Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Degree-of-Freedom System Modal Parameters Wavelet Transform Identification Method

It is precondition for realizing the mechanical dynamic optimization design and the dynamic modification to analyze the characteristic of multi-degree-of-freedom system. The paper discusses a kind of modal parameters wavelet transform identification method based on Modified Particle Swarm Optimization (PSO) algorithm, furthermore, proposes the PSO wavelet ridge extraction algorithm, and gives the formulas identifying modal parameters from a wavelet ridge. The proposed method applies to the dynamic characteristic identification of a flow meter to explore the effectiveness. The results prove the method is precision and insensitive to noise.
Wind Speed Analysis of Hurricane Sandy

The database of the HWind project sponsored by the National Oceanic and Atmospheric Administration (NOAA) for hurricanes between 1994 and 2013 is analysed. This is the first objective of the current research. Among these hurricanes, Hurricane Sandy was selected for a detailed study due to the number of files available and its social relevance, with this being the second objective of this study. Robust wind speed statistics showed a sharp increase in wind speed, around 6 m s−1 at the initial stage as Category 1, and a linear progression of its interquartile range, which increased at a rate of 0.54 m s−1 per day. Wind speed distributions were initially right-skewed. However, they evolved to nearly symmetrical or even left-skewed distributions. Robust kurtosis was similar to that of the Gaussian distribution. Due to the noticeable fraction of wind speed intermediate values, the Laplace distribution was used, its scale parameter increasing slightly during the hurricane’s lifecycle. The key features of the current study were the surface and recirculation factor calculation. The surface area with a category equal to, or higher than, a tropical storm was calculated and assumed to be circular. Its radius increased linearly up to 600 km. Finally, parcel trajectories were spirals in the lower atmosphere but loops in the mid-troposphere due to wind translation and rotation. The recirculation factor varied, reaching values close to 0.9 and revealing atmospheric stratification.
Identification of DELLA Genes and Key Stage for GA Sensitivity in Bolting and Flowering of Flowering Chinese Cabbage

Flowering Chinese cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) is an important and extensively cultivated vegetable in south China, and its stalk development is mainly regulated by gibberellin (GA). DELLA proteins negatively regulate GA signal transduction and may play an important role in determining bolting and flowering. Nevertheless, no systematic study of the DELLA gene family has been undertaken in flowering Chinese cabbage. In the present study, we found that the two-true-leaf spraying of gibberellin A3 (GA3) did not promote bolting but did promote flowering, whereas the three-true-leaf spraying of GA3 promoted both bolting and flowering. In addition, we identified five DELLA genes in flowering Chinese cabbage. All five proteins contained DELLA, VHYNP, VHIID, and SAW conserved domains. Protein-protein interaction results showed that in the presence of GA3, all five DELLA proteins interacted with BcGID1b (GA-INSENSITIVE DWARF 1b) but not with BcGID1a (GA-INSENSITIVE DWARF 1a) or BcGID1c (GA-INSENSITIVE DWARF 1c). Their expression analysis showed that the DELLA genes exhibited tissue-specific expression, and their reversible expression profiles responded to exogenous GA3 depending on the treatment stage. We also found that the DELLA genes showed distinct expression patterns in the two varieties of flowering Chinese cabbage. BcRGL1 may play a major role in the early bud differentiation process of different varieties, affecting bolting and flowering. Taken together, these results provide a theoretical basis for further dissecting the DELLA regulatory mechanism in the bolting and flowering of flowering Chinese cabbage.
Time-Dependent Effects of Exposure to Nature and Perceived Behavioral Control over Social Distancing in Shared Open Spaces on Psychological Distress of Residents during Pandemic of COVID-19

Background: Further research into the influence of the COVID-19 epidemic on mental health is needed. Some research has highlighted the positive effects of nature exposure on mental health, as well as the importance of subjective assessments of green spaces. Methods: Considering both private and semi-public environments, the current longitudinal study examines the moderating effects of exposure to nature and perceived behavioral control over social distancing on the relationship between perceived interior crowding, social isolation, and psychological distress. Finally, it investigates whether these connections change through time. During the Iranian national lockdowns, data from 718 middle-aged women who completed an online questionnaire survey in two waves were used. Results and Conclusion: The findings of structural equation modeling back up the idea that social isolation plays a key role in the association between perceived interior crowding and psychological distress. Multi-Group Analysis revealed that exposure to nature reduced the negative effects of perceived interior crowding on psychological distress, as predicted. Nevertheless, this relationship is time-dependent and nature exposure during the time did not necessarily assist in reducing negative impacts. The current research adds to the body of knowledge by identifying perceived behavioral control as a buffer against the adverse effects of social isolation on psychological distress over time. These findings provide for a better understanding of psychological processes and could help in the promotion of design policies in the event of a pandemic.
Flow and transient heat transfer in a high speed free piston tunnel: CFD simulations on turbulent and laminar flow conditions

This paper presents CFD simulations of a high-speed free piston tunnel with particular attention to the transient heat transfer during the rapid compression process involving shock waves. Commercial CFD software, STAR-CD was used to simulate the flow conditions of the transonic compressible flow. The study was done on the effect of turbulence modeling to the convective heat flux values. Turbulent model utilized was two equations K-Epsilon RNG with Prandtl number set to values of 0.7, 0.9 and 1.1. It was found that maximum pressure obtained at a measuring vertex for turbulent-Pr 0.7 was 3826 kPa and maximum heat flux value from the simulation was 631.8 kW/m2
Deep Learning for Securing Software-Defined Industrial Internet of Things: Attacks and Countermeasures

Software-defined networking (SDN) has become an attractive solution to carry out centralized and efficient control in Industrial Internet of Things (IIoT). However, its security has received little attention when applied to IIoT, and no comprehensive consideration has been given to attacks against forwarding nodes (FNs), the basic elements of the data plane. Therefore, in this article, we aim to investigate attacks against FNs from multiple perspectives in software-defined IIoT. To the best of our knowledge, we are the first to systematically consider this kind of attacks. Since it is difficult to predeploy all defense methods against various attacks, we propose a deep reinforcement learning (DRL)-based general attack tolerance scheme to guide the benign traffic flow bypass the attacked FNs. Furthermore, in view of the situation that the real data set is rare and the standard model-based data set is likely to be impractical, we use generative adversarial network (GAN), a representative deep generative model (DGM), to flexibly generate real-like network traffic for more sufficient and effective experimental verification on the attack tolerance scheme. Experimental results show that our proposed scheme can significantly improve the successful arrival rate of IIoT traffic and achieve near-optimal results.
Global Genomic Variations of Pathogenic Bacteria Salmonella Reading

US Centers for Disease Control and Prevention (CDC) estimates food-borne pathogenic Salmonella bacteria cause about 1.35 million infections, 26,500 hospitalizations, and 420 deaths in the U.S. every year. Outbreaks of multidrug-resistant Salmonella Reading linked mainly to raw turkey products and alfalfa sprouts have accounted for human illness including mortality. Therefore, we downloaded and compared genome sequences of 897 Salmonella Reading isolated from 27 resources in 11 countries at 21 year-points from the National Center for Biotechnology Information database. Isolates were clustered into 10 clades which consisted of 3 major and 7 minor or single node clades. Although 3 major clades included both Europe and American isolates, one clade consisted of 605 North American isolates out of 614 isolates. Evolutionary distance is more related to the continent than the source of isolation. The host source, continent (North America and Europe) and phylogenetic clade were related to the prevalence of isolates encoding Antimicrobial Resistance Genes (ARGs). Prevalence of prophages was greater in bovine and swine isolates than poultry and human isolates and the least prevalence was found in human isolates. Between continents, the prevalence of phage was greater in North American isolates than European. The diversity of virulence factors in swine isolates differed from poultry isolate while no difference was found among continents. In conclusion, evolutionary distance is related to isolation host source rather than the continent, and genome features were distinguished by host and cluster. Our genomic analysis implies that Salmonella Reading evolved indeHow to cite this paper: Lee, W.H. , Jeon, Y. , Chung, J.S. , Shim, W.J., Shin, M.J., Han, S.G. , Baek, S. , Kim, J., Kim, J. and Chung, P.S. (2021) Global Genomic Variations of Pathogenic Bacteria Salmonella Reading. Computational Molecular Bioscience, 11, 57-68. https://doi.org/10.4236/cmb.2021. 113004 Received: September 1, 2021 Accepted: September 27, 2021 Published: September 30, 2021 Copyright © 2021 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access
Insurers' risk management as a business process: a prospective competitive advantage or not?

PurposeInsurance companies exist to manage the risk of others, which is why they are perceived to be competitive in risk management (RM). Considering this, we investigate how different RM capabilities make insurers effective in RM. These capabilities include understanding risk and risk management (URRM), risk identification (RI), risk assessment and analysis (RAA) and risk monitoring (RMON) activities in insurance companies. In addition, the authors probe how these capabilities can jointly yield a competitive advantage for the insurance industry under the resource-based view (RBV) and dynamic capabilities perspective (DCP). Design/methodology/approachThe authors present a latent variable RM model for the insurance industry and employ structural equation modeling (SEM) to test the hypotheses. Furthermore, the authors also conduct confirmatory factor analysis (CFA) and convergent and discriminant validity analysis for model fit and invariance testing, respectively.FindingsThe results show that insurers who investigated RM-related capabilities directly influence their risk management practices (RMPs). Moreover, improving these capabilities will make insurers more effective in managing the risks of others. Thus, RM as a business process will yield a competitive advantage for the insurance sector. The findings are supported by the theoretical insights presented by the RBV and DCP. Furthermore, the model also adheres to the convergent and discriminant validity cut-off values.Originality/valueTo the best of the authors’ knowledge, this is the first study examining insurers' RM practices as a source of a competitive advantage.
Quasi-Synchronization of Delayed Stochastic Multiplex Networks via Impulsive Pinning Control

The quasi-synchronization problems of the delayed multiplex networks with stochastic perturbations are investigated in this article. Different from previous studies on complete synchronization for the delayed multiplex networks, quasi-synchronization, which is more practical and more challenging than complete synchronization, is discussed. To adopt a better cost-saving control, an impulsive pinning scheme is designed. That is, the key nodes in the multiplex networks are pinned and controlled at impulsive instants. At the same time, a virtual leader is introduced. Based on the theory of stochastic impulsive differential equations and the Lyapunov–Krasovskii functional method, the sufficient conditions are derived for the error systems to converge into a bounded region in the sense of the mean square. Therefore, stochastic quasi-synchronization is guaranteed. Finally, the two-layer Newman–Watts (NW) small-world networks and two-layer Barabási–Albert (BA) scale-free networks are provided to illustrate the effectiveness of the theoretical results.
Communicating Risk: Developing an “Efficiency Index” for Dementia Screening Tests

Diagnostic and screening tests may have risks such as misdiagnosis, as well as the potential benefits of correct diagnosis. Effective communication of this risk to both clinicians and patients can be problematic. The purpose of this study was to develop a metric called the “efficiency index” (EI), defined as the ratio of test accuracy and inaccuracy, to evaluate screening tests for dementia. This measure was compared with a previously described “likelihood to be diagnosed or misdiagnosed” (LDM), also based on “numbers needed” metrics. Datasets from prospective pragmatic test accuracy studies examining four brief cognitive screening instruments (Mini-Mental State Examination; Montreal Cognitive Assessment; Mini-Addenbrooke’s Cognitive Examination (MACE); and Free-Cog) were analysed to calculate values for EI and LDM, and to examine their variation with test cut-off for MACE and dementia prevalence. EI values were also calculated using a modification of McGee’s heuristic for the simplification of likelihood ratios to estimate percentage change in diagnostic probability. The findings indicate that EI is easier to calculate than LDM and, unlike LDM, may be classified either qualitatively or quantitatively in a manner similar to likelihood ratios. EI shows the utility or inutility of diagnostic and screening tests, illustrating the inevitable trade-off between diagnosis and misdiagnosis. It may be a useful metric to communicate risk in a way that is easily intelligible for both clinicians and patients.
Active learning and reflection in product development engineering education

Traditional engineering courses at tertiary level have been traditionally theory-based, supported by laboratory work, but there is now a world-wide trend towards project-based learning. In product development education, project-based learning is essential in order to integrate the disciplines of design, marketing and manufacturing towards the common goal of creating a new product. The process of development consists of a set of activities, tools and methods that are themselves evolving and improving with practice. A successful active learning course in product development education is presented in this paper as part of undergraduate engineering education, within the context of a fourth year course in New Zealand. The industry sponsored individual projects support active real-life learning and reflection. The paper presents ways in which active learning can be enhanced within the learning outcomes of a project-based paper. These methods can be adopted in educating engineering students of any discipline to better prepare them for their future careers.
Made to stick : why some ideas take hold and others come unstuck

What is that makes urban myths so persistent but many everyday truths so eminently forgettable? How do newspapers set about ensuring that their headlines make you want to read on? And why do we remember complicated stories but not complicated facts? In the course of over ten years of study, Chip and Dan Heath have established what it is that determines whether particular ideas or stories stick in our minds or not, and "Made to Stick" is the fascinating outcome of their painstaking research.Packed full of case histories and thought-provoking anecdotes, it shows, among other things, how one Australian scientist convinced the world he'd discovered the cause of stomach ulcers by drinking a glass filled with bacteria, how a gifted sports reporter got people to watch a football match by showing them the outside of the stadium, and how high-concept pitches such as 'Jaws on a spaceship' ("Alien") and 'Die Hard on a bus' ("Speed") convince movie executives to invest vast sums of money in a project on the basis of almost no information. Entertaining and informative by turns, this is a fascinating and multi-faceted account of a key area of human behaviour. At the same time, by showing how we can all use such cleverly devised strategies as the 'Velcro Theory of Memory' and 'curiosity gaps', it offers superbly practical insights, setting out principles we all can adopt to make sure that we get our ideas across effectively.
Drain Induced Barrier Widening and Reverse Short Channel Effects in Tunneling FETs: Investigation and Analysis

In this paper, using calibrated TCAD simulations, we demonstrate how the performance of a Tunneling FET (TFET) can be improved by using a new phenomenon called drain induced barrier widening (DIBW) at the source-channel junction. Our results indicate that TFETs in which DIBW dominates exhibit a steep subthreshold swing (≈35 mV/dec) and a low OFF-state current (<inline-formula> <tex-math notation="LaTeX">$\approx 10^{-16}\text{A}/\mu \text{m}$ </tex-math></inline-formula>) without affecting the ON-state current. We also show that TFETs exhibit a reverse short channel effect due to an increase in the tunneling width at the source-channel junction.
An active learning framework improves tumor variant interpretation

For precision medicine to reach its full potential for treatment of cancer and other diseases, protein variant effect prediction tools are needed that characterize variants of unknown significance (VUS) in a patient’s genome with respect to their likelihood to influence treatment response and outcomes. However, the performance of most variant prediction tools is limited by the difficulty of acquiring sufficient training and validation data. To overcome these limitations, we applied an iterative active learning approach starting from available biochemical, evolutionary, and functional annotations. The potential of active learning to improve variant interpretation was first demonstrated by applying it to synthetic and deep mutational scanning (DMS) datasets for four cancer-relevant proteins. We then probed its utility to guide interpretation and functional validation of tumor VUS in a potential biomarker for cancer therapy sensitivity, the nucleotide excision repair (NER) protein Xeroderma Pigmentosum Complementation Group A (XPA). A quantitative high-throughput cell-based NER activity assay, fluorescence-based multiplex flow-cytometric host cell reactivation (FM-HCR), was used to validate XPA VUS selected by the active learning strategy. In all cases, selecting VUS for validation by active learning yielded an improvement in performance over traditional learning. These analyses suggest that active learning is well-suited to significantly improve interpretation of VUS and cancer patient genomes.
Bilateral Investment Treaty and Foreign Direct Investment in India

India has one of the largest Bilateral Investment Treaty (BIT) networks with other counties around the world. The BITs is to promote foreign investment by increasing investor confidence, empowering individual private parties to take international arbitral proceedings against the threat of appropriation by the government of the host country. This paper analyses the effect of BITs on FDI inflows in India using panel data for 76 countries for the time period 2000-2016 applying a dynamic panel generalised method of moments instrumental variable estimation method. The differenced GMM and system GMM estimates show a significant negative effect of bilateral investment treaties on the FDI inflows in India. While the lagged FDI has a significant positive effect, the financial openness of the source nations is reducing FDI inflows to India. The POLCON index shows that the countries with lesser political constraints have positive FDI outflow towards India. As opposed to domestic variables, the Chinn-Ito and POLCON indices have a greater share of change in FDI inflows to India. It seems that the BITs is not efficient enough to create investor confidence to invest in India.
Technology Acceptance in Healthcare: A Systematic Review

Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that offers extensive understanding into what affects healthcare technologies and services and covers distinctive trends in large-scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self-efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemedicine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full potential of technology acceptance in healthcare and opening the door for further research opportunities.
Characterization of noise in semiconductor lasers and its influence in a practical FSK link

Quantum fluctuations in a semiconductor laser produce noise both in the amplitude and in the phase of the output field. These noise sources have a significant effect in optical coherent systems. Measurements of the field spectrum and RIN spectrum of a DFB laser, at three different optical power levels, are fitted to the corresponding analytical curves. The shapes of the measured curves are closely matched by the analytical expressions and the fitting produced very similar values for the common parameters in the field and RIN expressions, providing a good validation for the theory. These analytical/experimental results show a non-lorentzian shape for the field spectrum, as opposed to the commonly used lorentzian shape. Using the more accurate laser models derived from our work, we were able to predict successfully by simulation, the experimental values obtained for the BER in a practical FSK system.