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Enviromentally friendly elements along with risks of intellectual impairment as well as dementia: A planned out assessment and also meta-analysis.
This Perspective briefly summarizes the available theoretical and computational non-Born-Oppenheimer methods and presents examples illustrating how analytical theories and nonadiabatic dynamics simulations can elucidate the fundamental principles of chemical and biological processes. These examples also highlight how theoretical calculations are able to guide the interpretation of experimental data and provide experimentally testable predictions for nonadiabatic processes. This article is part of the theme issue 'Chemistry without the Born-Oppenheimer approximation'.In order to simplify the numerical solution of the time-dependent or time-independent Schrödinger equations associated with atomic and molecular motions, the use of well-adapted coordinates is essential. Usually, this set of curvilinear coordinates leads to a Hamiltonian operator that is as separable as possible. Although their corresponding kinetic energy operator (KEO) expressions can be derived analytically for small systems or special kinds of coordinates, a numerical and exact approach allows one to compute them in terms of sophisticated curvilinear coordinates. Furthermore, the numerical approach enables one to easily define reduced-dimensionality or constrained models. We present here a recent implementation of this numerical approach that allows nested coordinate transformations, therefore leading to great flexibility in the definition of the curvilinear coordinates. Furthermore, this implementation has no limitations in terms of numbers of atoms or coordinate transformations. The quantum dynamics of the cis-trans photoisomerization of part of the retinal chromophore illustrates the construction of the coordinates and KEO part of a three-dimensional model. This article is part of the theme issue 'Chemistry without the Born-Oppenheimer approximation'.The impact of different initial conditions in non-adiabatic trajectory surface hopping dynamics within a hybrid quantum mechanical/molecular mechanics scheme is investigated. The influence of a quantum sampling, based on a Wigner distribution, a fully thermal sampling, based on classical molecular dynamics, and a quantum sampled system, but thermally equilibrated with the environment, is investigated on the relaxation dynamics of solvated fulvene after light irradiation. We find that the decay from the first singlet excited state to the ground state shows high dependency on the initial condition and simulation parameters. The three sampling methods lead to different distributions of initial geometries and momenta, which then affect the fate of the excited state dynamics. We evaluated both the effect of sampling geometries and momenta, analysing how the ultrafast decay of fulvene changes accordingly. The results are expected to be of interest to decide how to initialize non-adiabatic dynamics in the presence of the environment. This article is part of the theme issue 'Chemistry without the Born-Oppenheimer approximation'.Nonadiabatic dynamics simulations in the long timescale (much longer than 10 ps) are the next challenge in computational photochemistry. This paper delimits the scope of what we expect from methods to run such simulations they should work in full nuclear dimensionality, be general enough to tackle any type of molecule and not require unrealistic computational resources. We examine the main methodological challenges we should venture to advance the field, including the computational costs of the electronic structure calculations, stability of the integration methods, accuracy of the nonadiabatic dynamics algorithms and software optimization. Based on simulations designed to shed light on each of these issues, we show how machine learning may be a crucial element for long time-scale dynamics, either as a surrogate for electronic structure calculations or aiding the parameterization of model Hamiltonians. We show that conventional methods for integrating classical equations should be adequate to extended simulations up to 1 ns and that surface hopping agrees semiquantitatively with wave packet propagation in the weak-coupling regime. We also describe our optimization of the Newton-X program to reduce computational overheads in data processing and storage. This article is part of the theme issue 'Chemistry without the Born-Oppenheimer approximation'.Factoring a wave function into marginal and conditional factors partitions the subsystem kinetic energy into two terms. The first depends solely on the marginal wave function, through its gauge-covariant derivative, while the second depends on the quantum metric of the conditional wave function over the manifold of marginal variables. We derive an identity for the rate of change of the second term. This article is part of the theme issue 'Chemistry without the Born-Oppenheimer approximation'.Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with enhanced transmissibility, pathogenicity, and immune escape ability have ravaged many countries and regions, which has brought substantial challenges to pandemic prevention and control. Real-time reverse transcriptase PCR (rRT-PCR) is widely used for SARS-CoV-2 detection but may be limited by the continuous evolution of the virus. However, the sensitivity of Chinese commercial rRT-PCR kits to critical SARS-CoV-2 variants remains unknown. In this study, contrived MS2 virus-like particles were used as reference materials to evaluate the analytical sensitivity of Daan, BioGerm, EasyDiagnosis, Liferiver, and Sansure kits when detecting six important variants (Alpha, Beta, Gamma, Delta, Omicron, and Fin-796H). The Beta and Delta variants adversely affected the analytical sensitivity of the BioGerm ORF1ab gene assay (9.52% versus 42.96%, P = 0.014, and 14.29% versus 42.96%, P = 0.040, respectively), whereas the N gene assay completely failed in terms of the Fin-796H variant. The Gamma and Fin-796H variants impeded the PCR amplification efficiency for the Sansure ORF1ab gene assay (33.33% versus 66.67%, P = 0.031, and 66.67% versus 95.24%, P = 0.040, respectively), and the Delta variant compromised the E gene assay (52.38% versus 85.71%, P = 0.019). The Alpha and Omicron variants had no significant effect on the kits. This study highlights the necessity of identifying the potential effect of viral mutations on the efficacy and sensitivity of clinical detection assays. It can also provide helpful insights regarding the development and optimization of diagnostic assays and aid the strategic management of the ongoing pandemic.Lowering the dose in single-photon emission computed tomography (SPECT) imaging to reduce the radiation damage to patients has become very significant. In SPECT imaging, lower radiation dose can be achieved by reducing the activity of administered radiotracer, which will lead to projection data with either sparse projection views or reduced photon counts per view. Direct reconstruction of sparse-view projection data may lead to severe ray artifacts in the reconstructed image. Many existing works use neural networks to synthesize the projection data of sparse-view to address the issue of ray artifacts. selleck chemicals llc However, these methods rarely consider the sequence feature of projection data along projection view. This work is dedicated to developing a neural network architecture that accounts for the sequence feature of projection data at adjacent view angles. In this study, we propose a network architecture combining Long Short-Term Memory network (LSTM) and U-Net, dubbed LU-Net, to learn the mapping from sparse-view projection data to full-view data. In particular, the LSTM module in the proposed network architecture can learn the sequence feature of projection data at adjacent angles to synthesize the missing views in the sinogram. All projection data used in the numerical experiment are generated by the Monte Carlo simulation software SIMIND. We evenly sample the full-view sinogram and obtain the 1/2-, 1/3- and 1/4-view projection data, respectively, representing three different levels of view sparsity. We explore the performance of the proposed network architecture at the three simulated view levels. Finally, we employ the preconditioned alternating projection algorithm (PAPA) to reconstruct the synthesized projection data. Compared with U-Net and traditional iterative reconstruction method with total variation regularization as well as PAPA solver (TV-PAPA), the proposed network achieves significant improvement in both global and local quality metrics.The collapse is the most frequent and harmful geological hazard during the construction of the shallow buried tunnel, which seriously threatens the life and property safety of construction personnel. To realize the process control of collapse in the tunnel construction, a three-stage risk evaluation method of collapse in the whole construction process of shallow tunnels was put forward. Firstly, according to the engineering geology and hydrogeology information obtained in the prospecting stage, a fuzzy model of preliminary risk evaluation based on disaster-pregnant environment factors was proposed to provide a reference for the optimization design of construction and support schemes in the design stage. Secondly, the disaster-pregnant environment factors were corrected based on the obtained information, such as advanced geological forecast and geological sketch, and the disaster-causing factors were introduced. An extension theory model of secondary risk evaluation was established to guide the reasonable excavation and primary support schemes. Finally, the disaster-pregnant and disaster-causing factors were corrected according to the excavation condition, an attribute model of final risk evaluation for the collapse was constructed combined with the mechanical response index of the surrounding rock. Meanwhile, the risk acceptance criteria and construction decision-making method of the collapse in the shallow buried tunnels were formulated to efficiently implement the multi-level risk control of this hazard. The proposed method has been successfully applied to the Huangjiazhuang tunnel of the South Shandong High-Speed Railway. The comparison showed that the evaluation results are highly consistent for these practical situations, which verify the application value of this study for guiding the safe construction of shallow buried tunnels.In order to resolve the imbalance of demand-capacity and airspace congestion, improve the performance of the en route air traffic management, promote the development of air traffic control automation system in the future, this paper proposes an En route air traffic control process model from the perspective of operation requirements. Taking the minimization of operation time, instantaneous density, maximum lateral displacement and air traffic controllers' workload as the optimization objectives, the commonly used air traffic control instructions such as climb and descent and speed restriction are set as constraints, the algorithm is designed based on the air traffic control scheme, and a complete air traffic control process are modeled which outputs instructions for each aircraft. Finally, the model is applied to a case study in the northwest region of China. The simulation results show that compared with the actual operation process, the total operation time is reduced by 18.6%, the variance of the lateral displacement and the vertical separation are efficiently reduced, and the en route air traffic capacity is improved.
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