Miracle-dz/UniFAR-FTUs huggingface dataset:
A Numerical Study of Lossy Multipole Debye Dispersive Media Using a Recursive Integral-FDTD Method

A dispersive finite-difference time-domain (FDTD) method based on the recursive integration (RI) technique for the modeling of the lossy multipole Debye dispersive media is described in this article. The interaction between the electromagnetic (EM) field and the human tissues is simulated by means of the RI method, and the frequency-dependent formulations which possess good compatibility with the main FDTD algorithm are achieved. Next, the expression of the multipole Debye dispersion model is similar to the multipole complex frequency shift (CFS) stretching function which is utilized to build the multipole perfectly matched layer (MPML). Therefore, a significant feature of using the RI method is its overall modeling of both multipole Debye dispersive media and MPML boundary conditions. Furthermore, the stability analysis of the RI method in solving the lossy multipole Debye dispersive model indicates that the Courant–Friedrich–Levy (CFL) stability limit of the regular FDTD can be maintained. At last, the numerical cases performed in this work demonstrate the correctness of the RI method in modeling dispersive media.
Premature Ventricular Contraction Beat Classification via Hyperdimensional Computing

Hyperdimensional computing (HD) is an emerging brain-inspired paradigm used for machine learning classification tasks. It manipulates ultra-long vectors-hypervectors- using simple operations, which allows for fast learning, energy efficiency, noise tolerance, and a highly parallel distributed framework. HD computing has shown a significant promise in the area of biological signal classification. This paper addresses group-specific premature ventricular contraction (PVC) beat detection with HD computing using the data from the MIT-BIH arrhythmia database. Temporal, heart rate variability (HRV), and spectral features are extracted, and minimal redundancy maximum relevance (mRMR) is used to rank and select features for classification. Three encoding approaches are explored for mapping the features into the HD space. The HD computing classifiers can achieve a PVC beat detection accuracy of 97.7 % accuracy, compared to 99.4% achieved by more computationally complex methods such as convolutional neural networks (CNNs).
Combining Support Vector Machine – Fast Fourier Transform (SVM – FFT) For Improving Accuracy on Broken Bearing Diagnosis

Electric motor has critical component that called as bearing. Bearing condition be monitored through vibration signal that is produced by vibration sensor. Vibration signal is analysed to detect condition of bearing and also to diagnose the broken bearing. In order to intelligently diagnose the broken bearing, signal generated by vibration -usually mentioned as vibration signal is utilized. Currently, machine learning is spready utilized to diagnose the broken bearing either by utilizing shallow architecture or deep architecture. This report presents the result of improving accuracy on diagnosing Broken Bearing using combined Support Vector Machines (SVM) and Fast Fourier Transform (FFT). The result showed that by using SVM - FFT could outperform combined SVM - Statistic and also outperform others shallow classifiers such as Decision Tree, Random Forest, and Naive Bayes. The combined SVM - FFT could be used for diagnosing Broken Bearing with higher accuracy (accuracy> 99%).
On Graphical Modeling of High-Dimensional Long-Range Dependent Time Series

We consider the problem of inferring the conditional independence graph (CIG) of a high-dimensional stationary, multivariate long-range dependent (LRD) Gaussian time series. In a time series graph, each component of the vector series is represented by a distinct node, and associations between components are represented by edges between the corresponding nodes. In a recent work on graphical modeling of short-range dependent (SRD) Gaussian time series, the problem was cast as one of multi-attribute graph estimation for random vectors where a vector is associated with each node of the graph. At each node, the associated random vector consists of a time series component and its delayed copies. A theoretical analysis based on short-range dependence has been given in Tugnait (2022 ICASSP). In this paper we analyze this approach for LRD Gaussian time series and provide consistency results regarding convergence in the Frobenius norm of the inverse covariance matrix associated with the multi-attribute graph.
Transforming towards 6G: Critical Review of Key Performance Indicators

With the experiences acquired upon the successful implementation of 5G networks academia, researchers, and industry are envisioning the need for 6G networks. The vision of the 6G communication network is supposed to completely assist the creation of a Ubiquitous Intelligent Mobile Society. Already 5G technologies are in place and still few extended features of 5G are continuously being introduced. Even though the 6G communication network is expected to have greater capabilities than the existing 5G, there are no clear specifications on how far these capabilities shall be capitalized in 6G. The 6G technologies shall move past ordinary mobile internet services and advance to support ubiquitous Artificial Intelligent (AI) services from the network's core to end-to-end service devices/applications. The architecture, protocols, and operations which are the primary constituents of the 6G network shall implement AI technologies for self-optimization and actualization. This article brings an all-inclusive deliberation of 6G based on an assessment of preceding generations' evolving technology developments.
Aerodynamic Shape Optimization for Supersonic Aircraft

Introduction onsidering the remarkable developments in many high technology fields during the past few decades, it is surprising that virtually no change in the speed of commercial air transportation has occurred since the introduction of the Boeing 707 in the late 1950s. Instead, the aging Concorde continues to fly at supersonic speeds for an extremely limited market and all other commercial aircraft have kept well below Mach 1. Several attempts have been mounted by U. S. , European and Japanese organizations to develop the technologies needed for the next generation supersonic commercial transport. All of these endeavors, while succeeding in developing or improving key technologies, have still failed to produce a viable aircraft. Nevertheless, some of the technologies are noteworthy because they also have applications to other flight regimes and disciplines. One of the most rewarding technologies fostered during the supersonic aircraft programs of the 1990s (NASA's High Speed Research (HSR) program, the European Supersonic Research Program (ESRP), and the SST Program of NAL in Japan) was aerodynamic shape optimization (ASO). In contrast to the evolutionary development process that progressed slowly over the last four decades to produce the current generation of transonic commercial transports, the next supersonic transport will have to achieve a level of efficiency that somehow allows it to become economically competitive with its transonic rivals. Achieving this level of efficiency in all aspects of the aircraft's engineering can be addressed mainly with advanced approaches to aircraft design. A critical engineering element of any commercial transport, and particularly for a supersonic transport, is its aerodynamic efficiency. The NASA HSR program therefore nurtured the rapid technology development of ASO methods, which were proven through wind tunnel validation to far surpass the " cut-and-try " approaches used in the past. Both during and subsequent to the HSR program, ASO methods have been adopted as a workhorse by many aerodynamic design teams. Their success has, in turn, facilitated the advent of even more sophisticated design strategies that attempt true high-fidelity multidisciplinary optimization (MDO). In many respects, the real hurdles that must be overcome in achieving cost-effective commercial supersonic transportation are not the daunting engineering challenges that still need years of work, but instead the environmental and economical hurdles. With current technologies, large commercial supersonic transports will produce significant sonic booms, present a hazard to the ozone layer and require design, development, test and evaluation (DDT&E) costs that exceed any single corporation's …
Knowledge as a mediator for innovativeness and risk-taking tolerance of female entrepreneurs in Indonesia

Abstract In the rapidly evolving small business environment of Indonesia, female entrepreneurs also play a crucial role. Information sharing and knowledge exchange are inseparable factors in the rapid growth of small and medium enterprises (SMEs), thus the influence of knowledge management influences risk-taking attitude and is closely related to innovativeness. Only a small number of studies, however, have looked at knowledge management in connection to innovation, optimism, and whether the impact is influenced by the firm’s risk-taking attitude. This study aims to investigate the connection between the innovativeness, information management, optimism, and risk-taking tolerance of female companies. By disseminating structured surveys, we took a quantitative strategy. To analyze the data we gathered, we used the SEM-PLS method. Our findings demonstrate how creativity significantly improves risk tolerance and information management. We also conclude that optimism greatly enhances risk tolerance and information management. Additionally, knowledge management increases a person’s ability for taking calculated risk.
Qualitative model-based multisensor data fusion and parameter estimation using ∞-norm Dempster-Shafer evidential reasoning

This paper is concerned with model-based parameter estimation for noisy processes when the process models are incomplete or imprecise. The underlying representation of our models is qualitative in the sense of Interval Arithmetic and Qualitative Reasoning and Qualitative Physics from the Artificial Intelligence literature. We adopt a specific qualitative representation, namely that advocated by Kuipers, in which a well defined mathematical description of a qualitative model is given in terms of operations on intervals of the reals. We investigate an weighted opinion pool formalism for multi-sensor data fusion, develop a definition for unbiased estimation on quantity-spaces and derive a consistent mass assignment function for mean estimators for two state systems. This is extended to representations involving more than two states by utilizing the relationships between coarse (i.e. two state) and fine (i.e. N state) representations explored by Shafer. We then generalized the Dempster-Shafer Theory of Evidence to a finite set of theories and show how an extreme theory can be used to develop mean minimum-mean-square-error estimators applicable to situations with correlated noise. We demonstrate our theory using real data from a mobile robot application which utilizes sonar and laser time-of-flight and gyroscope information to disseminate surface curvature.
Interspecific Drought Cuing in Plants

Plants readily communicate with their pollinators, herbivores, symbionts, and the predators and pathogens of their herbivores. We previously demonstrated that plants could exchange, relay, and adaptively utilize drought cues from their conspecific neighbors. Here, we studied the hypothesis that plants can exchange drought cues with their interspecific neighbors. Triplets of various combinations of split-root Stenotaphrum secundatum and Cynodon dactylon plants were planted in rows of four pots. One root of the first plant was subjected to drought while its other root shared its pot with one of the roots of an unstressed target neighbor, which, in turn, shared its other pot with an additional unstressed target neighbor. Drought cuing and relayed cuing were observed in all intra- and interspecific neighbor combinations, but its strength depended on plant identity and position. Although both species initiated similar stomatal closure in both immediate and relayed intraspecific neighbors, interspecific cuing between stressed plants and their immediate unstressed neighbors depended on neighbor identity. Combined with previous findings, the results suggest that stress cuing and relay cuing could affect the magnitude and fate of interspecific interactions, and the ability of whole communities to endure abiotic stresses. The findings call for further investigation into the mechanisms and ecological implications of interplant stress cuing at the population and community levels.
Exact Testing for Spatial Autocorrelation among Regression Residuals

This paper develops the exact distribution of the Moran or Cliff-Ord test for spatial autocorrelation among regression residuals. It shows how practical, computational methods can be used to implement the exact test. Higher order approximations to the distribution are also discussed. Empirical applications are provided, and the value of exact testing is discussed.
Relationship between the design characteristics of activity-based flexible offices and users’ perceptions of privacy and social interactions

ABSTRACT Activity-based flexible offices (AFOs) provide a variety of workspaces to meet the need for social interactions and privacy at work. This study investigates the relationship between the design characteristics of AFOs and users’ perceptions of visual and acoustic privacy and social interactions. This case study is based on post-occupancy evaluations in three AFO layouts at a public service organization in Sweden. A mixed-method approach is adopted that combines questionnaires and layout analysis. In general, the results showed that while aesthetics received the highest satisfaction scores, office functionality, task support, storage and visual and acoustic privacy received the lowest ratings. Key design characteristics for AFOs were operationalized, observed and exemplified: zone diversity, proportion, readability, spatial enclosure, sharing ratios and functionality of furniture and tools. These insights may contribute to better-informed decisions about the design characteristics that influence privacy and social interactions in AFOs.
Obesity during Pregnancy and SARS-CoV-2/COVID-19-Case Series of the Registry Study “COVID-19 Related Obstetric and Neonatal Outcome Study” (CRONOS-Network)

(1) Background: Obesity is an increasing challenge in the care of pregnant women. The aim of our study was to investigate whether obesity is an independent risk factor for severe maternal and neonatal outcomes in pregnant women with COVID-19. (2) Methods: Data from the COVID-19 Related Obstetric and Neonatal Outcome Study (CRONOS), a prospective multicenter registry for SARS-CoV-2 positive pregnant women, was used to analyze the effect of obesity on selected individual and combined outcome parameters (3) Results: With 20.1%, the prevalence of obesity in the CRONOS registry exceeds the German background rate of 17.5%. Obese women showed significantly higher rates of GDM (20.4% vs. 7.6%; p < 0.001), hypertensive pregnancy disorders (6.2% vs. 2%; p = 0.004) and C-sections (50% vs. 34.5%; p < 0.001). BMI was revealed to be an individual risk factor for the severe combined pregnancy outcome (maternal death, stillbirth or preterm birth < 32 weeks) (OR 1.050, CI 1.005–1.097). (4) Conclusions: Maternal BMI is a predictor for the most severe outcome as maternal or neonatal death and preterm delivery <32 weeks of gestation. Unexpectedly, categorized obesity seems to have limited independent influence on the course and outcome of pregnancies with COVID infections.
Design of a thermal probe for the plasma diagnostics

When a solid material is inserted into plasma, it receives heat flux and electrical current according to its bias voltage. The heat flux‐voltage characteristic is more complicated than the current‐voltage characteristic and depend on ion temperature as well as electron temperature. Thermal probe method uses this characteristic to determine plasma parameter. We propose here to use it to deduce ion temperature and show the results on related work. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Perceived Stress Is Directly Associated with Major Consumption of Sugar-Sweetened Beverages among Public University Students

Stress is a condition that has been related to the development of risk behaviors for health such as sugar-sweetened beverages (SSBs) consumption. The aim of this study was to examine the link between SSBs consumption and perceived stress level in university students. This was an observational, cross-sectional and single-time-point study where the subjects were recruited as a non-probabilistic sample of first-year university students. The students reported their SSBs consumption through a validated questionnaire, as well as their perceived stress level, evaluated through the Cohen scale. Comparisons were made between the means of all variables. Factorial analysis of variance was conducted to explore the effect of the variables’ interaction on the stress level. One-way analysis of variance was performed to assess differences between the sexes. Men consumed more SSBs (6101.17 ± 3772.50 mL/week) compared to women (4294.06 ± 3093.8 mL/week). However, women had higher scores of perceived stress and showed a strong association of stress with the SSBs consumption pattern (r and p-value). This study shows for the first time the association that exists between stress and SSBs consumption and indicates that it is related to sex in the young population.
Backcalculation of Thickness and Dielectric Properties of Asphalt Concrete Using Numerical Simulation of Ground Penetrating Radar

Ground penetrating radar (GPR) is an essential tool for the non-destructive evaluation of pavement conditions. The accuracy of GPR analysis depends on the correct prediction of the layer dielectric property. In this study, a finite-difference time domain simulation-based two-step backcalculation algorithm was developed to determine the layer thickness and dielectric constant from air-coupled GPR data. The two-step backcalculation algorithm was developed by combining a surrogate-based global optimizer and artificial neural network for efficiency and accuracy. The GPR backcalculated results were compared with the field conditions of three pavement sections. The results show that the backcalculation algorithm could predict the layer thicknesses with almost 95% to 99.7% accuracy, whereas the prediction accuracy of the dielectric constants was between 90% and 98.5%. Furthermore, it was observed that the variations of dielectric constants in each lift of the asphalt concrete layer could be used to predict the presence of probable distress in the field.
The Effect of Service Learning on the Development of Trait Emotional Intelligence and Adversity Quotient in Youths: An Experimental Study

This study aims to determine the effect of service learning (SL) on the trait emotional intelligence and adversity quotient of Chinese undergraduate students in Hong Kong. The SL programme lasted six months (at least 80 service hours). In a pre-test–post-test experimental design, students who participated in the SL during that time period were classified as the experimental group (n = 139; 69 male, 70 female), whereas students who never participated in SL were classified as the comparison group (n = 133; 66 male, 67 female). Both groups of participants were asked to finish the Wong and Law Emotional Intelligence Scale (WLEIS) and the Adversity Response Profile® (ARP) before and after the SL programme. The results showed that there were no significant differences in WLEIS and ARP scores (at the pre-test) between the experimental and comparison groups. The results further revealed that students in the experimental group had better improvements in WLEIS and ARP than those in the comparison group after they completed SL. These findings provide valuable insights and implications for incorporating components of SL into interventions for youths to improve their ability to process emotions and overcome adversity.
Development of an emission-transmission CT system combining X-ray CT and SPECT

We describe the design and initial evaluation of a prototype emission-transmission imaging system which incorporates a third-generation X-ray CT scanner and a single-headed SPECT scanner. The two commercial imaging devices are juxtaposed such that the CT table can move the patient directly into the SPECT scanner prior to the X-ray scan. The integrated system is being used to investigate correlation of X-ray CT images with conventional SPECT images and to attempt correction of attenuation errors in the SPECT image. We have evaluated two potential sources of error in the acquisition and analysis of data from this system. First, the observed flux of scattered X-rays passing through the SPECT collimator into the gamma camera is 17,000 cps-well below the rated maximum of 180,000 cps. Second, attenuation correction of SPECT will be performed by generating an attenuation map from the CT image. Previous experience suggests that directly calculated values can be generated within 3% of actual attenuation values, but more complex approaches will be implemented if necessary. Preliminary results of applying CT-derived attenuation correction are presented for a myocardial perfusion phantom. <<ETX>>
Robust Strategies for Optimal Order Execution in the Almgren–Chriss Framework

Abstract Assuming geometric Brownian motion as unaffected price process , Gatheral and Schied (2011; Optimal trade execution under geometric Brownian motion in the Almgren and Chriss framework, International Journal of Theoretical and Applied Finance, 14, pp. 353–368) derived a strategy for optimal order execution that reacts in a sensible manner on market changes but can still be computed in closed form. Here, we will investigate the robustness of this strategy with respect to misspecification of the law of . We prove the surprising result that the strategy remains optimal whenever is a square-integrable martingale. We then analyse the optimization criterion of Gatheral and Schied (2011) in the case in which is any square-integrable semimartingale and we give a closed-form solution to this problem. As a corollary, we find an explicit solution to the problem of minimizing the expected liquidation costs when the unaffected price process is a square-integrable semimartingale. The solutions to our problems are found by stochastically solving a finite-fuel control problem without assumptions of Markovianity.
Empirical Eigenvectors of Sea-Level Pressure, Surface Temperature and Precipitation Complexes over North America

Abstract The combined representation of fields of three climatic variables with empirical orthogonal functions, herein referred to as eigenvectors, is discussed. The eigenvectors are derived from measurements of monthly mean sea-level pressure, surface temperature and precipitation at 23 points in North America for 25 Januarys. Selected eigenvectors of the individual climatic variables are presented; however, the major part of the paper is devoted to the presentation of eigenvectors consisting of combinations of three climatic variables. Empirical eigenvectors derived from fields of two or more meteorological variables have been used in statistical prediction models, but none of the studies to date displayed examples of these eigenvectors or discussed the internal consistency of the combined representations. In this paper it is shown that the structure of the covariances between the three climatic variables,as portrayed by the combined representations, is consistent with synoptic experience. This result i.