Miracle-dz/UniFAR-FTUs huggingface dataset:
Electronic, Optic and Dynamic Properties of Rhombohedral BiTeBr

Electronic, optic and thermodynamic properties of rhombohedral BiTeBr crystal were investigated under the local density approximation (LDA) using the density functional theory. No spin-orbit interaction (SOI) were taken into account during calculations and band gap of 0.946 eV was found as which is different from experimental results but in good agreement with previous theoretical studies without SOI. For this new type ferroelectric BiTeBr from ground states features lattice parameters, electronic total density of states (TDOS), partial density of states (PDOS) and electronic band structure, from optical properties dielectric function, refraction index, extinction, reflection and absorption coefficients, number of effective valance electrons, volume and surface loss functions, among thermodynamic properties change in Helmholtz free energy, internal energy, entropy and constant volume specific heat as a function of temperature were studied.
Detecting initial aragonite and calcite variations in limestone–marl alternations

Limestone–marl alternations are commonly used for high‐resolution cyclostratigraphic studies and palaeoenvironmental reconstructions, but diagenetic studies indicate that not all limestone–marl alternations reflect genuine differences in the initial sediment composition driven by environmental changes. Differences in the ratios of diagenetically inert trace elements between limestones and marls indicate changes affecting the terrigenous fraction of the precursor sediment. Contrarily, limestone–marl alternations without these differences can be the product of: (i) variations in CaCO3 input (aragonite, calcite); (ii) distortion of the latter by diagenetic CaCO3 redistribution; or (iii) diagenetic CaCO3 redistribution in a homogeneous precursor sediment. The aim of this study is to provide a method to differentiate these cases and to identify variations in the proportion of calcite and aragonite in the precursor sediment composition. The model of differential diagenesis assumes that the concentration of diagenetically inert elements is inversely proportional to the amount of redistributed CaCO3. Consequently, the difference between ratios of diagenetically inert elements from two adjacent beds is a measure for CaCO3 redistribution. This is quantifiable by the vector length between ratios from two adjacent beds. The approach is illustrated here by evaluation of a case study from the Silurian of Gotland, Sweden. Trace elements were compared according to their solubility during diagenesis. All elements bound to clay minerals or calcite show similar patterns of vector length, while vector length of elements which fit into the aragonite lattice, and are diagenetically mobile, differ. The vector length approach provides a tool to test the diagenetic origin of limestone–marl alternations, to identify initial variations in CaCO3 input and to test a limestone–marl alternation’s suitability for cyclostratigraphic analyses.
Assessing Machine Learning Techniques for Identifying Field Line Resonance Frequencies From Cross‐Phase Spectra

Monitoring the plasmasphere is an important task to achieve in the Space Weather context. A consolidated technique consists of remotely inferring the equatorial plasma mass density in the inner magnetosphere using Field Line Resonance (FLR) frequencies estimates. FLR frequencies can be obtained via cross‐phase analysis of magnetic signals recorded from pairs of latitude separated stations. In the last years, machine learning (ML) has been successfully applied in Space Weather, but this is the first attempt to estimate FLR frequencies with these techniques. We survey several supervised ML algorithms for identifying FLR frequencies by using measurements of the European quasi‐Meridional Magnetometer Array. Our algorithms take as input the 2‐hour cross‐phase spectra of magnetic signals and return the FLR frequency as output; we evaluate the algorithm performance on four different station pairs from L = 2.4 to L = 5.5. Results show that tree‐based algorithms are robust and accurate models to achieve this goal. Their performance slightly decreases with increasing latitude and tend to deteriorate during nighttime. The estimation error does not seem to depend on the geomagnetic activity, although at high latitudes the error increases during highly disturbed geomagnetic conditions such as the main phase of a storm. Our approach may represent a prominent space weather tool included into an automatic monitoring system of the plasmasphere. This work represents only a preliminary step in this direction; the application of this technique on a more extensive data set and on more pairs of stations is straightforward and necessary to create more robust and accurate models.
Ambient Levels, Emission Sources and Health Effect of PM2.5-Bound Carbonaceous Particles and Polycyclic Aromatic Hydrocarbons in the City of Kuala Lumpur, Malaysia

With increasing interest in understanding the contribution of secondary organic aerosol (SOA) to particulate air pollution in urban areas, an exploratory study was carried out to determine levels of carbonaceous aerosols and polycyclic aromatic hydrocarbons (PAHs) in the city of Kuala Lumpur, Malaysia. PM2.5 samples were collected using a high-volume sampler for 24 h in several areas in Kuala Lumpur during the north-easterly monsoon from January to March 2019. Samples were analyzed for water-soluble organic carbon (WSOC), organic carbon (OC), and elemental carbon (EC). Secondary organic carbon (SOC) in PM2.5 was estimated. Particle-bound PAHs were analyzed using gas chromatography-flame ionization detector (GC-FID). Average concentrations of WSOC, OC, and EC were 2.73 ± 2.17 (range of 0.63–9.12) µg/m3, 6.88 ± 4.94 (3.12–24.1) µg/m3, and 3.68 ± 1.58 (1.33–6.82) µg/m3, respectively, with estimated average SOC of 2.33 µg/m3, contributing 34% to total OC. The dominance of char-EC over soot-EC suggests that PM2.5 is influenced by biomass and coal combustion sources. The average of total PAHs was 1.74 ± 2.68 ng/m3. Source identification methods revealed natural gas and biomass burning, and urban traffic combustion as dominant sources of PAHs in Kuala Lumpur. A deterministic health risk assessment of PAHs was conducted for several age groups, including infant, toddler, children, adolescent, and adult. Carcinogenic and non-carcinogenic risk of PAH species were well below the acceptable levels recommended by the USEPA. Backward trajectory analysis revealed north-east air mass brought pollutants to the studied areas, suggesting the north-easterly monsoon as a major contributor to increased air pollution in Kuala Lumpur. Further work is needed using long-term monitoring data to understand the origin of PAHs contributing to SOA formation and to apply source-risk apportionment to better elucidate the potential risk factors posed by the various sources in urban areas in Kuala Lumpur.
A New High-Gain DC-DC Converter with Continuous Input Current for DC Microgrid Applications

The growth of renewable energy in the last two decades has led to the development of new power electronic converters. The DC microgrid can operate in standalone mode, or it can be grid-connected. A DC microgrid consists of various distributed generation (DG) units like solar PV arrays, fuel cells, ultracapacitors, and microturbines. The DC-DC converter plays an important role in boosting the output voltage in DC microgrids. DC-DC converters are needed to boost the output voltage so that a common voltage from different sources is available at the DC link. A conventional boost converter (CBC) suffers from the problem of limited voltage gain, and the stress across the switch is usually equal to the output voltage. The output from DG sources is low and requires high-gain boost converters to enhance the output voltage. In this paper, a new high-gain DC-DC converter with quadratic voltage gain and reduced voltage stress across switching devices was proposed. The proposed converter was an improvement over the CBC and quadratic boost converter (QBC). The converter utilized only two switched inductors, two capacitors, and two switches to achieve the gain. The converter was compared with other recently developed topologies in terms of stress, the number of passive components, and voltage stress across switching devices. The loss analysis also was done using the Piecewise Linear Electrical Circuit Simulation (PLCES). The experimental and theoretical analyses closely agreed with each other.
Numerical Study on the Fracture Properties of Concrete Shield Tunnel Lining Segments

Shield tunnel lining structure is usually under very complex loading conditions in the underground space. As a kind of the common concrete structures, any defect in the tunnel lining segment may deteriorate its bearing capacity and even cause severe disasters. Three-dimensional numerical models of shield tunnel lining segments with initial cracks are built using the Symmetric Galerkin Boundary Element Method- (SGBEM-) Finite Element Method (FEM) Alternating Method. The cracking load and ultimate load of the tunnel segments are obtained, and crack propagation under fatigue load is also simulated by employing the Paris Fatigue Law. Results show that loading eccentricity has a very large influence on the bearing capacity of the cracked lining segment; the larger the loading eccentricity, the smaller the bearing capacity. Deformation and damage of the lining segment show obvious phases, which consist of the initial crack, cracking stage, steady crack propagation, unsteady crack propagation, and eventual failure.
Cu Sub-Nanoparticles on Cu/CeO2 as an Effective Catalyst for Methanol Synthesis from Organic Carbonate by Hydrogenation

Cu/CeO2 works as an effective heterogeneous catalyst for hydrogenation of dimethyl carbonate to methanol at 433 K and even at low H2 pressure of 2.5 MPa, and it provided 94% and 98% methanol yield based on the carbonyl and total produced methanol, respectively. This is the first report of high yield synthesis of methanol from DMC by hydrogenation with H2 over heterogeneous catalysts. Characterization of the Cu/CeO2 catalyst demonstrated that reduction of Cu/CeO2 produced Cu metal with <1.0 nm (subnanoparticles). Cu metal subnanoparticles are easily formed by the interaction with the CeO2 surface, which is responsible for the high catalytic performance.
Pareto Approach in Designing Optimal Semicontinuous Water Networks

Pareto optimization approach was used in searching for the particular successions of topologies and operating conditions for a semicontinuous water network (batch with respect to the raw materials) that minimize both the freshwater consumption and the investment and operating costs. The semicontinuous wastewater network (SWN) formed of N water-using units (WUs), each handling at most K contaminants, and one storage tank (ST) is optimized on time intervals by increasing the wastewater reuse opportunities. A single freshwater source is available and later a regeneration unit (RU) is added to the network as a fine-tuning tool in the Pareto optimization, and further its influence is discussed. The WUs are ordered simultaneously by schedule and by maximum allowable outlet concentrations of contaminants. Thus, the rules concerning the internal reuse between the overlapping WUs correspond to an oriented graph: no recycle is allowed against the ordered sequence. The differential-algebraic equations model consisting in a set of differential equations for the ST and a nonlinear system mass balance equations with restrictions for the overlapping WUs is optimized using the Matlab built in functions for both the genetic algorithms (GA) and the RK-type integrator. The topology of the SWN changes for each time interval, its optimal structure depending upon the minimum freshwater consumption and the costs. A complex synthetic case study was subjected to optimization, and several scenarios are analyzed.
Choice of Implant Surface-Feature May Affect the Viability of the Adherent Cells

Objective: In vitro evaluation of implant materials' effects on cell adhesion and viability can provide useful information for predicting implant biocompatibility. Therefore by using a simple and inexpensive method, it was aimed to investigate whether different implant surface-features might have distinct effects on the viability and adherence of the cells. Material and Methods: Different dental implant surfaces (anodized (AN), blasted wrinkled (BW), grit/acid etched (GA), and hydroxylapatite sprayed (HB)) were tested for their possible effects on adhesion and viability of the adherent human osteoblast cells by using an agar-based in vitro technique. Viability of the cells was assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and trypan blue staining. Results: The rate of cell adhesion did not seem to be significantly affected by the differences in surface features of dental implants (AN:78.21±0.52; BW:78.22±0.48; GA:78.44±0.85; HB:77.26±0.96). The surface features of the dental implants had an impact on the viability of the attached cells on the implants. Viability of the attached cells was significantly higher on AN, BW, GA surfaces when compared to the HB surface (AN: 72.28±6.04, BW: 67.02±3.47, GA: 85.82±5.05, and HB: 27.98±10.47). Conclusions: In vitro findings suggests that AN, BW, GA surfaces may provide a better platform than HB surfaces to maintain the viability of bound cells.
Synthesis of amphipathic superparamagnetic Fe3O4 Janus nanoparticles via a moderate strategy and their controllable self-assembly

Recently, amphipathic Janus Fe3O4 nanoparticles, with distinct hydrophilic and hydrophobic “faces”, have attracted tremendous attention. However, conventional synthesis methods for these nanoparticles either influenced the superparamagnetism of Fe3O4 nanoparticles or failed to endow Fe3O4 nanoparticles evident amphipathy, and thus as-prepared amphipathic Janus Fe3O4 nanoparticles hardly reveal remarkable self-assembly properties. To address these critical issues, a versatile strategy that combines activators regenerated by electron transfer for atom transfer radical polymerization (ARGET-ATRP) and Pickering emulsions is proposed here to synthesize amphipathic Janus Fe3O4 nanoparticles for the first time. Owing to the masterly integration all of the unique features of ARGET-ATRP and Pickering emulsions, Janus Fe3O4 nanoparticles with high saturation magnetization and diverse self-assembly behaviors in different solvents are successfully prepared in a gentle, simple and highly controllable reaction condition. Furthermore, these Janus Fe3O4 nanoparticles exhibit prominent emulsification in toluene–water model system as their superior amphipathy. These emulsions could not only maintain excellent stability even in an extremely low usage of Janus Fe3O4 nanoparticles, but also be easily separated by an external magnetic field because of the intrinsic superparamagnetism of Fe3O4. The result implies that the as-received Janus Fe3O4 nanoparticles may have extensive application prospects in oil purification from oil–water compounds. This synthesis strategy likewise could be a guidance to synthesize similar Janus materials.
Arsenic fixation in relation to the sterilization of soils with sodium arsenite

Abstract does not appear. First page follows. Introduction Data from greenhouse experiments on the toxicity of arsenic to oats in 80 California soils are reported by Crafts and Rosenfels (7)5 in another paper of this issue. In most of the soils tested, texture predominated as a determiner of toxicity; that is, toxicity was greatest in light and least in heavy soils. The few exceptions to this general rule are explained by the content of iron compounds of the soils (as indicated by their reddish color) or by the properties of the soil colloids. A similar relation between toxicity and soil texture has been noted by Cooper, et al. (4) and (5), Albert and Arndt (2), and Albert (1) working with South Carolina soils, and Reed and Sturgis (11), working with Louisiana soils. The total arsenic content of a soil has not proved to be a satisfactory criterion of toxicity. As Vandecaveye, Horner, and Keaton (15) have shown, arsenic toxicity to barley is more closely correlated with the fraction soluble in 0.1 N ammonium acetate solution than with the fraction soluble in hot concentrated HNO3. The results of Reed and Sturgis (11) show that the total arsenic content of the soil does not determine toxicity to rice. They indicate that toxicity is more closely correlated with arsenic soluble in 0.05 N HCl than with that soluble in water. According to Albert and Arndt (2), arsenic soluble in a collodion-bag dialysate is a reliable index of toxicity, whereas total arsenic is not. Greaves (9) has found no correlation between total and water-soluble arsenic in orchard soils.
Litinium gludi sp. nov. (Nematoda, Oxystominidae) from Kermadec Trench, Southwest Pacific Ocean

Recent work on the taxonomy of nematodes in Southwest Pacific Ocean trenches has led to the discovery of taxa which so far appear to be restricted to the oceans’ deepest environments. Here, Litinium gludi sp. nov. is described based on specimens obtained from a deep basin within the Kermadec Trench at 9540 m water depth. The new species differs from other species of the genus in having a conico-cylindrical tail, papillose labial sensilla, and heart- or leaf-shaped amphideal fovea. Both SSU and LSU phylogenetic analyses provide strong support for the placement of the new species within a clade containing both Thalassoalaimus and Litinum, and within Oxystomininae, which is consistent with the structure of the female reproductive system with only one posterior ovary in this subfamily. Our molecular analyses also indicate that the new species is most closely related to Thalassoalaimus despite lacking a caudal capsule, the main trait characterizing the latter genus, and despite being most morphologically similar to Litinium, particularly in the unusual shape of the amphideal fovea. However, given the changing definitions of the closely-related genera Thalassoalaimus and Litinium in recent years, available GenBank sequences may have been misidentified, which makes the interpretation of molecular phylogenetic analyses problematic. Given the current morphological definitions of Litinium and Thalassoalaimus, we choose to place the new species within Litinium, despite the apparently contradictory findings of molecular phylogenetic analyses. The placement of Cricohalalaimus in a clade with Thalassoalaimus and Litinium in both SSU and LSU analyses indicates that this genus should be placed within the Oxystomininae and not the Halalaiminae as in the current classification. This new proposed grouping is consistent with variation in the structure of the female reproductive system, a feature which appears more taxonomically informative than amphid shape for subfamily-level classification.
Ag-doped ZnO nanorods synthesized by two-step method

A two-step method is adopted to synthesize Ag-doped ZnO nanorods. A ZnO seed layer is first prepared on a glass substrate by thermal decomposition of zinc acetate. Ag-doped ZnO nanorods are then assembled on the ZnO seed layer using the hydrothermal method. The influences of the molar percentage of Ag ions to Zn ions (RAg/Zn) on the structural and optical properties of the ZnO nanorods obtained are carefully studied using X-ray diffractometry, scanning electron microscopy and spectrophotometry. Results indicate that Ag ions enter into the crystal lattice through the substitution of Zn ions. The (002) c-axis-preferred orientation of the ZnO nanorods decreases as RAg/Zn increases. At RAg/Zn > 1.0%, ZnO nanorods lose their c-axis-preferred orientation and generate Ag precipitates from the ZnO crystal lattice. The average transmissivity in the visible region first increases and then decreases as RAg/Zn increases. The absorption edge is first blue shifted and then red shifted. The influence of Ag doping on the average head face, and axial dimensions of the ZnO nanorods may be optimized to improve the average transmissivity at RAg/Zn < 1.0%.
Compensated two-step Maruyama methods for stochastic differential equations with Poisson jumps

In this article we present the compensated two-step Maruyama methods for the stochastic differential equations with Poisson jumps. Mean-square (M-S) order convergence is established and M-S stability analysis are displayed. In particular, compensated two-step Maruyama methods of Adams type are considered, their linear M-S stability regions compared with compensated Euler–Maruyama method (EM) are plotted. Numerical results show the M-S convergence and stability are given.
Risk Factors of Domestic Violence Reported Before and During the Covid-19 Pandemic: A Systematic Review

The presence of COVID-19 as a global pandemic has triggered the implementation of several policies such as lockdowns to limit public interactions and controlling the spread of the virus. Meanwhile, pandemic also impacts business and jobs resulted in downsizing and reduced income, leading to increased stress on workers. Stay at home in high tension can easily trigger conflict and domestic violence. The study aims to investigate the risk factors for domestic violence reported before the COVID-19 pandemic and during the pandemic. We performed a systematic literature review on the following databases: Scopus, Science Direct, and ProQuest by using the following keywords: Risk AND Factor AND Domestic AND Violence. From 3965 articles found, we selected 33 articles matched to the inclusion criteria and eligible for review. Our analysis revealed that socioeconomic conditions and mental health are still the risk factors of domestic violence before and during the pandemic. However, no studies reported childhood experiences as a risk factor of domestic violence during the pandemic, though studies before the pandemic highlighted childhood experiences as a risk factor for domestic violence. Reports on risk factors of domestic violence during a pandemic are scarce. Future studies are needed to investigate domestic violence factors during the pandemic to develop an effective prevention program.
DFC: A Performant Dagging Approach of Classification Based on Formal Concept

Knowledge discovery data (KDD) is a research theme evolving to exploit a large data set collected every day from various fields of computing applications. The underlying idea is to extract hidden knowledge from a data set. It includes several tasks that form a process, such as data mining. Classification and clustering are data mining techniques. Several approaches were proposed in classification such as induction of decision trees, Bayes net, support vector machine, and formal concept analysis (FCA). The choice of FCA could be explained by its ability to extract hidden knowledge. Recently, researchers have been interested in the ensemble methods (sequential/parallel) to combine a set of classifiers. The combination of classifiers is made by a vote technique. There has been little focus on FCA in the context of ensemble learning. This paper presents a new approach to building a single part of the lattice with best possible concepts. This approach is based on parallel ensemble learning. It improves the state-of-the-art methods based on FCA since it handles more voluminous data.