Multiple Vectors Research Articles

Simulating flexibility, variability and decentralisation with an integrated energy system model for Great Britain

Energy system models allow the development and assessment of ambitious transition pathways towards a sustainable energy system. However, current models lack adequate spatial and temporal resolution to capture the implications of a shift to decentralised energy supply and storage across multiple local energy vectors to meet spatially variable energy demand. There is also a lack of representation of interactions with the transport sector as well as national and local energy system operation. Here, we bridge these gaps with a high-resolution system-of-systems modelling framework which is applied to Great Britain to simulate differences between the performance of decarbonised energy systems in 2050 through two distinct strategies, an electric strategy and a multi-vector strategy prioritising a mix of fuels, including hydrogen. Within these strategies, we simulated the impacts of decentralised operation of the energy system given the variability of wind and across flexibility options including demand side management, battery storage and vehicle to grid services. Decentralised operation was shown to improve operational flexibility and maximise utilisation of renewables, whose electricity supplies can be cost-effectively converted to hydrogen or stored in batteries to meet peak electricity demands, therefore reducing carbon-intensive generation and the requirement for investment in expanding the electricity transmission network capacity.

Using Disasters to Estimate the Impact of Uncertainty

Abstract Uncertainty rises in recessions and falls in booms. But what is the causal relationship? We construct cross-country panel data on stock market returns to proxy for first- and second-moment shocks and instrument these with natural disasters, terrorist attacks, and political shocks. Our IV regression results reveal a robust negative short-term impact of second moments (uncertainty) on growth. Employing multiple vector autoregression estimation approaches, relying on a range of identifying assumptions, also reveals a negative impact of uncertainty on growth. Finally, we show that these results are reproducible in a conventional micro–macro business cycle model with time-varying uncertainty.

Semi-online parameter identification methodology for maritime power lithium batteries

Due to the special working environment of power lithium battery (P-LiB) on all-electric ships, the high-efficiency battery management system (BMS) is required. In this study, a novel semi-online parameter identification methodology integrated with large-scale global optimization algorithm is developed, in order to ensure the high-quality performance of subsequent BMS functions like the equalization control and state-of-charge estimation. Firstly, the P-LiB parameter identification model is established based on the first-order Thevenin equivalent circuit. Then, the evolution strategy of identification model is developed for dynamically updating the model along with the entire charging/discharging process of P-LiB. Considering that the model complexity increases exponentially with dimensionality, the AMCCDE algorithm developed in our previous work is employed to optimize the dynamic model repeatedly. Moreover, the semi-online operation mechanism for AMCCDE is proposed, in which the multiple context vectors are used to exchange information and inherit the optimal solution between each two adjacent semi-online cycles, and the identification solutions can be dynamically corrected and output at the end of each cycle. Finally, the developed semi-online identification methodology is verified using the USTC-DST and USTC-UDDS datasets. Experimental results show that the developed methodology can well balance the identification accuracy and timeliness, and dynamically output the accurate identification solutions in real-time.

Analysis of bioconvection and oxytactic microorganisms in a porous cavity with nano-enhanced phase change materials and quadrant heater: Application of support vector regression based model

In this numerical study, two-dimensional simulations are performed to obtain the bioconvection flow in a quadrant heater located porous closed space saturated with nano-enhanced phase change material (NePCM). The Galerkin finite element method (GFEM) is employed to compute the governing equations for the considered system by developing a computer code. The study is performed for wide range of parameters as aspect ratio, Darcy number, Hartmann number, nanoparticle fraction, bioconvection Rayleigh number and natural convection Rayleigh number were tested. Also, the analysis is supported via artificial intelligent by using multiple support vector regression based model. The tests were performed with artificial intelligence on temperature values. After the analysis, it is found that radius of the curvilinear shaped heater plays role on both heat transfer and fluid flow, oxygen distribution, density of motile microorganism and heat capacity. Application of support vector machine technique gives good agreement with computational fluid dynamics results. Heat and mass transfer are improved with increasing of nanoparticle addition but decrease with increasing of strength of magnetic field.

Cracks that Let the Light in: Collective Reflections on Integrating Lived Experience of Psychosis in Research and Policy in the Context of a Global Commission.

Within psychiatric research fields, there has been a marked uptick of interest in service user involvement in recent years. Nevertheless, it is often unclear how robust or impactful common forms of inclusion are, and the extent to which they have included individuals with psychosis. Using collective auto-ethnography, this paper describes the experiences of 8 academic and non-academic members of the 'lived experience' and participatory research workgroup of a global psychosis Commission and our navigation of power and power hierarchies, differences in background and training, and multiple vectors of identity, diversity, and privilege. We conclude that the realities of "involvement" are much messier, more fraught, and less intrinsically empowering than often signaled in calls for involvement and co-production. We nevertheless stress the power of collective dialogue and support-between and among a pluralistic group-and of honesty and transparency about challenges, barriers, and the colonial underpinnings and geopolitics of global mental health.

Adaptive Weighted K-Nearest Neighbor Trilateration Algorithm for Visible Light Positioning

An adaptive weighted K-nearest neighbor (AWKNN) trilateration positioning algorithm fused with the channel state information (CSI) is proposed to optimize the accuracy of the visible light positioning. The core concept behind this algorithm is to combine the WKNN algorithm with ranging based on the CSI. The direct path distance estimated by the CSI is utilized to construct a position set consisting of multiple positions and a corresponding distance database containing multiple distance vectors. The error parameters of the weighted combinations of different distance vectors are calculated iteratively to evaluate the impact of different K-values and weights on the positioning accuracy. The proposed algorithm can achieve high-precision trilateration positioning by adaptively selecting the K-value and weight. A typical 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to simulate the positioning performance. The simulation results reveal that the mean error of the CSI-based AWKNN algorithm achieves 1.84 cm, with a root mean square error (RMSE) of 2.13 cm. Compared with the CSI-based least squares (LS) method, the CSI-based nonlinear LS method, and the CSI-based WKNN method, the average error of this method is decreased by 29%, 16%, and 17%, whereas the RMSE is reduced by 35%, 14%, and 19%.

A New Learning Resource Recommendation Method for Improving the Efficiency of Students' Online Independent Learning

If the learning resource recommendation method fully considers the efficiency improvement of college students' online independent learning, it can save college students' learning energy and improve their learning quality, but also can promote students to develop such learning attitudes as active participation, willingness to explore and frequent practices. Therefore, this article studies the learning resource recommendation method for improving the efficiency of college students' online independent learning. This article quantitatively evaluates the learning efficiency of college students in the process of online independent learning, and takes the evaluation results as a reference for learning resource recommendation methods. The capsule network combined with self-attention routing algorithm is used to represent the various demands of college students' online independent learning with multiple vectors, and the sequence information layer based on Transformer model is set up to construct the learning resource recommendation model. Experimental results verify the effectiveness of the model.

Active IRS Aided Multiple Access for Energy-Constrained IoT Systems

In this paper, we investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming vectors across time and resource allocation. To this end, we first study two typical active IRS aided MA schemes, namely time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), by analytically comparing their achievable sum throughput and proposing corresponding algorithms. Interestingly, we prove that given only one available IRS beamforming vector, the NOMA-based scheme generally achieves a larger throughput than the TDMA-based scheme, whereas the latter can potentially outperform the former if multiple IRS beamforming vectors are available to harness the favorable time selectivity of the IRS. To strike a flexible balance between the system performance and the associated signaling overhead incurred by more IRS beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with device grouping, where the devices in the same group transmit simultaneously via NOMA while devices in different groups occupy orthogonal time slots. By controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for any given number of IRS beamforming vectors available. Despite of the non-convexity of the considered optimization problem, we propose an efficient algorithm based on alternating optimization, where each subproblem is solved optimally. Simulation results illustrate the practical superiorities of the active IRS over the passive IRS in terms of the coverage extension and supporting multiple energy-limited devices, and demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly balancing the performance-cost tradeoff.

Torque Ripple Reduction for Direct Torque Control of Dual Three-Phase PMSM Based on Multiple Virtual Voltage Vectors

The switching-table-based direct torque control (ST-DTC) has the merits of simple structure, easy implementation, and fast response, but suffers from large torque ripples. Previous literature, i.e., the classical DTC, can achieve torque ripple reduction in the high-speed region but has a limited capability for torque ripple reduction in the low-speed region. In this paper, the issue of torque ripple reduction in the full-speed region, i.e., both the low-speed and high-speed regions, is addressed. The effects of each group in all possible six groups of virtual voltage vectors (VVs) on torque variation are firstly investigated. Then, a multiple VV DTC based on only three groups of VVs is proposed and the corresponding optimal selections of three VV groups and level of torque regulator are presented. The proposed DTC not only can effectively reduce torque ripples in the full speed region, but also is simple and easy to implement. The superiority of the proposed DTC with optimal selections of VVs and level of torque regulator are verified by experimental results.

Strain-Modulated Perfect Valley Precession and Valley Transistor in Graphene

We propose the realization of perfect valley precession by use of strain, which gives rise to an effective vector potential and moves two valleys in opposite directions in momentum space. It is shown that the spatial precession period is independent of both the Fermi energy and the transverse wave vector. This is crucial to perfect precession in a practical device with multiple transverse wave vectors or modes. To theoretically demonstrate the perfect valley precession, we investigate the conductivity of a Kekul\'e graphene superlattice/graphene/Kekul\'e graphene superlattice junction that acts as a valley transistor. Due to the valley precession, the conductivity exhibits perfect oscillations with the strain and the conductivity minima are very close to zero. Our finding provides a strategy to design ideal valleytronics and straintronics devices.

LinVec: A Stacked Ensemble Machine Learning Architecture for Analysis and Forecasting of Time-Series Data

Objectives: The proposed work integrates multiple Machine Learning approaches in a single model to be used for the analysis and forecasting of Time Series data. Methods: In the present work, the concept of Stacked Ensemble learning is proposed that uses Multiple Linear Regression and Support Vector Regression techniques as the base models. A Meta Model is constructed based on Multiple Linear Regression with necessary modifications. The outputs from the base models are fed into the meta-model which is mended with the capability of combining the predictions from the two base models to produce better results than the individual constituent parts, after running a k-fold training procedure. Findings: The proposed model is capable of analyzing and predicting any Time Series data. In the present study, stock data of six companies enlisted in the National Stock Exchange of India are analyzed for the prediction of the next day’s Open, High, and Low prices. The proposed work achieves better accuracy and reduces the error in prediction when compared to similar works done in the same field. Novelty: The amalgamated technique used in this work can be considered as a generalization of the stacked ensemble method in a broader aspect. The proposed model combines the strengths of multiple Machine Learning methods into a single model to achieve better performance than its individual counterparts. Further, several recent works have tried to predict only the next day’s Open and Closing Prices of stocks, but for an intraday trader, prediction of the next day’s Low and High prices of a stock are more significant than the closing prices. Very few works have predicted all of the Open, High and Low prices in a single study, our present work achieved this quite successfully. Keywords: Machine Learning; Stacked Ensemble Model; Support Vector Regression; Multiple Linear Regression; Time Series data analysis; Stock Price Prediction

Vector communities under global change may exacerbate and redistribute infectious disease risk.

Vector-borne parasites may be transmitted by multiple vector species, resulting in an increased risk of transmission, potentially at larger spatial scales compared to any single vector species. Additionally, the different abilities of patchily distributed vector species to acquire and transmit parasites will lead to varying degrees of transmission risk. Investigation of how vector community composition and parasite transmission change over space due to variation in environmental conditions may help to explain current patterns in diseases but also informs our understanding of how patterns will change under climate and land-use change. We developed a novel statistical approach using a multi-year, spatially extensive case study involving a vector-borne virus affecting white-tailed deer transmitted by Culicoides midges. We characterized the structure of vector communities, established the ecological gradient controlling change in structure, and related the ecology and structure to the amount of disease reporting observed in host populations. We found that vector species largely occur and replace each other as groups, rather than individual species. Moreover, community structure is primarily controlled by temperature ranges, with certain communities being consistently associated with high levels of disease reporting. These communities are essentially composed of species previously undocumented as potential vectors, whereas communities containing putative vector species were largely associated with low levels, or even absence, of disease reporting. We contend that the application of metacommunity ecology to vector-borne infectious disease ecology can greatly aid the identification of transmission hotspots and an understanding of the ecological drivers of parasite transmission risk both now and in the future.

Multi-Concept Representation Learning for Knowledge Graph Completion

Knowledge Graph Completion (KGC) aims at inferring missing entities or relations by embedding them in a low-dimensional space. However, most existing KGC methods generally fail to handle the complex concepts hidden in triplets, so the learned embeddings of entities or relations may deviate from the true situation. In this article, we propose a novel M ulti- c oncept R epresentation L earning (McRL) method for the KGC task, which mainly consists of a multi-concept representation module, a deep residual attention module, and an interaction embedding module. Specifically, instead of the single-feature representation, the multi-concept representation module projects each entity or relation to multiple vectors to capture the complex conceptual information hidden in them. The deep residual attention module simultaneously explores the inter- and intra-connection between entities and relations to enhance the entity and relation embeddings corresponding to the current contextual situation. Moreover, the interaction embedding module further weakens the noise and ambiguity to obtain the optimal and robust embeddings. We conduct the link prediction experiment to evaluate the proposed method on several standard datasets, and experimental results show that the proposed method outperforms existing state-of-the-art KGC methods.

An Ensemble and Multi-View Clustering Method Based on Kolmogorov Complexity

The ability to build more robust clustering from many clustering models with different solutions is relevant in scenarios with privacy-preserving constraints, where data features have a different nature or where these features are not available in a single computation unit. Additionally, with the booming number of multi-view data, but also of clustering algorithms capable of producing a wide variety of representations for the same objects, merging clustering partitions to achieve a single clustering result has become a complex problem with numerous applications. To tackle this problem, we propose a clustering fusion algorithm that takes existing clustering partitions acquired from multiple vector space models, sources, or views, and merges them into a single partition. Our merging method relies on an information theory model based on Kolmogorov complexity that was originally proposed for unsupervised multi-view learning. Our proposed algorithm features a stable merging process and shows competitive results over several real and artificial datasets in comparison with other state-of-the-art methods that have similar goals.

A Novel Ensemble of Support Vector Machines for Improving Medical Data Classification

In recent years, the increasing volume and availability of healthcare and biomedical data are opening up new opportunities for computational methods to enhance healthcare in many hospitals. Medical data classification is regarded as the challenging task to develop intelligent medical decision support systems in hospitals. In this paper, the ensemble approaches based on support vector machines are proposed for classifying medical data. This research’s key contribution is that the ensemble multiple support vector machines use the function kernel in the style of gradient boosting and bagging to produce a more accurate fusion model than the mono-modality models. Extensive experiments have been conducted on forty benchmark medical datasets from the University of California at Irvine machine learning repository. The classification results show that there is a statistically significant difference (p-values < 0.05) between the proposed approaches and the best classification models. In addition, the empirical analysis of forty medical datasets indicated that our models can predict diseases with an accuracy rate of 82.82 and 81.76 percent without feature selection in the preprocessing data stage.

Understanding and Applications of Tensors in Ecosystem Services: A Case Study of the Manas River Basin

Ecosystem services (ESs) are a multiple whole composed of multiple services and their multiple relations, which can be expressed as tensors (multiple functions of multiple vectors). This study attempts to introduce the concept and method of tensors into ES research to solve problems caused by the multiplicity of ESs, such as multiple descriptions and perceptions of ESs, repeated calculation of ES values, and cascading relationships with the social economy. Taking the Manas River Basin composite ecosystem as an example, we constructed five different types of ES tensors based on different understandings and applications: (1) As multiple vectors, three eigenvectors were extracted from the ES state tensor (ESST), including farmland service (FS), vegetation service (VS) and water service (WS). According to the ES response tensor (ESRT), an increase in FS may lead to a decline in overall services. (2) As multiple functions, the ES value (ESV) of the basin was measured by the ESV metric tensor (ESVMT), with a gross value of 14.8 billion USD and a net value of 10.17 billion USD. From different stakeholders perceptions constructed by the ecosystem services to human well-beings (ES-HW) tensor, the human well-being values (HWV) were ranked as citizens > farmers ≈ herdsmen > public. (3) The HWV output efficiency of different LULC per unit of water use was calculated by a fourth-order mixed tensor constructed by water–LULC–ES–HW multiple cascading relations. Among them, the HWV efficiency of water areas and wetlands was the highest, but the area was the smallest. Cultivated land and unused land had the lowest HWV efficiency and largest area. In general, the ES tensor is the extension and integration of the ES scalars/indicators to the ES vectors/bundles, which can provide tools for the integral expression, objective measurement and multiple perceptions of ESs.

Jacobi polynomials and design theory I

In this paper, we introduce the notion of Jacobi polynomials of a code with multiple reference vectors, and give the MacWilliams type identity for it. Moreover, we derive a formula to obtain the Jacobi polynomials using the Aronhold polarization operator. Finally, we describe some facts obtained from Type III and Type IV codes that interpret the relation between the Jacobi polynomials and designs.

MIRN: A multi-interest retrieval network with sequence-to-interest EM routing.

Vector-based retrieval have been widely adopted to process online users' diverse interests for recommendations. However, most of them utilize a single vector to represent user multiple interests (UMI), inevitably impairing the accuracy and diversity of item retrieval. In addition, existing work often does not take into account the scale and speed of the model, and high-dimensional user representation vectors need high computation cost, leading to inefficient item retrieval. In this paper, we propose a novel lightweight multi-interest retrieval network (MIRN) by incorporating sequence-to-interest Expectation Maximization (EM) routing to deal with users' multiple interests. By leveraging representation ability of the Capsule network, we design a multi-interest representation learning module that clusters multiple Capsule vectors from the user's behavior sequence to represent each of their interests respectively. In addition, we introduce a composite capsule clustering strategy for the Capsule network framework to reduce the scale of the network model. Furthermore, a Capsule-aware module incorporating an attention mechanism has been developed to guide model training by adaptively learning multiple Capsule vectors of user representations. The experimental results demonstrate MIRN outperforms the state-of-the-art approaches for item retrieval and gains significant improvements in terms of metric evaluations.

Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming

Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.

Comparison of data-driven prediction methods for comprehensive coke ratio of blast furnace

Abstract The emission of blast furnace (BF) exhaust gas has been criticized by society. It is momentous to quickly predict the comprehensive coke ratio (CCR) of BF, because CCR is one of the important indicators for evaluating gas emissions, energy consumption, and production stability, and also affects composite economic benefits. In this article, 13 data-driven prediction techniques, including six conventional and seven ensemble methods, are applied to predict CCR. The result of ten-fold cross-validation indicates that multiple linear regression (MLR) and support vector regression (SVR) based on radial basis function are superior to the other methods. The mean absolute error, the root mean square error, and the coefficient of determination (R 2) of the MLR model are 1.079 kg·t−1, 1.668, and 0.973, respectively. The three indicators of the SVR model are 1.158 kg·t−1, 1.878, and 0.975, respectively. Furthermore, AdaBoost based on linear regression has also strong prediction ability and generalization performance. The three methods have important significances both in theory and in practice for predicting CCR. Moreover, the models constructed here can provide valuable hints into realizing data-driven control of the BF process.