Computational Point Research Articles

Cost-utility and cost–benefit analysis of a multi-component intervention (NEXpro) for neck-related symptoms in Swiss office workers

BackgroundNeck pain is a significant public health issue, especially among office workers, with a prevalence ranging from 42 to 68%. This study aimed to evaluate the cost-utility and cost-benefit of a multi-component intervention targeting neck pain in the general population of office workers in Switzerland. The 12-week multi-component intervention consisted of neck exercises, health promotion information workshops, and workplace ergonomics sessions.MethodsThe study was designed as a stepped-wedge cluster randomized controlled trial and assessed using an employer’s perspective. The main analysis focused on the immediate post-intervention period. Long-term effects were examined in a subsample at the 4, 8, and 12-month follow-ups. The intervention effects on costs and quality-adjusted life years (QALYs) were estimated using generalized linear mixed-effects models, controlling for confounding factors. Incremental cost-effectiveness ratios (ICERs) and cost-effectiveness acceptability curves were presented, along with calculations of the break-even point and the return on investment. Various sensitivity analyses were performed.ResultsA total of 120 office workers participated in the trial, with 100 completing the intervention period and 94 completing the entire study. The main analysis included 392 observations. The intervention had a significant positive effect on QALYs and a nonsignificant effect on costs. The ICER was estimated at -25,325 per QALY gain, and the probability of the intervention being cost saving was estimated at 88%. The break-even point was reached one week after the end of the intervention.ConclusionThe multi-component intervention is likely to reduce company costs and simultaneously improve the quality of life of employees. However, the implementation of such interventions critically depends on evidence of their cost-effectiveness. As there is still a large research gap in this area, future studies are needed.Trial registrationClinicalTrials.gov, NCT04169646. Registered 15 November 2019-Retrospectively registered.Trial protocolAegerter AM, Deforth M, Johnston V, Ernst MJ, Volken T, Luomajoki H, et al. On-site multi-component intervention to improve productivity and reduce the economic and personal burden of neck pain in Swiss office-workers (NEXpro): protocol for a cluster-randomized controlled trial. BMC Musculoskelet Disord. 2020;21(1):391. https://doi.org/10.1186/s12891-020-03388-x.

Feasibility Analysis of Timber Sales Business Reviewed from Financial Aspects (Case Study of UD. Langgeng Jaya in Tawangsari Village, Jombang)

The furniture industry is an industry that converts wood into processed wood in the form of goods such as tables, chairs, cupboards, etc. Demand for processed wood is increasing along with population growth. The financial feasibility of UD Langeng Jaya was the focus of this research for five years, namely from 2017 to 2021. This location was chosen deliberately because there had been no previous research at UD. Langgeng Jaya is one of the wood sales industries that is currently developing. The data analysis used is the calculation of Net Present Value (NPV), Payback Period (PP), and Break Event Point (BEP). The results of the research show that UD Langgeng Jaya in Tawangsari Village, Mojowarno District, Jombang Regency, is financially feasible to develop in terms of Net Present Value (NPV) of IDR 2,742,319,730,- > 0, Payback Period (PP) for wood sales is 1 year, 9 months 7 days and the Break Even Point for sales of UD Langgeng Jaya wood is IDR 3,123,716,-, while the breakeven point for sales of beams and wood is 51 pieces.

TIP 4 P 2005 Ice : Simulating water with two molecular states.

Rigid, non-polarizable water models are very efficient from a computational point of view, and some of them have a great ability in predicting experimental properties. There is, however, little room for improvement in simulating water with this strategy, whose main shortcoming is that water molecules do not change their interaction parameters in response to the local molecular landscape. In this work, we propose a novel modeling strategy that involves using two rigid non-polarizable models as states that water molecules can adopt depending on their molecular environment. During the simulation, molecules dynamically transition from one state to another depending on a local order parameter that quantifies some local structural feature. In particular, molecules belonging to low- and high-tetrahedral order environments are represented with the TIP4P/2005 and TIP4P/Ice rigid models, respectively. In this way, the interaction between water molecules is strengthened when they acquire a tetrahedral coordination, which can be viewed as an effective way of introducing polarization effects. We call the resulting model TIP4P2005Ice and show that it outperforms either of the rigid models that build it. This multi-state strategy only slows down simulations by a factor of 1.5 compared to using a standard non-polarizable model and holds great promise for improving simulations of water and aqueous solutions.

Accounting for melting properties in the multi-solid paraffin prediction model for analyzing well production at fields in the Republic of Kazakhstan

The deposition of paraffin on pipeline walls is a serious flow issue. leading to a reduction in oil production due to the decreased flow cross-sectional area. and in severe cases. to complete blockage. This problem also results in increased energy consumption and causes failure of surface equipment due to paraffin plugs. Traditional methods predict paraffin crystallization temperature by considering the impact of temperature on the solubility parameters of individual components in both liquid and solid phases as well as their molar volumes. The goal of this study is to improve the prediction of paraffin deposition in Kazakhstan crude oil by incorporating an evaluation of melting properties into the multi-solid (MS) body prediction model. The process of calculating and adjusting melting properties to enhance the accuracy of the MS model is described in detail. This approach helps to overcome the limitations faced by most existing models when adapting to different types of crude oil. In this study, the focus is on optimizing the multi-solid (MS) body prediction model for paraffin deposition by integrating the melting properties of crude oil components. The improvement of the MS model is crucial because paraffin deposition is a significant issue in Kazakhstan's oil fields, where the wide range of reservoir conditions can cause standard prediction models to fall short. By enhancing the accuracy of melting point calculations, the updated model can better predict the onset of paraffin crystallization and its deposition tendencies under varying pressure and temperature conditions. Keywords: melting properties; multi solid solution; petroleum; solid solution; paraffin deposition

Frustrated Magnetism and Spin Anisotropy in a Buckled Square Net YbTaO4.

The interplay between quantum effects from magnetic frustration, low-dimensionality, spin-orbit coupling, and crystal electric field in rare-earth materials leads to nontrivial ground states with unusual magnetic excitations. Here, we investigate YbTaO4, which hosts a buckled square net of Yb3+ ions with Jeff = 1/2 moments. The observed Curie-Weiss temperature is about -1 K, implying an antiferromagnetic coupling between the Yb3+ moments. The heat capacity shows no long-range ordering down to 0.10 K, instead shows a field-dependent broad maximum indicative of short-range correlations. The magnetic entropy recovered and magnetization measurements confirm a spin-orbit driven Jeff = 1/2 Kramers doublet ground state. Point charge calculations show that the Yb3+ ions do not host the quintessential XY spin anisotropy observed in typical Yb-based quantum magnets like NaYbO2, YbMgGaO4, and pyrochlores but rather exhibit an almond-shaped anisotropy with an easy axis. Thus, YbTaO4 can serve as a model system to study frustrated magnetism in a square lattice using the J1-J2 Heisenberg model. This work also emphasizes the significance of small perturbations to the local crystal electric field that can alter the spin anisotropy and change the collective behavior of the system.

Moving Mesh Partial Differential Equation Modelling of a 5CB Nematic Liquid Crystal Confined in Symmetric and Asymmetric Pi-Cells: A Review

The switching properties of nematic liquid crystals under electrical and mechanical stresses play a fundamental role in the design and fabrication of electro-optical devices. Depending on the stress applied to a nematic texture confined in a pi-cell, different nematic configurations are allowed inside the cell, while the induced distortion is relaxed by means of growing biaxial domains which can end with the order reconstruction phenomenon, a transition connecting two topologically different nematic textures which can occur in different regions of the pi-cell. Due to the different space and time scales involved, modelling in the frame of the Landau–de Gennes order tensor theory is mandatory to correctly describe the fast-switching mechanisms involved, while from a computational point of view, sophisticated numerical techniques are required to grasp tiny and fast features which can be predicted by the mathematical modelling. In this paper, we review the results obtained from the mathematical and numerical modelling of a 5CB liquid crystal confined in a pi-cell performed by using a numerical technique based on the equidistribution principle, tailored for the description of a complex physical system in which fast switching phenomena are coupled with strong distortions. After a recap on the underneath theory and on the numerical method, we focus on the switching properties of the nematic material when subjected to variable mechanical and electrical stresses in both symmetric and asymmetric conditions.

Correlated states in super-moiré materials with a kernel polynomial quantics tensor cross interpolation algorithm

Abstract Super-moiré materials represent a novel playground to engineer states of matter beyond the possibilities of conventional moiré materials. However, from the computational point of view, understanding correlated matter in these systems requires solving models with several millions of atoms, a formidable task for state-of-the-art methods. Conventional wavefunction methods for correlated matter scale with a cubic power with the number of sites, a major challenge for super-moiré materials. Here, we introduce a methodology capable of solving correlated states in super-moiré materials by combining a kernel polynomial method with a quantics tensor cross interpolation matrix product state algorithm. This strategy leverages a mapping of the super-moiré structure to a many-body Hilbert space, that is efficiently sampled with tensor cross interpolation with matrix product states, where individual evaluations are performed with a Chebyshev kernel polynomial algorithm. We demonstrate this approach with interacting super-moiré systems with up to several millions of atoms, showing its ability to capture correlated states in moiré-of-moiré systems and domain walls between different moiré systems. Our manuscript puts forward a widely applicable methodology to study correlated matter in ultra-long length scales, enabling rationalizing correlated super-moiré phenomena.

Identification of Physical Dynamical Processes via Linear Structure Models (Part 1)

The article discusses methods for identifying parameters of partial differential equations. Identification problems are often called poorly conditioned. However, the reason is the ambiguity of the solution, even at the point of minimality of the criterion. In particular, the article discusses: (1) An analysis of singular values for identify the unambiguous solution. The basis of these methods is a singular value decomposition of the matrix of experimental data, what makes it possible to abandon the inversion of matrices and, as a consequence, translate the problem of ill-conditioned problems into the problem of ambiguity of the solution. (2) The issues of anomalous measurements and combination of various experiments. (3) A universal optimization method for identifying parameters by their complete simple enumeration. The method is based on fast calculation of points on a multidimensional sphere. (4) The issues of identifiability of linear structure models and construction of experiments guaranteeing identification. (5) A method for identifying parameters via projecting linear structure model elements onto the plane of guarantors. (6) An approach to constructing histograms of unknown parameters of dynamic systems before calculating them using any algorithm. The approach is based on linear structure models with parameters on a sphere and a rather unexpected application of singular value decomposition. (7) The methods are accompanied by examples of heat equations. The Appendix containsalgorithmsintheMATLABlanguageforallexamples. (8)The presented optimization, projection and statistical methods based on the concept of linear structure models allow solving the same identification problem in fundamentally different ways, which significantly increases the reliability of the results obtained.

Finite element analysis of rock deformation in deep twin tunnels

Relying upon a three-dimensional finite element analysis, this contribution investigates the instantaneous irreversivel response induced by the constitutive behavior of the rock mass in the convergence profile of twin tunnels. At the rock material level, elastoplastic state equations based on a Drucker-Prager yield surface with an associated flow rule are adopted in the modeling. As regards the tunnel support, the formulation accounts for the presence of an elastic shotcrete-like lining. From a computational point of view, the deactivation-activation method is used to simulate the excavation process and the installation of the lining. The accuracy of the finite element predictions is assessed through comparisons with the available analytical solutions formulated in a simplified scenario for the twin tunnel configuration. A parametric study investigates the mutual interaction induced by the proximity of the tunnels.

Vibration of extremely flexible beam axially tensioned by incremental force

Cables can be associated with extremely flexible beams. Particularly, electrical system cables supported on transmission towers are subject to climatic conditions whose effects are capable of mobilizing their vibration modes. The first mode is considered to be of special importance due to the shape assumed by the cable when subjected to the Earth's gravitational field. Under these conditions, cable deformation is influenced by its geometric and material properties, including viscoelastic behavior, when considered, making these systems intrinsically nonlinear in terms of geometry and material. The balance of these structural parts can only be found in the deformed configuration of the system. As a rule, cables need to be pulled for proper and correct use in transmission lines. The use of a traction force modifies the rigidity of the system, causing an increase in the natural frequencies of vibration. This occurs because the force that pulls the cable mobilizes the geometric stiffness portion of the total system stiffness. In solving the vibration problem, numerical methods can be employed. To this end, the model adopted is analogous to a double-supported beam, for which the successive integration of the bending moment differential equation leads to displacements of the axis, or the elastic line, for each stage of the iterative process. These iterations are repeated until the sum of the portions equals the total axial force to be applied. The smaller the force portion in each iteration, the better the result obtained. However, the number of iterations required will be greater, making processing potentially costly from a computational point of view. To solve the proposed problem, a programming routine was developed in the “Python Jupyter Notebook” language that allows calculating the deformation of the structural system and the natural frequency of vibration in each iteration. The process begins with the analytical solution of the approximate elastic line under the effect of gravity and the subsequent tension in the cable, with the definition of the natural frequency of vibration following the Rayleigh method. In the end, the first natural vibration frequency of the cable was raised non-linearly from 0.358 Hz to 5.944 Hz.

A Phase-State-Radius-Based Multiphase-State Identification Acceleration Approach of the Hydrocarbons-CO2-H2O System for Compositional Reservoir Simulation

Summary The CO2-enhanced oil recovery (CO2-EOR) technique could increase the oil recovery factor and achieve subsurface CO2 storage. To evaluate the feasibility of CO2-EOR operation, an equation-of-state-based (EOS-based) compositional simulation is inevitable. However, time-consuming multiphase flash calculations limit the application of the conventional EOS-based compositional simulation in large-scale reservoir simulations. Therefore, there is an urgent need to accelerate the EOS-based compositional simulation by reducing the time spent on the multiphase flash calculations. In this study, we develop a fast, multiphase-state identification approach to achieve high computational efficiency of the compositional simulation. First, taking one given point in the compositional space composed by pressure and the overall composition as the original point, the phase-state radius (PSR) is defined as a radius inside which all points have the same phase state as the given point. Subsequently, the results of the multiphase flash calculations of the points in the compositional space are stored. Finally, when a new point is inside the PSR of one calculated point, the stored results are used to accelerate the new multiphase flash calculations of the new point by omitting the phase stability analysis (PSA) and possible additional multiphase split (MS) calculations. Case studies show a 6–10% reduction in multiphase flash computation time compared with two existing methods when the fluid is far from the phase boundary and a reduction of at least 17.5% when the fluid is near the phase boundary.

Promoter recognition specificity of Corynebacterium glutamicum stress response sigma factors σD and σH deciphered using computer modeling and point mutagenesis

This study aimed to reveal interactions of the stress response sigma subunits (factors) σD and σH of RNA polymerase and promoters in Gram-positive bacterium Corynebacterium glutamicum by combining wet-lab obtained data and in silico modeling. Computer modeling-guided point mutagenesis of C. glutamicum σH subunit led to the creation of a panel of σH variants. Their ability to initiate transcription from naturally occurring hybrid σD/σH-dependent promoter Pcg0441 and two control canonical promoters (σD-dependent PrsdA and σH-dependent PuvrD3) was measured and interpreted using molecular dynamics simulations of homology models of all complexes. The results led us to design the artificial hybrid promoter PD35H10 combining the −10 element of the PuvrD3 promoter and the −35 element of the PrsdA promoter. This artificial hybrid promoter PD35-rsdAH10-uvrD3 showed almost optimal properties needed for the bio-orthogonal transcription (not interfering with the native biological processes).

Automatic variogram calculation and modeling

The variogram is one of the most used tools in geostatistics. It represents a key step for the results of estimation and simulation. This paper presents a methodology of the experimental variogram points calculation and subsequent modeling, including some practical considerations. The proposed methodology infers the variogram parameters directly from the dataset to require minimum user input. Autovar is a program that implements the described methodology, giving an initial variogram model for disseminated and tabular deposits.

Data-Efficient Active Learning for Thermodynamic Integration: Acidity Constants of BiVO4 in Water.

The protonation state of molecules and surfaces is pivotal in various disciplines, including (electro-)catalysis, geochemistry, biochemistry, and pharmaceutics. Accurately and efficiently determining acidity constants is critical yet challenging, particularly when explicitly considering the electronic structure, thermal fluctuations, anharmonic vibrations, and solvation effects. In this research, we employ thermodynamic integration accelerated by committee Neural Network potentials, training a single machine learning model that accurately describes the relevant protonated, deprotonated, and intermediate states. We investigate two deprotonation reactions at the BiVO4 (010)-water interface, a promising candidate for efficient photocatalytic water splitting. Our results illustrate the convergence of the required ensemble averages over simulation time and of the final acidity constant as a function of the Kirkwood coupling parameter. We demonstrate that simulation times on the order of nanoseconds are required for statistical convergence. This time scale is currently unachievable with explicit ab-initio molecular dynamics simulations at the hybrid DFT level of theory. In contrast, our machine learning workflow only requires a few hundred DFT single point calculations for training and testing. Exploiting the extended time scales accessible, we furthermore asses the effect of commonly applied bias potentials. Thus, our study significantly advances calculating free energy differences with ab-initio accuracy.

Artificial Neural Network-Based Non-Linear Modeling and Simulation of CaO-SiO2-Al2O3-MgO Blast Furnace Slag Viscosity

Blast furnace slags are formed by CaO-SiO2-Al2O3-MgO systems and have several physical characteristics, one of which is viscosity. Viscosity is an important variable for the operation and blast furnace performance. This work aimed to model viscosity through linear and non-linear models in order to obtain a model with precision and accuracy. The best model constructed was a non-linear model by artificial neural networks that presented 23 nodes in the first hidden layer and 24 nodes in the second hidden layer with 6 input variables and 1 output variable named ANN 23-24. ANN 23-24 obtained better statistical evaluations in relation to 11 different literature equations for predicting viscosity in CaO-SiO2-Al2O3-MgO systems. ANN 23-24 was also subjected to numerical simulations in order to demonstrate the validation of the non-linear model and presented applications such as viscosity prediction, calculation of the inflection point in the viscosity curve by temperature, the construction of ternary diagrams with viscosity data, and the construction of iso-viscosity curves.

Numerical simulation and validation of heavy rainfall flood inundation in small watersheds in undocumented mountainous areas

ABSTRACT With global warming and the increase in extreme precipitation events, floods are becoming more frequent in mountainous areas, and the safety of lives and property of people is seriously threatened. However, understanding of the flooding process in uninformative mountainous areas is limited due to the lack of high-quality hydrometeorological data. Hence, this study adopts the MIKE21 model to simulate flood inundation in the Shadai River basin in the Qilian Mountain region of the northern Tibetan Plateau as an example. This validates the model-simulated flow and inundation extent using the flow data obtained from the calculation of the flood trace points, extent of inundation, and high-resolution remote sensing images. The results show that the flash flood inundation mainly occurs at 12:00–01:00 AM on 18 August 2022, and the simulated and actual maximum inundation areas are 7.9 and 9.5 km2, respectively. The fitted F-statistic value is 0.81, and the relative error between the calculated flow rate of the flood trace point and the model-simulated flow rate is 8%, indicating good consistency. Furthermore, an in-depth exploration of the model parameter sensitivity reveals that the use of distributed Manning's roughness coefficient value has higher simulation accuracy.

ON HIGHER ORDER DYNAMIC MODE DECOMPOSITION

This paper introduces an alternative variant of Higher Order Dy namic Mode Decomposition (HODMD), which improves the standard approach from a computational point of view. In the new scheme, time delayed snapshots are used along with the special form of the Koopman operator. An algorithm is derived that allows the calculation of novel decomposition in a stable and efficient way. This method is suitable in cases where standard Dynamic Mode Decomposition (DMD) is not applicable. These are dynamics that show limited spatial complexity, and a very large number of included frequencies. We illustrate and explain the new method using some classical and sample dynamics.

A novel computer vision and point cloud-based approach for accurate structural analysis of a tall irregular timber structure

Wood material has been widely used in critical heritage structures such as pagodas, totem poles, and large-scale sculptures. Conducting rigorous structural analysis is crucial to protect these structures in high-seismic regions. Typically, these types of structures are modeled using an equivalent finite element (FE) model which used simple cylinder with a constant cross section equal to the average of the top and bottom cross section. However, simplified equivalent FE models may not accurately consider the irregularities and complexities of these structures. In this paper, advanced computer vision and point cloud techniques were adopted to accurately and rapidly construct a FE model of a 30-meter irregular timber sculpture. This was achieved using video scans, computer vision-aided 3D reconstruction, point cloud processing, and mesh to solid element conversion. The refined FE model was used to conduct capacity check, mesh sensitivity study, pushover analysis, and response spectrum analysis. The results of the refined FE model were compared to an equivalent FE model. The results show: 1) the proposed numerical modeling methodology for structural analysis can efficiently and accurately measure the dimension of the irregular sculpture up to 98.2 % accuracy; 2) the lateral stiffnesses of the 30-meter irregular sculpture vary significantly (up to 42.6 %) from one direction to the other; 3) the equivalent FE model overestimated the shear and moment capacities by 20.6 % and 13.2 %, respectively; 4) on average, the equivalent FE model overestimated the shear and moment demands by 8.9 % and 5.5 %, respectively. Overall, the proposed application has demonstrated a fast, economical and accurate method to conduct seismic evaluation and design for irregular structures.

Light culling based on the limitations of human visual perception

Abstract We propose a method to remove virtual point light sources (VPLs) in the scene to reduce the time overhead of lighting calculation for shading points in non-visually sensitive areas. We quantify the degree of light stimulation to the human eye and use it to calculate the lighting contribution of VPLs in the scene to visually sensitive areas. Based on this contribution, we remove VPLs that contribute less to visually sensitive areas to improve rendering speed. Experimental results show that the FLIP evaluation index difference between this method and FLC [1] is only 0.00077, while the average number of frames is 1.15 times that of the method in FLC. This proves that this paper can effectively improve rendering speed.

A computer vision and point cloud-based monitoring approach for automated construction tasks using full-scale robotized mobile cranes

A computer vision and point cloud-based monitoring approach for automated construction tasks using full-scale robotized mobile cranes