhanced performance of a static modulated Fourier transform spectrometer with a spectral reconstruction method that can compensate for the insufficient data points. An enhanced spectrum can be reconstructed by applying a linear regression method to a measured interferogram. We obtain the transfer function of a spectrometer by analyzing what interferogram can be detected with different values of parameters such as focal length of the Fourier lens, mirror displacement, and wavenumber range, instead of direct measurement of the transfer function. Additionally, the optimal experimental conditions for the narrowest spectral width are investigated. Application of the spectral reconstruction method achieves an improved spectral resolution from 74 cm-1 when spectral reconstruction is not applied to 8.9 cm-1, and a narrowed spectral width from 414 cm-1 to 371 cm-1, which are close to the values of the spectral reference. In conclusion, the spectral reconstruction method in a compact static modulated Fourier transform spectrometer effectively enhances its performance without any additional optic in the structure.

A Bayesian Optimization Algorithm for the Optimization of Mooring System Design Using Time-Domain Analysis

Dynamic analysis can consider the complex behavior of mooring systems. However, the relatively long analysis time of the dynamic analysis makes it difficult to use in the design of mooring systems. To tackle this, we present a Bayesian optimization algorithm (BOA) which is well known as fast convergence using a small number of data points. The BOA evaluates design candidates using a probability-based objective function which is updated during the optimization process as more data points are achieved. In a case study, we applied the BOA to improve an initial mooring system that had been designed by human experts. The BOA was also compared with a genetic algorithm (GA) that used a pre-trained surrogate model for fast evaluation. The optimal designs that were determined by both the BOA and GA have a 50% lower maximum tension than the initial design. However, the computation time of the GA needed 20 times more than that of the BOA because of the training time of the surrogate model.

Specialized 3D Distinct element limit analysis approach for a fast seismic vulnerability evaluation of massive masonry structures: Application on traditional pagodas

A 3D limit analysis model specialized in the analysis at the collapse of massive masonry structures subjected to seismic loads is proposed. The approach belongs to the wide family of distinct element methods, because is based on a discretization of the structure by means of infinitely resistant hexahedron elements and quadrilateral interfaces where all dissipation occurs. Any failure surface -after proper linearization- can be adopted for interfaces, as for instance a Mohr-Coulomb failure criterion with tension and compression cutoffs. Having discretized the continuum in such a way that internal dissipation is allowed only between adjoining elements, the corresponding linear programming problem necessary to estimate the collapse load, can be obtained through both a kinematic and static approach. The first is more straightforward and the primal problem provides a failure mechanism, interfaces plastic multipliers and collapse multipliers. By virtue of the duality theorem, the second lower bound approach is associated with the same failure load, also providing the internal forces acting on interfaces. The model is validated on three examples of historical value, namely-three pagodas in China, all characterized by complex 3D geometries and massive walls. The results show how the procedure proposed is able to provide fast estimations of the load carrying capacity under the application of horizontal seismic actions and how the corresponding failure mechanism is rather complex, thus precluding the utilization of simplified approaches on idealized geometries.

Prediction of thermo-mechanical performance for effusion cooling by machine learning method

Fast and accurate evaluation of performance of cooling structures is very critical to the design of hot-section components in gas turbines. In the present study, machine learning method was developed to predict thermo-mechanical performance for effusion cooling on flat plate. The geometric and thermodynamic parameters including blowing ratio, density ratio, hole inclination angle, hole pitch and spacing were studied. Latin hypercube sampling was used for the design of numerical experiments, and the data sets for training model were generated by computational fluid dynamics (CFD) solutions. A kind of deep neural networks (DNNs) was used to predict the distributions of solid temperature and stress. Based on the outputs of neural network, the effects of the geometric and thermodynamic parameters on thermo-mechanical performance were investigated in detail. The results show that thermal stress decreases with the increases of density ratio and blowing ratio, while the increases of hole spacing and pitch cause the increase of thermal stress. Compared with the CFD results, the proposed DNN method has mean absolute percent error of 0.5% and 0.08% and root mean square error of 6.95 K and 5.1 MPa as applying for predicting solid temperature and stress. On the whole, machine learning is a promising method for modelling effusion cooling with high accuracy.