Domain Mapping Research Articles

Novel ANN Method for Solving Ordinary and Time-Fractional Black–Scholes Equation

The main aim of this study is to introduce a 2-layered artificial neural network (ANN) for solving the Black–Scholes partial differential equation (PDE) of either fractional or ordinary orders. Firstly, a discretization method is employed to change the model into a sequence of ordinary differential equations (ODE). Subsequently, each of these ODEs is solved with the aid of an ANN. Adam optimization is employed as the learning paradigm since it can add the foreknowledge of slowing down the process of optimization when getting close to the actual optimum solution. The model also takes advantage of fine-tuning for speeding up the process and domain mapping to confront the infinite domain issue. Finally, the accuracy, speed, and convergence of the method for solving several types of the Black–Scholes model are reported.

Toward the incorporation of temporal interaction analysis techniques in modeling and understanding sociotechnical systems

AbstractThe increased complexity of modern sociotechnical systems (STS) necessitates the need for a manageable representation of their attributes, to augment our understanding and enable the development of ways through which we can increase their effectiveness, efficiency, and safety. Although many of the methodologies developed in the Human Factors domain map and investigate system properties and network structures, the inclusion of the temporal dimension in the analysis of STS remains limited. In this paper we present how modeling and visualization of STS can be augmented with the incorporation of temporal interaction analysis techniques that enable a micro‐level, fine‐grained analysis of data. We provide an overview of temporal analysis techniques by breaking down their main function, requirements, types of research questions they can address, and the visualization properties they offer, attempting to enhance their use in system analysis. This overview can assist researchers in selecting an analysis technique, enabling the incorporation of temporality in STS analysis, and helping towards the design of improved and safer systems and interventions.

Key issues influencing the future internationalization of higher education: the case of Thailand

Purpose The rapidly changed global context for internationalization (IZN) over the next decade prompted a decision to use a futurist perspective for identifying issues to be considered in the organization’s next strategic plan. This paper aims to report on this project to identify current and strategic issues influencing the future strategy of the higher education (HE) IZN for Thailand on behalf of the Bureau of International Cooperation Strategy and the Office of the Higher Education Commission. Design/methodology/approach The research approach was a customized version of the University of Houston’s Framework Foresight method. It involved framing the domain with a description and domain map, scanning for signals of change within the domain and emerging issues analysis to produce a set of current and emerging issues. A planning step synthesizes a set of recommended actions. Findings The key findings reported in this paper are the identification of 14 current and emerging issues influencing the future of the IZN of HE in Thailand. The issues were organized along with the three horizons framework: H1: how are we [currently] doing? H2: what should we do next and H3, where do we want to go? The primary recommendation of this research reported on in this study is to consider the 14 issues for inclusion into the next strategic plan. Seven specific strategic options mapped over three phases were identified as well. The research reported here was carried out for Thailand, but the process could easily be adapted by other countries and other topics. Research limitations/implications The modified version of the University of Houston Framework Foresight approach has been applied successfully to many topics. The topic explored here is focused on one nation, Thailand. The authors feel the lessons are, however, broadly applicable. Practical implications The ability to use a futurist perspective to identify current and emerging issues is highlighted. The organizing of the issues using the three horizons framework proved to be particularly useful in helping the client to develop a sense of timing regarding the future, that is, when and to what degree to pay attention to the many issues that typically confront any organization. Originality/value The use of the three horizons framework in the analysis of the emerging issues provide benefits in two ways in situating the likely timing of signals of change in horizon scanning and “scan hits” both scanning for the identification of issues and organizing the resulting current and emerging issues along the three horizons with H1 current issues: how are we [currently] doing?; H2 emerging issues: what should we do next and H3 emerging issues, where do we want to go? The paper also includes a section exploring the impact of Covid-19 on the likely timing of the issues identified just before the pandemic hit, finding that timing of some issues would speed up, some would stay the same and some would slow down.

Coatomer protein COPƐ, a novel NS1-interacting protein, promotes the replication of Porcine Parvovirus via attenuation of the production of type I interferon

Porcine Parvovirus (PPV) is a pathogen causing porcine reproductive disorders. Non-structural protein NS1 appears diverse functions acting as a predominant regulator in promoting PPV replication. In this study, we identified a PPV NS1 binding protein coatomer subunit epsilon (COPƐ), and found that COPƐ is a critical regulator during PPV replication. In NS1 transfected or PPV infected cells, COPƐ was interacted with NS1 and translocated into nucleus together with NS1. Knockout of COPƐ could inhibit PPV production by increasing the expression levels of IFN-β, while overexpression of COPƐ enhanced PPV production by reducing the expression levels of IFN-β. Furthermore, the domain mapping assay showed that the N-terminal amino acids domain of NS1 (25-EAFSYVF-31) were required for the interaction of COPƐ with NS1. Sequence alignment result displays that parvovirus NS1 (EAFSYVF) amino acids domain is highly conservative among PPV, CPV, FPV and MEV, and down-regulation of COPƐ could also significantly reduce the replication of these viruses. Notably, we found that the interaction of COPƐ with NS1 play an important role in promoting the production of type I interferon during PPV or CPV infection, which affect the replication of these viruses. Taken together, the results presented here show a novel function of NS1 interaction with COPƐ that regulates the parvovirus replication through modulating the type I interferons signaling pathway, provided a potential target for the control of parvovirus-associated diseases.

Topography and motion of acid-sensing ion channel intracellular domains.

Acid-sensing ion channels (ASICs) are trimeric cation-selective channels activated by decreases in extracellular pH. The intracellular N and C terminal tails of ASIC1 influence channel gating, trafficking, and signaling in ischemic cell death. Despite several X-ray and cryo-EM structures of the extracellular and transmembrane segments of ASIC1, these important intracellular tails remain unresolved. Here, we describe the coarse topography of the chicken ASIC1 intracellular domains determined by fluorescence resonance energy transfer (FRET), measured using either fluorescent lifetime imaging or patch clamp fluorometry. We find the C terminal tail projects into the cytosol by approximately 35 Å and that the N and C tails from the same subunits are closer than adjacent subunits. Using pH-insensitive fluorescent proteins, we fail to detect any relative movement between the N and C tails upon extracellular acidification but do observe axial motions of the membrane proximal segments toward the plasma membrane. Taken together, our study furnishes a coarse topographic map of the ASIC intracellular domains while providing directionality and context to intracellular conformational changes induced by extracellular acidification.

On Reasonable Inquiry and Analysis Domains of Sympérasmology

Sympérasmology was proposed as the theory of synthetic system knowledge (SSK), which is seen as the fuel for the engine of systelligence. There are two main reasons why the proposal is made: (i) rapidly growing, SSK represents a third category of knowledge beside common personal knowledge and testified scientific knowledge, and (ii) though important, neither modern gnoseology nor contemporary epistemology studies its nature, principles, progression, and impacts. The need for rational and empirical studies of SSK is also underpinned by the on-going intelligence revolution, in which knowledge is deemed to be a productive power, a cognitive enabler of smart systems, and a strong transformer of social life. Sympérasmology is still in an embryonic state. Notwithstanding, a map of possible inquiry and analysis domains is released for a public debate in this paper. These domains can be sorted into four categories: (i) rudiments, (ii) principles, (iii) faculties, and (iv) implications. This paper explains these categories and the related domains of interest, and discusses some relevant aspects of study. Without striving for exhaustiveness, it elaborates on many relevant discussion topics and issues. The paper emphasizes that a precise specification of the scope and objectives of sympérasmology needs a stream of exploratory research studies as well as further insightful philosophical discussions.

Optomechanical Mapping of Ferroelectric Domains and the Piezo‐Photovoltaic Effect in Ba‐ and Ni‐Doped (K0.5Na0.5)NbO3

The piezo‐photovoltaic effect has been recently proposed as an analogy of the flexo‐photovoltaic effect in noncentrosymmetric ferroelectrics that are also piezoelectric in nature. It has been demonstrated to boost the photovoltaic performance of ferroelectrics under applied uniaxial mechanical loads. The impact of the piezo‐photovoltaic effect on ferroelectric domains, however, has not yet been studied. In this context, a nanoscale insight into mechanical as well as optomechanical control of domains in a novel bandgap‐engineered ferroelectric, namely—KNBNNO ((K0.5Na0.5)NbO3–2 mol% Ba(Ni0.5Nb0.5)O3−δ), is provided. It is found that the applied mechanical force of 1 μN (pressure: 0.25 GPa) using a scanning probe tip evinces a 67% amplification in piezoresponse in the material, and on removing mechanical load, the material retains 33% higher piezoresponse than the pristine state. Mechanically induced property changes in the material are found to be strongly influenced by light illumination. Applied mechanical stress due to an atomic force microscopy (AFM) tip is highly nonuniform and can induce additional polarization via the flexoelectric effect which further enhances the photovoltaic charge screening.

Talking the same language on patient empowerment: Development and content validation of a taxonomy of self-management interventions for chronic conditions.

ContextThe literature on self‐management interventions (SMIs) is growing exponentially, but it is characterized by heterogeneous reporting that limits comparability across studies and interventions. Building an SMI taxonomy is the first step towards creating a common language for stakeholders to drive research in this area and promote patient self‐management and empowerment.ObjectiveTo develop and validate the content of a comprehensive taxonomy of SMIs for long‐term conditions that will help identify key characteristics and facilitate design, reporting and comparisons of SMIs.MethodsWe employed a mixed‐methods approach incorporating a literature review, an iterative consultation process and mapping of key domains, concepts and elements to develop an initial SMI taxonomy that was subsequently reviewed in a two‐round online Delphi survey with a purposive sample of international experts.ResultsThe final SMI taxonomy has 132 components classified into four domains: intervention characteristics, expected patient/caregiver self‐management behaviours, outcomes for measuring SMIs and target population characteristics. The two‐round Delphi exercise involving 27 international experts demonstrated overall high agreement with the proposed items, with a mean score (on a scale of 1‐9) per component of 8.0 (range 6.1‐8.8) in round 1 and 8.1 (range 7.0‐8.9) in round 2.ConclusionsThe SMI taxonomy contributes to building a common framework for the patient self‐management field and can help implement and improve patient empowerment and facilitate comparative effectiveness research of SMIs.Patient or public contribution.Patients’ representatives contributed as experts in the Delphi process and as partners of the consortium.

Using Machine Learning to Map Western Australian Landscapes for Mineral Exploration

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. Geological processes at depth ultimately control and are linked to the resulting surface features. Large regions in Australia, West Africa, India, and China are blanketed by cover (intensely weathered surface material and/or later sediment deposition, both up to hundreds of metres thick). Mineral exploration through cover poses a significant technological challenge worldwide. Classifying and understanding landscape types and their variability is of key importance for mineral exploration in covered regions. Landscape variability expresses how near-surface geochemistry is linked to underlying lithologies. Therefore, landscape variability mapping should inform surface geochemical sampling strategies for mineral exploration. Advances in satellite imaging and computing power have enabled the creation of large geospatial data sets, the sheer size of which necessitates automated processing. In this study, we describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available digital elevation model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve approximately 98% classification accuracy with a reasonable runtime of 48 minutes on a single Intel® Core™ i7-8550U CPU core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, whereas smaller tiles result in a more detailed and, at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region. Mapping landscape variability at a large scale can be used globally as a fundamental tool for guiding more efficient mineral exploration programs in regions under cover.

Nonlinear Analysis of a Quasi-Zero Stiffness Air Suspension Based on the Cell-Mapping Method

The quasi-zero stiffness system has the characteristics of low dynamic stiffness and high static stiffness, which can bring a better driving experience and lower road dynamic load at high speed on irregular roads. This paper studies a type of interconnected quasi-zero stiffness air suspension system, which has two states, namely, the non-interconnected quasi-zero stiffness air suspension and the interconnected quasi-zero stiffness air suspension, to meet the performance requirements under different loads and vehicle speed. First, the mathematical model of the nonlinear system is established based on the basic principles of fluid mechanics and thermodynamics. Then, the stability of the equilibrium point is analyzed using the Lyapunov first method, where the quantitative analysis of the attractive region of the system is conducted through the bifurcation diagram and phase diagram. By using the Taylor series expansion, cell-mapping theory and domain map of attraction, the attractive region of the system is quantitatively analyzed to obtain the parametric feasible domain under stable conditions. Finally, the performance of the quasi-zero stiffness suspension system with the selected parameters under the stability constraint is verified by simulation analysis and experiment. The results show that the system represented in this paper provides higher suspension comfort and stability.

Improving Heterogeneous Network Knowledge Transfer Based on the Principle of Generative Adversarial

Deep learning requires a large amount of datasets to train deep neural network models for specific tasks, and thus training of a new model is a very costly task. Research on transfer networks used to reduce training costs will be the next turning point in deep learning research. The use of source task models to help reduce the training costs of the target task models, especially heterogeneous systems, is a problem we are studying. In order to quickly obtain an excellent target task model driven by the source task model, we propose a novel transfer learning approach. The model linearly transforms the feature mapping of the target domain and increases the weight value for feature matching to realize the knowledge transfer between heterogeneous networks and add a domain discriminator based on the principle of generative adversarial to speed up feature mapping and learning. Most importantly, this paper proposes a new objective function optimization scheme to complete the model training. It successfully combines the generative adversarial network with the weight feature matching method to ensure that the target model learns the most beneficial features from the source domain for its task. Compared with the previous transfer algorithm, our training results are excellent under the same benchmark for image recognition tasks.

Multiwavelength anomalous X-ray diffraction for combined imaging of atomic displacement and strain.

The X-ray Bragg coherent diffractive imaging (CDI) technique assumes that the structure factor holds constant over the measured crystal. This approximation breaks down for materials exhibiting variations in the unit-cell configuration, such as piezo- and ferroelectrics. In that case, the strain field cannot be reliably determined from the reconstruction because the lattice deformation and the structure factor contribute concomitantly. Proposed here is a solution to this problem achieved by combining Bragg CDI and the multiwavelength anomalous diffraction approach that measures a Friedel pair of reflections at two different photon energies near an absorption edge. Comparing the obtained reconstructions with a parametric model that includes calculating the scattering amplitude as a function of wavelength and the unit-cell configuration, the contributions of the lattice deformation and the structure factor are separated. Simulations of the ferroelectric material BaTiO3 demonstrate the possibility of simultaneous probing of the strain and displacement of the Ti atoms. The proposed method opens up an opportunity to apply coherent X-ray diffraction for nanoscale-resolved 3D mapping of polarization domains in micro- and nanocrystals.

Nonsingular Global Fixed-Time Stabilization for Nonlinear Systems

Since existing results about fixed-time stabilization are only applied to strict feedback systems, this paper investigates the nonsingular fixed-time stabilization of more general high-order nonlinear systems. Based on a novel concept named coordinate mapping of time domain, a control method is first proposed to transform the nonsingular fixed-time convergence problem into the finite-time convergence problem of a transformed time-varying system. By extending the existing, adding a power integrator technique into the considered time-varying system, a periodic controller is constructed to stabilize the original system in fixed time. The results of simulations verify the effectiveness of the proposed method.

Ferroelectric and magnetic domain mapping of magneto-dielectric Ce doped BiFeO3 thin films

Co-existent ferroelectric and magnetic ordering at room temperature in multiferroics has generated significant interest for potential applications in low-power sensors, transducers, and memory devices. Despite tremendous efforts centered on site-substitution in BiFeO3 to attain enhanced functionality, local probing of multiferroic domain dynamics with dopant concentration remains overlooked. Here we report a detailed investigation on ferroelectric and magnetic domain mapping of Bi1−XCeXFeO3; 0 ≤ X ≤ 0.2 (BCFOX) thin films and discussed concentration-dependent behavior of piezo-response switching, piezo-actuation butterfly-loop and phase-hysteresis, and magneto-dielectric coupling. Large piezo-response amplitude change of 30 mV and near to complete phase reversal of ~150° together with homogeneous magnetic domain distribution and an enhanced magneto-dielectric coupling coefficient of 2.15% at 0.3 T unveils improved ferroelectric ordering in polycrystalline BCFO0.12 thin film. We believe, our findings will develop a comprehensive understanding of sensitive force microscopy technique for the custom-made advancement of BiFeO3 based multiferroic family.

Peptide-tiling screens of cancer drivers reveal oncogenic protein domains and associated peptide inhibitors.

Gene fragments derived from structural domains mediating physical interactions can modulate biological functions. Utilizing this, we developed lentiviral overexpression libraries of peptides comprehensively tiling high-confidence cancer driver genes. Toward inhibiting cancer growth, we assayed ~66,000 peptides, tiling 65 cancer drivers and 579 mutant alleles. Pooled fitness screens in two breast cancer cell lines revealed peptides, which selectively reduced cellular proliferation, implicating oncogenic protein domains important for cell fitness. Coupling of cell-penetrating motifs to these peptides enabled drug-like function, with peptides derived from EGFR and RAF1 inhibiting cell growth at IC50s of 27-63μM. We anticipate that this peptide-tiling (PepTile) approach will enable rapid de novo mapping of bioactive protein domains and associated interfering peptides.

Learning to Enhance Visual Quality via Hyperspectral Domain Mapping (Student Abstract)

Deep learning based methods have achieved remarkable success in image restoration and enhancement, but a majority of such methods rely on RGB input images. These methods fail to take into account the rich spectral distribution of natural images. We propose a deep architecture, SpecNet which computes spectral profile to estimate pixel-wise dynamic range adjustment of a given image. First, we employ an unpaired cycle-consistent framework to generate hyperspectral images (HSI) from low-light input images. HSI are further used to generate a normal light image of the same scene. In order to infer a plausible HSI from a RGB image we incorporate a self-supervision and a spectral profile regularization network. We evaluate the benefits of optimizing the spectral profile for real and fake images in low-light conditions on the LOL Dataset.

Modeling and Estimating Host Country Values in International Projects to Facilitate In-Country Value Creation

International companies are more and more seeking to act proactively by proposing In-Country Value (ICV) strategies to create sustainable local values in the host countries in which they carry out projects. Still, such sustainable local values are complex to identify because they are often indirectly related to their own value chains, project activities, and outcomes. There are, therefore, both theoretical and industrial needs to model and estimate sustainable values brought by complex projects in host countries, considering direct and indirect effects. In this paper, a systems thinking-based approach combined with a frequency analysis first permitted to build up a model of the sustainable values created by the project in a host country. Then, after underlining the complexity of such a model, a Domain Mapping Matrix (DMM) approach was proposed to help build a process to estimate project impacts in terms of ICV creation. An application to a case study built up with an industrial practitioner (an oil and gas company) permitted to test and validate the overall model and approach. It notably showed how such a model permitted to facilitate discussions among stakeholders and laid the foundations of ICV creation-oriented decision-making processes.

Geometric series expansion of the Neumann–Poincaré operator: Application to composite materials

The Neumann–Poincaré (NP) operator, a singular integral operator on the boundary of a domain, naturally appears when one solves a conductivity transmission problem via the boundary integral formulation. Recently, a series expression of the NP operator was developed in two dimensions based on geometric function theory [34]. In this paper, we investigate geometric properties of composite materials using this series expansion. In particular, we obtain explicit formulas for the polarisation tensor and the effective conductivity for an inclusion or a periodic array of inclusions of arbitrary shape with extremal conductivity, in terms of the associated exterior conformal mapping. Also, we observe by numerical computations that the spectrum of the NP operator has a monotonic behaviour with respect to the shape deformation of the inclusion. Additionally, we derive inequality relations of the coefficients of the Riemann mapping of an arbitrary Lipschitz domain using the properties of the polarisation tensor corresponding to the domain.

Hybrid Convolutional Neural Network for Localization of Epileptic Focus Based on iEEG.

Epileptic focus localization by analysing intracranial electroencephalogram (iEEG) plays a critical role in successful surgical therapy of resection of the epileptogenic lesion. However, manual analysis and classification of the iEEG signal by clinicians are arduous and time-consuming and excessively depend on the experience. Due to individual differences of patients, the iEEG signal from different patients usually shows very diverse features even if the features belong to the same class. Accordingly, automatic detection of epileptic focus is required to improve the accuracy and to shorten the time for treatment. In this paper, we propose a novel feature fusion-based iEEG classification method, a deep learning model termed Time-Frequency Hybrid Network (TF-HybridNet), in which short-time Fourier transform (STFT) and 1d convolution layers are performed on the input iEEG in parallel to extract features of the time-frequency domain and feature maps. And then, the time-frequency features and feature maps are fused and fed to a 2d convolutional neural network (CNN). We used the Bern-Barcelona iEEG dataset for evaluating the performance of TF-HybridNet, and the experimental results show that our approach is able to differentiate the focal from nonfocal iEEG signal with an average classification accuracy of 94.3% and demonstrates an improved accuracy rate compared to the model using only STFT or one-dimensional convolutional layers as feature extraction.

A generalized Spatio-Temporal Threshold Clustering method for identification of extreme event patterns

Abstract. Extreme weather and climate events such as floods, droughts, and heat waves can cause extensive societal damages. While various statistical and climate models have been developed for the purpose of simulating extremes, a consistent definition of extreme events is still lacking. Furthermore, to better assess the performance of the climate models, a variety of spatial forecast verification measures have been developed. However, in most cases, the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme events. In order to alleviate inconsistencies when defining extreme events within different scientific communities, we propose a new generalized Spatio-Temporal Threshold Clustering method for the identification of extreme event episodes, which uses machine learning techniques to couple existing pattern recognition indices with high or low threshold choices. The method consists of five main steps: (1) construction of essential field quantities; (2) dimension reduction; (3) spatial domain mapping; (4) time series clustering; and (5) threshold selection. We develop and apply this method using a gridded daily precipitation dataset derived from rain gauge stations over the contiguous United States. We observe changes in the distribution of conditional frequency of extreme precipitation from large-scale well-connected spatial patterns to smaller-scale more isolated rainfall clusters, possibly leading to more localized droughts and heat waves, especially during the summer months. The proposed method automates the threshold selection process through a clustering algorithm and can be directly applicable in conjunction with modeling and spatial forecast verification of extremes. Additionally, it allows for the identification of synoptic-scale spatial patterns that can be directly traced to the individual extreme episodes, and it offers users the flexibility to select an extreme threshold that is linked to the desired geometrical properties. The approach can be applied to broad scientific disciplines.