Multi-domain Analysis Research Articles

Non-intrusive implementation of the Generalized Finite Element Method with Global-Local Enrichment for multi-domain analysis

This work proposes a non-intrusive implementation of the Generalized Finite Element Method with Global-Local enrichment (GFEMgl-IGL) for multi-domain analysis. In GFEMgl-IGL, the global problem is initially discretized with a coarse mesh and without considering localized phenomena. The solution of this domain is obtained through the standard formulation of the Finite Element Method, using the commercial software Abaqus in this work. Following, mesoscales, as many as necessary, are defined as intermediate problems between the global and local domains (where localized phenomena of interest, such as cracks, are effectively represented). The global-local enrichment of the GFEMgl determines the association of each mesoscale with the respective local problem. The coupling of mesoscales with the global problem is established through the transfer of displacements and generalized forces, defining the non-intrusive strategy denominated Iterative Global-Local (IGL). Numerical simulations via GFEMgl are executed in the computational system INSANE (INteractive Structural ANalysis Enviroment - www.insane.dees.ufmg.br). The combination of solvers indicated in the solution methodology focuses on endorsing the application of algorithms developed in the academy as instruments with the capacity to solve complex models utilizing commercial software. This work proposes the expansion of the implementation of the non-intrusive strategy in INSANE, enabling the consideration of multiple local domains. A numerical example is presented to show the simulation's performance and to investigate the influence of the main parameters related to the proposed strategy.

Multi-domain analysis of ultra-short-term HRV for breathing pattern classification in wearable health devices

This research paper introduces an innovative approach to classify heart rate variability (HRV) time series into paced and spontaneous breathing patterns to reflect changes in the autonomic nervous system. This type of classification is beneficial in wearable devices for stress/relaxation level detection and in deciding therapeutic interventions. The “Multi-Domain Approach” methodology integrates three different techniques: standard HRV features, fuzzy recurrence plot (FRP)-based FRP_GLCM, and empirical mode decomposition-based IMF_FRP_GLCM. The study concentrates on analyzing HRV time series within shorter data segments, aligning with the requirements of contemporary wearable health devices and biofeedback systems. HRV data collected during spontaneous and slow-paced breathing were analyzed across data segments of 5, 4, 3, 2, and 1 min, incorporating feature selection and reduction methods. Results demonstrated that standard HRV features yielded optimal performance for 5-min segments, achieving an average accuracy of 90%. Interestingly, IMF_FRP features achieved comparable accuracy even for 1-min segments. As segment duration decreased, standard HRV feature accuracy declined while IMF_FRP accuracy stayed intact, eventually matching 5-min segment accuracy levels. The study underscores the surging demand for shorter data segment HRV analysis, driven by advancements in wearable smart watches technology and mobile applications for monitoring health and managing stress.

The Synergistic Impact of Human-AI Collaboration: A Multi-Domain Analysis

This comprehensive article explores the transformative impact of human-AI collaboration across healthcare, manufacturing, and scientific research. The article examines how the synergistic integration of artificial intelligence with human expertise is revolutionizing key industries, enhancing efficiency, accuracy, and innovation. In healthcare, we discuss the implementation and impact of AI-assisted diagnostics and AI-augmented surgery, highlighting improvements in diagnostic accuracy and surgical outcomes. The manufacturing sector is analyzed through the lens of AI-driven quality control systems and the introduction of collaborative robots (cobots), demonstrating significant gains in productivity and worker safety. In scientific research, we explore AI's role in data-driven research and hypothesis testing, particularly in areas such as drug discovery and genomics. The article also provides a critical analysis of the complementary strengths of humans and AI, addresses ethical considerations in AI implementation, and speculates on the future potential of this collaboration. Drawing on recent research and case studies, this article presents a holistic view of how human-AI synergy is driving progress across multiple domains, while also considering the challenges and ethical implications of this technological integration. The findings suggest that the future of human-AI collaboration holds immense potential for accelerating scientific discovery, improving healthcare outcomes, and revolutionizing manufacturing processes, ultimately benefiting society as a whole.

Retraction Note: HDFRMAH: design of a high-density feature representation model for multidomain analysis of human health issues

Retraction Note: HDFRMAH: design of a high-density feature representation model for multidomain analysis of human health issues

Multi-domain analysis and prediction of inductively coupled plasma jet dynamics via high-speed imaging of visible light emission

Inductively coupled plasma wind tunnels are crucial for replicating hypersonic flight conditions in ground testing. Achieving the desired conditions (e.g., stagnation-point heat fluxes and enthalpies during atmospheric reentry) requires a careful selection of operating inputs, such as mass flow, gas composition, nozzle geometry, torch power, chamber pressure, and probing location along the plasma jet. The study presented herein focuses on the influence of the torch power and chamber pressure on the plasma jet dynamics within the 350 kW Plasmatron X ICP facility at the University of Illinois at Urbana-Champaign. A multi-domain analysis of the jet behavior under selected power-pressure conditions is presented in terms of emitted light measurements collected using high-speed imaging. We then use Gaussian Process Regression to develop a data-informed learning framework for predicting Plasmatron X jet profiles at unseen pressure and power test conditions. Understanding the physics behind the dynamics of high-enthalpy flows, particularly plasma jets, is the key to properly design material testing, perform diagnostics, and develop accurate simulation models.

IMNMAGN: Integrative Multimodal Approach for Enhanced Detection of Neurodegenerative Diseases Using Fusion of Multidomain Analysis With Graph Networks

IMNMAGN: Integrative Multimodal Approach for Enhanced Detection of Neurodegenerative Diseases Using Fusion of Multidomain Analysis With Graph Networks

Tactical urban pocket parks (TUPPs) for subjective and objective multi-domain comfort enhancement

Cities face growing anthropogenic overheating phenomena, such as Urban Heat Island Effect and more intense and frequent heatwaves, impacting livability and wellbeing of local citizens and tourists. To mitigate such effects, passive mitigation strategies have been widely studied in the past decades, to be integrated within the built environment. The insertion of green areas, i.e. parks, in urban areas is among the most common passive strategies, however it presents numerous challenges, as traditional parks are difficult to insert in an existing packed urban texture. Hence, in this study, we examine the significance of strategic small urban parks related to various construction types. These parks can be seamlessly integrated through tactical urbanism interventions to enhance both the objective and subjective perception of overall comfort. By coupling human-centric microclimate monitoring campaigns in the small parks and surrounding blocks (objective analysis) with questionnaire surveys to parks’ users (subjective multidomain analysis) we aim at assessing their effectiveness. Results show that Tactical Urban Pocket Parks (TUPP) can slightly improve objective whole comfort and significantly enhance the improvement of subjective comfort (from neutral/bad to good/very good).

HDFRMAH: design of a high-density feature representation model for multidomain analysis of human health issues

HDFRMAH: design of a high-density feature representation model for multidomain analysis of human health issues

Human Motion Recognition With Spatial-Temporal-ConvLSTM Network Using Dynamic Range-Doppler Frames Based on Portable FMCW Radar

As a noncontact and noninvasive sensor, radar offers attractive modality for human motion recognition in many fields, such as surveillance, smart homes, and assisted living. As a dynamic process, a comprehensive multidomain analysis in stream is crucial for human motion recognition. In this article, a novel spatial–temporal convolution long short-term memory (ST-ConvLSTM) network using dynamic range-Doppler frames (DRDF) based on portable frequency-modulated continuous-wave (FMCW) radar is proposed to address this problem. First, DRDF is introduced to characterize different human motions in time, range, Doppler, and radar cross section (RCS) domains with a time-sequenced range-Doppler maps. Then, ConvLSTM cell is investigated to extract continuous 2-D image features by combining both convolution operation and LSTM cell. Furthermore, a spatial attention module is utilized to emphasize the spatial distribution of human motion in the range-Doppler domain, while a temporal attention module is designed to obtain optimal temporal weights considering behavior continuity. Finally, an extensive radar dataset from 16 volunteers demonstrated its feasibility and superiority in the classification of six typical daily human motions. The contributions of attention modules and its robustness facing individual diversity are also investigated and discussed.

An effective tremor-filtering model in teleoperation: Three-domain Wavelet Least Square Support Vector Machine

Proper elimination of hand tremors in robot-assisted minimally invasive surgery has become a key concern due to its significant impacts on control precision and surgical success. In this study, a Three-domain Wavelet Least Square Support Vector Machine with Improved Sparrow Search Algorithm (ISSA-TDWLSSVM) model is proposed to forecast and eliminate tremor signals in teleoperation. The multi-domain analysis layer is proposed for redesigning the structure of the LSSVM. The multi-domain analysis layer is divided into three parts, complex-frequency domain, frequency domain, and time domain. The functions corresponding to three domains are the Time series, Mexican hat wavelet, and Gauss–Laplace functions. The other contribution of this study is that a novel wavelet kernel-function is proposed. Meanwhile, an improved sparrow search algorithm is proposed for optimizing the wavelet kernel-function parameter to improve the forecasting accuracy. To prove the generalization ability of the proposed model, the ISSA-TDWLSSVM and existing models are used in three examples. Compared with the existing models, the result showed that the ISSA-TDWLSSVM model has the highest forecasting accuracy. The wavelet kernel function has a good mapping ability. As compared with the LSSVM used in three-axis teleoperation robot, the Accuracy index of the ISSA-TDWLSSVM model is increased by 34.52%. In a word, an effective model is proposed to eliminate tele-operated tremor signals in this study.

Intelligent compaction quality evaluation based on multi-domain analysis and artificial neural network

Intelligent compaction technology monitors the compaction quality of pavement materials in real-time by observing the sensor signals installed on the roller. At present, the existing intelligent compaction detection methods cannot always accurately evaluate the comprehensive compaction quality of soil from shallow layer to deep layer. In addition, the time domain features of the signal and the frequency domain features of the power spectrum have never been considered as an observation values to evaluate the soil compaction quality. In this paper, we combine these signal features with an artificial neural network to accurately evaluate the overall soil compaction quality. First, to achieve the minimum amount of data required by an artificial neural network, each original signal is split into one hundred pieces. Then the time-domain features and frequency-domain features of the signal fragment in the power spectrum is calculated and the correlation between these characteristics and the comprehensive compaction quality is analyzed. Finally, the eight features with the best correlation are used as the input of an artificial neural network to build a nonlinear model. The test results show that the nonlinear model can accurately classify the overall compaction quality of soil into three categories: under compaction, the best compaction, and over compaction.

Seismic Wave Propagation Characteristics and Their Effects on the Dynamic Response of Layered Rock Sites

To investigate the seismic response of layered rock sites, a multidomain analysis method was proposed. Three finite element models with infinite element boundaries for layered sites were analysed. The results of this multidomain analysis show that stratum properties and elevation have an impact on wave propagation characteristics and the dynamic response of layered sites. Compared with the rock mass, the overlying gravel soil has a greater dynamic amplification effect at the sites. A time domain analysis parameter PGA(IMF) was proposed to analyse the effects of different strata on the seismic magnification effect of layered sites, and its application was also discussed in comparison with PGA. According to the frequency domain analysis, the interface of the rock mass strata has a low impact on the Fourier spectrum characteristics of the sites, but gravel soil has a great magnification effect on the spectrum amplitude in the high-frequency band (≥30 Hz) of waves. Moreover, the stratum properties have a great influence on the shape and peak value of the Hilbert energy and marginal spectrum at layered sites. When waves propagate from hard rock to soft rock, the peak value of the Hilbert energy spectrum changes from single to multiple peaks; then, in gravelly soil, the Hilbert energy spectral peak, its nearby amplitude and the amplitude in the high-frequency band (28–36 Hz) are obviously amplified. The frequency components and amplitude of the marginal spectrum become more abundant and larger from rock to gravelly soil in the high-frequency band (28–35 Hz).

Solar accessibility at the neighborhood scale: A multi-domain analysis to assess the impact of urban densification in Nordic built environments

This article presents the preliminary stage of a wider study that aims to determine the effect of a densification process on solar accessibility in a consolidated Nordic built environment through the use of a multi-domain approach. A set of solar accessibility analyzes are conducted for actual and future scenarios at three urban domains of analysis: (i) outdoor, (ii) envelope, and (iii) indoor, using as metrics (i) hours of direct sunlight on the 21st of March, (ii) seasonal and annual irradiation, and (iii) average daylight factor. The approach is applied to a case study located at high latitude (Trondheim, Norway; latitude 63.43° N) for the development of the university campus. The results show a reduction in solar accessibility fin all the tested domains when the new project is inserted into the plot. Almost two-thirds of the outdoor area fails to comply with the recommended target of five hours of direct sunlight access, while none of the 21 analyzed facades is considered suitable for the installation of active solar systems. The indoor daylight level is also severely compromised, with all four ground floors of the tested buildings falling short of the target 2% average daylight factor in the future scenario. However, the selection of high reflective materials as the finishing cladding for the newly designed buildings can compensate for the losses due to the new buildings’ masses. This study demonstrates how a set of solar analyzes in different urban domains can identify the critical aspects of a densification process in its preliminary design stages and open future possibilities for urban morphology, districts and buildings’ design optimization.

Multi-domain analysis of non-surgical risk factors amenable to pre-operative optimization in microvascular head and neck surgery

PurposeThe goal of this study was to conduct a multi-domain, organ system-based analysis of non-surgical comorbidities amenable to pre-operative optimization in patients undergoing free tissue transfer, in order to better understand factors that influence patient outcomes. Study designRetrospective review. SettingsTertiary academic center. Materials and methodsA retrospective analysis of 546 patients in a prospectively maintained database who underwent free tissue transfer reconstruction between 2007 and 2016 was performed. Analysis of the relationship between binary-coded system-based domains and log-transformed length of stay (LOS), rehabilitation requirement, 30-day readmission, and post-operative complications was conducted with multiple linear regression or logistic regression models. ResultsPoor nutritional status and the presence of anxiety/depression independently increased median hospital LOS. Endocrine and metabolic deficits, poor nutrition status, and psychiatric comorbidities were significant predictors for rehabilitation facility requirement upon discharge. ConclusionInterventions targeted to patient psychiatric and nutritional health may yield substantially improved outcomes in the head and neck cancer population receiving free tissue transfer surgery.

Seizure Detection in Epileptic EEG Using Short-Time Fourier Transform and Support Vector Machine

Epilepsy is the most common form of neurological disease. The electroencephalogram (EEG) is the main tool in the observation of epilepsy. The detection and prediction of seizures in EEG signals require multi-domain analysis, one of which is the time domain combined with other approaches for feature extraction. In this study, a method for detecting seizures in epileptic EEG is proposed using analysis of the distribution of the signal spectrum in the time range t. The EEG signal which includes normal, inter-ictal and ictal is transformed into the time-frequency domain using the Short-Time Fourier Transform (STFT). Simulations were carried out on varying window length, overlap and FFT points to find the highest detection accuracy. The frequency distribution and first-order statistics were then calculated as feature vectors for the classification process. A support vector machine was employed to evaluate the proposed method. The simulation results showed the highest accuracy of 92.3% using 25-20-512 STFT and quadratic SVM. The proposed method in this study is expected to be a basis for the detection and prediction of seizures in long-term EEG recordings or real-time EEG monitoring of epilepsy patients.

Lumped parameter modeling of two dimensional mechanical and acoustical components

Multidomain lumped parameter models used to predict transducer performance are an efficient way of modeling full system responses. To increase the accuracy of such models, transmission lines can be used. Discretizing the line into small spatial segments allows for lumped parameters to be used for modeling partial differential equations. Segmentation in onedimension has long been used in circuit simulators and multidomain analysis codes. This work presents a solution in two dimensions for both mechanical and acoustical physical domains. For example, the analysis of the mechanical motion of membranes and the acoustical behavior of thin cavities can be analyzed. This enables increased accuracy in modeling a transducer as the shape function of the membrane is accounted for. Further, the mechanical and acoustical physics can be coupled for an appropriate Multiphysics interaction. This is a simple demonstration of the possibilities that could be achieved using a spatially discretized multidomain models for more complex transducers.

Lexical borrowing in the Middle English period: a multi-domain analysis of semantic outcomes

The Middle English period is well known as one of widespread lexical borrowing from French and Latin, and scholarly accounts traditionally assume that this influx of loanwords caused many native terms to shift in sense or to drop out of use entirely. The study analyses an extensive dataset, tracking patterns in lexical retention, replacement and semantic change, and comparing long-term outcomes for both native and non-native words. Our results challenge the conventional view of competition between existing terms and foreign incomers. They show that there were far fewer instances of relexification, and far more of synonymy, during the Middle English period than might have been expected. When retention rates for words first attested between 1100 and 1500 are compared, it is loanwords, not native terms, which are more likely to become obsolete at any point up to the nineteenth century. Furthermore, proportions of outcomes involving narrowing and broadening (often considered common outcomes following the arrival of a co-hyponym in a semantic space) were low in the Middle English period, regardless of language of origin.

A Health Monitoring Method of Machine Tool Spindle Based on Multi-domain Analysis and Convolutional Neural Network

This paper presents a method for monitoring machine tool spindle health based on multi-domain analysis and convolutional neural network. By extracting the characteristics of data from time domain, frequency domain and time frequency domain, the data information is fully excavated automatically. Meanwhile, Convolutional neural network is used to realize fault diagnosis and classification. As a result, the fault classification results reveal high accuracy by datasheet. It proves that the method in this paper which has strong practicability and application value.

A proposed combined Cornell-Sokolow modeling approach to determine the left ventricular hypertrophy accurately based on multi-domain information processing

Left Ventricular Hypertrophy (LVH) is associated with cardiomyopathy and many other heart diseases. In this paper, the authors have proposed a combined Cornell-Sokolow (CCS) methodology to achieve significant improvement in detecting LVH by ECG/EKG with multi-domain analysis. Considering two famous voltage criteria simultaneously helps prevent the concealing of LVH in the patient’s data. The advancement of multi-domain analysis gives access to reach the algorithm from any common and cost-effective platform. This multi-domain analysis consists of both image and signal processing of the ECG data. In a MATLAB environment, these processes are quite accurate and optimized. With the help of the CCS model, the outcome of this research is satisfactory, both in terms of feature detection and LVH observation.

Investigation on PMSM for electric vehicle applications using co-simulation of MATLAB and magnet software

Transportation plays an important role in all our day to day life. Advancements in technology have uplifted the transportation system into the next level by the introduction of E-Vehicle. E-vehicles are the replacement of gasoline vehicles which are the major reason for depletion of ozone layer. The main component of the E-vehicle is the motor which basically decides the efficiency of the vehicles. The design of the machine has a great impact on the efficiency and as well as on the engines output. Due to the poor analysis of electromagnetic properties of the motor, undesired fluctuations and pulsations would occur causing harmonics to take place thereby there is a huge increase in iron loss. This will also affect the control over the vehicle at low speed. A reasonable method to avoid such issues is to introduce an additional step of analyzing the electromagnetic properties before prototyping the machine. Analysis of the machine is done on the perspective with the integration of the motor and the other components. A new design phase where multi-domain analysis defines these phenomena will assist in the task of achieving an optimal design. A specific simulation tool for each domain is needed for analysis of the entire sys-tem. The co-simulation is carried out on a 10-pole, 12-slot interior Permanent Magnet Synchronous Motor (PMSM) in this paper to do finite element analysis in order to achieve the necessary efficiency.