Background Vector Research Articles

High-efficient sample point transform algorithm for large-scale complex optimization

Decomposition algorithms and surrogate model methods are frequently employed to address large-scale, intricate optimization challenges. However, the iterative resolution phase inherent to decomposition algorithms can potentially alter the background vector, leading to the repetitive evaluation of samples across disparate iteration cycles. This phenomenon significantly diminishes the computational efficiency of optimization. Accordingly, a novel approach, designated the Sample Point Transformation Algorithm (SPTA), is put forth in this paper as a means of enhancing efficiency through a process of mathematical deduction. The mathematical deduction reveals that the difference between sample points in each iteration loop is a simple function related to the inter-group dependent variables. Consequently, the SPTA method achieves the comprehensive transformation of the sample set by establishing a surrogate model of the difference between the sample sets of two cycles with a limited number of sample points, as opposed to conducting a substantial number of repeated samplings. This SPTA is employed to substitute the most time-consuming step of direct calculation in the classical optimization process. To validate the calculation efficiency, a series of numerical examples were conducted, demonstrating an improvement of approximately 75 % while maintaining optimal accuracy. This illustrates the advantage of the SPTA in addressing large-scale and complex optimization problems.

Generalized Background Vector Method for Tailored Three-Dimensional Nonperiodic Woven Composite Designs

In critical aerospace applications such as Ceramic Matrix Composite structures, novel three-dimensional nonperiodic textile composite preforms hold great promise for creating advanced composite structures with strategically aligned fiber tows. However, the inherently challenging nature of determining tow locations, a large-scale combinatorial problem, presents a significant obstacle in textile architecture design. This study generalizes the previously developed Background Vector Method (BVM) to address diverse design requirements and constraints, effectively circumventing combinatorial design complexities via a game theoretic approach. This approach allows for the creation of tunable designs for woven architectures with complex geometries, such as channels and tapering features, through simple control parameter adjustments. The method demonstrates exceptional computational efficiency, making it suitable for large-scale nonperiodic textile structures. Case studies including woven sandwich and airfoil structures highlight the generalized BVM’s versatility and effectiveness in addressing complex design challenges within the aerospace sector.

Planar anisotropic CPT-odd systems: A field theoretical approach

The primary objective of this study is to introduce field-theoretical tools into the realm of physical properties within planar systems exhibiting possible anisotropic features. This goal is achieved by fitting a specific field-theoretical model to simulate the presence of such a system. The proposed approach enables the investigation of in-plane physical phenomena using analytic methods. Specifically, our focus is on phenomena related to stationary point-like field sources that can mimic defects in material layers. We employ a dimensional reduction procedure on the well-known Carroll–Field–Jackiw model to derive a planar theory. This theory includes an electromagnetic sector governed by Maxwell-Chern–Simons electrodynamics, a scalar sector described by a massless Klein–Gordon field, and a mixed sector where the background vector controls the interactions between the scalar and gauge fields. Across all sectors of this planar theory, we explore physical phenomena arising from interactions with external sources. Specifically, we analyze perturbative effects up to second order in the background vector, examining contributions from both electric and scalar planar charges as well as Dirac points.

Gravitational waves effects in a Lorentz–violating scenario

This paper focuses on how the production and polarization of gravitational waves are affected by spontaneous Lorentz symmetry breaking, which is driven by a self–interacting vector field. Specifically, we examine the impact of a smooth quadratic potential and a non–minimal coupling, discussing the constraints and causality features of the linearized Einstein equation. To analyze the polarization states of a plane wave, we consider a fixed vacuum expectation value (VEV) of the vector field. Remarkably, we verify that a space–like background vector field modifies the polarization plane and introduces a longitudinal degree of freedom. In order to investigate the Lorentz violation effect on the quadrupole formula, we use the modified Green function. Finally, we show that the space–like component of the background field leads to a third–order time derivative of the quadrupole moment, and the bounds for the Lorentz–breaking coefficients are estimated as well.

Nonneutral Weibel model plasma in the non-minimal CPT-odd coupling

Inspired by the extension of the Standard Model, we investigate the effects of the space-time anisotropy caused by Lorentz symmetry violation (LSV) on a plasma column confinement configuration. The model of Plasma taken into account is the z-pinch model that was in the earliest efforts in fusion power research. This model comprises particles in a nonequilibrium cylindrical distribution, which remains stationary in the absence of collisions. We propose a disturbance in the distribution by a Lorentz violation environment. As proposed by Carroll, Field, and Jackiw, in a scenario of (LSV), a background field vector could couple with the electromagnetic field, modifying the classical behavior of the electromagnetic field. As reported here, considering the presence of a background field vector, the intensities of the fields and particle densities would be disturbed by the influence of the LSV. For different values of the background field vector coupling constant, the contribution of the background vector field could modify the intensity of the electromagnetic fields, and concentrate even more the electrons densities in the edge of the plasma column, evidencing a behavior similar to a skin effect in this plasma column.

HostNet: improved sequence representation in deep neural networks for virus-host prediction

BackgroundThe escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a challenging and pressing demand.ResultsTo overcome the challenges of virus-host prediction, we present HostNet, a deep learning framework that utilizes a Transformer-CNN-BiGRU architecture and two enhanced sequence representation modules. The first module, k-mer to vector, pre-trains a background vector representation of k-mers from a broad range of virus sequences to address the issue of data deficiency. The second module, an adaptive sliding window, truncates virus sequences of various lengths to create a uniform number of informative and distinct samples for each sequence to address the issue of data imbalance. We assess HostNet's performance on a benchmark dataset of “Rabies lyssavirus” and an in-house dataset of “Flavivirus”. Our results show that HostNet surpasses the state-of-the-art deep learning-based method in host-prediction accuracies and F1 score. The enhanced sequence representation modules, significantly improve HostNet's training generalization, performance in challenging classes, and stability.ConclusionHostNet is a promising framework for predicting virus hosts from genomic sequences, addressing challenges posed by sparse and varying-length virus sequence data. Our results demonstrate its potential as a valuable tool for virus-host prediction in various biological contexts. Virus-host prediction based on genomic sequences using deep neural networks is a promising approach to identifying their potential hosts accurately and efficiently, with significant impacts on public health, disease prevention, and vaccine development.

A compact yet flexible design space for large-scale nonperiodic 3D woven composites based on a weighted game for generating candidate tow architectures

Three-dimensional non-periodic woven composite preforms have sufficient design flexibility that tows can be aligned along principal loading paths even in shaped structural components with detailed local features. While this promises competitive performance, the feasible design space is combinatorially large, far beyond exhaustive search. Seeking a design space that is compact and easily searched yet can span the full potential of 3D weaving, we propose a method for generating candidate designs called the Background Vector Method (BVM) which treats weaving tows as agents in a game competing to match background vectors derived from different design requirements. The BVM generates candidate designs that adapt local architecture to global design goals by adjusting scalar weights. A manufacturing-based parameterization assures fabricability. The scope of possible designs and the speed of the BVM are illustrated by re-creating common periodic 3D weaving patterns and novel complex non-periodic architectures, with a route demonstrated to forming cavities, ducts, and other open volumes. How the BVM might be incorporated within an optimization algorithm is outlined and pathways are shown for systematically enlarging the design space as individual design problems may require.

Enhancing biosecurity against virus disease threats to Australian grain crops: current situation and future prospects

Australia is a major grain exporter, and this trade makes an important contribution to its economy. Fortunately, it remains free of many damaging virus diseases and virus vectors found elsewhere. However, its crop biosecurity is under increasing pressure from global ecological, climatic, and demographic challenges. Stringent biosecurity and plant health programs safeguard Australian grain production from damaging virus and virus vector incursions entering via different pathways. These programs formerly relied upon traditional testing procedures (indicator hosts, serology, PCRs) to intercept incoming virus-contaminated plant material. Recently, the integration of rapid genomic diagnostics innovation involving High Throughput Sequencing (HTS) smart tools into sample testing schedules is under exploration to improve virus testing accuracy, efficiency, and cost effectiveness under diverse circumstances. This process includes evaluating deployment of Illumina and Oxford Nanopore Technology shotgun sequencing. It also includes evaluating targeted viral genome HTS and virus vector metabarcoding approaches. In addition, using machine learning and deep learning capacities for big data analyses and remote sensing technologies will improve virus surveillance. Tracking damaging virus variants will be improved by surveillance networks which combine virus genomic-surveillance systems with an interoperable virus database. Sequencing Australian virus specimen collections will help ensure the accuracy of virus identifications based solely on genetic information. Enhancing routine diagnosis and data collection using these innovations will improve post entry virus interception and background virus and vector surveillance. This will help reduce the frequency of new incursions, improve virus management during eradication, containment and other plant health activities, and achieve more profitable Australian grain production.

Topical repellents for malaria prevention.

Insecticide-based interventions, such as long-lasting insecticide-treated nets (LLINs) and indoor residual spraying (IRS), remain the backbone of malaria vector control. These interventions target mosquitoes that prefer to feed and rest indoors, but have limited capacity to prevent transmission that occurs outdoors or outside regular sleeping hours. In low-endemicity areas, malaria elimination will require that these control gaps are addressed, and complementary tools are found. The use of topical repellents may be particularly useful for populations who may not benefit from programmatic malaria control measures, such as refugees, the military, or forest goers. This Cochrane Review aims to measure the effectiveness of topical repellents to prevent malaria infection among high- and non-high-risk populations living in malaria-endemic regions. To assess the effect of topical repellents alone or in combination with other background interventions (long-lasting insecticide-treated nets, or indoor residual spraying, or both) for reducing the incidence of malaria in high- and non-high-risk populations living in endemic areas. We searched the following databases up to 11 January 2023: the Cochrane Infectious Diseases Group Specialised Register; CENTRAL (in the Cochrane Library); MEDLINE; Embase; CAB Abstracts; and LILACS. We also searched trial registration platforms and conference proceedings; and contacted organizations and companies for ongoing and unpublished trials. We included randomized controlled trials (RCTs) and cluster-randomized controlled trials (cRCTs) of topical repellents proven to repel mosquitoes. We also included non-randomized studies that complied with pre-specified inclusion criteria: controlled before-after studies (CBA), controlled interrupted time series (ITS), and controlled cross-over trials. Four review authors independently assessed trials for inclusion, and extracted the data. Two authors independently assessed the risk of bias (RoB) using the Cochrane RoB 2 tool. A fifth review author resolved any disagreements. We analysed data by conducting a meta-analysis, stratified by whether studies included populations considered to be at high-risk of developing malaria infection (for example, refugees, forest goers, or deployed military troops). We combined results from cRCTs with RCTs by adjusting for clustering and presented results using forest plots. We used the GRADE framework to assess the certainty of the evidence. We only included data on Plasmodium falciparum infections in the meta-analysis. Thirteen articles relating to eight trials met the inclusion criteria and were qualitatively described. We included six trials in the meta-analysis (five cRCTs and one RCT). Effect on malaria incidence Topical repellents may slightly reduce P falciparum infection and clinical incidence when both outcomes are considered together (incidence rate ratio (IRR) 0.74, 95% confidence interval (CI) 0.56 to 0.98; 3 cRCTs and 1 RCT, 61,651 participants; low-certainty evidence); but not when these two outcomes were considered independently. Two cRCTs and one RCT (12,813 participants) evaluated the effect of topical repellents on infection incidence (IRR 0.76, 95% CI 0.56 to 1.02; low-certainty evidence). One cRCT (48,838 participants) evaluated their effect on clinical case incidence (IRR 0.66, 95% CI 0.32 to 1.36; low-certainty evidence). Three studies (2 cRCTs and 1 RCT) included participants belonging to groups considered at high-risk of being infected, while only one cRCT did not include participants at high risk. Adverse events Topical repellents are considered safe. The prevalence of adverse events among participants who used topical repellents was very low (0.6%, 283/47,515) and limited to mild skin reactions. Effect on malaria prevalence Topical repellents may slightly reduce P falciparum prevalence (odds ratio (OR) 0.81, 95% CI 0.67 to 0.97; 3 cRCTs and 1 RCT; 55,366 participants; low-certainty evidence). Two of these studies (1 cRCT and 1 RCT) were carried out in refugee camps, and included exclusively high-risk populations that were not receiving any other background vector control intervention. There is insufficient evidence to conclude that topical repellents can prevent malaria in settings where other vector control interventions are in place. We found the certainty of evidence for all outcomes to be low, primarily due to the risk of bias. A protective effect was suggested among high-risk populations, specially refugees, who might not have access to other standard vector control measures. More adequately powered clinical trials carried out in refugee camps could provide further information on the potential benefit of topical repellents in this setting. Individually randomized studies are also likely necessary to understand whether topical repellents have an effect on personal protection, and the degree to which diversion to non-protected participants affects overall transmission dynamics. Despite this, the potential additional benefits of topical repellents are most likely limited in contexts where other interventions are available.

Spatial mapping on the vector indices of Aedes aegypti in a rural area of Bangalore, India

Background Vector indices such as the house index HI breteau index BI and container index CI are widely used to infer the density of Aedes aegypti in surveillance programs.Objective To assess the vector indices and spatial map of the positive households using a Quantum Geographic Information System QGIS.Materials and Methods This cross-sectional study was conducted in the rural field practice area of a medical college near Bangalore. A total of 320 households were sampled and surveyed through household visits for vector indices. Smartphones with Epicollect5 application and Global Positioning Systemand GPS were used for data collection. QGIS was used for spatial mapping of the households.Results From 320 households surveyed 507 water collection sites were examined for Aedes aegypti larvae. The most common site of Aedes breeding identified was 53 73.61 in plastic containers. The CI was 14.20 HI was 17.81 and BI was 22.5.Conclusion Vector indices from the study area showed the risk potential to be high for the spread of dengue and chikungunya. Spatial mapping describes the hotspots of Aedes breeding in households and neighborhoods. There is a requirement for strengthening the integrated vector control measures.

Vector dark radiation and gravitational-wave polarization

We consider conformal vector models which could play the role of a cosmological dark radiation component. We analyse the propagation of gravitational waves in the presence of this vector background and find a suppression in the tensor transfer function at large scales. We also find that although the cosmological background metric is isotropic, anisotropies are imprinted in the tensor power spectrum. In addition, the presence of the background vector fields induces a net polarization of the gravitational wave background and, for certain configurations of the vector field, a linear to circular polarization conversion. We also show that this kind of effects are also present for vector models with more general potential terms.

A Study on Chinese-English Machine Translation Based on Migration Learning and Neural Networks

This paper constructs a neural machine translation model for Chinese and English using encoder feature extraction with migration learning, allowing the machine to automatically perform feature migration learning, transforming Chinese and English data into word vectors using a centralized representation, and using neural networks to achieve a direct mapping between Chinese and English languages. In this paper, we study the construction of a neural machine translation model by the neural network structure. Firstly, the neural machine translation model is constructed using the neural network structure, which is simplified based on the neural network structure and achieves good performance while reducing the training complexity. To address the problem that neural machine translation cannot make good use of linguistic knowledge, a neural machine translation model with lexical sequence information is proposed. Based on the translation model of a bidirectional neural network incorporating attention mechanism, syntactic analysis is performed to obtain lexical sequence information, and then it is incorporated into the encoder part of the translation model in the form of bidirectional encoding, and the background vector is formed together by vector splicing. To address the problem that the Chinese-English language sequences of any length in the encoder part are encoded into a fixed dimensional background vector, an attention mechanism is introduced to dynamically adjust the degree of influence of the context at the source language end on the target language sequences to improve the translation model’s ability to deal with long-distance dependencies. To better reflect the contextual information, this paper further proposes a machine translation model based on migration learning and neural network and conducts a comparative analysis for various translation models to verify the effectiveness of the model performance improvement.

Comparison of Different Kernels in a Support Vector Machine to Classify Prostate Cancerous Tissues in T2-Weighted Magnetic Resonance Imaging

Background and objectivesA support vector machine (SVM) is one of the most powerful classifiers in machine learning that can be applied when a data set is introduced in two classes in a high dimensional feature space. The objective of this study is to compare different kernels of SVMs to classify prostate cancerous tissues.

Semi-transparent boundaries in CPT-even Lorentz violating electrodynamics

Some aspects of the nonbirefringent CPT-even gauge sector of the Standard Model Extension (SME), in the vicinity of a semi-transparent mirror, are investigated in this paper. We first consider a model where the Lorentz symmetry breaking is caused by a single background vector vμ, and we obtain perturbative results up to second order in vμ. Specifically, we compute the modified propagator for the gauge field due to the presence of the mirror and we analyze the corresponding interaction between the mirror and a stationary point-like charge. We show that when the charge is placed in the vicinity of the mirror, a spontaneous torque emerges, which is a new effect with no counterpart in Maxwell electrodynamics. We also compare these results with the corresponding ones obtained for the Lorentz violating scalar field theory. As expected, in the limiting case of perfect mirrors, we recover the interaction found via the image method. Finally, we discuss how we can extend these results for a more general Lorentz violating background.

Torsion braneworlds in a tensor-vector gravity

In this paper, we study the properties of gravity and bulk fields living in a torsion warped braneworld. The torsion is driven by a background vector whose norm provides a source for the bulk cosmological constant. For a vector as the derivative of a scalar field, we find new isotropic thick brane geometry. We analyze the features of bosonic and fermionic fields in this scenario. The background vector provides nonminimal coupling between the fields and the geometry leading to modifications in the Kaluza–Klein (KK) states. For a scalar field, the background vector breaks the [Formula: see text] symmetry, whereas for the vector gauge field, a geometric coupling to the torsion tensor traps the massless mode. The spinor connection is modified by the torsion and a geometric derivative Yukawa-like coupling is proposed. The effects of these new couplings are investigated.

Novel Microsoft Excel spreadsheet calculator for vector analysis of mass astigmatism data and comparison between Alpins and Thibos methods

Background Vector analysis is mandatory for reporting astigmatism and for the evaluation of different refractive procedures.Aim The aim of this study was to describe and evaluate a new spreadsheet calculator for vector analysis of mass astigmatism data and to compare between Alpins and Thibos methods.Patients and methods Using Microsoft Office Excel 2016, a new astigmatic calculator was designed, based on Alpins and Thibos methods of vector analysis. Mass astigmatic data input involved power in diopters and axis direction in degrees. The calculator was investigated on 20 eyes for which photorefractive keratectomy was performed. Preoperative and postoperative manifest refractions were measured. Comparisons were done between the results of Alpins and Thibos methods of analysis.Results The mean surgically induced astigmatism (SIA) was 2.547±0.721 D and the change in astigmatism power vector (ΔAPV) calculated with the Thibos method was 1.27±0.36 D. A perfect positive correlation (r=1; P<0.000) was present between SIA and ΔAPV with SIA=2ΔAPV. A perfect positive correlation (r=1; P<0.000) was also present between the index of success and APV ratio.Conclusion Vector analysis of mass astigmatism data could be performed using the new Excel calculator. Alpins and Thibos methods showed interchangeability between their results.

EFFICIENT RISK MEASURES CALCULATIONS FOR GENERALIZED CREDITRISK+ MODELS

Numerical calculations of risk measures and risk contributions in credit risk models amount to the evaluation of various forms of quantiles, tail probabilities and tail expectations of the portfolio loss distribution. Though the moment generating function of the loss distribution in the [Formula: see text] model is available in analytic closed form, efficient, accurate and reliable computation of risk measures (Value-at-Risk and Expected Shortfall) and risk contributions for the [Formula: see text] model poses technical challenges. We propose various numerical algorithms for risk measures and risk contributions calculations of the enhanced [Formula: see text] model under the common background vector framework using the Johnson curve fitting method, saddlepoint approximation method, importance sampling in Monte Carlo simulation and check function formulation. Our numerical studies on stylized credit portfolios and benchmark industrial credit portfolios reveal that the Johnson curve fitting approach works very well for credit portfolios with a large number of obligors, demonstrating high level of numerical reliability and computational efficiency. Once we implement the systematic procedure of finding the saddlepoint within an approximate domain, the saddlepoint approximation schemes provide efficient calculation and accurate numerical results. The importance sampling in Monte Carlo simulation methods are easy to implement, but they compete less favorably in accuracy and efficiency with other numerical algorithms. The less commonly used check function formulation is limited to risk measures calculations. It competes favorably in accuracy and reliability, but an extra optimization algorithm is required.

A field theory in Randers-Finsler spacetime

Finsler geometry is a natural arena to investigate the physics of spacetimes with local Lorentz violation. The directional dependence of the Finsler metric provides a way to encode the Lorentz-violating effects into the geometric structure of the spacetime. Here, a classical field theory is proposed in a special Finsler geometry, the so-called Randers-Finsler spacetime, where the Lorentz violation is produced by a background vector field. By promoting the Randers-Finsler metric to a differential operator, a Finsler-invariant action for the scalar, gauge and fermions are proposed. The theory contains nonlocal terms, as in the Very Special Relativity based theories. By expanding the Lagrangian, minimal and nonminimal Standard Model Extension terms arise, revealing a perturbative Lorentz violation. For a CPT-even term, the Carrol-Field-Jackiw and derivative extensions are obtained.

Effective field theory for non-relativistic hydrodynamics

We write down a Schwinger-Keldysh effective field theory for non-relativistic (Galilean) hydrodynamics. We use the null background construction to covariantly couple Galilean field theories to a set of background sources. In this language, Galilean hydrodynamics gets recast as relativistic hydrodynamics formulated on a one dimension higher spacetime admitting a null Killing vector. This allows us to import the existing field theoretic techniques for relativistic hydrodynamics into the Galilean setting, with minor modifications to include the additional background vector field. We use this formulation to work out an interacting field theory describing stochastic fluctuations of energy, momentum, and density modes around thermal equilibrium. We also present a translation of our results to the more conventional Newton-Cartan language, and discuss how the same can be derived via a non-relativistic limit of the effective field theory for relativistic hydrodynamics.

Suppressed cosmic growth in coupled vector-tensor theories

We study a coupled dark energy scenario in which a massive vector field $A_{\mu}$ with broken $U(1)$ gauge symmetry interacts with the four-velocity $u_c^{\mu}$ of cold dark matter (CDM) through the scalar product $Z=-u_c^{\mu} A_{\mu}$. This new coupling corresponds to the momentum transfer, so that the background vector and CDM continuity equations do not have explicit interacting terms analogous to the energy exchange. Hence the observational preference of uncoupled generalized Proca theories over the $\Lambda$CDM model can be still maintained at the background level. Meanwhile, the same coupling strongly affects the evolution of cosmological perturbations. While the effective sound speed of CDM vanishes, the propagation speed and no-ghost condition of a longitudinal scalar of $A_{\mu}$ and the CDM no-ghost condition are subject to nontrivial modifications by the $Z$ dependence in the Lagrangian. We propose a concrete dark energy model and show that the gravitational interaction on scales relevant to the linear growth of large-scale structures can be smaller than the Newton constant at low redshifts. This leads to the suppression of growth rates of both CDM and total matter density perturbations, so our model allows an interesting possibility for reducing the tension of matter density contrast $\sigma_8$ between high- and low-redshift measurements.