Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Optimization of Current Dynamic Control in Induction Motor Field-Weakening Region

The maximum-torque-output (MTO) trajectory can achieve optimal current distribution for induction motor (IM) field-weakening (FW) control. To provide the dynamic model for MTO, this letter proposes quantitative analysis of the current dynamic character in FW control. As the current vector moves along MTO trajectory, the current changing rate and voltage margin requirement are calculated to study the dynamic control objective and the realization condition of FW control. According to the summarized changing law of voltage margin requirement, the d-axis voltage margin priority approach is applied to the IM FW system for current dynamic optimization. The effectiveness of the studied method is verified through comparison experiments on an advanced RISC machine (ARM)-based 3.7-kW IM platform.
Nutritional Recommendations for Sport Team Athletes

Bonfanti N1 and Jimenez-Saiz SL2* 1PhD, Professor of San Jorge University of Zaragoza, Autovía Mudéjar, km. 299, 50830 Villanueva de Gállego, Zaragoza, Spain 2PhD, Director of the Master's Degree in Sports Training and Nutrition, European University of Madrid, 8, Av. de Fernando Alonso, 28108 Alcobendas, Spain *Corresponding author: Jimenez-Saiz SL, PhD, Director of the Master's Degree in Sports Training and Nutrition, European University of Madrid, 8, Av. de Fernando Alonso, 28108 Alcobendas, Spain, Tel: +34 91 211 3503; E-mail: sergiolorenzo.jimenez@universidadeuropea.es
DNS tunneling Detection Using Elasticsearch

Domain Name System (DNS) Protocol is a popular medium used by malware to perform ‘command and control’ in taking over victim’s computer, this technique called as DNS tunneling. Moreover, DNS tunneling can also be used to bypass captive portal hotspot in public places and worsen the network quality. However, in more dangerous stage, DNS tunneling can also be used to exfiltrate data from the victim’s computer. Instead of using DNS Protocol to translate domain name, the medium misused to bootleg the data. Those are the weaknesses which frequently used by the attacker to deceive network administrator. Our approach to this problem is analyzing the traffic using unique hostname as indicator of compromise and utilizing Elasticsearch tool to detect DNS tunneling. Elasticsearch will send an email to notify the administrator about DNS tunneling. The email contains information about domain suspected as perpetrator of DNS tunneling. The result from Elasticsearch can be used to add the domain blacklist, so the domain can no longer be used to perform DNS tunneling. Hopefully those combinations are able to support the network administrator to secure the network from DNS tunneling. Moreover, the result of network quality analysis shows that there is a rise in jitter value and packet lost when DNS tunneling happens.
Coiled-Coil Domains for Self-Assembly and Sensitivity Enhancement of Protein–Polymer Conjugate Biosensors

While protein self-assembly has been used to improve the performance of biosensors and biocatalysts, few general techniques have been demonstrated to achieve well-ordered protein arrays under the highly concentrated conditions required to optimize device performance. The strong association strength of coiled-coils, supercoiled bundles of alpha helices wrapped around each other, makes these structures promising scaffolds from which proteins can be linked into ordered assemblies in concentrated solution. Herein, the two component alpha helices of a heterodimeric coiled-coil are fused to a weakly ordering protein, 2x rcSso7d.SA (2x), to improve its self-assembly as a protein–poly(N-isopropylacrylamide) (PNIPAM) conjugate. Three fusion protein conjugates are developed, from which two distinct heterodimeric coiled-coil conjugates are formed, but only one of these heterodimer conjugates significantly improves the ordering quality of 2x. Additionally, the alpha helical segment in one of the three synthesized fus.
Do Banks Change Their Liquidity Ratios Based on Network Characteristics?

Abstract By applying interbank network simulation, this paper investigates the impact of interbank network topology on bank liquidity ratios. Whereas regulators have put more emphasis on liquidity requirements since the global financial crisis of 2007–2008, how differently shaped interbank networks affect individual bank liquidity behavior remains an open issue. We look at how banks' interconnectedness within interbank loan and deposit networks affects their decisions to hold more or less liquidity during normal times and distress times. Our sample consists of commercial, investment, and real estate and mortgage banks in 28 European countries and allows us to differentiate large and small networks. Our results show that accounting for bank connections within a network is important to understand how banks set their liquidity ratios. Our findings have critical implications for the implementation of Basel III liquidity requirements and bank supervision more generally.
An ensemble automatic modulation classification model with weight pruning and data preprocessing

Automatic Modulation Classification (AMC) detects the modulation type and order of the received signal using limited prior knowledge within a short observation interval. In this thesis, we aim to provide a computation-efficient and highperformance AMC model for resource-constrained mobile devices. We use a public RadioML dataset and introduce three data pre-processing methods including noise reduction, normalization, and label smoothing before training the raw signals. Besides four common signal representations, we propose a new signal representation called a three-dimensional constellation image. For each signal representation, we carefully design a Deep Learning (DL) model. In addition to the traditional Convolutional Neural Network (CNN), two new AMC model structures are proposed. The attention module is integrated into the AMC model structure based on conventional Long Short-term Memory (LSTM) networks. Another proposed AMC model structure connects CNN, LSTM, and densely connected neural networks with two additional connections. After training the AMC models, we analyze the overall and per-class performance. We also study the computational complexity of trained AMC models in terms of memory consumption and detection efficiency. Overall, the results indicate that the proposed data pre-processing methods and the new AMC model structures can significantly improve the classification performance. To reduce the complexity of proposed AMC models, we introduce weight pruning to remove unnecessary connections in DL models. After weight pruning, the proposed AMC models have negligible performance degradation.
Are Federated Cloud Sharing Systems Sustainable?: On Dynamic Sharing Markets and Their Stability

The recent emergence of the small cloud (SC), both in concept and in practice, has been driven mainly by issues related to service cost and complexity of commercial cloud providers (e.g., Amazon) employing massive data centers. However, the resource inelasticity problem faced by the SCs due to their relatively scarce resources might lead to a potential degradation of customer QoS and loss of revenue. A proposed solution to this problem recommends the federated sharing of resources between competing SCs to alleviate the resource inelasticity issues that might arise. Based on this idea, a recent effort proposed SC-Share, a performance-driven static market model for competitive small cloud environments that results in an efficient market equilibrium jointly optimizing customer QoS satisfaction and SC revenue generation. However, an important question with a non-obvious answer still remains to be answered, without which SC sharing markets may not be guaranteed to sustain in the long-run - is it still possible to achieve a stable market efficient state when the supply of SC resources is dynamic in nature? In this article, we take a first step to addressing the problem of efficient market design for single SC resource sharing in dynamic environments. We answer our previous question in the affirmative through the use of Arrow and Hurwicz's disequilibrium process in economics, and the gradient play technique in game theory that allows us to iteratively converge upon efficient and stable market equilibria.
Solvent-Free Powder Synthesis and Thin Film Chemical Vapor Deposition of a Zinc Bipyridyl-Triazolate Framework

MAF-252, a non-porous crystalline coordination polymer, is obtained from the solvent-free reaction of ZnO with 3-(2-pyridyl)-5-(4-pyridyl)-1,2,4-triazole. MAF-252 can be synthesized in powder form and deposited as thin films, starting from ZnO powder and layers, respectively. Chemical vapour deposition (CVD) of MAF-252 enables conformal and patterned thin films, even on high aspect ratio features.
Three‐dimensional profiling of collimated radio‐frequency orbital angular momentum beams

The use of orbital angular momentum (OAM) modes in radio communication is thought to enhance capacity. This work focuses on using the l = +1 mode transmitted from a 180 mm diameter, 8-element circular antenna array. The transmitted OAM beam was collimated by using a spherical mirror and the intensity and phase were investigated. A xyz scanning stage was used to profile the propagating OAM beam in three dimensions, resulting in a detailed investigation into the effects of collimation on the OAM beam. The proposed system was shown to reduce the beam divergence from 36.6° to 1.2°, without affecting the OAM mode purity of the beam for a frequency range of 4–6 GHz. This investigation showed a step towards realising practical control over the divergence of OAM-carrying beams.
Stressful life events and problematic smartphone usage among Chinese boarding-school adolescents: a moderated mediation model of peer support and depressive symptoms

Abstract Background: Problematic smartphone usage has become a worldwide problem due to its high prevalence, rapid growth, and serious adverse consequences. Objective: The purpose of this study was to explore the mediating role of depressive symptoms in the relationship between stressful life events and problematic smartphone usage and the protective role of peer support between stressful life events and depressive symptoms in boarding-school adolescents. Method: 124 rural boarding-school adolescents (61 boys, 49%) completed questionnaires about stressful life events (wave 1), peer support (wave 1), depressive symptoms (wave 2), and problematic smartphone usage (wave 3) between 2016 and 2018. Results: A structural equation modeling and multi-group analysis showed that (1) depressive symptoms mediated the effect of stressful life events on later problematic smartphone usage; (2) peer support had a negative effect on depressive symptoms, but it did not moderate the relationship between stressful life events and depressive symptoms; and (3) no gender difference were found on these associations. Conclusions: Depressive symptoms fully mediated the association between stressful life events and problematic smartphone usage among boarding-school adolescents. Peer support can alleviate depressive symptoms, thereby reducing the risk of problematic smartphone usage.
Bradyrhizobium spp. as attenuators of water deficit stress in runner peanut genotypes based on physiological and gene expression responses

The peanut plant has high plasticity and great adaptability to adverse conditions, including drought. To mitigate the negative effects of drought on legumes, nitrogen-fixing microorganisms have been investigated in some plant species, such as soybeans and beans. We analyzed the role Bradyrhizobium strains in peanut genotypes subjected to water deficit by means of plant growth, physiological and gene expression analysis. The research was conducted under greenhouse conditions with the runner peanut genotypes (IAC Runner 886, 2012-33 and 2012-47) and two Bradyrhizobium strains (ESA 123 and SEMIA 6144). After 20 days of germination, the water supply was completely interrupted and gas exchange analysis were carried out using an infrared gas analyzer, up to the 10 th day of stress. Leaves were collected for the analysis of proline content and the strains obtained the best results. The genotypes inoculated with the ESA 123 strain obtained superior responses compared to the non-stressed treatment inoculated with the same bacteria and the stressed control without bacterial inoculation. In the ESA 123 inoculated treatments, the water stressed plants had higher RDM (28.5% higher, on average), NN (two fold higher), and gene expression (approximately six and three-fold higher for ERF8 and NCED genes, respectively). The increase in the expression of NCED and ERF8 genes , in the three genotypes inoculated with ESA 123, suggests a key role of this inoculant in the activation of metabolic cascades for plant protection under water deficit.
Winners and Losers from Supervisory Enforcement Actions Against Banks

We investigate how supervisory enforcement actions (EAs) against banks affect their business borrowers. We find negative short-term valuation effects of EAs for large relationship borrowers, which are reversed after new loans are granted. Large non-relationship borrowers' valuations are unaffected by EAs, but turn negative after relationships are established with sanctioned banks. Additionally, sanctioned banks appear to offset uncertainty and reputational damage of EAs by improving credit terms and availability for relationship and non-relationship large businesses, but decrease credit availability to small businesses. The small business credit contraction may have significant negative economic consequences due to bank dependency and credit constraints.
Machine Learning Algorithmic Study of the Naira Exchange Rate

In this study, we discuss various machine learning algorithms and architectures suitable for the Nigerian Naira exchange rate forecast. Our analyses were focused on the exchange rates of the British Pounds, US Dollars and the Euro against the Naira. The exchange rate data was sourced from the Central Bank of Nigeria. The performances of the algorithms were evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute Error and the coefficient of determination (R-Squared score). Finally, we compared the performances of these algorithms in forecasting the exchange rates.
An introgressed gene causes meiotic drive in Neurospora sitophila

Significance In order to survive, most organisms must deal with parasites. Such parasites can be other organisms or, sometimes, selfish genes found within the host genome itself. While much is known about parasitic organisms, the interaction with their hosts, and their ability to spread within and between species, much less is known about selfish genes. We here identify a selfish “spore killer” gene in the fungus Neurospora sitophila. The gene appears to have evolved within the genus but has entered the species through hybridization and introgression. We also show that the host can counteract the gene through RNA interference. These results shed light on the diversity of selfish genes in terms of origin, evolution, and host interactions. Meiotic drive elements cause their own preferential transmission following meiosis. In fungi, this phenomenon takes the shape of spore killing, and in the filamentous ascomycete Neurospora sitophila, the Sk-1 spore killer element is found in many natural populations. In this study, we identify the gene responsible for spore killing in Sk-1 by generating both long- and short-read genomic data and by using these data to perform a genome-wide association test. We name this gene Spk-1. Through molecular dissection, we show that a single 405-nt-long open reading frame generates a product that both acts as a poison capable of killing sibling spores and as an antidote that rescues spores that produce it. By phylogenetic analysis, we demonstrate that the gene has likely been introgressed from the closely related species Neurospora hispaniola, and we identify three subclades of N. sitophila, one where Sk-1 is fixed, another where Sk-1 is absent, and a third where both killer and sensitive strain are found. Finally, we show that spore killing can be suppressed through an RNA interference-based genome defense pathway known as meiotic silencing by unpaired DNA. Spk-1 is not related to other known meiotic drive genes, and similar sequences are only found within Neurospora. These results shed light on the diversity of genes capable of causing meiotic drive, their origin and evolution, and their interaction with the host genome.
"Information Security Is Not Really My Job": Exploring Information Security Role Identity in End-Users

Given the significant role individuals play on the welfare of organizations’ security, end users are encouraged to see themselves as part of the information security solution and are expected to perform certain end-user security roles. However, there is often a divide between the organization’s expectations of the enduser’s information security role and the end-user’s functional role. We explore the concept of role identity in order to understand the factors that increase the importance ascribed to the information security end user role, which in turn affects performance and actions towards security behaviors. We develop a model that focuses on two issues: (1) factors that increase information security role identity (ISRI) and (2) consequents of ISRI, specific to security behaviors. A survey was used to explore the relationships in the model. Theoretical and practical implications of this research are presented.
Complete plastome sequence of magnolia fordiana var. hainanensis (dandy) Noot. (Magnoliaceae), an endemic and ornamental tree in South China

Abstract Magnolia fordiana var. hainanensis (Dandy) Noot. (Magnoliaceae) is an endemic and ornamental tree distributed in Hainan, China. In this study, we report and characterize the complete plastid genome sequence of M. fordiana var. hainanensis in order to provide genomic resources helpful for promoting its conservation and garden utilization. The complete plastome is 160,157 bp in length and contains the typical quadripartite structure of angiosperm, including two Inverted Repeat (IRs) regions of 26,573 bp, a Large Single-Copy (LSC) region of 88,255 bp and a Small Single-Copy (SSC) region of 18,756 bp. The plastome contains 114 genes, consisting of 80 unique protein-coding genes, 30 unique tRNA gene and 4 unique rRNA genes. The overall A/T content in the plastome of M. fordiana var. hainanensis is 60.70%. The complete plastome sequence of M. fordiana var. hainanensis will provide a useful resource for the conservation and garden utilization of this species as well as for the phylogenetic studies of Magnoliaceae.
Culture and Health Persuasion: Differences between Koreans and Americans

Abstract Four studies examined the perceived persuasiveness of pro-and anti-health messages among Koreans and Americans. Results indicated that the perceived effectiveness of messages in the two countries depends on whether a healthy (e.g., anti-smoking) or an unhealthy (pro-smoking) behavior is advocated. In general, Americans believed that they were more persuaded than others by messages advocating healthy behavior, whereas Koreans believed that they were less persuaded than others by such messages. In contrast, Americans believed that they were less persuaded than others by messages advocating unhealthy behavior, whereas this tendency was not found among Koreans. Implications and avenues for future research are discussed.
An adaptive replica placement approach for distributed key‐value stores

The use of distributed key‐value stores (KVS) has experienced fast adoption by various applications in recent years due to key advantages such as hypertext transfer protocol‐based RESTful application programming interface, high availability and elasticity. Due to great scalability characteristics, KVS systems commonly use consistent hashing as data placement mechanism. Although KVS systems offer many advantages, they were not designed to dynamically adapt to changing workloads which often include data access skew. Furthermore, the underlying physical storage nodes may be heterogeneous and do not expose their performance capabilities to higher level data placement layers. In this paper, we address those issues and propose an essential step toward a dynamic autonomous solution by leveraging deep reinforcement learning. We design a self‐learning approach that incrementally changes the data placement, improving the load balancing. Our approach is dynamic in the sense that is capable of avoiding hot spots, that is, overloaded storage nodes when facing different workloads. Also, we design our solution to be pluggable. It assumes no previous knowledge of the storage nodes capabilities, thus different KVS deployments may make use of it. Our experiments show that our method performs well on changing workloads including data access skew aspects. We demonstrate the effectiveness of our approach through experiments in a distributed KVS deployment.
Using Physiological Recordings for Studying User Experience: Case of Conversational Agent-Equipped TV

ABSTRACT This study explores TV viewers’ user experience (UX) of the conversational agent (CA) assisted interactions while watching TV. In the human-computer interaction field, the user experience is primarily measured through performance and subjective self-reported data. But TV is an entertainment medium, the user experience of TV viewers should be determined based on how much the TV viewers enjoyed and immersed, rather than productivity or performance. It is also necessary to collect objective data as well as subjective self-report results of emotional experiences. Although the CA is becoming increasingly common in everyday life, the user satisfaction is still not high. However, this is mainly a result of evaluation in the performance aspect, and few studies have been carried out on the overall user experience including emotional aspects. To comparatively analyze the user experience with the CA interface and remote control unit (RCU) interface, we adopted physiological measurements for objective data as well as self-report questionnaires. Among the physiological measurement results, skin conductance (SC) differed between CA and RCU interactions. SC was high at the beginning of the CA interaction but decreased over time. In RCU sessions, SC maintained a constant value or rather increased. In the self-reporting results, the ergonomic quality of the RCU was higher than that of the CA. However, CA was evaluated more positively in terms of hedonic quality. CA had a greater effect on the overall attractiveness while having more emotional appeal. Additionally, a partial correlation between objective data and self-report results could be observed. In sum, subjective self-reports showed that CA provides a more positive TV UX with regard to emotional aspects. Physiological measurement results provide objective data for further understanding user experience by showing specific differences in cognitive involvement and emotional response changes during user interactions.
Approximate CPU Design for IoT End-Devices with Learning Capabilities

With the rise of Internet of Things (IoT), low-cost resource-constrained devices have to be more capable than traditional embedded systems, which operate on stringent power budgets. In order to add new capabilities such as learning, the power consumption planning has to be revised. Approximate computing is a promising paradigm for reducing power consumption at the expense of inaccuracy introduced to the computations. In this paper, we set forth approximate computing features of a processor that will exist in the next generation low-cost resource-constrained learning IoT devices. Based on these features, we design an approximate IoT processor which benefits from RISC-V ISA. Targeting machine learning applications such as classification and clustering, we have demonstrated that our processor reinforced with approximate operations can save power up to 23% for ASIC implementation while at least 90% top-1 accuracy is achieved on the trained models and test data set.