Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrical double layer forces: a Monte Carlo study

Using a novel method the force between two charged surfaces with an intervening electrolyte solution has been determined from Monte Carlo simulations. We find large deviations from the standard Poisson–Boltzmann treatment of the so called double layer force for divalent counterions at high surface charge densities and at short separations. The deviations have two causes: (i) Due to the inclusion of the effect of ion–ion correlations the counterions concentrate more towards the charged wall reducing the overlap between the double layers; and (ii) correlated fluctuations in the ion clouds of the two surfaces lead to an attractive interaction of a van der Waals type. For some realistic values of the parameters the attraction overcomes the repulsive part and there is a net attractive force between similarly charged surfaces. This finding leads to a modification of our conceptual understanding of the interaction between charged particles and it shows that the DLVO theory is qualitatively deficient under some, .
Semantic Segmentation of Lesions from Dermoscopic Images using Yolo-DeepLab Networks

Accurate segmentation of lesions from dermoscopic images is very important for timely diagnosis and treatment of skin cancers. Due to the variety of shapes, sizes, colors, and locations of lesions in dermoscopic images, automatic segmentation of skin lesions remains a challenge. In this study, a two-stage method for the segmentation of skin lesions based on deep learning is presented. In the first stage, convolutional neural networks (CNNs) estimate the approximate size and location of the lesion. A sub-image around the estimated bounding box is cropped from the original image. The sub-image is resized to an image of a predefined size. In order to segment the exact area of the lesion from the normal image, other CNNs are used in the DeepLab structure. The accuracy of the normalization stage has a significant impact on the final performance. In order to increase the normalization accuracy, a combination of four networks in the structure of Yolov3 is used. Two approaches are proposed to combine Yolov3 structures. The segmentation results of two networks in the DeepLab v3+ structure are also combined to improve the performance of the second stage. Another challenge is the small number of training images. To overcome this problem, the data augmentation is used, as well as using different modes of an image in each stage. In order to evaluate the proposed method, experiments are performed on the well-known ISBI 2017 dataset. Experimental results show that the proposed lesion segmentation method outperforms the state-of-the-art methods.
STATISTICAL POTENTIALS EXTRACTED FROM PROTEIN STRUCTURES : ARE THESE MEANINGFUL POTENTIALS?

We present an inventory of factors that determine the stability of proteins. It is shown that the (true) potential of mean force between pairs of amino acid residues does not belong to this inventory. Therefore, such quantities are not needed for either estimating the stability of a protein or for predicting its structure. It is also shown that the so-called “statistical potential” as derived from the data bank of protein structures is neither the potential nor the potential of mean force for pairs of amino-acid residues. A critical examination of the nonadditivity of the many body potential of the mean force is also presented.
A PRECISE FAULT LOCATION SCHEME FOR LOW-VOLTAGE DC MICROGRIDS SYSTEMS USING MULTI-LAYER PERCEPTRON NEURAL NETWORK

Nowadays, microgrids have attracted much attention in developed countries. The protection of DC systems, unlike conventional AC systems, is a highly challenging task. The acquaintance on fault location in distribution network causes for quick restoration, maintenance and decrease unnecessary power outage period. Neural Networks (NNs) are among the powerful, reliable approaches and are used in many different engineering applications. Also, Multi-Layer Perceptron (MLP) NNs are used for different estimating problems. This paper presents an accurate protection method for Low-Voltage DC (LVDC) ring-bus microgrid systems based on MLP NN. The aim of the proposed method is precise fault location estimation in microgrids, irrespective of the type and magnitude of fault, current, and the power supply quantity, by instantaneous current monitoring of each segment of the microgrid. Simulation results demonstrate the NN fault location estimation in percent of line length are in a suitable range. The results show that the estimation error is small and is within the permissible range. According to the results, efficiency and accuracy of MLP NN are confirmed. To do so, an LVDC ring-bus microgrid is used that utilizes solid-state bidirectional switches along with master and slave controllers
An Evaluation of Left and Right Brain Dominance using Electroencephalogram Signal

—The left and right brain dominance theory has been established for decades. Besides, the left and right brain balancing education concept and training have also been developed for years. Currently, the only way to determine a person whether is left or right brain dominance is by making a questionnaire assessment. There is no scientific data that can directly reflect brain activity to prove the left and right brain theory as well as the effectiveness of the left and right brain development training. Hence, in this research, it is aimed to determine whether the electroencephalography (EEG) signal has any correlation with the brain dominance level. The brain dominance level of the subject is determined and benchmarked by using the Hermann Brain Dominance Instrument (HBDI) test, a popular testing tool utilized by innumerable multinational companies to determine employee’s brain dominance level. As the captured raw EEG signal is complicated and noisy, several preprocessing methods are utilized to eliminate the unwanted noise and artifacts efficiently from the acquired signal. The techniques are namely baseline correction method, electrical line noise removal, and independent component analysis (ICA). Besides, significant features can be hardly determined from the time-based EEG signal with high complexity. Hence, the EEG Topographical Power Spectral Density Percentage (EEGTPSDP) method is implemented to analyze the EEG signal. By using the results computed by EEGTPSDP method, it proves that there is a strong correlation between the brain dominance level and EEG power spectral density on one hemisphere. Hence, this research is able to validate the left and right brain dominance theory according to the EEG signal. The implemented EEGTPSDP method can be used to classify the dominant brain of a person. In this way, this research is able to contribute to the education field by determining the students’ brain dominance level and track their learning progress based on the EEG signal in a scientific approach.
Copy-scAT: An R package for detection of large-scale and focal copy number alterations in single-cell chromatin accessibility datasets

The single-cell assay for transposase accessible chromatin (scATAC) is an invaluable asset to profile the epigenomic landscape of heterogeneous cells populations in complex tissue and organ systems. However, the lack of tools that enable the use of scATAC data to discriminate between malignant and non-malignant cells has prevented the widespread application of this technique to clinical tumor samples. Here we describe Copy-scAT, a new computational tool that uses scATAC data to infer both large-scale and focal copy number alterations. Copy-scAT can call both clonal and subclonal copy number changes, allowing identification of cancer cells and cell populations that putatively constitute the tumor microenvironment. Copy-scAT therefore enables downstream chromatin accessibility studies that focus on malignant or non-malignant cell populations in clinical samples that are profiled by scATAC.
Coronavirus-associated molecular mimicry through homology to a SARS-CoV-2 peptide could be leading to susceptibility in patients with HLA-A*02:01 and HLA-A*24:02 serotypes

This study aims to predict autoimmunity-related pathological mechanisms that possess risk for individuals with specific human leukocyte antigen (HLA) serotypes and shared by certain coronaviruses including SARS-CoV-2, based on homology to a SARS-CoV-2 peptide. With the given aim, 1-) coronavirus-associated sequences, which are homologous to the 15mer SARS-CoV-2 peptide CFLGYFCTCYFGLFC, are obtained. 2-) Human peptides that have at least 7 residue matches with those coronavirus sequences, and the SARS-CoV-2 15mer, are found. 3-) Epitope pairs, which are sourced by those aligned coronavirus and human sequences are identified. 4-) Epitope pairs that are predicted to bind strongly not only to the same HLA allele with each other but also to the same HLA allele as those of the respective alignment of the SARS-CoV-2 peptide are selected. Following are the identified proteins or peptides (with HLA-A*02:01 or HLA-A*24:02 epitopes), as described in 1-to-4: Immunoglobulin heavy chain junction regions, CRB1 isoform I precursor, slit homolog 2 protein, hCG1995581, hCG2028737, phospholipid phosphatase-related protein type 2. Among those, CRB1 isoform I precursor sequence with the predicted HLA-A*24:02 epitope aligns with the highest number of different sequences. Results imply autoimmunity risk in COVID-19 patients with HLA-A*02:01 and HLA-A*24:02 serotypes, through molecular mimicry, as a shared pathogenicity risk that can be prevalent upon getting infected with certain coronaviruses. These can pave way to improved risk groups’ assessment and autoimmunity treatment options, for COVID-19 and its associated diseases. Also, the approach in this study can be used to predict prospective pathologies of the transmissible variants in susceptible humans.
SYNTHESIS AND REACTIVITY IN INORGANIC AND METAL-ORGANIC CHEMISTRY

Photoactivities of some metallophthalocyanine complexes for the generation of singlet oxygen were estimated by a kinetic study of the photooxidation of 1,3-diphenylisobenzofuran in aerated dimethyl sulfoxide. *Correspondence: Kuninobu Kasuga, Department of Material Science, Faculty of Science and Engineering, Shimane University, Matsue 690-8504, Japan; Email: kasuga@riko.shimane-u.ac. jp.
Structural and magnetic properties of ion-beam bombarded Co/Pt multilayers

A series of [Pt(2 nm)/Co(2 nm)]10/Pt(30 nm) multilayers were deposited by using an ion-beam technique. X-ray diffraction and transmission electron microscopy results have shown that as-deposited samples consist of h.c. p. Co and f.c. c. Pt phases. Disordered CoPt3 phases were developed with increasing End-Hall voltage (VEH) that induces greater ion-beam bombardment energy during deposition. This indicates that intermixing of Co and Pt increases with ion-beam bombardment. The coercivities (ranging from 100 Oe to 300 Oe) of Co/Pt multilayers decreased with increasing VEH. After annealing, the formation of CoPt3 was observed in these ion-beam bombarded samples, resulting in lower coercivities (Hc ∼ 50 Oe). The depressed transition temperature of CoPt3 for films deposited with the largest VEH was attributed to distorted CoPt3 structures that appeared with annealing. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Optimisation of a laser ablation cell for detection of hetero-elements in proteins blotted onto membranes by use of inductively coupled plasma mass spectrometry

For imaging of elemental distribution in protein spots blotted onto membranes with a conventional size, we have investigated three different laser ablation cell geometries to find a design of a cell showing a constantly high signal intensity across the cell and possessing short wash-out times. For this purpose the resulting intensity after optimisation (He carrier gas flow rate, argon make-up gas, generator forward power) was measured for single laser shots at different positions on a steel metal sheet and for continuous ablation of nitrocellulose (NC) membranes during linear translation of the cell. In the latter experiment, ink bar structures have been printed on the NC membranes to simulate blotted protein spots and used to evaluate the local resolution and to determine an adequate translation velocity. The final cell has a volume of about 11.3 cm3 in which single shot signals are washed out in less than a second so that translation velocities of up to 1.5 mm s−1 can be applied to baseline separate structures with a distance of less than 2 mm, which will be demonstrated using an example of a phosphoprotein mixture separated by SDS-PAGE and blotted onto a NC membrane.
A Survey On Load Balancing in Software Defined Networks

: Software-defined networks brings elastic control so enterprises can respond to altering business requirements more quickly. SDN separates the physical network control plane from the data plane. It is the new emerging network technology for refining and simplifying network administration with high flexibility. Several load balancing approaches based on SDN had been recommended to improve the performance of the networks. Related with the traditional SDN networks, it revealed great advantages in many characteristics, but also arise the challenging of the load imbalance. If the load distribution is abnormal within the SDN networks, it will significantly affect the performance of network. Many SDN-based load balancing approaches have been planned to improve the performance of the SDN networks. Therefore, this paper premises the load balancing organizations in SDN networks, and analyzes the advantages and drawbacks of them.
Effective catalysis of poly(ethylene terephthalate) (PET) degradation by metallic acetate ionic liquids

Poly(ethylene terephthalate) (PET) is widely used for beverage bottles, electrical and electronic instruments, household wares, and so on. As a consequence of dramatically increasing consumption, recycling of post-consumer PET products has become an important environmental opportunity for sustainable usage in society. In this paper, we investigated the use of chlorine-free metallic acetate ionic liquids (ILs) as catalysts for the degradation of PET because of their lower toxicity, corrosivity, and cost. 1,3-Diethylimidazolium triaceticzincate ([deim][Zn(OAc)3]) behaved as the best in this group. The synthesized ILs and the major product, characterized by a variety of techniques and factors affecting glycolysis, were examined. Under optimum conditions, conversion of PET reached 98.05 %, and the selectivity of the bis(hydroxyethyl) terephthalate (BHET) monomer was 70.94 %. A probable mechanism for the glycolysis of PET catalyzed by [deim][Zn(OAc)3] was given. In our opinion, catalysis accounted for the synergic effect of the cation and anion of the IL.
Electromagnetic Scattering by Networks of High-Permittivity Thin Wires

The electromagnetic scattering from interconnections of high-permittivity dielectric thin wires with sizes smaller than (or almost equal to) the operating wavelength is investigated. A simple lumped element model for the polarization current intensities induced in the wires is proposed. The circuit elements are capacitances and inductances between the wires. An analytical expression for the induced polarization currents in terms of the magneto-quasistatic current modes is obtained. The connection between the spectral properties of the loop inductance matrix and the network's resonances is established. The number of the allowed current modes and resonances is deduced from the topology of the circuit's digraph. The coupling to radiation is also included, and the radiative frequency shifts and the quality factors are derived. The introduced concept and methods may find applications both at the microwaves and in nanophotonics.
Transferability of Neural Network-based De-identification Systems

Methods and Materials: We investigated transferability of neural network-based de-identification sys-tems with and without domain generalization. We used two domain generalization approaches: a novel approach Joint-Domain Learning (JDL) as developed in this paper, and a state-of-the-art domain general-ization approach Common-Specific Decomposition (CSD) from the literature. First, we measured trans-ferability from a single external source. Second, we used two external sources and evaluated whether domain generalization can improve transferability of de-identification models across domains which rep-resent different note types from the same institution. Third, using two external sources with in-domain training data, we studied whether external source data are useful even in cases where sufficient in-domain training data are available. Finally, we investigated transferability of the de-identification mod-els across institutions. Results and Conclusions: We found transferability from a single external source gave inconsistent re-sults. Using additional external sources consistently yielded an F1-score of approximately 80%, but domain generalization was not always helpful to improve transferability. We also found that external sources were useful even in cases where in-domain training data were available by reducing the amount of needed in-domain training data or by improving performance. Transferability across institutions was differed by note type and annotation label. External sources from a different institution were also useful to further improve performance.
Split Modeling for High-Dimensional Logistic Regression

A novel method is proposed to learn an ensemble of logistic classification models in the context of high-dimensional binary classification. The models in the ensemble are built simultaneously by optimizing a multi-convex objective function. To enforce diversity between the models the objective function penalizes overlap between the models in the ensemble. We study the bias and variance of the individual models as well as their correlation and discuss how our method learns the ensemble by exploiting the accuracy-diversity trade-off for ensemble models. In contrast to other ensembling approaches, the resulting ensemble model is fully interpretable as a logistic regression model and at the same time yields excellent prediction accuracy as demonstrated in an extensive simulation study and gene expression data applications. An open-source compiled software library implementing the proposed method is briefly discussed.
Robust Mean Estimation in High Dimensions via Global Outlier Pursuit

We study the robust mean estimation problem in high dimensions, where less than half of the datapoints can be arbitrarily corrupted. Motivated by compressive sensing, we formulate the robust mean estimation problem as the minimization of the $\ell_0$-`norm' of an \emph{outlier indicator vector}, under a second moment constraint on the datapoints. We further relax the $\ell_0$-`norm' to the $\ell_p$-norm ($0<p\leq 1$) in the objective and prove that the global minima for each of these objectives are order-optimal for the robust mean estimation problem. Then we propose a computationally tractable iterative $\ell_p$-minimization and hard thresholding algorithm that outputs an order-optimal robust estimate of the population mean. Both synthetic and real data experiments demonstrate that the proposed algorithm outperforms state-of-the-art robust mean estimation methods. The source code will be made available at GitHub.
Performance Analyses of MRT/MRC in Dual-Hop NOMA Full-Duplex AF Relay Networks with Residual Hardware Impairments

This paper analyzes the performance of maximum-ratio transmission (MRT)/maximum-ratio combining (MRC) scheme in a dual-hop non-orthogonal multiple access (NOMA) full-duplex (FD) relay networks in the presence of residual hardware impairments (RHIs). The effects of channel estimation errors (CEEs) and imperfect successive interference cancellation are also considered for a realistic performance analysis. In the network, the base station and multiple users utilize MRT and MRC, respectively, while a dedicated relay consisting of two antennas, one for receiving and the other for broadcasting, operates in amplify-and-forward mode. For performance criterion, exact outage probability (OP) expression is derived for Nakagami-m fading channels. Furthermore, a tight lower bound and asymptotic expressions are also derived to provide more insights into the obtained OP in terms of diversity order and array gain. The obtained numerical results demonstrate the importance of loop-interference cancellation process at FD relay in order for the investigated system to perform better than half-duplex-NOMA counterpart. Also, a performance trade-off between the MRT and MRC schemes is observed in the presence of CEEs among users. Furthermore, it is shown that RHIs have a significant effect on the performance of users with lower power coefficients, however it does not change the diversity order. RHIs and CEEs have the most and least deterioration effects on the system performance, respectively.
MOND-like behavior in the Dirac–Milne universe

Aims. Observational data show that the observed luminous matter is not sufficient to explain several features of the present universe, from gravitational structure formation to the rotational velocities in galaxies and clusters. The mainstream explanation is that the missing mass, although gravitationally active, interacts very weakly with ordinary matter. Competing explanations involve changing the laws of gravity at low accelerations, as in MOdified Newtonian Dynamics (MOND). Here, we suggest that the Dirac-Milne cosmology, a matter-antimatter symmetric cosmology where the two components repel each other, is capable of accounting for such an apparent modification of the Newtonian law, without invoking dark matter. Methods. Using a simple analytical approximation and 1D and 3D simulations, we study rotation curves and virial velocities and compare the mass observed in the simulations to the mass derived assuming Newtonian gravity. Using a modified version of the RAMSES code, we study the Faber-Jackson scaling relation and the intensity of the additional gravitational field created by antimatter clouds. Results. We show that, in the Dirac-Milne universe, rotation curves are generically flat beyond a characteristic distance of ≈2.5 virial radii, and that the Tully-Fisher and Faber-Jackson scaling relations with an exponent ≈3 are satisfied. We show that the mass derived from the rotation curves assuming Newtonian gravity is systematically overestimated compared to the mass really present. In addition, the Dirac-Milne universe, featuring a polarization between its matter and antimatter components, presents a behavior similar to that of MOND, characterized by an additional surface gravity compared to the Newtonian case. We show that in the Dirac-Milne universe, at the present epoch, the intensity of the additional gravitational field gam due to the presence of clouds of antimatter is on the order of a few 10−11 m s−2, similar to the characteristic acceleration of MOND. We study the evolution of this additional acceleration gam and show that it depends on the redshift, and it is therefore not a fundamental constant. Conclusions. Combined with its known concordance properties on the SNIa luminosity distance, age, nucleosynthesis, and structure formation, the Dirac-Milne cosmology may then represent an interesting alternative to the standard cosmological model ΛCDM, MOND, and other scenarios for explaining the dark matter (or missing gravity) and dark energy conundrum.