Multidimensional Research Articles

Exponential sine chaotification model for enhancing chaos and its hardware implementation

Chaotic systems have been intensively studied for their roles in many applications, such as cryptography, secure communications, nonlinear controls, etc. However, the limited complexity of existing chaotic systems weakens chaos-based practical applications. Designing chaotic maps with high complexity is attractive. This paper proposes the exponential sine chaotification model (ESCM), a method of using the exponential sine function as a nonlinear transform model, to enhance the complexity of chaotic maps. To verify the performance of the ESCM, we firstly demonstrated it through theoretical analysis. Then, to exhibit the high efficiency and usability of ESCM, we applied ESCM to one-dimensional (1D) and multi-dimensional (MD) chaotic systems. The effects were examined by the Lyapunov exponent and it was found that enhanced chaotic maps have much more complicated dynamic behaviors compared to their originals. To validate the simplicity of ESCM in hardware implementation, we simulated three enhanced chaotic maps using a digital signal processor (DSP). To explore the ESCM in practical application, we applied ESCM to image encryption. The results verified that the ESCM can make previous chaos maps competitive for usage in image encryption.

Dynamic Estimation of Multi-Dimensional Deformation Time Series From InSAR Based on Kalman Filter and Strain Model

With the increasing amount of synthetic aperture radar (SAR) data with various imaging geometries (at least ascending/descending tracks), it is possible to obtain accurate multi-dimensional (MD) deformation time series with long time span. However, in most cases SAR data of different geometries are un-synchronously acquired over the same region, making it impossible to directly solve the underdetermined observation model (OSM) between the interferometric SAR (InSAR) measurements and the MD deformations. Kalman filter (KF), as one of the most famous dynamic estimators, can obtain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information of the unknowns based on the preexisting time series, therefore it can be used to deal with this InSAR underdetermined problem. This article employs the KF to realize the dynamic estimation of MD deformations with short-baseline interferograms. The innovation lies in the establishment of the KF state transition model (STM) and OSM, which aims to make the InSAR monitoring problem better adapt to the KF. Particularly, by assuming a smooth deforming process, existing deformation time series are used to establish the STM and to predict the deformations at current moment. Besides, a strain model (SM) is employed to assist the establishment of the OSM. Simulation and real experiments in the Geysers geothermal field (GGF), U.S. demonstrate that, compared with the state-of-the-art methods, the proposed KF method allows more robust deformation estimation and achieves higher computational efficiency for dynamic estimation.

Linguistic characteristics of online academic forum posts across subregisters, L1 backgrounds, and grades

This study examines an under-explored interactive register of discipline-specific online academic forum posts in higher education. 881 academic forum posts written by postgraduate students were analysed, using an additive multi-dimensional (MD) analysis. Four dimensions of linguistic variation were compared across the two first language backgrounds (L1 Chinese and L1 English), three subregisters (application design, business scenario, and discussion), and grades. The results indicated that the forum posts written by two L1 backgrounds, three subregisters, and grades significantly differed in terms of dimensions of linguistic variation. The findings have important implications for writing instruction and assessment in higher education. One of the significant implications of the present study is that both first and second language writers need to have mastery of informational and elaborated discourse features (nouns, phrasal coordination) to write successful academic forum posts at graduate level. Writing instructors may provide successful model texts for relatively new registers so that L2 writers can meet the multiple demands of online academic forum posts, including interacting with peers and supporting their ideas with academic literature.

Schema Evolution in Multiversion Data Warehouses

Data warehouses (DWs) evolve in both their content and schema due to changes of user requirements, business processes, or external sources to name a few. Although multiple approaches using temporal and/or multiversion DWs have been proposed to handle these changes, an efficient solution for this problem is still lacking. The authors' approach is to separate concerns and use temporal DWs to deal with content changes, and multiversion DWs to deal with schema changes. To address the former, previously, they have proposed a temporal multidimensional (MD) model. In this paper, they propose a multiversion MD model for schema evolution to tackle the latter problem. The two models complement each other and allow managing both content and schema evolution. In this paper, the semantics of schema modification operators (SMOs) to derive various schema versions are given. It is also shown how online analytical processing (OLAP) operations like roll-up work on the model. Finally, the mapping from the multiversion MD model to a relational schema is given along with OLAP operations in standard SQL.

A Multi-Dimensional Analysis of Research Article Discussion Sections in an Engineering Discipline: Corpus Explorations and Scientists’ Perceptions

Writing discussion sections of research articles (RAs) is difficult for novice scientists. The study investigates patterns of linguistic characterizations in discussion sections of RAs in chemical engineering. Around 240,000-word corpus was compiled using 213 discussion sections extracted from 20 disciplinary journals. Multi-dimensional (MD) analysis proposed by Biber was used to capture linguistic co-occurrence patterns based on a constellation of features across collected texts. The MD results show six salient linguistic patterns: (1) Involvement and interactivity; (2) Description versus Narration; (3) Expression of attitude; (4) Informational production; (5) Framing scientific claims; and (6) Expression of denial. Discourse-based interviews were then conducted with eight professional scientists to elicit their perception of MD findings concerning their reading experience and understanding of established writing conventions. The implications for EAP professionals are proposed as to the explicit instruction on teaching novice writers how to employ stance expressions strategically in academic writing.

Methodological synthesis of research using multi-dimensional analysis

Since 1984 when Douglas Biber first conducted a multi-dimensional (MD) analysis, MD analyses have expanded in scope. However, there has yet to be a comprehensive survey of this body of research. This methodological synthesis conducts a large-scale survey of MD analyses dating back to Biber’s (1984) dissertation. An unprecedented total of 230 studies including peer-reviewed articles and dissertations were coded for a variety of study characteristics and methodological choices. The results of this survey show that researchers have made a wide variety of methodological decisions including the choice of tagger, factor loading and communality cut-off values, and types of additional statistical analyses conducted. It was also found that reporting practices of MD analyses have been less than optimal, lacking information necessary for the replicability of the study. The study concludes with a set of recommendations for future MD analyses in relation to reporting practices and the goals of future studies.

Spacetime Frequency-Multiplexed Digital-RF Array Receivers With Reduced ADC Count

Wireless systems operating at mm-wave frequencies require dense antenna arrays to achieve directional gain for overcoming high path loss. Digital mm-wave arrays retain spatial degrees of freedom, but require a dedicated analog to data converter (ADC) per spatial channel, leading to undesirably high receiver complexity, large ADC count, and power consumption. This brief exploits directional sparsity to reduce the number of receivers and ADCs with minimal loss in performance. A multidimensional (MD) linear transformation using transmission lines and a K : 1 combiner is used to reduce the number of ADCs by a factor K. Simulations verify that the proposed method can lead to better than 50% ADC complexity reductions (for K ≥ 2) for linear arrays and more than 75% ADC complexity reduction (for K ≥ 4) for rectangular arrays when sparsity conditions are met. Unlike in analog-digital hybrid beamforming, where a phased-array combines K channels to a single ADC, the proposed method does not lead to loss of spatial degrees of freedom.

Register variation in L1 and L2 student writing

Abstract While there have been many studies describing L2 academic writing, most of these studies have used corpora of first year or assessment writing (Crosthwaite 2016; Weigle &amp; Friginal 2014). The present study seeks to describe linguistic variation in L2 writing for content classes and to compare these linguistic patterns to those found in L1 writing. A multi-dimensional (MD) analysis was conducted in two corpora, BAWE and BrAWE, extracting five dimensions. The L2 corpus contained 379 texts written by Brazilian students doing part of their undergrad in the UK and the L1 corpus contained 395 texts from BAWE. The results of this study indicate that L1 and L2 writers use similar linguistic resources to convey the purpose of university registers, with the exception of case studies, designs, exercises and research reports. This linguistic variation between L1 and L2 writers might be explained by students’ interpretation of these registers’ communicative purposes.

MS-Net: A lightweight separable ConvNet for multi-dimensional image processing

As the core technology of deep learning, convolutional neural networks have been widely applied in a variety of computer vision tasks and have achieved state-of-the-art performance. However, it’s difficult and inefficient for them to deal with high dimensional image signals due to the dramatic increase of training parameters. In this paper, we present a lightweight and efficient MS-Net for the multi-dimensional(MD) image processing, which provides a promising way to handle MD images, especially for devices with limited computational capacity. It takes advantage of a series of one dimensional convolution kernels and introduces a separable structure in the ConvNet throughout the learning process to handle MD image signals. Meanwhile, multiple group convolutions with kernel size 1 × 1 are used to extract channel information. Then the information of each dimension and channel is fused by a fusion module to extract the complete image features. Thus the proposed MS-Net significantly reduces the training complexity, parameters and memory cost. The proposed MS-Net is evaluated on both 2D and 3D benchmarks CIFAR-10, CIFAR-100 and KTH. Extensive experimental results show that the MS-Net achieves competitive performance with greatly reduced computational and memory cost compared with the state-of-the-art ConvNet models.

Multidimensional Digital Bioassay Platform Based on an Air-Sealed Femtoliter Reactor Array Device.

Single-molecule experiments have been helping us to get deeper inside biological phenomena by illuminating how individual molecules actually work. Digital bioassay, in which analyte molecules are individually confined in small compartments to be analyzed, is an emerging technology in single-molecule biology and applies to various biological entities (e.g., cells and virus particles). However, digital bioassay is not compatible with multiconditional and multiparametric assays, hindering in-depth understanding of analytes. This is because current digital bioassay lacks a repeatable solution-exchange system that keeps analytes inside compartments. To address this challenge, we developed a digital bioassay platform with easy solution exchanges, called multidimensional (MD) digital bioassay. We immobilized single analytes in arrayed femtoliter (10-15 L) reactors and sealed them with airflow. The solution in each reactor was stable and showed no cross-talk via solution leakage for more than 2 h, and over 30 rounds of perfect solution exchanges were successfully performed. With multiconditional assays based on our system, we could quantitatively determine inhibitor sensitivities of single influenza A virus particles and single alkaline phosphatase (ALP) molecules, which has never been achieved with conventional digital bioassays. Further, we demonstrated that ALPs from two origins can be precisely distinguished by a single-molecule multiparametric assay with our system, which was also difficult with conventional digital bioassays. Thus, MD digital bioassay is a versatile platform to gain in-depth insight into biological entities in unprecedented resolution.

Multiple‐input multiple‐output system design of multidimensional orthogonal frequency division multiplexing system with coded direct index modulation

AbstractIn this paper, two kinds of design methods of combining the multidimensional (MD) orthogonal frequency division multiplexing (OFDM) with coded direct index modulation (CDIM) system and multiple‐input multiple‐output (MIMO) technology are proposed to increase the spectrum efficiency more. These systems can be called as dimension‐dependent system and dimension‐independent system, respectively. MD‐OFDM‐CDIM was proposed to overcome the limitations of the spectral efficiency of MD‐OFDM. It has some advantages in terms of spectral efficiency and the performance is comparable to OFDM. However, there is a lack of compatibility studies with MIMO to increase the spectral efficiency. Therefore, in this paper, the compatibility of MD‐OFDM‐CDIM with MIMO is shown and the performance of the combined MIMO MD‐OFDM‐CDIM systems is evaluated. The simulation results show that the performance of the two proposed systems are similar under the conditions for the same spectral efficiency. However, the dimension‐independent system is better than the dimension‐dependent system because the reception processing complexity of the dimension‐dependent system increases significantly as the numbers of transmission and reception antennas go to higher.

Normal stress differences in the consolidation of strong colloidal gels

Macroscopic models for the uniaxial consolidation of strong colloidal gels typically characterise the compressive strength of the particulate network in terms of the compressive yield stress Py(ϕ) or uniaxial elastic modulus K ′ (ϕ). Almost all industrial applications involve multi-dimensional (MD) configurations with arbitrary tensorial stress states, and it is unclear how to generalise these 1D constitutive models to MD consolidation. Several studies have attempted to extend these 1D constitutive models to MD by assuming either isotropic consolidation or zero Poisson’s ratio, but the validity of these assumptions is currently unknown. Lacking is a validated tensorial rheology for the consolidation of strong colloidal gels that is capable of predicting the consolidation of these materials. One step toward the development of such tensorial rheology is the consideration of normal stress differences (NSDs) during uniaxial consolidation. Thus, a tensorial constitutive model for the consolidation of colloidal gels cannot be developed without accounting for these NSDs. We address this problem by performing discrete element model (DEM) simulations of the uniaxial consolidation of a two-dimensional (2D) strong colloidal gel and investigate evolution of the tensorial stress state during consolidation. We show that during consolidation, the Poisson ratio increases from zero near the gel point to almost unity near close-packing and uncover the particle-scale mechanisms that underpin these observations. These results provide the first steps toward a complete tensorial rheology of colloidal gels that is capable of resolving the evolution of these complex materials under superposed differential compression and shear. Graphical abstract

When the Stars Shine Too Bright: The Influence of Multidimensional Ratings on Online Consumer Ratings

Scholars generally assume that consumer ratings reflect consumer satisfaction, but ratings can be influenced by the design of the rating system. We examine two rating designs—single-dimensional rating systems, which elicit overall ratings only, and multidimensional (MD) rating systems, which elicit both dimensional and overall ratings—and how they impact overall ratings. Drawing on the accessibility–diagnosticity framework, we argue that dimensional ratings in MD systems influence overall ratings based on how the dimensions have been rated. We support this explanation with seven experiments. Our results suggest that across various experimental settings, rating objects, dimensions, and numbers of dimensions, overall ratings are systematically influenced by the design of the rating system.This paper was accepted by Chris Forman, information systems.

A Temporal Multidimensional Model and OLAP Operators

Usually, data in data warehouses (DWs) are stored using the notion of the multidimensional (MD) model. Often, DWs change in content and structure due to several reasons, like, for instance, changes in a business scenario or technology. For accurate decision-making, a DW model must allow storing and analyzing time-varying data. This paper addresses the problem of keeping track of the history of the data in a DW. For this, first, a formalization of the traditional MD model is proposed and then extended as a generalized temporal MD model. The model comes equipped with a collection of typical online analytical processing (OLAP) operations with temporal semantics, which is formalized for the four classic operations, namely roll-up, dice, project, and drill-across. Finally, the mapping from the generalized temporal model into a relational schema is presented together with an implementation of the temporal OLAP operations in standard SQL.

Multidimensional Spectral Super-Resolution With Prior Knowledge With Application to High Mobility Channel Estimation

The problem of estimating high-mobility channels is a special case of the general problem of recovering multi-dimensional (MD) complex sinusoids. This paper is concerned with estimation of multiple frequencies with prior knowledge from incomplete and/or noisy samples. Suppose that it is known a priori that the frequencies lie in some given intervals, we develop efficient super-resolution estimators by exploiting such prior knowledge based on frequency-selective (FS) atomic norm minimization. We study the MD Vandermonde decomposition of block Toeplitz matrices in which the frequencies are restricted to lie in given intervals. We then propose to solve the FS atomic norm minimization problems for the low-rank spectral tensor recovery by converting them into semidefinite programs based on the MD Vandermonde decomposition. We also develop fast solvers for solving these semidefinite programs via the alternating direction method of multipliers (ADMM), where each iteration involves a number of refinement steps to utilize the prior knowledge. Extensive simulation results are presented to illustrate the high performance of the proposed methods.

An Improved Framework for Modelling Data Warehouse Systems Using UML Profile

Data Warehouse (DW) applications provide past detail for judgment process for the companies. It is acknowledged that these systems depend on Multidimensional (MD) modelling different from traditional database modelling. MD modelling keeps data in the form of facts and dimensions. Some proposals have been presented to achieve the modelling of these systems, but none of them covers the MD modelling completely. There is no any approach which considers all the major components of MD systems. Some proposals provide their proprietary visual notations, which force the architects to gain knowledge of new precise model. This paper describes a framework which is in the form of an extension to Unified Modelling Language (UML). UML is worldwide known to design a variety of perspectives of software systems. Therefore, any method using the UML reduces the endeavour of designers in understanding the novel notations. Another exceptional characteristic of the UML is that it can be extended to bring in novel elements for different domains. In addition, the proposed UML profile focuses on the accurate representations of the properties of the MD systems based on domain specific information. The proposed framework is validated using a specific case study. Moreover, an evaluation and comparative analysis of the proposed framework is also provided to show the efficiency of the proposed work

Linguistic variation in the discourse of corporate annual reports: A multi-dimensional analysis

Combining the frameworks of multi-dimensional (MD) analysis and rhetorical structure theory (RST), this study examines the linguistic co-occurrence patterns in the discourse of corporate annual reports (CARs) and interprets their underlying functional dimensions. Our corpus consists of texts of corporate 10K reports from firms listed on New York Stock Exchange (NYSE; N of texts = 642, totally 14,674,047 tokens). Five functional dimensions are quantitatively extracted and qualitatively interpreted: (1) expression of direct persuasion; (2) expression of impersonal stance; (3) subjective versus objective positioning; (4) integrative expression of stance versus fragmented expression; and (5) expression of reliability. All these dimensions contribute to the communicative function of persuasion. The analysis of the rhetorical structures of excerpts with high concentrations of co-occurring linguistic features on each dimension further indicates the communicative strategy of persuasion. The proposed MD model is then applied to analyze the effect of firm performance on the linguistic variation in CAR discourse. We found that firm performance can significantly affect the linguistic variation in CAR discourse. CAR discourse from firms with good performance is more reliable. The result reveals managements’ use of concealment strategy in impression management. This study has implications for MD analysis, business discourse analysis, language pedagogy and accounting research.

Comparison of Fused-Ring Electron Acceptors with One- and Multidimensional Conformations.

Three fused-ring electron acceptors (FXIC-1, FXIC-2, and FXIC-3) were designed and synthesized. This FXIC series has similar electron-rich central units and the same electron-poor termini. Due to the different steric structures of fluorene, bifluorenylidene, and spirobifluorene, FXIC-1 is a one-dimensional (1D) crystal, while FXIC-2 and FXIC-3 are multidimensional (MD) amorphous materials. The conformations of the FXIC series have a slight impact on their absorption and energy levels. FXIC-1 has higher electron mobility than FXIC-2 and FXIC-3. When blending with different polymer donors (PTB7-Th, J71, and PM7), the FXIC-1-based organic solar cells have efficiencies higher than those of the FXIC-2/FXIC-3-based cells. Meanwhile, the ternary-blend cells based on PTB7-Th:F8IC with FXIC-1, FXIC-2, and FXIC-3 show similar efficiencies, which are all better than those of the binary-blend devices.

A data warehouse hybrid design framework using domain ontologies for local good-governance assessment

Purpose Most local governance assessment tools are entirely or partially based on stakeholders’ surveys, focus groups and benchmarks of different local governments in the world. These tools remain a subjective way of local governance evaluation. To measure the performance of local good-governance using an unbiased assessment technique, the authors have developed a framework to help automate the design process of a data warehouse (DW), which provides local and central decision-makers with factual, measurable and accurate local government data to help assess the performance of local government. The purpose of this paper is to propose the extraction of the DW schema based on a mixed approach that adopts both i* framework for requirements-based representation and domain ontologies for data source representation, to extract the multi-dimensional (MD) elements. The data was collected from various sources and information systems (ISs) deployed in different municipalities. Design/methodology/approach The authors present a framework for the design and implementation of a DW for local good-governance assessment. The extraction of facts and dimensions of the DW’s MD schema is done using a hybrid approach, where the extraction of requirement-based DW schema and source-based DW schema are done in parallel followed by the reconciliation of the obtained schemas to obtain the good-governance assessment DW final design. Findings The authors developed a novel framework to design and implement a DW for local good-governance assessment. The framework enables the extraction of the DW MD schema by using domain ontologies to help capture semantic artifacts and minimize misconceptions and misunderstandings between different stakeholders. The introduction and use of domain ontologies during the design process serves the generalization and automation purpose of the framework. Research limitations/implications The presently conducted research faced two main limitations as follows: the first is the full automation of the design process of the DW and the second, and most important, is access to local government data as it remains limited because of the lack of digitally stored data in municipalities, especially in developing countries in addition to the difficulty of accessing the data because of regulatory aspects and bureaucracy. Practical implications The local government environment is among the public administrations most subject to change-adverse cultures and where the authors can face high levels of resistance and significant difficulties during the implementation of decision support systems, despite the commitment/engagement of decision-makers. Access to data sources stored by different ISs might be challenging. While approaching the municipalities for data access, it was done in the framework of a research project within one of the most notorious universities in the country, which gave more credibility and trust to the research team. There is also a need for further testing of the framework to reveal its scalability and performance characteristics. Originality/value Compared to other local government assessment ad hoc tools that are partially or entirely based on subjectively collected data, the framework provides a basis for automated design of a comprehensive local government DW using e-government domain ontologies for data source representation coupled with the goal, rationale and business process diagrams for user requirements representations, thus enabling the extraction of the final DW MD schema.

Comparing web-crawled and traditional corpora

Using a multi-dimensional (MD) analysis of register variability, the study compares two corpora of Czech: Koditex, a “traditional” corpus carefully designed using various sources with rich metadata, and Araneum Bohemicum Maximum, a web-crawled corpus with an opportunistic composition representative of the “searchable” web. Both types of corpora are projected onto the space induced by the MD model, with the main objective being to find out whether they overlap in the linguistic variation they cover, or whether one introduces some specific variation which cannot be found in the other. We also document a crucial methodological point which has broader relevance for MD analyses in general, namely that texts have to be of similar lengths in order for their scores on the dimensions to be comparable. Results indicate that some traditional text categories, such as journalism or non-fiction, are characterized by language phenomena which are equally well covered by web-crawled data, though of course traditional corpora keep their edge in terms of the richness of the accompanying metadata. But overall, the range of variation in Koditex is broader as it contains texts which have no adequate substitute (i.e. texts with a comparable set of linguistic characteristics, regardless of their extratextual label) in data acquired through general-purpose web-crawling techniques. These include informal conversations, private correspondence, some types of fiction, but also user-generated content (comments on Facebook, forums etc.).