Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of molecular energy using deep tensor neural networks

In this paper, we propose a combined scheme called Quantum Me-chanics and Deep Tensor Neural Network (QM-DTNN) to address the challenges related to the prompt and accurate calculation of the physicochemical properties of a protein. In QM-DTNN, a protein is decomposed into individual amino acid units that are treated with molecular caps. The physicochemical properties (molecular energy) of amino acid units are predicted using DTNNs, which are trained by QM ( ab Initio ) data. The training, validating, and testing data sets are made of conformations drawn through enhanced sampling in speciﬁc collective variable spaces. The inputs of DTNNs include pair-wise inter-distance and nuclear charges of an amino acid unit. The outputs of DTNNs, which are the physicochemical properties of amino acid units, are calculated using QM. The three typical amino acid units (i.e., Arginine, Lysine, and Tryptophan) are used to demonstrate the feasibility of QM-DTNN. The prediction re-sults demonstrated good correlations to QM data. The proposed scheme reduces the computational time considerably compared to that of QM calculation with acceptable precision loss.
Scaffolding in Supporting Senior High School Students’ Critical Thinking Skills in Sequences and Series Problems

Critical thinking skills are important for every individual, that they need to be developed in classroom learning, one of which is through learning sequences and series material. However, there are still many students who have difficulty solving problems on sequences and series material, so they need help using scaffolding. This study aims to describe students' thinking skills on sequences and series material, as well as scaffolding which helps students' critical thinking skills in solving sequence and series material questions. The subjects of this study were 2 students who failed to complete the two critical thinking skills tests given. The two students were then interviewed and given scaffolding. The interviews were conducted in a semi-structured manner, that the researcher prepared several questions which could indicate indicators of critical thinking skills, as well as scaffolding which could assist students in solving problems, but the researcher was free to improvise by asking for other information according to the conditions during the interview. The data obtained was then analyzed by reducing the data, presenting the data, and drawing conclusions. The results showed that scaffolding in the form of reviewing played a dominant role for supporting the failure of critical thinking skills on the indicators of interpretation, analysis, evaluation and explanation. Restructuring helps the failure of critical thinking skills on the indicators of interpretation. Explaining helps the failure of critical thinking skills on evaluation indicators. Developing conceptual thinking helps indicators of critical thinking skills analysis, inference, and self-regulation.
A General Approach to Testing Volatility Models in Time Series

Volatility models have been playing important roles in economics and finance. Using a generalized spectral second order derivative approach, we propose a new class of generally applicable omnibus tests for the adequacy of linear and nonlinear volatility models. Our tests have a convenient asymptotic null N(0,1) distribution, and can detect a wide range of misspecifications for volatility dynamics, including both neglected linear and nonlinear volatility dynamics. Distinct from the existing diagnostic tests for volatility models, our tests are robust to time-varying higher order moments of unknown form (e.g., time-varying skewness and kurtosis). They check a large number of lags and are therefore expected to be powerful against neglected volatility dynamics that occurs at higher order lags or display long memory properties. Despite using a large number of lags, our tests do not suffer much from the loss of a large number of degrees of freedom, because our approach naturally discounts higher order lags, which is consistent with the stylized fact that economic or financial markets are affected more by the recent past events than by the remote past events. No specific estimation method is required, and parameter estimation uncertainty has no impact on the convenient limit N(0,1) distribution of the test statistics. Moreover, there is no need to formulate an alternative volatility model, and only estimated standardized residuals are needed to implement our tests. We do not have to calculate tedious and model-specific score functions or derivatives of volatility models with respect to estimated parameters, which are required in some existing popular diagnostic tests for volatility models. We examine the finite sample performance of the proposed tests. It is documented that the new tests are rather powerful in detecting neglected nonlinear volatility dynamics which the existing tests can easily miss. They are useful diagnostic tools for practitioners when modelling volatility dynamics.
A Survey of Blockchain-Based Schemes for Data Sharing and Exchange

Data immutability, transparency and decentralization of blockchain make it widely used in various fields, such as Internet of things, finance, energy and healthcare. With the advent of the Big Data era, various companies and organizations urgently need data from other parties for data analysis and mining to provide better services. Therefore, data sharing and data exchange have become an enormous industry. Traditional centralized data platforms face many problems, such as privacy leakage, high transaction costs and lack of interoperability. Introducing blockchain into this field can address these problems, while providing decentralized data storage and exchange, access control, identity authentication and copyright protection. Although many impressive blockchain-based schemes for data sharing or data exchange scenarios have been presented in recent years, there is still a lack of review and summary of work in this area. In this paper, we conduct a detailed survey of blockchain-based data sharing and data exchange platforms, discussing the latest technical architectures and research results in this field. In particular, we first survey the current blockchain-based data sharing solutions and provide a detailed analysis of system architecture, access control, interoperability, and security. We then review blockchain-based data exchange systems and data marketplaces, discussing trading process, monetization, copyright protection and other related topics.
Changes in Autonomic Nervous System Function in Patients >60 Years of Age with Coronary Heart Disease, and Normotension or Hypertension: An Observational Study

Aim: The aim of this observational study was to perform in-depth analysis of autonomic nervous system function in patients older than 60 years of age with coronary artery disease, and normotension or hypertension. Method: A total of 104 patients older than 60 years with coronary heart disease (CHD) were divided into a normotension group and hypertension (HT) group, and 24-hour Holter monitoring was performed to assess autonomic function. Result: Among the 104 patients with CHD analyzed, 52 had normotension, and 52 had hypertension. The 24-hour Holter results based on time-domain methods indicated that the values of the time-domain parameters of heart rate variability were significantly lower in the CHD+HT group than the CHD group. Furthermore, during both the daytime and nighttime, the time-domain parameters were significantly lower in the CHD+HT group than the CHD group. No difference was observed in autonomic function during the daytime and nighttime in each group. Values of frequency-domain parameters of heart rate variability were also significantly lower in the CHD+HT group than the CHD group. More patients in the CHD+HT group than the CHD group received percutaneous coronary intervention (57.69% vs. 50% χ2=0.619, P=0.55). In 12 months of follow-up, we found no significant differences in rehospitalization for unstable angina and target lesion revascularization between patients with CHD with normotension versus hypertension. Conclusion: The heart autonomic nervous system dysfunction in patients older than 60 years with CHD with hypertension was more severe than that in patients with CHD with normotension, and therefore, should receive greater clinical attention.
A 73% Peak PDP Single-Photon Avalanche Diode Implemented in 110 nm CIS Technology With Doping Compensation

In this article, we present 10 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m diameter SPADs fabricated in 110 nm CIS technology based on an N<sup>+</sup>/HVPW junction, with enhanced sensitivity at short wavelengths. To reduce tunneling noise due to the highly-doped layers in the process, a doping compensation technique is used, which allows to adjust the doping profile of the HVPW. Thanks to this technique, DCR is reduced by a factor of 24 at 2 V excess bias voltage when compared to non-compensated devices. Furthermore, the maximum achievable PDP is enhanced by 49% thanks to the much lower DCR leading to a PDP of 73%, the highest ever reported at 440 nm, while the DCR is 12.5 cps/<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>m<sup>2</sup>, all at the 5 V excess bias. Since the junction is formed very close to the surface, the SPAD has excellent sensitivity in the UV spectrum, with a PDP of 43% at a wavelength of 350 nm. The proposed SPAD also achieves a PDP of 7% with a timing jitter of 68 ps at 850 nm at 5 V excess bias, which makes the device very useful for RGB-Z (RGB-D) sensors.
A REVIEW ON K-MODE CLUSTERING ALGORITHM

The main purpose of the process of data mining is to extract useful information from a huge amount of dataset. As one of the most important tasks in data mining, clustering is the process of grouping object attributes and features such that the data objects in one group are more similar than data objects in another group. It is a form of unsupervised learning that means how data should be grouped the data objects (similar types) together will be not known in advance. The algorithms used for clustering are k-means algorithm, k-medoid algorithm, k-nearest neighbour algorithm, k-mode algorithm etc. The K-Mode Algorithm is an eminent algorithm which is an extension of the K-Means Algorithm for clustering data set with categorical attributes and is famous for its simplicity and speed. The ‘Simple Matching Dissimilarity’ measure is used instead of Euclidean distance and the ‘Mode’ of clusters is used instead of ‘Means’. In this paper, review on the K-Mode Algorithm is done.
NANOGOLD AS A COMPONENT OF ACTIVE DRUGS AND DIAGNOSTIC AGENTS

Nanotechnology is a fast-growing field of science that dates back to the late 1950s. Nanoparticles can be divided into organic, inorganic, and carbon-based. An example of inorganic nanoparticles, in which relatively high hopes for the development of both pharmacy and medicine are placed, are gold nanoparticles. They possess beneficial properties, such as small size (ranging from several to several hundred nanometers), a large specific surface area to volume, and characteristic optical properties, as well they are relatively easy to synthesize with the ability to control the parameters of the final product to obtain desired sizes and shapes. Moreover, they exhibit high biocompatibility and low toxicity, which is especially important when administered internally (per os, i. v.). Several methods for the synthesis of gold nanoparticles (AuNPs) have been described in the literature, including chemical, physical, and biological methods. Microorganisms such as fungi, plants, and algae are used to produce gold nanoparticles. Due to their particle size and ability to penetrate cell membranes, gold nanoparticles are being considered as drug carriers. Many attempts have been made to attach gold nanoparticles to drugs, focusing mainly on antimicrobial and anticancer drugs. Treatment with these drugs in combination with nanoparticles is more effective than applying free drugs without the carrier. AuNPs have also been used with great success in the photothermal therapy of cancer. Additionally, work is underway to use them in diagnostics to prepare flow assays, increasing the sensitivity and specificity of the tests. Due to a large amount of scientific data on nanogold, this review focuses on presenting methods for obtaining gold nanoparticles and approximating their applications in areas of medical science.
Analyse de primitives symétriques

Cette these a pour objet d'etude les algorithmes de chiffrement par blocet les fonctions de hachage cryptograpiques, qui sont deux primitives essentielles de la cryptographie dite «symetrique». Dans une premiere partie, nous etudions des elements utiles pour la conception de chiffres par bloc: tout d'abord des matrices de diffusion de grande dimension issues de codes correcteurs geometriques, puis une boite de substitution offrant une bonne diffusion. Dans le second cas, nous montrons aussi comment utiliser cet element pour construire un chiffre compact et efficace sur petits processeurs.Dans une seconde partie, nous nous interessons a des attaques en collision a initialisation libre sur la fonction de hachage SHA-1. Nous montrons comment les attaques classiques sur cette fonction peuvent etre rendues plus efficaces en exploitant la liberte supplementaire offerte par ce modele. Ceci nous permet en particulier de calculer explicitement des collisions pour la fonction de compression de SHA-1 non reduite.
Discussion on temperature reconstruction with sediment core data in Ilvonen et al.

We congratulate the authors on an important contribution to temperature reconstruction methodology applicable to pollen data. Pollen data is more challenging to model than other climate proxies, such as measurements on tree rings, because of the categorical nature of pollen assemblage data and the dating uncertainty inherent to any sedimentary proxy. The authors are commended on developing a reconstruction methodology that incorporates Bchron and Bummer. In this discussion, we discuss several ideas prompted by our reading of this interesting and novel paper. Copyright © 2016 John Wiley & Sons, Ltd.
Towards Quantum Logic Inspired Cooling and Detection for Single (Anti-)Protons

Teresa Meiners, Malte Niemann, Anna-Greta Paschke, Johannes Mielke, Alexander Idel, Matthias Borchert, Kai Voges, Amado Bautista-Salvador, Stefan Ulmer, Christian Ospelkaus Institut für Quantenoptik, Leibniz Universität Hannover, Welfengarten 1, 30167 Hannover, Germany Laboratory of Nano and Quantum Engineering, Leibniz Universität Hannover, Schneiderberg 39, 30167 Hannover Physikalisch-Technische Bundesanstalt, Bundesallee 100, 38116 Braunschweig, Germany Ulmer Initiative Research Unit, RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
Characteristics of Spinning Black Holes

This work carried out in collaboration among both authors. Author DM collected the materials and designed the study and plotted the graphs with statistical data analysis. He wrote the first draft of the manuscript and managed literature searches. Author AR managed the analyses of the study and literature searches. Both authors read and approved the final manuscript. ABSTRACT Aims: The present paper derives an expression for the frequency or wavelength Hawking radiation from black holes by Mahto et al. published in International Journal of Astrophysics and Space Science (2013). Place and Duration of Study: This work has been completed in the department of Physics of Marwari College under T.M. B.U. Bhagalpur from Jan 2016 to Aug 2016. Methodology: This research work is completely theoretical and the total work has been doneusing Laptop at Marwari College Bhagalpur and University Department of Physics, T.M. B.U. Bhagalpur. Results: The astrophysical objects which emit the radiations of wavelengths (3.707x10 5 m to 14.828x10 5 m) or frequencies (8.092x10 2 Hz to 2.023x10 2 Hz)in XRBs and wavelengths ( 10 7.414 10 · m to 13 37.070 10 · m) or frequencies ( 3 4 .0 4 6 1 0 - · Hz to 6 0 .8 0 9 1 0 - · Hz) in AGN may be classified as spinning as well as non-spinning black holes. Conclusion: The frequencies/wavelengths of Hawking radiation either emitted from spinning black holes or non-spinning black holes are the same and only depends on their mass. This may be also regarded as the characteristics of spinning black holes in addition to the mass, spin and charge. The mass of black holes are mainly responsible for their identification and characterization and independent from spinning character.
Growth performance, egg quality and selection intensity of 6th generation of BLRI improved native duck genotypes

This research was taken to improve the performance of native duck. Two native duck varieties, Rupali and Nageswari of 6th generation were selected aiming to increase their laying performances. A total of 540 ducklings of both genotypes were hatched and brooded in brooder house. Male and female ducks were separated and marked with wing band at 12 weeks of age. At 40 weeks of age, a total of 300 ducks of both genotype were selected on the basis of selection index comprising the parameters of age at first egg, body weight at first egg, egg production % and egg weight. The individual with the higher total score was selected for breeding purposes. Selected male and female ratio were 1:5 using natural mating. The selection intensity and expected selection responses of selection criteria of two duck genotypes were estimated. Selection responses for ASM (day), egg weight (g) and egg production rate was -1.84, 1.08 g and 0.45 in case of Rupali and -1.66, 1.35 and 0.73 for Nageswari, respectively. Egg production % was numerically higher in Rupali (60.12%) than Nageswari (58.41%). Egg weight (EW) and feed intake were significantly differences between two native ducks where both higher values were found in Rupali (EW 64.83g and feed intake 134g) than Nageswary. Egg shape index was significantly higher in Rupali (76%) than Nageswari (75%) duck. In conclusion, Rupali ducks showed better production performance and it is suggested that selective breeding of duck may have significant impact on the development of native duck. SAARC J. Agric. , 21(1): 253-263 (2023)
Power analysis for stratified cluster randomisation trials with cluster size being the stratifying factor

ABSTRACT Stratified cluster randomisation trial design is widely employed in biomedical research and cluster size has been frequently used as the stratifying factor. Conventional sample size calculation methods have assumed the cluster sizes to be constant within each stratum, which is rarely true in practice. Ignoring the random variability in cluster size leads to underestimated sample sizes and underpowered clinical trials. In this study, we proposed to directly incorporate the variability in cluster size (represented by coefficient of variability) into sample size calculation. This approach provides closed-form sample size formulas, and is flexible to accommodate arbitrary randomisation ratio and varying numbers of clusters across strata. Simulation study shows that the proposed approach achieves desired power and type I error over a wide spectrum of design configurations, including different distributions of cluster sizes. An application example is presented.
On the Power and Performance of a Doubly Latent Residual Approach to Explain Latent Specific Factors in Multilevel-Bifactor-(S-1) Models

A doubly latent residual approach (DLRA) is presented to explain latent specific factors in multilevel bifactor-(S-1) models. The new approach overcomes some important limitations of the multiple indicators multiple causes (MIMIC) approach and allows researchers to predict latent specific factors at different levels. The DLRA is illustrated using real data from a large-scale assessment study. Furthermore, the statistical performance and power of the DLRA is examined in a Monte Carlo simulation study. The results show that the new DLRA performs well if more than 50 clusters and more than 10 observations per cluster are sampled. The power to test structural parameters at level 2 was lower than at level 1. To test a medium effect at level 2, we recommend to sample at least 100 clusters with a minimum cluster size of 10. The advantages and limitations of the new approach are discussed and guidelines for applied researchers are provided.
Star operations on overrings of Prüfer domains

ABSTRACT Let R be a Prüfer domain. We begin with two questions: (Q1) If R has only finitely many star operations, is the same true for each overring of R? and (Q2) If each proper overring of R has only finitely many star operations, is the same true for R? We show that both questions have negative answers in general, we give a complete description of when (Q1) has a positive answer (loosely, R must be “almost” strongly discrete), and we show that for a finite dimensional Prüfer domain, the answer to (Q2) is always “yes”. We then study star regular Prüfer domains, that is, Prüfer domains R such that |Star(T)|≤|Star(R)| for each overring T of R, and we compute the number of star operations on some particular Prüfer domains.
Experimental Study of a Two-Phase Immersion Cooling System for the CPU in a PC and a 2U Server Operated at the Overclocking Frequency

Overclocking the operation frequency of a central processing unit (CPU) could improve a processor’s performance. However, this may cause hindrance to heating issues. In this study, a two-phase immersion cooling (2PIC) system using 3M Novec 7100 engineered fluid was applied on a personal computer (PC) and a 2U server operated at an overclocked frequency of 5.0 GHz. The experimental results indicated that the heat transfer efficiency had been improved by attaching the Al heat sink (HS) and evaporator to the CPU. The temperature of the CPU in the 2PIC system was lower than that of a forced-air convection cooling system. The 2PIC system had energy savings of up to 24% and shows the potential of turbo boost technology for computer and communication-related industries.
On the Link between the Release of Solar Energetic Particles Measured at Widespread Heliolongitudes and the Properties of the Associated Coronal Shocks

Under the paradigm that the main agents in the acceleration of solar energetic particles (SEPs) are shocks initially driven by coronal mass ejections, we analyze whether the properties of the shocks in the corona inferred from combining extreme-ultraviolet (EUV) and white-light (WL) observations from multiple vantage points together with magnetohydrodynamic (MHD) simulations of the corona can be used to determine the release of SEPs into different regions of the heliosphere and hence determine the longitudinal extent of the SEP events. We analyze the SEP events observed on 2011 November 3, 2013 April 11, and 2014 February 25 over a wide range of heliolongitudes. MHD simulations provide the characteristics of the background medium where shocks propagate, in particular the Alfvén and sound speed profiles that allow us to determine both the extent of the EUV waves in the low corona and the fast magnetosonic Mach number (MFM) of the shocks. The extent of the EUV waves in the low corona is controlled by this background medium and does not coincide with the extent of the SEP events in the heliosphere. Within the uncertainties of (i) the extent and speed of the shock inferred from EUV and WL images and (ii) the assumptions made in the MHD models, we follow the evolution of MFM at the region of the shock magnetically connected to each spacecraft. The estimated release times of the first SEPs measured by each spacecraft does not coincide with the time when the MFM at this region exceeds a given threshold.