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Dimensional confinement and superdiffusive rotational motion of uniaxial colloids in the presence of cylindrical obstacles
In biological systems such as cells, the macromolecules, which are anisotropic particles, diffuse in a crowded medium. In the present work, we have studied the diffusion of spheroidal particles diffusing between cylindrical obstacles by varying the density of the obstacles as well as the spheroidal particles. Analytical calculation of the free energy showed that the orientational vector of a single oblate particle will be aligned perpendicular, and a prolate particle will be aligned parallel to the symmetry axis of the cylindrical obstacles in equilibrium. The nematic transition of the system with and without obstacles remained the same, but in the case of obstacles, the nematic vector of the spheroid system always remained parallel to the cylindrical axis. The component of the translational diffusion coefficient of the spheroidal particle perpendicular to the axis of the cylinder is calculated for the isotropic system, which agrees with analytical calculation. When the cylinders overlap such that the spheroidal particles can only diffuse along the direction parallel to the axis of the cylinder, we can observe dimensional confinement. This was observed by the discontinuous fall of the diffusion coefficient, when plotted against the chemical potential both for a single particle and for a finite volume fraction. The rotational diffusion coefficient quickly reached the bulk value as the distance between the obstacles increased in the isotropic phase. In the nematic phase, the rotational motion of the spheroid should be arrested. We observed that even though the entire system remained in the nematic phase, the oblate particle close to the cylinder underwent a flipping motion. The consequence is that when the rotational mean squared displacement was calculated, it showed a super-diffusive behavior even though the orientational self-correlation function never relaxed to zero, showing this to be a very local effect.
Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism
Trajectory analysis of anomalous dynamics in optical lattice
We apply the trajectory formulation to analyze the anomalous dynamics of cold atoms in an optical lattice. The phase space probability density function of cold atoms, their dynamics, and the mechanism of dynamic evolution from an initial Gaussian distribution to a power-law distribution are analyzed. The results of the trajectory formulation are in good agreement with the previously reported experimental results for the exponent of position variance for a long time and the position–momentum correlation. The self-similar natures of trajectories in phase space are found for Lévy distributions. Our results unify the raw moments that can be expressed as the summation of a number of independent, identically distributed variables and the anomalous dynamics, which holds promise for an intuitive interpretation anomalous behavior and their kinetic mechanisms from initial Gaussian to anomalous distributions for a long time.
Therapeutic experience and key techniques of tubeless percutaneous nephrolithotomy
Out-of-phase ELDOR spectroscopy: A precise tool for investigating structure and dynamics of charge-transfer states in organic photovoltaic blends
We developed a technique allowing the direct observation of photoinduced charge-transfer states (CTSs)—the weakly coupled electron–hole pairs preceding the completely separated charges in organic photovoltaic (OPV) blends. Quadrature detection of the electron spin echo (ESE) signal enables the observation of an out-of-phase ESE signal of CTS. The out-of-phase Electron–Electron Double Resonance (ELDOR) allows measuring electron–hole distance distributions within CTS and its temporal evolution in the microsecond range. The technique was applied to OPV bulk heterojunction blends of different donor polymers, including the benchmark polymer P3HT and the high-performance polymer PCDTBT, with the fullerene PC61BM acceptor. The corresponding electron–hole distance distributions were obtained using the Tikhonov regularization. It was found that not only the dipolar interaction but also the exchange interaction contributes to the formation of the out-of-phase ELDOR signal. By varying the delay time after photoexcitation, we observed CTSs at different stages of charge separation. The initial distribution of the electron–hole distances for different blends correlates with their photoelectric conversion efficiency, with shorter average thermalization distances found for the blends of PC61BM with the less efficient regiorandom polymer P3HT. Spin-selective recombination of the CTS was unambiguously demonstrated for the blend of regioregular P3HT with PC61BM. It produces characteristic features in the out-of-phase ELDOR trace for small “dipolar” evolution times. These data allow us to estimate the CTS recombination rate for a certain distance between the electron and the hole within the CTS. The proposed method can be used to probe CTS in a variety of OPV active layer materials.
Ensemble learning based sustainable approach to rebuilding metal structures prediction
The theory of Barlow packings: Basic properties and cohesive energies from exact lattice summations within the sticky hard-sphere model
The theory of periodic Barlow multi-lattices (X1X2…XN)∞ with Xi ∈ {A, B, C} and Xi ≠ Xi+1 of stacked two-dimensional hexagonal close-packed layers is presented and used to derive exact lattice sum expressions in terms of fast converging Bessel function expansions for inverse power potentials. We describe in detail the mathematical properties of Barlow sphere packings and demonstrate that only two basic lattice sums are required to describe all periodic packings. For the sticky hard-sphere model with an attractive inverse power law potential, we find a linear correlation between the cohesive energies of different Barlow packings and the face-centered cubic packing fraction. We introduce an efficient algorithm for enumerating the unique periodic Barlow sequences for any given period N. The theory and lattice sums introduced here pave the way for the future treatment of Barlow multi-lattices.
Association between behavioral and sociodemographic factors and high subjective health among adolescents: a nationwide representative study in South Korea
Active learning of molecular data for task-specific objectives
Active learning (AL) has shown promise to be a particularly data-efficient machine learning approach. Yet, its performance depends on the application, and it is not clear when AL practitioners can expect computational savings. Here, we carry out a systematic AL performance assessment for three diverse molecular datasets and two common scientific tasks: compiling compact, informative datasets and targeted molecular searches. We implemented AL with Gaussian processes (GP) and used the many-body tensor as molecular representation. For the first task, we tested different data acquisition strategies, batch sizes, and GP noise settings. AL was insensitive to the acquisition batch size, and we observed the best AL performance for the acquisition strategy that combines uncertainty reduction with clustering to promote diversity. However, for optimal GP noise settings, AL did not outperform the randomized selection of data points. Conversely, for targeted searches, AL outperformed random sampling and achieved data savings of up to 64%. Our analysis provides insight into this task-specific performance difference in terms of target distributions and data collection strategies. We established that the performance of AL depends on the relative distribution of the target molecules in comparison to the total dataset distribution, with the largest computational savings achieved when their overlap is minimal.
Tackling misinformation in mobile social networks a BERT-LSTM approach for enhancing digital literacy
Abstract The rapid proliferation of mobile social networks has significantly accelerated the dissemination of misinformation, posing serious risks to social stability, public health, and democratic processes. Early detection of misinformation is essential yet challenging, particularly in contexts where initial content propagation lacks user feedback and engagement data. This study presents a novel hybrid model that combines Bidirectional Encoder Representations from Transformers (BERT) with Long Short-Term Memory (LSTM) networks to enhance the detection of misinformation using only textual content. Extensive evaluations revealed that the BERT-LSTM model achieved an accuracy of 93.51%, a recall of 91.96%, and an F1 score of 92.73% in identifying misinformation. A controlled user study with 100 participants demonstrated the model’s effectiveness as an educational tool, with the experimental group achieving 89.4% accuracy in misinformation detection compared to 74.2% in the control group, while showing increased confidence levels and reduced decision-making time. Beyond its technical efficacy, the model exhibits significant potential in fostering critical thinking skills necessary for digital literacy. The findings underscore the transformative potential of advanced AI techniques in addressing the challenges of misinformation in the digital age.
Controlling crystal planes of biomass-derived carbon based Mo2C NPs and the electrochemical performance
The electrochemical property of Mo2C nanoparticles (NPs) depends on the structure and crystal planes. Herein, Mo2C nanoparticles were prepared and dispersed on carbon nanosheets by the construction of a biomass-derived carbon precursor, and the exposed dual crystal planes were also controlled by optimal conditions. The structure, compositions, and morphology of the carbon-based Mo2C were characterized, and the Mo2C NPs were well dispersed on the carbon nanosheets. The electrochemical study shows that optimal Mo2C exhibits excellent electrochemical properties for the oxidation of nicotine compared with other materials in the broad linear range of 0.2–300 μM. In particular, it displays a remarkable oxidation ability for the low-concentration nicotine (0.2–5 μM), and the detection limit is about 0.17 μM. Furthermore, the exposed dual crystal planes of Mo2C play a critical role in the oxidation. Notably, this characteristic of Mo2C NPs makes it possible to detect nicotine from the extracted solution and be used for chip electrodes to detect nicotine quickly via wireless response, which exhibits actual application prospects for portable detection. The results indicate that the as-prepared Mo2C material could be effective and low-cost for nicotine analysis in the sectors of health management and medical fields.
AL161431.1 is identified as a biomarker for bladder cancer progression and immunotherapy response
Amorphous-dominated MgO hollow spheres enhanced fluoride adsorption: Mechanism analysis and machine learning prediction
Amorphous-dominated magnesium oxide hollow spheres (A-MgO) were prepared using a spray-drying method in this study. These hollow spheres exhibited excellent sphericity, large specific surface areas, and abundant porosity. A-MgO exhibited outstanding fluoride adsorption properties, with a maximum adsorption capacity of 260.4 mg/g. When the pH value was less than 8, the fluoride removal percentage remained more than 87.4%. Moreover, the removal percentage remained above 75% even after five application cycles. In addition, the research revealed that SO42−, CO32−, and PO43− exerted a more pronounced effect on fluoride removal, whereas coexisting ions such as Br−, Cl−, NO3−, and HCO3− had minimal impact on this process. An in-depth analysis of the adsorption mechanism demonstrated that the process of fluoride adsorption by A-MgO involves various synergistic mechanisms, such as electrostatic adsorption, ion exchange, oxygen vacancy adsorption, physical adsorption, and pore filling. To predict the fluoride ion adsorption performance of A-MgO under complex conditions, a high-performance machine learning model, GBDT-S, was developed using hyperparameter optimization. The R2 of 0.99 and 0.80 for the training and testing datasets, respectively, with the RMSE of 3.26 and 3.89. Interpretative analysis using SHapley Additive exPlanations technology indicated that reaction time, PO43− concentration, and pH were key factors influencing the fluoride ion removal percentage.
Pre-stack seismic inversion for reservoir characterization in Pleistocene to Pliocene channels, Baltim gas field, Nile Delta, Egypt
AbstractThe Nile Delta, North Africa’s leading gas-producing region, was the focus of this study aimed at delineating gas-bearing sandstone reservoirs from the Pleistocene to Pliocene formations using a combination of pre-stack inversion and rock physics analysis. This research employed seismic inversion techniques, including full-angle stack seismic volumes, well logs, and 3-D with rock physics modeling to refine volumes of P-wave velocity (Vp), S-wave velocity (Vs), and density. Traditional seismic attributes, such as far amplitude, proved insufficient for confirming gas presence, highlighting partial angle stacks, integrated the need for advanced methods. Extended Elastic Impedance (EEI) analysis was used to predict fluids and identify lithology in clastic reservoir environments. The EEI approach facilitated the determination of optimal projection angles for key petrophysical properties such as porosity, shale volume, and water saturation. This method was applied to the middle Pliocene (Kafr El Sheikh Formation) and the Pleistocene (El Wastani Formation), revealing promising drilling sites. In the Kafr El Sheikh Formation, porosity ranged from 16 to 29%, shale volume from 21 to 40%, and hydrocarbon saturation from 25 to 90%. The study concludes that integrating pre-stack seismic inversion with EEI significantly enhances the likelihood of identifying gas-bearing sands while reducing exploration risks. The improved POS for the Pleistocene anomaly gas bearing sand (from 49 to 69%) and the middle Pliocene anomaly (from 46 to 66%) underscores the effectiveness of this approach in the Baltim Field, Offshore Nile Delta, and supports further drilling and development wells.
Hydrogen bonding blues: Vibrational spectroscopy of the TIP3P water model
The computational spectroscopy of water has proven to be a powerful tool for probing the structure and dynamics of chemical systems and for providing atomistic insight into experimental vibrational spectroscopic results. However, such calculations have been limited for biochemical systems due to the lack of empirical vibrational frequency maps for the TIP3P water model, which is used in many popular biomolecular force fields. Here, we develop an empirical map for the TIP3P model and evaluate its efficacy for reproducing the experimental vibrational spectroscopy of water. We observe that the calculated infrared and Raman spectra are blueshifted and narrowed compared to the experimental spectra. Further analysis finds that the blueshift originates from a shifted distribution of frequencies, rather than other dynamical effects, suggesting that the TIP3P model forms a significantly different electrostatic environment than other three-point water models. This is explored further by examining the two-dimensional infrared spectra, which demonstrates that the blueshift is significant for the first two vibrational transitions. Similarly, spectral diffusion timescales, evaluated through both the center line slope and the frequency–frequency time correlation function demonstrate that TIP3P exhibits significantly faster spectral dynamics than other three-point models. Finally, sum-frequency generation spectroscopy calculations suggest that despite these challenges, the TIP3P empirical map can provide phenomenological, qualitative, insight into the behavior of water at the air–water and lipid–water interfaces. As these interfaces are models for hydrophobic and hydrophilic environments observed in biochemical systems, the presently developed empirical map will be useful for future studies of biochemical systems.
Database of soil properties incorporating organic content from roots and soil organisms for regional slope stabilisation
High-throughput computational screening of auxetic two-dimensional metal dichalcogenides and dihalides
Auxetic materials hold tremendous potential for many advanced applications, but candidates are quite scarce, especially at two dimensions. Here, we focus on two-dimensional (2D) metal dichalcogenides and dihalides with the chemical formula MX2 by screening structures sharing the P4̄m2 space group among 330 MX2 compounds from the computational 2D materials database. Via high-throughput first-principles computations, 25 stable MX2 (M = Mg, Ca, Mn, Co, Ni, Cu, Zn, Ge, Cd, Sn; X = F, Cl, Br, I, O, S, Se) systems with in-plane negative Poisson’s ratios (NPRs) are successfully identified. Within these structures, 2D NiCl2 has the largest NPR value of −0.34, with a magnitude significantly higher than those of black phosphorene (−0.027) and SnO2 (−0.1). The distinct auxetic effect in MX2 originates from both the unique local corner-sharing tetrahedral structural motif under the low-dimensional effect and the strong orbital interaction between the d orbitals of M and the p orbitals of halogen/chalcogen atoms. As a result, Poisson’s ratio can be effectively tuned by enhancing the d–p interaction through an external biaxial strain. We reveal that these auxetic materials exhibit rich electronic and magnetic properties, covering nonmagnetic, ferromagnetic, or anti-ferromagnetic metals, semiconductors, and insulators. The extraordinary auxetic behaviors in combination with rich physical properties could lead to multifunctional nanomechanical, optoelectronic, and spintronic applications.
Prediction of white matter hyperintensities evolution one-year post-stroke from a single-point brain MRI and stroke lesions information
AbstractPredicting the evolution of white matter hyperintensities (WMH), a common feature in brain magnetic resonance imaging (MRI) scans of older adults (i.e., whether WMH will grow, remain stable, or shrink with time) is important for personalised therapeutic interventions. However, this task is difficult mainly due to the myriad of vascular risk factors and comorbidities that influence it, and the low specificity and sensitivity of the image intensities and textures alone for predicting WMH evolution. Given the predominantly vascular nature of WMH, in this study, we evaluate the impact of incorporating stroke lesion information to a probabilistic deep learning model to predict the evolution of WMH 1-year after the baseline image acquisition, taken soon after a mild stroke event, using T2-FLAIR brain MRI. The Probabilistic U-Net was chosen for this study due to its capability of simulating and quantifying the uncertainties involved in the prediction of WMH evolution. We propose to use an additional loss called volume loss to train our model, and incorporate stroke lesions information, an influential factor in WMH evolution. Our experiments showed that jointly segmenting the disease evolution map (DEM) of WMH and stroke lesions, improved the accuracy of the DEM representing WMH evolution. The combination of introducing the volume loss and joint segmentation of DEM of WMH and stroke lesions outperformed other model configurations with mean volumetric absolute error of 0.0092 ml (down from 1.7739 ml) and 0.47% improvement on average Dice similarity coefficient in shrinking, growing and stable WMH.
Accurate DFT simulation of complex functional materials: Synergistic enhancements achieved by SCAN meta-GGA
Complex functional materials are characterized by intricate and competing bond orders, making them an excellent platform for evaluating the newly developed strongly constrained and appropriately normed (SCAN) density functional. In this study, we explore the effectiveness of SCAN in simulating the electronic properties of displacive ferroelectrics (BaTiO3 and PbTiO3) and magnetoelectric multiferroics (BiFeO3 and YMnO3), which encompass a broad spectrum of bonding characteristics. Due to a significant reduction in self-interaction error, SCAN manifests its improvements over the Perdew–Burke–Ernzerhof (PBE) method in three aspects: SCAN predicts more accurate ionicity, produces more compact orbitals, and better captures d-orbital anisotropy. Particularly, these synergistic enhancements lead to notable phenomena in calculating the bandgap of YMnO3: while the PBE+U simulation may suggest a strong correlation appearance attributed to high Hubbard-like U values (∼5 eV), the value is dramatically lower (∼1 eV) in the SCAN+U method. Furthermore, we provide an intuitive analysis of SCAN’s operational principles by examining the complex electron densities involved. These insights are theoretically intriguing and have practical implications, potentially encouraging wider adoption of SCAN in the computational modeling of complex functional materials.