Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum-correlated fluctuations, phonon-induced bondpolarization, enhanced tunneling, and low-energy nuclearreactions in condensed matter

In heavily (deuterated or hydrogenated) palladium, some of the crystallinity is lost. As a consequence, the localized phonon modes of the crystal/damaged-region interface have a much higher frequency than the host. These high-frequency modes create electrostatic fields that interact strongly with electrons of the local atoms. A resulting instantaneous potential inversion, from polarization, leads to the formation of lochons (local charged bosons–electron pairs in the singlet state, perhaps isolated from the Pd d-orbital energy levels) and of an associated H + or D + ion (with its two shared electrons instantaneously isolated into the adjacent Pd d-levels). The Coulomb repulsion between the nuclei of these pairs is greatly reduced by strong screening from the lochons that can even generate an attractive polarization potential. Furthermore, the mutual tunneling penetration probability of the Coulomb barrier is enhanced by correlated fluctuations. This arises from the generalized uncertainty relations, Δx Δp x Δ E Δt ≥ (n+ 1/2)h /(1-ρ 2 ) 0.5 , where n may be on the order of 10–100 and where results from two models are combined. The integer n values represent excitations in the phonon modes of the H or D sub-lattice and ρ is the correlation coefficient with 0 < ρ < 1. Higher values of n and ρ , for a particle in a potential well, imply less localization and greater uncertainties in location (i.e., extending its probability distribution further into the barrier). These periodic fluctuations into the barrier are an interference effect similar to that of beat frequencies.
Absorption power cycles for low‐temperature heat sources using aqueous salt solutions as working fluids

There are many low‐temperature heat sources; however, current technologies for their utilization have a relatively low efficiency and high cost. The leading technology in the low‐temperature domain for heat‐to‐work conversion is the organic Rankine cycle (ORC). Absorption power cycles (APCs) are a second option. Nearly all currently known APCs, most importantly the Kalina cycle, use a water‐ammonia mixture as their working fluids. This paper offers a theoretical exploration of the possibility of utilizing aqueous solutions of three salts (lithium bromide, lithium chloride and calcium chloride), known mainly from absorption cooling, as working fluids for APCs. The cycles are compared with a typical steam Rankine cycle, a water‐ammonia APC, and (subcritical) ORCs with a range of working fluids explored. The analysis includes a parasitic load for heat rejection by a cooling tower or air‐cooled condenser. The absorption cycles exhibit better performance than all Rankine‐based cycles analysed in temperatures below 120°C. For the LiBr‐based APC, a detailed thermal design of the cycle is provided for 100°C water as a heat source and a sensitivity analysis is performed of the parameters controlling the main cycle. Mechanical design considerations should not pose a problem for small power units, especially in the case of expansion machines, which are often problematic in ORCs. The salt‐based APCs also carry environmental benefits, as the salts utilized in the working fluids are non‐toxic. Copyright © 2016 John Wiley & Sons, Ltd.
Predicting Household power consumption: Using Gradient Boosting and Deep Quantile Regression Model

The rapid development of technology, cities, and the introduction of IoT has caused high fluctuations in energy consumption. Therefore, efficient energy management and forecasting energy consumption for buildings are important in decision-making for effective energy-saving and keeping the world a better place. Over the years researchers have tried to propose machine learning approaches to forecast and monitor abnormal electricity consumption, however, there is still a need for more new approaches to forecasting energy consumption correctly and precisely. This paper attempts to address the global issue of efficient electricity management through a data science approach. The paper proposes a quantile learning approach for predicting household electricity consumption. We propose a deep quantile regression model and a gradient boosting model to forecast power consumption. In addition, we propose an approach to detect abnormal patterns in energy consumption data using estimated consumption intervals. We tested our approach on a publicly available univariate time series dataset collected from a house located in Sceaux.
Two-stage Topology Control Strategy for Software-defined UAV Networks Against Jamming Attack

Software-defined network (SDN) technology brings new methods to the autonomous control of UAV networks. Aiming at the deterioration of UAV network wireless communication quality caused by jamming attacks, we propose a two-stage topology control algorithm to improve network service quality. The first stage is the redeployment of the control plane consisted by the relay UAVs. We calculate the position of the jammers based on Received Signal Strength Indication (RSSI), and then we first redeploy the position of the Relay UAVs. The second stage is the redeployment of the data plane consisted by the mission UAVs. Mission UAVs that lose control links due to jamming are redeployed according to the historical state of controllers, and other UAVs are redeployed according to the new state of controllers. The performance metric is the weighted average of load balancing, redeployment delay and Signal to Interference plus Noise Ratio (SINR). The experimental results show that compared with the Distance Closest Migration (DCM) strategy and the Distributed Hopping Algorithm (DHA), the comprehensive performance of the proposed algorithm is better.
Chemical bonding of polycaprolactone on the surface of hydroxyapatite via silanization and ring opening polymerization

In this study, surface modification of hydroxyapatite (HAp) by grafting polycaprolactone (PCL) is described. This method is carried out into two steps: (1) A treatment of HAp powder with a mixture of tetraorthosilicate (TEOS) and 3-aminopropyl-etriethoxysilane (APTES) to introduce amine groups and enhance nucleophilicity of surface functionalities; (2) Initiation of the Ring Opening Polymerization (ROP) of the e-caprolactone (CL) in the presence of the silanized HAp. Moreover, the impact of the reaction time (24, 48 and 72 h) on the grafting amount of PCL is performed. The results of FTIR analysis confirmed the presence of carbonyl groups on the silanized HAp, providing evidence of grafting of PCL on the HAp surface. WAXS pattern showed no formation of a secondary phase after surface modification and the crystallinity is preserved. SEM/TEM analysis revealed a homogenous surface powder composed of irregular nanoscale crystals. TGA analysis indicated the dependence of PCL graft amount on the reaction time. Finally, the effect of surface modification on HAp/PCL nanocomposites morphology was investigated.
On the modification and convergence of unconstrained optimal control using pseudospectral methods

A modified pseudospectral (PS) method is presented based on direct Legendre interpolation for unconstrained optimal control problems (OCP). The conditions for the convergence of the proposed PS method are provided and it is proved that the method possesses the spectral accuracy for solutions in appropriate Sobolev spaces. Different from existing convergence results in the literature, in this new analysis neither special cases of the problem nor relaxing the discretized dynamics is considered. Moreover, the proof does not use necessary conditions of optimal control. An iterative procedure is then proposed for nonlinear OCPs. The nonlinear problem is first replaced with a sequence of linear‐quadratic OCPs by utilizing the quasilinearization technique. Then, this sequence of problems are successively solved using the modified PS method. In some nonlinear OCPs where the resulting nonlinear programming problem is dense, the PS method becomes computationally intractable. In such a this problems, the iterative PS method reduces the computational complexity and saves work. In addition, a new efficient costate estimation procedure is derived by employing the necessary optimality conditions. This new costate estimation method is flexible in that it allows us to define three different schemes based on the structure of the problem. Several examples of varying complexity are included to demonstrate the efficiency and accuracy of the proposed methods.
Pengembangan Model Migrasi Database Relational ke NoSQL Memanfaatkan Metadata SQL

Penyimpanan data merupakan isu krusial pada teknologi Big Data karena membutuhkan teknologi penyimpanan data yang profisien agar dapat menyimpan data (terstruktur dan tidak terstruktur) secara cepat dalam jumlah besar. Hal ini sudah tidak bisa lagi dipenuhi oleh model database relational (SQL) yang saat ini masih banyak digunakan. Kelemahan tersebut dapat diatasi dengan menggunakan database NoSQL, namun sayangnya proses migrasi data dari relational/SQL database ke NoSQL masih sulit dilakukan karena perbendaan skema dan format penyimpanan data. Berdasarkan masalah tersebut, maka penelitian mengenai migrasi database relational ke NoSQL sangat diperlukan. Penelitian ini mencoba mengajukan pengembangan model perangkat lunak untuk migrasi database relational ke NoSQL menggunakan pendekatan aturan migrasi dan data transformasi yang memanfaatkan metadata SQL. Berdasarkan eksperimen yang telah dilakukan aturan migrasi yang diterapkan pada model yang dikembangkan berhasil melakuakn migrasi database SQL ke NoSQL dengan kecepatan rerata 0.978 detik untuk 5 table dalam 1 database.
Understanding the Patterns of Processed Seafood Exports from Sri Lanka: Application of the Gravity Model to Quantify the Determinants

1 Scientist, Socioeconomics and marketing research division of National Aquatic Resources Research and Development Agency, National Aquatic Resources Research and Development Agency (NARA), Craw Island, Mattakkuliya, Colombo 15, Sri Lanka 2 PhD Scholar, Department of Economics and Politics, VidyaBhavana, VisvaBharati University, Bolpur, Santiniketan, Dist: Birbhum, West Bengal, India * Corresponding Author (kariyawasam.lahiru@gmail.com)
Mind-Mapping Technique Implementation into English for Specific Purposes Training

The conducted research reveals the efficacy of a mind-mapping training technique within the English for Specific Purposes university course. The study aims at assessing the influence of mind maps on the students' motivation and language academic performance. To achieve the objective, it became relevant to study the step-by-step educational development of a university entrant, junior student and, then, a graduate as the result of practicing mind-mapping activities in classes. To accomplish the research tasks successfully it was necessary to apply the following methods: observation research, questioning, testing, output data calculating, modelling, foreseen data checking. The methods of statistical analysis, namely: data correlation, factor analysis, consequent implication, and content analysis were chosen. The three-stage experiment was fulfilled within the 2016–2018 academic years at the Faculty of Computer Sciences and Cybernetics (Taras Shevchenko National University of Kyiv, Ukraine) and encompassed three groups of participants namely, the university entrants, the students of the first and fourth years of study. The research results show foreign language academic proficiency enhancement. The data prove that the respondents from the experimental research groups (in contrast to the control groups' outcomes) considered mind-mapping activities interesting and engaging. Such a learning motivation boost is due to the participants' associative, spatial and geometric thinking ability activation. Further investigation correlating students' motivation and mind maps' content is needed to facilitate the English for Specific Purposes university training.
Fear of Missing Out (FoMO) in Analysis of Cognitive Behavior Therapy (CBT)

The phenomenon that appears at this time is every time we have a mobile phone and have a social media application, there will be many positive and negative impacts. Especially from the negative effects of both mental health, many of us are directly experiencing the disorder but we have not been aware of it. The purpose of this study is to analyze how the fear of missing out is analyzed through the cognitive behavioral therapy counseling model. The method used in this research is literature study. The result of this study is the concept of a flow of personal conditions that experience fomitus, which can be continued in more complex studies. The conclusion of this research is to facilitate in the process of categorizing the process of developing symptoms of (FoMO) disorders in each individual in general through cognitive behavioral therapy (CBT) counseling models.
Broadcast Programming and Strategies in the Spanish TV Duopoly System: A Comparative Analysis on Atresmedia and Mediaset (2013-2019)

Over the last decade, the private television market in Spain has gone through a paradigmatic model of duopoly between two large corporations, Atresmedia and Mediaset, both forged out of two major mergers: that of Telecinco with Cuatro, and that of Antena 3 with La Sexta. We intended to analyze the change that the private television sector has undergone in our country since that double merger and how the content and audiences of the four channels have evolved. We resorted to a methodological triangulation of historical-systemic review, content analysis and comparative method. One of the most interesting conclusions drawn from the investigation has been the discovery that Mediaset has turned Cuatro into an almost mimetic channel and subsidiary of Telecinco, while Atresmedia has largely respected the differentiation of its two main channels: Antena 3 and La Sexta TV.
What Is the Current Development Status of Wearable Device in Industrial 4.0? Using Technology Acceptance Model to Explore the Willingness and Pattern of Usage of the Consumers

The object of the study is to use the technology acceptance model to explore the willingness and pattern of usage of the consumers. 205 valid questionnaires were collected by using the Google online survey platform. Using IBM SPSS and AMOS Statistics 20.0 software, first background information was analyzed, then CFA was used to analyze the relationship between variables, and SEM was used to verify the rationality of the measurement model. The result discovered that there is a positive effect on perceived usefulness by perceived utility of usage by the users. There is a positive effect on usage intention by perceived utility. There is a positive effect on perceived curiosity by perceived utility. There is a positive effect on usage intention by perceived usefulness, and there is a positive effect on usage willingness by social support for the wearable device users. However, there is no positive effect on usage willingness by perceived curiosity. Conclusion. If the industry can provide consumers with a good experience, it will help enhance consumer attitudes, increase consumer willingness, and continue to enhance consumer curiosity. Simply satisfying consumers' curiosity cannot increase consumer willingness, but social support will affect consumers' willingness to use.
Theory-Informed Course Design: Applications of Bloom’s Taxonomy in Undergraduate Public Health Courses

The public health workforce needs an array of knowledge and skills to effectively address current and future public health challenges. While existing competency models establish educational objectives for public health degree programs, there is insufficient guidance on how academic programs should develop these competencies, and workforce assessments continue to identify skills gaps in areas such as critical thinking and problem analysis. In this article, we describe an approach for designing undergraduate public health courses based on the hierarchy of cognitive processes in Bloom’s taxonomy. Course activities are sequenced to provide students with opportunities to attain increasing mastery of course concepts and analytical skills, from remembering new concepts to applying them in case studies and creating original analyses and proposals. This simple approach has been applied to three separate courses taught by multiple instructors over 3 years and has received positive feedback from students. By explicitly communicating the theoretical basis for course activities, this approach also promotes metacognitive knowledge in students that can foster their continued learning success.
Studies of Sub-Mesoscale Variability of the Ocean Upper Layer Based on Satellite Observations Data

 bertrand.chapron@ifremer.fr Purpose. The approach represented in the article is applied to analysis of satellite scanner optical images of high spatial resolution for identifying and quantitative determining the characteristics of the sub-mesoscale dynamic processes in the ocean upper layer. Methods and Results. The Envisat AATSR and MERIS SAR-images are used as the satellite data, which permit to determine the ocean surface temperature and surface brightness in the visible range, respectively. Variations in the s ea surface glitter contrasts are associated with modulations of the sea surface roughness (rms slope of short waves) on the currents. It is shown that the surface roughness contrasts correlate with the spatial inhomogeneities of the ocean surface temperature, tracing submesoscale processes in the ocean (spiral eddies, filaments, local shears of currents). The described model of formation of surface manifestations is based on interaction between the Ekman current and the main flow vorticity. Conclusions. Possibility of detecting and quantitative assessing the intense current gradients in the vicinity of sub-mesoscale fronts is shown. These gradients are manifested in the optical satellite images through the ocean surface roughness modulations. The proposed approach makes it possible to study and to assess quantitatively the dynamic processes taking place in the vicinity of the submesoscale fronts. These processes, in their turn, affect the exchange of momentum, heat and gases between the ocean and the atmosphere. The prospects of applying the sub-mesoscale variability de-fined from the satellite measurements, to development of the models and the systems for the ocean global observations and monitoring
On using kernel integration by graphical LASSO to study partial correlations between heterogeneous data sets

Integration of unstructured and very diverse data is often required for a deeper understanding of the complex biological systems. In order to uncover communalities between heterogeneous data, the data are often harmonized by constructing a kernel and perform numerical integration. In this study, we propose a method for data integration in the framework of an undirected graphical model, where the nodes represent individual data sources of varying nature in terms of complexity and underlying distribution and where the edges represent the partial correlations between two blocks of data. We propose a modified GLASSO for estimation of the graph, with a combination of cross‐validation and extended Bayes Information Criterion for sparsity tuning. Furthermore, hierarchical clustering on the weighted consensus kernels from a fixed network is used to partitioning the samples into different classes. Simulations show increasing ability to uncover true edges with increasing sample size and signal to noise. Likewise, identification of nonexisting edges towards disconnected nodes is feasible. The framework is demonstrated for integration of longitudinal symptom burden data, from the second and third year of life, combined with 21 diseases precursors and information of the development of asthma and eczema at the age of 6 years, from 403 children from the COPSAC2010 mother‐child cohort. This suggests that maternal predisposition as well as being born preterm indirectly lead to a higher risk of asthma via an increased respiratory symptom burden.
Mining frequent approximate patterns in large networks

Frequent pattern mining (FPM) algorithms are often based on graph isomorphism in order to identify common pattern occurrences. Recent research works, however, have focused on cases in which patterns can differ from their occurrences. Such cases have great potential for the analysis of noisy network data. Most existing FPM algorithms consider differences in edges and their labels, but none of them so far has considered the structural differences of vertices and their labels. Discerning how to identify cases that differ from the initial pattern by any number of vertices, edges, or labels has become the main challenge of recent research works. As a solution, we suggest a novel FMP algorithm named mining frequent approximate patterns (MFAPs) with two central new characteristics. First, we begin by using the inexact matching technique, which allows for structural differences in edge, vertices, and labels. Second, we follow the approximate matching with a focus on mining patterns within the directed graph, as opposed to the more commonly explored case of patterns being mined from the undirected graph. Our results illustrate the effectiveness of this new MFAP algorithm in identifying patterns within an optimized time.
M 7C 3: The Story of a Misunderstood Carbide

Since the discovery of the M7C3-type carbide in 1935, it has been the subject of many experimental and theoretical studies. Initially, the structure was thought of as being either trigonal or hexagonal, while later publications indicated that the structure was more likely to be orthorhombic. Even today, 8 decades after its discovery, many publications claim that M7C3 carbides are either hexagonal or orthorombic “beyond doubt”, while others, acknowledge the lack of agreement in the literature and try to resolve it using first principles calculations which rely on the atomic positions in the previously suggested structures. To date, no hard evidence was presented to support either claim in an indisputable manner. Using EBSD (electron backscattered diffraction), TEM (transmission electron microscopy) diffractions and high-resolution STEM (scanning transmission electron microscopy) imaging gathered from 8 zone-axis of the (Cr,Fe)7C3 phase formed in the as-cast AlCrFe2Ni2 high entropy alloy, we were able to determine that only the hexagonal structure is a viable option. Furthermore, we demonstrate the co-existence of two variants of the newly revealed atomic structure which we believe were mistakenly identified as stacking faults in previous studies.
A Temple of “The Mistress of the Sea” That Was Far from the Shore (the Iseum in Beneventum)

The paper discusses the region of Campania as a zone of contact between Rome and Egypt, especially with regard to the cult of Egyptian Isis. Her cult first penetrates into port cities: Puteoli, Pompeii and, possibly at an even earlier date, Cumae. Later Rome, as the capital of the empire, affected each and every thing, including the spread of Isidic cult. The focus of our attention is the Iseum in Beneventum built by Domitian who obviously liked to present himself as a dominus et deus in a truly pharaonic spirit.
A Military Named Entity Recognition Method based on pre-training language model and BiLSTM-CRF

Military named entity recognition is the basis of the military intelligence analysis and operational information service. In order to solve the problems of inaccurate word segmentation, diverse forms and the lack of corpus in military texts, the author proposes a method of military named entity recognition based on Pre-training language model. On this basis, and taking advantage of Bi-directional Long Short-Term Memory (BiLSTM) neural network in dealing with the wide range of contextual information, the BERT-BiLSTM-CRF named entity recognition model was constructed. The experimental results on the tagged military text corpus show that the extraction effect of this method is better than that of the traditional methods.