Browse Articles

Interaction at pre-bonding distances and bond formation for open <i>p</i>-shell atoms with different orientations of their angular momenta

We employ the ΔSCF framework to build and optimize, via the Maximum Overlap Method (MOM), non-Aufbau Hartree–Fock determinants for molecular systems containing atoms with open p-shells. We use these determinants in the coupled-cluster (CC) Ansatz to calculate interaction energies at pre-bonding distances and to study bond formation pathways for the ground and excited states of the molecules. We propose that the MOM-CC combination presents a straightforward and general way to study interatomic forces between open-shell atoms with arbitrarily populated orbitals. As a practical application of the MOM combined with CC with singles, doubles, and perturbative triples, we demonstrate that fixed multipole Coulomb interactions between open p-shell atoms play an important role at pre-bonding distances and can lead to an overall repulsive interaction between two neutral atoms. Using three diatomic molecules, B2, Al2, and AlB, as examples, we illustrate how the mutual orientation of atomic p-orbitals at large separations determines the type of the established chemical bond. Implementation of the proposed method for atoms with other configurations of unfilled shells is straightforward. We also demonstrate that a previously proposed classical small dielectric spheres model, which has been shown to be highly accurate for interactions between closed-shell atoms, remains more accurate than density-functional theory calculations also for atoms with open shells. This suggests that rigorous classical electrostatics is capable of capturing a significant part of electron correlation and polarization effects and potentially can be used for accurate yet low-cost calculations of pre-bonding interactions between larger molecules, for which high-level quantum chemical methods would be computationally impractical.

Incremental self-organization of spatio-temporal spike pattern detection

Abstract Nervous systems utilize temporally precise patterns of activity. However, the mechanisms by which spike patterns are processed are not known. In particular, the fact that during learning different patterns are distributed over time raises the question of how groups of neurons become selective for new spike patterns without overwriting already learned patterns. A simple one-layer spiking neural network model is presented that learns to recognize spatiotemporal spike patterns sequentially. The approach integrates biological synaptic mechanisms, including Hebbian learning, heterosynaptic plasticity, and synaptic scaling, allowing groups of neurons to self-organize selectivity for a set of spike patterns. Spoken words, transformed by a cochlear model into spatio-temporal spike patterns, are learned without supervision. This work suggests how the brain can use temporal spike codes and provides a novel, scalable, efficient, and noise-tolerant solution to the stability-plasticity dilemma.

Self-assembled hemimicelles of perfluoroalkylalkanes: How chain length, shape, and dipole determine internal structure. A new (geometrical + electrostatic) model

Perfluoroalkylalkanes form nanostructured Langmuir films comprising discrete surface aggregates or hemimicelles. The aggregates are formed by a large number of molecules (∼2000–3000), are highly monodisperse and round-shaped, and display a characteristic pit in their center. In this study, the influence of the hydrogenated and the perfluorinated chain lengths on the size of the hemimicelles was probed by a systematic atomistic molecular dynamics simulation study and further rationalized in terms of a model that describes the internal structure of the hemimicelles. The model, while set on geometrical considerations, was developed using quantitative information obtained from the molecular dynamics simulations performed using an atomistic force field that includes detailed electrostatic and dispersive intramolecular and intermolecular interactions. These interactions are therefore intrinsically incorporated in the model, which is consequently designated geometrical + electrostatic (G+E model). Eleven PFAA molecules (F8Hm: F8H14, F8H16, F8H18, F8H20; FnH16: F6H16, F8H16, F10H16, F12H16; F10Hm: F10H14, F10H18, F10H20) and the corresponding hemimicelles were studied, covering a representative range of molecular structures. The results of both the molecular dynamics simulations and the new model reproduce the available experimental data within the respective uncertainties. The results provide a rational basis for a complete understanding of the self-assembly process of PFAA molecules into discrete hemimicelles, well-founded in physical principles and molecular properties.

Fenlong-ridging combined with composite modifier reconstructs soil microbiome to mitigate saline stress and enhance sustainable cultivation of Isatis indigotica Fortune

Understanding the heterogeneous nucleation of ice on silver iodide using deep potential molecular dynamics

Ice nucleation is one of the most unique and widespread phase transitions on Earth. Due to the relatively low phase transition energy barrier to overcome and the ubiquitous existence of foreign substrates, ice nucleation primarily occurs heterogeneously in nature. Despite extensive studies, our understanding of the molecular-scale heterogeneous nucleation process under the influence of silver iodide (AgI) substrate interactions, one of the most efficient ice nucleating agents, remains limited. Using a deep neural network potential, we perform molecular dynamics simulations with ab initio accuracy to investigate the heterogeneous nucleation of ice on AgI. By analyzing the free energy surface of water molecules at the AgI–water interface, we systematically elucidate the mechanism behind the formation of an ice-like hexagonal layer on AgI. The reconstruction of the metastable, disordered hydrogen bond network into this ice-like hexagonal layer facilitates ice nucleation and contributes to the asynchronous crystallization manner. Furthermore, we find that the influence of the AgI substrate propagates through the highly dynamical and collaborative hydrogen bond network, leading to a pre-ordered region at the ice–water interface that reduces the ice growth rate to approximately one-third compared to ice homogeneous nucleation conditions. These findings provide new insights into the early stages of ice heterogeneous nucleation on the AgI surface and expand our understanding of the role substrates play in this process.

Dynamical study of optical soliton solutions of time-fractional perturbed model in ultrafast optical fibers

Practical considerations for accurate estimation of diffusion parameters from single-particle tracking in living cells

Advances in fluorescence microscopy have enabled high-resolution tracking of individual biomolecules in living cells. However, accurate estimation of diffusion parameters from single-particle trajectories remains challenging due to static and dynamic localization errors inherent in these measurements. While previous studies have characterized how such errors affect mean-squared displacement (MSD) analysis, practical guidelines for minimizing them during data acquisition and correcting them during analysis are still lacking. Here, we combine theoretical modeling and simulations to evaluate how exposure time and sampling rate influence the accuracy of MSD-based inference under fractional Brownian motion (FBM), a canonical model of anomalous diffusion. We demonstrate that decoupling exposure and sampling times enables escape from the error-prone regime, thus improving inference accuracy, and that incorporating an offset in nonlinear MSD fitting substantially improves the estimation of the anomalous diffusion exponent. We validate this framework using trajectories of cytoplasmic particles in Escherichia coli, recovering consistent diffusion parameters across multiple datasets. We further prove that the framework extends beyond FBM to general cases of subdiffusion, thereby offering practical strategies to improve both experimental design and data analysis in single-particle tracking of live or synthetic systems.

Tracking control of air flow based on a fractional-order model of the lung impedance

Abstract A fractional order output feedback controller for a lung ventilator is designed. This is based on a state-of-the-art electrical analogue model of the human respiratory system in the form of a network of resistors and fractional capacitors. The electrical input impedance of the adopted analogue can be suitably tuned to fit experimental ventilation impedance data. Furthermore, it can explicitly account for the different physiological fractal type characteristics associated with lung formation such as branching morphogenesis associated to the treelike tubular network and alveolar differentiation associated with the generation of specialized epithelial cells for gas exchange. A description of this electrical analogue in pseudo-state space is then proposed. The aim is to finally provide a control methodology within the scope of output feedback control, when the measured output which is the airflow through the trachea is directed to follow a specified reference. The control provides adequate air pressure input to generate this nominal airflow. The proposed control design includes a pseudo-state observer and a double leaky integrator. The gains involved are designed using constraints imposed through linear matrix inequalities (LMIs), which enforce a regional allocation of eigenvalues. The robustness of the control loop is analysed through an uncertainty matrix analysis linked directly to the model. It is observed that the proposed design can tolerate a relatively wide variation in physiological parameters ( $$\pm 15\%$$ ). The proposed formulation advances current control design approaches for mechanical ventilators and provides a generic methodology for the control of complex system with emergent responses as encountered in bioengineering.

Computation of the heat capacity of water from first principles

Water is a unique solvent with many remarkable properties. An example is its exceptionally high heat capacity, which plays an important role in storing and transporting thermal energy, with implications for many processes from regulating the body temperature of living organisms to moderating our climate at the global scale. To elucidate the microscopic origin of the heat capacity of water from first principles, highly accurate computer simulations are required. Apart from a reliable description of the atomic interactions, the presence of light hydrogen atoms necessitates the explicit consideration of nuclear quantum effects through path integral molecular dynamics (PIMD) simulations. The high computational costs of PIMD simulations, which are even further increased by the need for an extensive statistical sampling of energy fluctuations to determine the heat capacity, can be strongly reduced by replacing first principles calculations with machine learning potentials to represent the atomic interactions. In this study, we use high-dimensional neural network potentials constructed from density functional theory calculations employing the RPBE-D3 and revPBE0-D3 functionals. To further enhance the computational performance, we introduce a highly efficient PIMD algorithm that computes in parallel not only the energies and forces but also the coordinate and thermostat time evolutions. Using this approach, we are able to determine converged data for the heat capacity from a 4 ns simulation employing 128 beads. In particular, for the revPBE0-D3 functional, we find excellent agreement with experiment, providing evidence that our approach represents a promising framework for the quantitative understanding of the thermodynamic properties of water and aqueous solutions.

Traffic flow prediction based on temporal attention and multi-graph adjacency fusion using DynamicChebNet

Abstract Accurate and timely traffic flow prediction plays a crucial role in improving road utilization, reducing congestion, and optimizing public transportation management. However, modern urban traffic faces challenges such as complex road network structures and the variation in traffic flow across different temporal and spatial scales. These issues lead to complex spatiotemporal correlations and heterogeneity, resulting in low prediction accuracy and poor real-time performance of existing models. In this work, we propose a novel traffic flow prediction model called TMDCN (Temporal Attention and Multi-Graph Adjacency Fusion Using DynamicChebNet), which integrates temporal attention and multi-graph adjacency matrix fusion. First, to address the difficulty of capturing dependencies across multiple time scales, we construct a Temporal Feature Extraction Block that combines attention mechanisms with multi-scale convolutional layers, enhancing the model’s ability to handle complex traffic pattern changes and capture flow variations and temporal dependencies. Next, we leverage multi-graph adjacency matrix fusion and dynamic Chebyshev graph convolutional networks to capture the spatial dependencies of the traffic network. Experiments on the PeMS04 and PeMS08 datasets show that, compared to conventional methods, the proposed method reduces the Mean Absolute Error (MAE) of traffic flow prediction one hour ahead to 18.33 and 13.72, respectively. The source code for this paper is available at https://github.com/tyut-zjb/TMDCN.

Levy–Perdew–Sahni equation and its application to perform atomic calculations

Levy–Perdew–Sahni (LPS) derived the connection between the asymptotic decay of density and the ionization potential of a many-electron system using the equation for the square root of density. For this, they employed an expression for the corresponding effective potential in terms of the wavefunction of the system. In this paper, we explore the possibility of solving the LPS equation in conjunction with approximate wavefunction. For this, we first perform the variational derivation of the equation and use this to set up a self-consistent cycle to get the ground state properties of two-electron systems using the modified form of the Le Sech wavefunction. Furthermore, using the observation that even the approximate wavefunctions give the accurate effective potential for the LPS equation, we show that accurate densities are obtained through using the LPS equation with these wavefunctions. We demonstrate our method by performing calculations for closed-shell atoms.

Enhancing prognostic accuracy in PMBCL: semiquantitative analysis of interim PET/CT scans

Abstract Primary mediastinal large B-cell lymphoma (PMBCL) is a rare, aggressive lymphoma affecting young adults. Interim PET/CT (iPET/CT) scans are used to assess treatment response, but the positive predictive value of standard Deauville score remains limited. This retrospective multicenter study analyzed 116 PMBCL patients treated with anthracycline-based chemoimmunotherapy, focusing on 90 patients with high quality iPET/CT. Semiquantitative radiomics metrics, including changes in maximum standardized uptake value (dSUVmax), metabolic tumor volume (dMTV), and total lesion glycolysis (dTLG), were assessed alongside event-free survival (EFS). All interim and final PET/CT scans were independently reviewed by two nuclear medicine physicians blinded to outcomes. Among the 90 patients, 62 (68.9%) were iPET-positive (Deauville scores 4–5). Event-free survival (EFS) at 3 years was significantly higher in iPET-negative patients compared to iPET-positive patients (75% vs. 29%; p &lt; 0.01). Radiomics analysis demonstrated that dSUVmax, dMTV, and dTLG provided superior predictive accuracy for EFS. Values below optimized cut-off thresholds demonstrated significantly better outcomes (e.g., 3-y EFS: 77.8% for dSUVmax ≥ 80% vs. 11.1% for dSUVmax &lt; 80%, p &lt; 0.01). Radiomics-based metrics outperformed visual iPET/CT assessment in identifying high-risk patients, underscoring their potential in guiding treatment. Future research should integrate radiomics with clinical factors to enhance PET-guided treatment strategies.

How special are the dynamics of deep eutectic solvents? A look at the prototypical case of ethaline

We investigated the molecular dynamics of the prototypical deep eutectic solvent (DES) ethaline. We disentangled the different motions of its two constituents, namely choline chloride and ethylene glycol, on a spatiotemporal range that extends from sub-nanometer to micrometer distances and from picoseconds to milliseconds. This was achieved by a combination of pulsed-field-gradient NMR, time-of-flight, and backscattering quasielastic neutron scattering experiments with isotopically labeled samples. On the micrometer scale, we observe that the translational motions of the two DES constituents obey classical hydrodynamics, with distinct diffusivities that reflect their different hydrodynamic radii. This is no longer valid at the nanometer scale, where the two DES components present similar short-ranged diffusivities, indicating a significant effect of their supramolecular association. The sub-nanometer scale motions include jumps that precede Fickian diffusion and localized dynamics that precede the breaking of the transient cage formed by neighboring molecules. Therein, the spatial amplitude of the localized motions mirrors their different molecular sizes and chemical structures, while their respective correlation times contrast with observations made for other choline-based DESs such as glyceline. This result underlines the importance of more subtle effects, such as the different H-bond propensities of the polyol donor, and demonstrates the difficulty in anticipating the nanoscale dynamic behavior of DESs from knowledge of their macroscopic properties.

Effects of reduced voluntary effort on muscle mechanical properties during loaded medicine ball throws

Spatial landscape and flux for exploring protein pattern formation in rod-shaped bacteria

Spatial patterns formed by biomacromolecules such as proteins are widely present in biological systems and are closely related to fundamental cellular processes. A classic example is the spatial patterning of Min proteins in bacteria, where pole-to-pole oscillations of these patterns guide symmetric cell division. To uncover the underlying mechanisms behind the formation and transition of spatial patterns in the Min protein system, we applied nonequilibrium landscape–flux theory combined with the mode expansion method. By quantifying and visualizing the potential landscape in mode space, we identified distinct stable spatial patterns as potential wells, providing a global perspective on the system’s stability. Moreover, we revealed that nonequilibrium flux acts as the driving force for spatial pattern switching with increasing cell length or molecular detachment rates. Peaks in the average flux and entropy production rate near phase boundaries highlight significant changes in dynamical nature and thermodynamic cost during critical transitions, offering deeper insights into the physical mechanisms underlying spatial pattern transitions. These findings not only underscore how spatial landscape topography and flux dynamics collectively govern the formation, stability, and switching of protein patterns but also establish a powerful framework for linking nonequilibrium physical mechanisms to biological functions. Furthermore, this framework holds potential applications, such as the detection of early warning signals for cell division.

Using social reinforcement in online Language learning to foster motivation through self-determination theory

<i>Ab initio</i> study of the far infrared spectrum and gas phase formation reactions of methyl ethyl ketone (CH3–CO–CH2–CH3)

Highly correlated ab initio calculations are employed for a complete spectroscopic characterization of methyl ethyl ketone (MEK). Thermochemical and kinetic properties of formation processes, suitable for the gas phase atmospheric and astrophysical environments, are determined. Among 13 formation processes, three bi-radical addition reactions were found as the most likely, for which the temperature-dependent rate coefficients are provided. The search of conformers at the CCSD(T)-F12 level of theory leads to two stable structures Ap (Cs) and Sp (C1), which depend strongly on the correlation energy. The stability of Ap-MEK is noticeable, whereas Sp can transform into Ap by vibrational excitations at very low temperatures since conformers are separated by low energy barriers. Three internal rotations, the torsion of ethyl group (α), and the torsions of the two methyl groups (θac and θet) interconvert 27 minima of the potential energy surface. In both conformers, V3ac &amp;lt;&amp;lt;&amp;lt; V3et. To explore the far infrared region and to map the low torsional energy levels and splittings, a variational procedure of reduced dimensionality is employed. The ground vibrational state splits into nine components distributed in two groups at 0.0 cm−1 (A1 and E2) and 0.289 cm−1 (E1, E3, and E4). Accurate rotational parameters are provided.

DEP-CEEMDAN-MPE-INHT a time-frequency analysis method for noisy blasting seismic waves with adaptive noise suppression and endpoint processing

Assessment of the time correlation function-based approach for absorption spectrum calculations using time-dependent density functional theory and molecular dynamics simulations

Electronic spectra, including absorption spectra, provide crucial insight into the electronic properties of molecular systems. Single-point excited-state calculations using quantum chemical (QC) methods, such as time-dependent density functional theory, can predict peak positions and their intensities of the spectra; however, these methods are inherently incapable of capturing spectral broadening effects. Herein, we present a comprehensive assessment of an approach based on time correlation functions (TCFs) that enables the computational prediction of spectra, including broadening effects, by integrating QC calculations with classical molecular dynamics simulations. We systematically compared the absorption spectral shapes calculated using five levels of TCF-based spectral formulas, including the second-order cumulant approach, which are hierarchically related through successive approximations. To evaluate the applicability of the TCF-based approach, we selected two small organic pigment molecules, 3,4,5,6-tetrachlorofluorescein (FLU) and crystal violet (CST), as test cases. By assessing the impact of different approximations on the predicted spectral shapes, we found that for FLU, all approximation levels yielded comparable results, whereas for CST, certain approximations led to significant deviations. These discrepancies may be caused by rapid fluctuations of transition dipole moments in CST, which has a flexible molecular skeleton, in contrast to FLU, which has a relatively rigid structure. In addition, using Kubo’s stochastic theory allowed us to investigate the relationship between the timescale of molecular fluctuations and spectral broadening. Our analysis confirms that molecular rigidity plays a critical role in determining the accuracy of spectral shape predictions.

Investigating invasion patterns of Callinectes sapidus and the relation with research effort and climate change in the Mediterranean Sea