Double Vector Research Articles

Release note: VBFNLO 3.0

Vbfnlo is a flexible parton level Monte Carlo program for the simulation of vector boson fusion (VBF), QCD-induced single and double vector boson production plus two jets, and double and triple vector boson production (plus jet) in hadronic collisions at next-to-leading order (NLO) in the strong coupling constant, as well as Higgs boson plus two and three jet production via gluon fusion at the one-loop level. For the new version – Version 3.0 – several major enhancements have been included. An interface according to the Binoth Les Houches Accord (BLHA) has been added for all VBF and di/tri-boson processes including fully leptonic decays. For all dimension-8 operators affecting vector boson scattering (VBS) processes, a modified T-matrix unitarization procedure has been implemented. Several new production processes have been added, namely the VBS Zγjj\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Z\\gamma jj$$\\end{document} and γγjj\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma \\gamma jj$$\\end{document} processes at NLO, γγjj\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma \\gamma jj $$\\end{document}, WWj and ZZj production at NLO including the loop-induced gluon-fusion contributions and the gluon-fusion one-loop induced Φjjj\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varPhi jjj$$\\end{document} (Φ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varPhi $$\\end{document} is a CP-even or CP-odd scalar boson) process at LO, retaining the full top-mass dependence. Finally, the code has been parallelized using Openmpi.

LINC00941 is a diagnostic biomarker for lung adenocarcinoma and promotes tumorigenesis through cell autophagy.

Non-small cell lung cancer (NSCLC) is a lethal malignancy. There is mounting evidence indicating that lncRNAs are crucial players with dual roles as both biomarkers and regulators across various cancers. It was reported that LINC00941 plays a cancer-promoting role in NSCLC. However, its impact on tumour autophagy remains poorly understood. In this study, we developed a risk assessment model and identified an autophagy-related lncRNA LINC00941, which has independent predictive and early diagnostic potential. Using RT-qPCR analysis, we confirmed the upregulation of LINC00941 in tumour tissues and cell lines of human lung adenocarcinoma (LUAD). Functional assays, such as CCK8, colony formation and xenograft models, demonstrated the cancer-promoting activity of LINC00941 both invitro and invivo. Further analysis using Western blotting analysis, mRFP-GFP-LC3 double fluorescence lentivirus vector and transmission electron microscopy (TEM) confirmed that the knockdown of LINC00941 triggered autophagy. These results indicate that knockdown of LINC00941 induces autophagy and impairs the proliferation of LUAD. Therefore, we propose LINC00941 as an independent biomarker for early diagnosis as well as a therapeutic target in LUAD.

Model predictive current control of Dual Three-phase Permanent Magnet Synchronous Motor based on double virtual vectors

Model predictive current control of Dual Three-phase Permanent Magnet Synchronous Motor based on double virtual vectors

Double-Vector Model-Free Predictive Current Control Method for Voltage Source Inverters With Sampling Noise Suppression

Model-free predictive current control (MFPCC) methods based on look-up tables (LUTs) have been widely applied in voltage source inverters (VSIs) due to their simple implementation and excellent robustness. However, the performance of the VSIs is affected by the voltage vector used, accuracy of current gradients and sampling noise. In order to improve these, a new double-vector MFPCC method is proposed in this paper. First, a synthetic voltage vector composed of double vectors is applied in each control period to reduce current ripples, where the durations of the double vectors are calculated using predicted current gradients. Next, an extended LUT is designed by using the current gradients of the applied synthetic voltage vectors to update all current gradients of eight basic voltage vectors, so as to increase the accuracy of current gradients. Then, the effect of the sampling noise on MFPCC is analyzed in detail and suppressed by a sliding-mode observer (SMO) for the VSIs. Finally, simulation and experimental results have verified the effectiveness of the proposed method.

Exclusive production of double light neutral mesons at the e+e− colliders

In this work we investigate the exclusive production of a pair of light neutral mesons in e+e− annihilation, where the final state bears an even C-parity. The production processes can be initiated via the photon fragmentation or the nonfragmentation mechanism. While the fragmentation contribution can be rigorously accounted, the nonfragmentation contributions are calculated within the framework of collinear factorization, where only the leading-twist light-cone distribution amplitudes (LCDAs) of mesons are considered. Mediately solely by the nonfragmentation mechanism, the production rates of double light neutral pseudoscalar mesons are too small to be observed at the commissioning e+e− facilities. In contrast, the production rates of a pair of light neutral vector mesons are greatly amplified owing to the significant kinematic enhancement brought by the fragmentation mechanism. It is found that, at s=3.77 GeV, after including the destructive interference between the nonfragmentation and fragmentation contributions, the production rates for e+e−→ρ0ρ0 and ρ0ω can be lowered by about 10% and 30% relative to the fragmentation predictions. Future precise measurement of these exclusive double neutral vector meson production channels at BESIII experiment may provide useful constraints on the LCDAs of light vector mesons. Published by the American Physical Society 2024

Drug Response Prediction Using XGBOOST

Abstract: An essential issue in computational personalised medicine is the prediction of drug responses. There have been several proposals for approaches to this problem that rely on machine learning, particularly deep learning. Nevertheless, these approaches often portray the medications as strings, an implausible representation of molecules. Furthermore, there has been a lack of comprehensive consideration of interpretation, such as whether mutations or copy number aberrations contribute to the medication response. Graph DRP, a new approach based on graph convolution networks, is suggested as a solution to the issue in this research. Cell lines were displayed as double vectors of genetic abnormalities in Graph DRP, whereas medications were shown as sub-atomic chartsthat straightforwardly caught the bonds among particles.

Model predictive control for Vienna rectifier with constant frequency based on inductance parameters online identification

When the finite control set model predictive control (FCS-MPC) is applied to the three-level converter, there are problems such as large current harmonics, high requirements for the computing efficiency of the microcontroller, complex multi-objective optimization and limited output vector switching. In addition,the mismatch of inductance parameter may directly affect the observation accuracy of FCS-MPC. To solve the above problem,a double vector model predictive control strategy with constant frequency strategy based on the inductance parameters on-line identification is presented.First, a single objective cost function based on the direct power is constructed by optimizing the redundant vector which is selected to balance the neutral-point potential, the design of weighting factor is avoided.At the same time, in order to effectively reduce the grid current ripple under single vector modulation, space vector pulse width modulation (SVPWM) is realized by combining with zero vector control mode. And, a parameter identification method based on model reference adaptive system(MARS) is proposed to overcome the effect of model parameter mismatch.Finally, the results show that the proposed F-MPCCF has good steady-state and dynamic performance from the static, transient and neutral-point potential control.

Linearizations of Cubic Two-Parameter Eigenvalue Problems and Its Vector Space

The paper considers the study of linearization techniques of cubic two-parameter matrix polynomial (CTMP). We analyze vector spaces of linearization of CTMP, namely ansatz vector space and double ansatz vector space. We also consider cubic two-parameter eigenvalue problem (CTEP) to study their linearization classes. A unified framework on linearization of CTMP will be established. The conditions under which a matrix pencil in the ansatz spaces is a linearization of CTMP will also be derived. Moreover, using these linearization techniques, CTEP is first converted into a singular linear two-parameter eigenvalue problem (2PEP) of larger size so that existing numerical method for (2PEP) can be applied.

Association of coronary angiography with ST-elevation and no ST-elevation in patients with refractory ventricular fibrillation – A substudy of the DOuble SEquential External Defibrillation for Refractory Ventricular Fibrillation (DOSE-VF randomized control trial)

BackgroundRefractory ventricular fibrillation or pulseless ventricular tachycardia (rVF/pVT) during out-of-hospital cardiac arrest (OHCA) is associated with poor survival. Double sequential defibrillation (DSED) and vector change (VC) improved survival for rVF/pVT in the DOSE-VF RCT. However, the role of angiography and percutaneous coronary intervention (angiography/PCI) during the trial is unknown. ObjectivesTo determine the incidence of ST-elevation (STE) and no ST-elevation (NO-STE) on post-arrest ECG and the use of angiography/PCI in patients with rVF/pVT during the DOSE-VF RCT. MethodAdults (≥18-years) with rVF/pVT OHCA randomized in the DOSE-VF RCT who survived to hospital admission were included. The primary analysis compared the proportion of angiography in STE and NO-STE. We performed regression modelling to examine association between STE, the interaction with defibrillation strategy, and survival to discharge controlling for known covariates. ResultsWe included 151 patients, 74 (49%) with STE and 77 (51%) with NO-STE. The proportion of angiography was higher in the STE cohort than NO-STE (87.8% vs 44.2%, p < 0.001); similarly the proportion of PCI was also higher (75.7% vs 9.1%, p < 0.001). Survival to discharge was similar between STE and NO-STE (63.5% vs 51.9%, p = 0.15). Use of angiography/PCI did not differ between defibrillation strategies. Decreased age (OR 0.95, 95% CI 0.92–0.98; p = 0.001) and angiography (OR 9.33, 95% CI 3.60–26.94; p < 0.001) were predictors of survival; however, STE was not. ConclusionWe found high rates of angiography/PCI in patients with STE compared to NO-STE, however similar rates of survival. Angiography was an independent predictor of survival. Improved rates of survival employing DSED and VC were independent of angiography/PCI.

Optimal configuration and power scheduling algorithm of distributed power system with large range power flow transfer

The distributed power system is composed of multiple distributed energy sources, loads and energy storage devices, which have the characteristics of decentralization and complexity. Therefore, a distributed power system optimal allocation and power dispatching algorithm considering large-scale power flow transfer is proposed. Calculate the power flow values of different nodes, and construct an optimization model according to the results to complete power flow transmission. In the inner loop, the current dispatching is set as the objective, and in the outer loop, the voltage dispatching is set as the objective. Distributed power system scheduling is completed by double closed-loop vectors. The experimental results show that the power oscillation amplitude of this method is small and the duration is short.

A geometrisation of [formula omitted]-manifolds

This paper proposes a geometrisation of N-manifolds of degree n as n-fold vector bundles equipped with a (signed) Sn-symmetry. More precisely, it proves an equivalence between the categories of [n]-manifolds and the category of (signed) symmetric n-fold vector bundles, by finding that symmetric n-fold vector bundle cocycles and [n]-manifold cocycles are identical.This extends the already known equivalences of [1]-manifolds with vector bundles, and of [2]-manifolds with involutive double vector bundles, where the involution is understood as an S2-action.

Optimum design method for structural configuration and fiber arrangement for fiber-reinforced composites

Fiber-reinforced composite materials, exemplified by CFRP, offer the possibility of achieving lightweight, high-stiffness, and high-strength structures by continuously and evenly distributing fibers. While topology and orientation optimization methods have been developed for anisotropic materials in the past, there remains a gap in design methods that consider manufacturability, especially for continuous fiber materials. In this study, we propose a design method that takes into account manufacturability, focusing on the aspects of continuity and uniformity in fiber-reinforced composite optimum design. Specifically, we introduce a two-stage optimization approach. In the first stage, we conduct concurrent optimization of topology and fiber orientation. We utilize a level-set function to represent topological configuration, while for orientation, we introduce a “double angle vector”, which enables us to consider fiber properties such as angular periodicity. These design variables are updated by solving partial differential equations based on reaction–diffusion equations. In the second stage, leveraging the optimal orientations obtained in the first stage, we optimize the path-line generation for the manufacture of continuous fiber materials. We introduce a scalar function representing path lines and formulate an optimization problem to ensure that the path lines are both evenly spaced and continuous. The update of design variables in this state is also achieved via solving the partial differential equation. Through the development of this two-stage optimization method, we aim to create an optimal structure with manufacturable continuous fiber materials, incorporating both the topology and fiber orientation that satisfy the requirements of continuity and uniformity.

Robust autoencoder feature selector for unsupervised feature selection

Unsupervised Feature Selection (UFS) methods are known to produce models with excellent ability to select high-quality features. This advantage, however, is challenged when analyzing noisy real-world data, where anomalies are prevalent. For example, there may be a feature (anomalous feature) that is corrupted across many samples or a sample (anomalous sample) that has more corruptions than its peers. Previous literature focused on addressing anomalous samples, and methods that are robust to both types of anomalies have been under-explored. This paper proposes a novel general framework for reconstruction-based UFS methods, which can be embedded into the feature learning process to simultaneously remove anomalous samples and features. Specifically, the framework learns double binary weight vectors to assign 0 weights to samples or features with the highest reconstruction errors and 1 weights to the others when computing reconstruction errors. By discarding the 0-weighted samples and features when updating the model parameters, the anomalies in the data are excluded. This allows the model to focus more on learning from the clean part of the noisy data. Our proposed framework is then integrated with AutoEncoder Feature Selector (AEFS [10]) to develop a new method, which jointly performs anomaly removal and feature selection. The experimental results demonstrate the effectiveness of the proposed framework. Particularly, processing both types of anomalies provides better robustness than processing only one type. Moreover, our proposed method outperforms several state-of-the-art methods on various real-world datasets.

A Low-Complexity Double Vector Model Predictive Current Control for Permanent Magnet Synchronous Motors

Compared to the conventional finite control set model predictive control (FCS-MPC), the double vector model predictive current control (DVMPCC) for permanent magnet synchronous motors (PMSMs) has a better steady-state performance without significantly increasing the switching frequency. However, determining optimal vectors with their dwell times requires a high computational burden. A low-complexity DVMPCC in the steady state was proposed in this study to address this problem. Firstly, the operating state of the motor was judged according to the speed error. During steady-state operation, the first optimal active vector was selected from three candidate vectors adjacent or identical to the active vector applied in the previous control period, reducing the number of comparisons by half. Next, the second optimal vector was selected from the other two active vectors, and the zero vector, the second optimal vector with the duty cycle, was determined according to the deadbeat condition of the q-axis current and cost function minimization. Finally, simulation and experimental results proved that the proposed low-complexity DVMPCC for surface-mounted permanent magnet synchronous motors is practical and feasible.

A novel Ca2+ double cone vector system to treat compromised skin.

Stressed, damaged or very aged skin is predominantly characterized by a malfunctioning skin barrier. Underlying skin barrier malfunction is a reduced or defective calcium gradient in the epidermis. Consequently, replenishing the compromised skin's calcium stores with topical calcium could be a potential therapeutic approach. We investigated the effect of our novel Ca2+ double cone vector system on improving the differentiation and barrier function of reconstructed human epidermis (RHE), cultured at low basal calcium (0.3 mM) to represent very aged skin. Furthermore, in a randomized placebo-controlled clinical study the skin barrier of 20 healthy volunteers was challenged with 2% sodium lauryl sulphate (SLS) for 24 h under occlusion, following and/or prior to treatment with a gel containing 2% of our calcium vector system. Culture in reduced basal calcium conditions (0.3 mM) strongly impeded the formation of a dense stratified epidermis. The apical treatment with 1.1 mM CaCl2 was not able to restore a functional differentiation. Treatment with 0.1% of the Ca2+ delivery system rescued the differentiation process and resulted in a normal stratified epidermis. Clinically, application of the Ca2+ vector system prior to and following SLS stress prevented increases in skin irritation and transepidermal water loss (TEWL) compared to placebo controls. Importantly, the treatment also significantly accelerated the recovery time following SLS stress. With our novel Ca2+ vector system, we highlight the delivery of bioavailable Ca2+ ions into the skin as a new and successful approach to treat a damaged barrier present in stressed, aged or atopic skin.

An Improved Model Predictive Control to Enhance Voltage Performance for $LC$ Filtered Three-Level Inverters With Voltage Feedback Only

This article proposes a double vector model predictive control (MPC) method for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LC$</tex-math></inline-formula> filtered three-level T-type inverter (3LT <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> I) to gain superior voltage performance, which is current sensorless and computationally efficient. In the proposed scheme, inverter voltage increment is induced in the multi-objectives cost function, and the desired voltage is derived based on the simplified discrete model, which optimizes the output voltage control performance. The prepositive prediction calculation is shifted out from the evaluation circle, and the computation is lessened simultaneously. Then, aiming to estimate the capacitor current of the simplified discrete model with voltage feedback only, the strong-tracking Kalman filter (STKF)-based observer is designed, which gets rid of the dependence on current sensors and lowers hardware cost. Furthermore, to improve the control accuracy of tracking the desired voltage, a two-layer decision algorithm based on the search tree is developed to find the optimal double vector easily, which obtains a low-complexity selection process using the univariate cost function. Finally, the simulation and experimental results are given to demonstrate the effectiveness of the theoretical analyses and the proposed method.

Double voltage vector model predictive control for grid-connected cascade H-bridge multilevel inverter with fixed switching frequency

Model predictive control (MPC) is an outstanding control method which can achieve superior dynamic response, nonlinear control and multi-objective collaborative control. However, because of unfixed switching frequency, the harmonic spectrum of the output current is dispersed, which would make it difficult to design the output filter. In this work, a double voltage vector model predictive control (DVV-MPC) algorithm for grid-connected cascade H-bridge (CHB) multilevel inverter is presented. The algorithm not only has the advantages of MPC but also produces fixed the switching frequency of the inverter. It is realized by a modulation stage with two adjacent voltage vectors in one switching cycle. And the duty cycle of the associated voltage level is obtained by minimizing an additional cost function. This method is suitable for the cascades of n cells inverter. For reducing the computation burden and eliminating the computation delay, adjacent regions prediction method and time-delay compensation method are applied to the inverter. Finally, in this paper, the proposed strategy is applied to a single-phase grid-connected 7-level CHB inverter. Simulation and experiment are executed to show the advantages of the proposed control algorithm in dynamic state and steady state.

Vector Potential Coupling From Outside the Loop Through the Superconducting Shield

We tested whether the Maxwell-Lodge effect, a classical version of the Aharonov-Bohm effect, occurs even when electromagnetic shielding is provided by a superconducting tube. A long, thin superconducting solenoid coil was confined inside a lead tube so that there was no flux leakage from the coil ends. The double semi-circular vector potential coils ensure that 1) there is no local magnetic field at the location of the copper wire in the secondary coil and 2) there is no magnetic flux inside the loop of the secondary coil. Three different materials with different permeability were used for the core. A coaxial wire secondary coil shielded by a lead tube was placed orthogonally to the center of the semicircle of the primary coil. The voltage generated in the secondary coil at 4.2 K was only reduced to about 1/10 of 300 K. This coupling between the primary and secondary coils is due to the vector potential, since it cannot be completely shielded even with a superconducting shield.

Research on performance evaluation and optimization of college budget management under the background of big data

Abstract At present, due to the limitations of technology and resources, the comprehensive budget management generally has problems such as imperfect foundation of establishment, weak binding force of execution, and lack of performance appraisal system. These problems become more prominent in the context of the era of big data. The use of big data technology, such as large capacity, real-time control, intelligent Settings and other characteristics, will be able to improve the current many problems. Therefore, this paper focuses on how to combine big data with comprehensive budget management. This paper holds that it is not enough for comprehensive budget management to stay in the current stage, but also need to use big data technology, which is a higher level of information stage. Therefore, this paper adopts the method of double subordination fuzzy support vector machine to study the effect of budget management of scientific research projects in Chinese universities. Based on the construction of comprehensive budget management performance evaluation index system throughout the whole process of college budget cycle, taking S university as an example, this paper analyzes the performance of comprehensive budget management in S university, and points out that big data can optimize and innovate the budget management mode, which has great significance for college budget management.

An improved global fast terminal high-order sliding mode control strategy based on novel reaching law for improving PMLSM dynamic performance

Due to its better power density and efficiency, permanent magnet linear synchronous motor (PMLSM) is ideal for generating electricity with wave power device (WPD). Nevertheless, when the PMLSM for the WPD control system is impacted by changes in motor characteristics and interference from the outside environment, it typically is unable to deliver high-quality dynamic performance on schedule. Although sliding mode control (SMC) has been introduced to improve the robustness of the system greatly in recent decades, traditional methods still have problems such as slow dynamic response, poor anti-disturbance performance, large chattering amplitude, and phase delay. To solve the above defects in SMC, an improved global fast terminal high-order SMC strategy based on novel reaching law for improving PMLSM dynamic performance is proposed in this paper, as well as the sliding mode surface and control law of the speed controller is designed based on this control strategy. The proposed strategy would not only shorten the dynamic response time but also weaken the chattering magnitude in the sliding mode stage, improving the dynamic performance of both speed and current. The simulation results demonstrate that in comparison to the traditional PI and SMC control methods, the improved strategy proposed in this paper can further enhance the control performance of the linear motor, demonstrating the method’s enormous potential in the double closed-loop vector control system of PMLSM for WPD.