Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermodynamic phase transition of uranium trihydride: Role of electronic strong correlation

The electronic structure and thermodynamical properties of uranium trihydrides ($\alpha$-UH$_{3}$ and $\beta$-UH$_{3}$) have been studied using first-principles density functional theory. We find that inclusion of strong electronic correlation is crucial in successfully depicting the electronic structure and thermodynamic phase stability of uranium hydrides. After turning on the Hubbard parameter, the uranium 5f states are divided into well-resolved multiplets and their metallicity is weakened by downward shift in energy, which prominently changes the hydrogen bond and its vibration frequencies in the system. Without Coulomb repulsion, the experimentally observed $\alpha\mathtt{\rightarrow}\beta$ phase transition cannot be reproduced, whereas, by inclusion of the on-site correlation, we successfully predict a transition temperature value of about 332 K, which is close to the experimental result.
Cloning and characterization of a CCoAOMT gene involved in rapid lignification of endocarp in dove tree (Davidia involucrata Baill.)

Abstract The long period of seed dormancy is one of the most important reasons leading to the low fertility of dove tree (Davidia involucrata Baill., Davidia hereinafter). During the fruit development process of Davidia, the endocarps will become particularly compact and hard, which was considered as a critical reason for the long seed dormancy of Davidia. However, the biological significance and regulatory mechanism of this process remain unclear. In this study, we identified a CCoAOMT gene, a key member of the lignin biosynthesis pathway, from Davidia endocarp. The gene named DiCCoAOMT1 has an ORF (open reading frame) of 744 bp and encodes a predicted protein of 239 amino acids. The expression of DiCCoAOMT1 is endocarp-specific and the expression levels increased along with the fruit development process. The encoding protein of DiCCoAOMT1 demonstrated a relatively high O-methyltransferase activity when using caffeic acid as a substrate in vitro. The tissue-specific CCoAOMT gene identified in this study might play a key role in specifically regulating the rapid lignification process in Davidia endocarp.
Co-Residence Data Theft Attacks on N-Version Programming-Based Cloud Services With Task Cancelation

Powered by virtualization, the cloud computing has brought good merits of cost effective and on-demand resource sharing among many users. On the other hand, cloud users face security risks from co-residence attacks when using this virtualized platform. Particularly, a malicious attacker may create side channels to steal data from a target user’s virtual machine (VM) that co-resides with the attacker’s VM on the same physical server. This article models a cloud service undergoing the co-residence data theft attacks. The threshold-voting-based ${N}$ -version programming (NVP) is implemented to improve the service reliability, where multiple service component versions (SCVs) are activated in parallel to perform the requested service. The final output is determined upon receiving a threshold number of identical outputs from the SCVs, immediately followed by canceling all outstanding SCVs to reduce expenses. Probabilistic models are first introduced to evaluate performance metrics of the considered service, including the data theft probability, service success probability, expected service operation time, and expected utility. Optimization problems are further solved to find the optimal number of SCVs maximizing the expected utility. Interactions among different model parameters and VM allocation policies, as well as their effects on the considered performance metrics and on the optimization solutions are studied through examples.
The Self-Aware Information Processing Factory Paradigm for Mixed-Critical Multiprocessing

In order to provide performance increases despite the end of Moore’s law and Dennard scaling, architectures aggressively exploit data- and thread-level parallelism using billions of transistors on a single chip, enabled by extreme geometry miniaturization. A resulting challenge is the control, optimization, and reliable operation of such complex multiprocessing architectures. Modern and future systems will be required to operate under multi-dimensional variability: from varying workload, quality-of-service (QoS) goals, and non-functional requirements to varying environmental and operating conditions. A trend has recently emerged to abstract such complex multiprocessing architectures as self-aware factories whose resources are monitored, configured and their use is planned during runtime. In this article, we present the Information Processing Factory (IPF) paradigm for mixed-criticality. We introduce its 5-layer hierarchical organization and a system configuration framework that ensures that the strict requirements of the safety-critical functions are always met while dynamically managing and optimizing the mixed-critical system at runtime. We illustrate the application of IPF in heterogeneous domains with two representative use-cases (healthcare and automotive), investigate the use of IPF to achieve long-term dependability, and highlight the open challenges. Experimental results report the reliability levels achievable with the proposed paradigm.
A Nonisolated High Step-Up Three-Port Soft-Switched Converter With Minimum Switches

In this article, a nonisolated high step-up three-port converter is proposed based on the single-ended primary-inductor converter. The proposed converter includes two switches, which are turned on under zero voltage switching or low-voltage condition intrinsically. The overall number of elements used in the converter is small. Furthermore, the conduction losses are reduced by reducing the number of the semiconductor elements in both battery and photovoltaic (PV) current flow paths. The input inductor is not coupled to any inductors to reduce the PV current ripple and enhance the realization of maximum power point tracking (MPPT). Since low-voltage switches with low on-resistance are selectable, conduction losses are reduced. All modes of operation in the steady-state condition are discussed and design considerations in each mode are presented. Finally, the experimental prototype of the proposed converter with a 24 V PV source, 36 V battery pack, and a 380 V-200 W load is presented.
Fast Self-Triggered MPC for Constrained Linear Systems With Additive Disturbances

This article proposes a robust self-triggered model predictive control (MPC) algorithm for a class of constrained linear systems subject to bounded additive disturbances, in which the intersampling time is determined by a fast convergence self-triggered mechanism. The main idea of the self-triggered mechanism is to select a sampling interval so that a rapid decrease in the predicted costs associated with optimal predicted control inputs is guaranteed. This allows for a reduction in the required computation without compromising performance. By using a constraint tightening technique and exploring the nature of the open-loop control between sampling instants, a set of minimally conservative constraints is imposed on nominal states to ensure robust constraint satisfaction. A multistep open-loop MPC optimization problem is formulated, which ensures recursive feasibility for all possible realizations of the disturbance. The closed-loop system is guaranteed to satisfy a mean-square stability condition. To further reduce the computational load, when states reach a predetermined neighborhood of the origin, the control law of the robust self-triggered MPC algorithm switches to a self-triggered local controller. A compact set in the state space is shown to be robustly asymptotically stabilized. Numerical comparisons are provided to demonstrate the effectiveness of the proposed strategies.
Noncontact Respiration Detection Leveraging Music and Broadcast Signals

Recent works have shown that acoustic signals can be leveraged to perform respiration monitoring with high accuracy and low energy consumption. Since smartphones, smart speakers, and many other IoT devices are already equipped with microphones and speakers, it is convenient to implement the acoustic sensing solutions on those devices. However, the existing technologies require the speaker to transmit certain ultrasonic signals to detect respiration. Although these signals are inaudible to adults, they are audible to children and pets and they may even have negative impacts on plants. In this article, instead of using ultrasonic signals, we are trying to leverage audible signals in daily lives, e.g., music or broadcasting audios, to detect human respiration. We design a respiration detection system which derives the respiration rate by continuously estimates the channel impulse response (CIR) using music and broadcast signals. We study the intersymbol interference (ISI) brought by the randomness of music and broadcast signal and give our strategy to minimize the interference. We also propose several techniques to resolve some practical issues, such as the multipath effect and sampling frequency offset between the speaker and the microphone. Extensive experiments are conducted to demonstrate the feasibility of our system. The result shows that our system can achieve high respiration detection accuracy with the mean error of less than 0.5 BPM when different audio signals are used.
Headspace volatiles isolated from twigs of Picea omorika from Serbia

The variability of volatiles isolated from twigs by the static headspace (HS) method in seven natural populations of Picea omorika from Serbia was investigated for the first time. In the overall chemical profile, monoterpenes strongly dominated hydrocarbons as the most volatile compounds (95.7%). The dominant compounds were ? -pinene (29.5 %), ? -pinene (25.7%) and myrcene (13.0%), totaling 68.2% of the volatiles on average. The following nine volatiles were found to be present in medium-to-high amounts (0.5-10%): tricyclene, camphene, ? -phellandrene, ? -3-carene, p-cymene, ? -phellandrene, terpinolene, (E)-caryophyllene, and germacrene D. Out of the 78 volatiles detected, the six most abundant (? -pinene, ? -pinene, myrcene, ? -3-carene, ? -phellandrene and camphene) were selected for principle component analysis (PCA) and cluster analyses (CA). PCA revealed a high degree of similarity between populations, while CA showed a degree of separation of two populations from the others. The presented results are in agreement with previous phytochemical and molecular analyses of this species that confirm high variability in both specialized metabolites and genetic markers.
Evaluation of hybrid sorghum for silage through in situ degradability.

The experiment was carried out in order to evaluate in situ degradability and the degradation kinetics of dry matter, crude protein, neutral detergent fiber and acid detergent fiber of silages of three sorghum genotypes. Two isogenic lines of grain sorghum (CMSXS 114 and CMSXS 165) and one sorghum hybrid (double purpose, BR-700) were used. The plantation of the genotypes was carried out in summer/2009, in seedbeds 5 m long, 3 m wide and 0.75 m row spacing. After cutting, the material was ensilaged. After the opening of the silos the samples were pre-dried, milled and stored in glass bottles for analyses of dry matter, crude protein, neutral detergent fiber, acid detergent fiber, hemicelluloses, cellulose and lignin, pH, NH3/NT and in situ digestibility of the dry matter. An entirely randomized experimental design was used, with four replications and four treatments, and the means were compared by the Tukey test at 5% significance. As for degradation rate of DM, the ensilage of CMSXS165 line without tannin was superior to all of the treatments. The isogenic line genotype without tannin (CMSXS 165) presented greater effective degradability of CP than the line with tannin (CMSXS 114). Thus, the results found in this experiment allow concluding that the presence of tannin can reduce the rumen degradability of the dry matter and crude protein of the ensilages.
The Denoising of Desert Seismic Data Based on Cycle-GAN With Unpaired Data Training

The seismic data with high quality are the essential foundation of imaging and interpretation. However, the real seismic data are inevitably contaminated by noise, which affects the subsequent processing and interpretation of seismic data. In desert seismic data, the energy of noise is stronger. Also, the frequency-band overlap between noise and effective signals is more serious. Recently, some methods based on supervised learning can suppress the desert seismic noise to some extent. Generally, supervised learning-based methods use synthetic noisy data and paired pure data as training sets to train model. However, the difference between synthetic noisy data of training and real seismic data of testing leads to the degradation of the model, and the denoising results often have many false seismic events when dealing with field seismic data. To solve the above problem, we introduce Cycle-generative adversarial networks (GANs) into the denoising of desert seismic records. Cycle-GAN is an unsupervised learning-based method. It can learn the domain mapping from noisy data domain to effective signal data domain through unpaired data training. So we use unpaired real desert common-shot-point data and synthetic pure data to train Cycle-GAN, so as to effectively improve the denoising ability of the method for real seismic data. Finally, the denoising of desert seismic data is realized. The experiment shows that the Cycle-GAN with unpaired data training can effectively suppress desert seismic noise and retain the effective signal amplitude. Also, the denoising result has less false seismic reflection.
Proactive & Time-Optimized Data Synopsis Management at the Edge

Internet of Things offers the infrastructure for smooth functioning of autonomous context-aware devices being connected towards the Cloud. Edge Computing (EC) relies between the IoT and Cloud providing significant advantages. One advantage is to perform local data processing (limited latency, bandwidth preservation) with real time communication among IoT devices, while multiple nodes become hosts of the collected data (reported by IoT devices). In this work, we provide a mechanism for the exchange of data synopses (summaries of extracted knowledge) among EC nodes that are necessary to give the knowledge on the data present in EC environments. The overarching aim is to intelligently decide on when nodes should exchange data synopses in light of efficient execution of tasks. We enhance such a decision with a stochastic optimization model based on the Theory of Optimal Stopping. We provide the fundamentals of our model and the relevant formulations on the optimal time to disseminate data synopses to network edge nodes. We report a comprehensive experimental evaluation and comparative assessment related to the optimality achieved by our model and the positive effects on EC.
Evidence of heterovalent europium in zoned Llallagua apatite using wavelength dispersive XANES

Abstract Eu L3 X-ray absorption near edge structure (XANES) spectroscopy was conducted with a wavelength dispersive (WDS) spectrometer to determine the valence state of europium in a natural, Mn-and REE-rich apatite from Llallagua, Bolivia. Europium exists in both the divalent and trivalent states in the Llallagua apatite with a Eu2+/Eu3+ ratio between 0.12 and 0.22. With the enhanced energy resolution of WDS the EuLα1 fluorescence line can be resolved from the MnKα line, allowing for significant reduction of background in the EuL3 absorption edge region and resolution of the Eu XAS, which is difficult by conventional methods because of fluorescence peak interference. The anomalous partitioning behavior of Eu in these samples (Rakovan and Reeder 1996) can be explained by the observed presence of Eu2+ and Eu3+ and is consistent with the suggested size effect on intrasectoral zoning of REEs in apatite.
Total Variation and Sparsity Regularized Decomposition Model With Union Dictionary for Hyperspectral Anomaly Detection

Anomaly detection in hyperspectral imagery has been an active topic among the remote sensing applications. It aims at identifying anomalous targets with different spectra from their surrounding background. Therefore, an effective detector should be able to distinguish the anomalies, especially for the weak ones, from the background and noise. In this article, we propose a novel method for hyperspectral anomaly detection based on total variation (TV) and sparsity regularized decomposition model. This model decomposes the hyperspectral imagery into three components: background, anomaly, and noise. In order to distinguish effectively these components, a union dictionary consisting of both background and potential anomalous atoms is utilized to represent the background and anomalies, respectively. Moreover, the TV and the sparsity-inducing regularizations are incorporated to facilitate the separation. Besides, we present a new strategy for constructing the union dictionary with the density peak-based clustering. The proposed detector is evaluated on both simulated and real hyperspectral data sets and the experimental results demonstrate its superiority compared with several traditional and state-of-the-art anomaly detectors.
Experimental and Analytical Study of the Responses of Nanoscale Devices to Neutrons Impinging at Various Incident Angles

In harsh radiation environments, it is well known that the angle of incidence of impinging particles against the surface of the operating devices has significant effects on their sensitivity. This article discusses the sensitivity underestimations that are made if particle isotropy is not taken into account, by means of an analytical study made with a single-event upset predictive platform. To achieve this goal, experimental results carried out with a commercial-off-the-shelf (COTS) bulk 130-nm nonvolatile static random access memory (SRAM) for various incident angles on 14.2 MeV neutrons are first discussed. Then, a modeling tool called multiscales single-event phenomena predictive platform (MUSCA-SEP3) is used to predict the sensitivity of this memory under the same environmental conditions. Predictions and experimental results will be cross-checked, and therefore, the feasibility of this tool will be demonstrated for testing any other incident angle. Finally, an isotropic environment and an XY SRAM array will be emulated with MUSCA in order to demonstrate that the asymmetrical cross sections that were observed experimentally for various incidence angles are due to the underlying asymmetry of the metalization/passivation layers within the device with respect to its active silicon. Conclusions will finally be drawn as for the importance of taking into account particle isotropy in radiation-ground tests.
Mushroom price forecasting in the major producing states

The present study is an attempt to forecast the prices of mushroom in the markets of Punjab, Jammu and Kashmir, Himachal Pradesh, and India. The time-series data on the monthly price of mushroom required for the study was collected from the AGMARKNET website for the period January 2008 to December 2019. The seasonal ARIMA model was used for the modeling of price using the Box-Jenkins technique and best-fitted models were selected based on lowest RMSE, AIC, BIC and MAPE values. The models ARIMA (2,1,2)(1,1,1)[12], ARIMA (1,0,4)(1,1,2)[12], ARIMA (1,1,1)(2,1,1)[12], and ARIMA (3,1,1)(1,1,2)[12] were found to be the best fitted model for Punjab, Jammu and Kashmir, Himachal Pradesh and India respectively. After model models validations, the mean absolute percentage error values were close to the value of fitted models. The results revealed that forecasted wholesale prices of mushroom were higher in the markets from September to November 2020-21. The best-identified models were used for predicting the future prices of 24 months (January 2020 to December 2021). The analysis was done in “R” statistical software.
Circular Business Models for Consumer Markets

Over the last decade, the circular economy has gained traction as a concept to transform the society and economy into more sustainable systems. In this context, research into circular business models arose to implement circular economy strategies at the company level. In this thesis, a consumer perspective on circular business models is taken. The research explored how business models can help achieve a circular economy and lead to sustainable consumption.
Exploring the Performance Characteristics of the Naïve Bayes Classifier in the Sentiment Analysis of an Airline’s Social Media Data

A R T I C L E I N F O A B S T R A C T Article history: Received: 20 April, 2020 Accepted: 08 July, 2020 Online: 28 July, 2020 Airline operators get much feedback from their customers which are vital for both operational and strategic planning. Social media has become one of the most popular platforms for obtaining such feedback. However, to analyze, categorize, and generate useful insight from the huge quantity of data on social media is not a trivial task. This study investigates the capability of the Naïve Bayes classifier for analyzing sentiments of airline image branding. It further examines the impact of data size on the accuracy of the classifier. We collected data about some online conversations relating to an incident where an airline's security operatives roughly handled a passenger as a case study. It was reported that the incident resulted in a loss of about $1 billion of the company's corporate value. Data were extracted from twitter, preprocessed and analyzed using the Naïve Bayes Classifier. The findings showed a 62.53% negative and 37.47% positive sentiments about the incident with a classification accuracy of over 0.97. To assess the impact of training size on the accuracy of the classifier, the training sets were varied into different sizes. A direct linear relationship between the training size and the classifier's accuracy was observed. This implies that large training data sets have the potentials for increasing the classification accuracy of the classifier. However, it was also observed that a continuous increase in the classification size could lead to overfitting. Hence there is a need to develop mechanisms for determining optimum training size for finest accuracy of the classifier. The negative perceptions of customers could have a damaging effect on a brand and ultimately lead to a catastrophic loss in the organization.
High-Performance Sensitive TE/TM Mode Switch With Graphene-Based Metal-Dielectric Resonances

We explore the performance of the monolayer graphene with a metal-dielectric compound grating via numerical simulation. The results show that the absorption peaks can be near 100% at 68.36 THz for both TE and TM polarizations. Different spectral line shapes are displayed under different polarization states, we attribute the excellent absorption performance to the excitation of magnetic resonance (MR) and cavity mode resonance (CMR), and then an equivalent capacitance-inductance (LC) circuit model and the waveguide theory are used to explain it theoretically. We have also studied the influence of the polarization angle on the absorption performance, and found that as the polarization angle increases, the resonance wavelength remains unchanged but the FWHM of absorption spectrum increases gradually. Based on this feature, we apply it in mode switch which can easily switch its function as absorption or sensing by adjusting the polarization states of incident radiations.