Forward Solution Research Articles

The Research on the Mental Health Influencing Factors of Chinese College Students in the Context of the Short Videos about the Hu Xinyu Incident

On October 14, 2022, high school student Hu Xinyu left school and disappeared. This incident is also known as the Hu Xinyu incident. After more than three months of searching by the police and the investigation team, Hu Xinyu's body was finally found. Based on the materials obtained, it was deduced that Hu Xinyu chose to commit suicide due to excessive pressure. Once this matter was reported, it immediately flooded the short video platform. Since it was a juvenile case, this incident caused many college students to express their views on the short video platform. In addition, the mental health of college students has once again received widespread attention from society. Affected by the Hu Xinyu incident, college students are also examining their current pressure, academic pressure, and the pressure to find a job in the future. Therefore, the Hu Xinyu incident greatly impacted the mental health of college students. Because the Hu Xinyu incident happened recently, and most college students also pay attention to the Hu Xinyu incident on the short video platform, and there is no relevant report on the impact of the incident on the mental health of college students, it is very important to explore its impact on the mental health of college students. important. The Hu Xinyu incident is related to the mental health of college students. College students can only face future challenges better if they maintain good mental health. This article adopts the form of a questionnaire survey for college students to explore the impact of the Hu Xinyu incident on college students, and through screening effective questionnaires, summarizing conclusions, and analyzing problems, the conclusions are discussed under the objective data. Finally, it is concluded that college students are generally under pressure, cannot resist pressure, cannot face pressure, and choose self-forgiveness in the face of pressure. At the same time, this paper also puts forward solutions such as schools should reduce the burden, parents should pay attention to students' mental health, and students should make plans for their future in advance so that college students can maintain good mental health and meet the future and new challenges.

Design of a Redundant 6-DOF Parallel Platform for Mirror Precision Pose Adjustment

The pose control of 6-DOF parallel mechanism depends on the measurement accuracy of the position of the active driving strut, which affects the pose control accuracy of 6-DOF parallel mechanism. In order to overcome the influence of heavy load large impact force on the measurement accuracy of the active drive strut, a redundant 6-DOF parallel mechanism that separates the measurement struts from the active drive struts was proposed. The fixed platform and the moving platform are connected by six identical active drive struts and six redundant measurement struts. The active driving strut is an active motion unit used to achieve various pose movements of the parallel mechanism, and the follower redundant measurement strut is a precision measurement unit, and does not constrain any DOF of the platform. Only the follower accurately measures the distance between the upper and lower hinge points of the redundant hinges. The accurate position and pose of the 6-DOF parallel mechanism are obtained by using the forward kinematics solution. The controller is used to implement the pose closed-loop control algorithm to achieve precise pose control. The design of this redundant 6-DOF parallel mechanism improves the movement accuracy of the parallel mechanism under heavy load. It can be applied to the precision adjustment of the primary mirror or secondary mirror under heavy load in large aperture optics, and has strong engineering application value.

Incorporating interface permeability into the diffusion MRI signal representation while using impermeable Laplace eigenfunctions

Objective. The complex-valued transverse magnetization due to diffusion-encoding magnetic field gradients acting on a permeable medium can be modeled by the Bloch–Torrey partial differential equation. The diffusion magnetic resonance imaging (MRI) signal has a representation in the basis of the Laplace eigenfunctions of the medium. However, in order to estimate the permeability coefficient from diffusion MRI data, it is desirable that the forward solution can be calculated efficiently for many values of permeability. Approach. In this paper we propose a new formulation of the permeable diffusion MRI signal representation in the basis of the Laplace eigenfunctions of the same medium where the interfaces are made impermeable. Main results. We proved the theoretical equivalence between our new formulation and the original formulation in the case that the full eigendecomposition is used. We validated our method numerically and showed promising numerical results when a partial eigendecomposition is used. Two diffusion MRI sequences were used to illustrate the numerical validity of our new method. Significance. Our approach means that the same basis (the impermeable set) can be used for all permeability values, which reduces the computational time significantly, enabling the study of the effects of the permeability coefficient on the diffusion MRI signal in the future.

Continuous purification of drugs by ionic liquid-drawn organic solvent forward osmosis and solute recovery

Classical purification of pharmaceuticals is energy-intensive and employs toxic solvents that are discarded, calling for more sustainable methods. Here, we purified tetracycline by organic solvent forward osmosis using ionic liquids. Results show the osmotic enrichment of feed solutions containing different concentrations of tetracycline in methanol. The solvent flux during the filtration process is mainly influenced by solvent properties, such as molecular size, viscosity, polarity, and the solvent–membrane interaction. We evaporated the diluted draw solution to recover the draw solute for reuse. Overall ionic liquids appear as suitable draw solutes for organic solvent forward osmosis for pharmaceutical compound enrichment with draw solute recovery and reuse.

Supercritical Froude Number Flow through Ducts with Statistically Roughened Walls

High-speed fluid flows over roughened surfaces occur in many engineering applications; one important application involves high velocity water flows in pipelines with roughened interior walls where the wall roughness affects head loss estimates necessary for engineering design purposes. The present analysis provides an analytical solution of the fluid physics underlying the induced static pressure profile resulting from high Froude number supercritical velocity through duct with random wall roughness. The analytic solution of the hyperbolic governing small perturbation velocity potential equation subject to high Froude number flows brings forward characteristic wave solutions that determine the static pressure profile in a duct with random height wall roughness. While current engineering practice utilizes semi-empirical engineering equations employing test data to determine the friction factor, velocity and static pressure profiles and head loss for different roughness types in different sized ducts as a function of Reynolds number (as summarized in a later section of the paper), the present analysis provides a new analytical method to determine the fluid physics involved in the static pressure change induced by wall random roughness in ducts subject to high Froude number supercritical flows.

Isogeometric multi-resolution full waveform inversion based on the finite cell method

Full waveform inversion (FWI) is an iterative identification process that serves to minimize the misfit of model-based simulated and experimentally measured wave field data. Its goal is to identify a field of parameters for a given physical object. For many years, FWI is very successful in seismic imaging to deduce velocity models of the earth or of local geophysical exploration areas. FWI has also been successfully applied in various other fields, including non-destructive testing (NDT) and biomedical imaging. The inverse optimization process of FWI relies on forward and backward solutions of the (elastic or acoustic) wave equation, as well as on efficient computations of adequate optimization directions. Many approaches employ (low order) finite element or finite difference methods, using parameterized material fields whose resolution is chosen in relation to the elements or nodes of the discretized wave field. In our previous paper (Bürchner et al., 2023), we investigated the potential of using the finite cell method (FCM) as the wave field solver. The FCM offers the advantage that highly complex geometric models can be incorporated easily. Furthermore, we demonstrated that the identification of the model’s density outperforms that of the velocity — particularly in cases where unknown voids characterized by homogeneous Neumann boundary conditions need to be detected. The paper at hand extends this previous study in the following aspects: The isogeometric finite cell analysis (IGA-FCM) – a combination of isogeometric analysis (IGA) and FCM – is applied as the wave field solver, with the advantage that the polynomial degree and subsequently also the sampling frequency of the wave field can be increased quite easily. Since the inversion efficiency strongly depends on the accuracy of the forward and backward wave field solutions and of the gradients of the functional, consistent, and lumped mass matrix discretization are compared. The resolution of the grid describing the unknown material density – thus allowing to identify voids in a physical object – is then decoupled from the knot span grid. Finally, we propose an adaptive multi-resolution algorithm that locally refines the material grid using an image processing-based refinement indicator. The developed inversion framework allows fast and memory-efficient wave simulations and object identification. While we study the general behavior of the proposed approach using 2D benchmark problems, a final 3D problem shows that it can also be used to identify void regions in geometrically complex spatial structures.

Design of Microbial Corrosion Assessment Model for Protective Coatings based on Combined Weight TOPSIS

Instrumentation has a wide range of applications in various fields. In today’s rapid development of information technology and the economy, it plays an indispensable role. The application environment of instrumentation in many fields is complex and harsh. Therefore, the requirements for instrumentation materials are also more stringent. This paper mainly focuses on the special fields of aerospace, petrochemical, marine, and nuclear energy. The application status of instrumentation and its harsh application environment have been researched. This paper researched the main corrosion failure modes of instrumentation materials, and put forward suggestions and solutions for corrosion protection methods.

A TPAA-SBFEM based full-scale forward and inverse interval analysis for dynamic viscoelastic problems

This paper aims to propose efficient algorithms for the full-scale forward and inverse interval analysis of dynamic viscoelastic problems. Underpinned by a reliable deterministic forward solution provided by the Temporally Piecewise Adaptive Algorithm and Scaled Boundary Finite Element Method (TPAA-SBFEM), the deterministic solutions of displacement/stress and their derivatives with respect to constitutive parameters can be acquired with high fidelity, and an efficient surrogate of deterministic forward solutions can be constructed with sufficient accuracy. Utilizing these deterministic solutions, derivatives and the surrogate as well, two sets algorithm are proposed to solve forward/inverse interval dynamic viscoelastic problems.In the forward interval analysis, Taylor series expansion (TSE) and interval arithmetic (IA) are combined to estimate the bounds of displacements and stresses when the interval scale of constitutive parameter is relatively small, and the acquirement of the first-order derivatives of displacements and stresses are stressed; when the interval scale is relatively large, the Particle Swarm Optimization (PSO) is employed to estimate the intervals of displacements and stresses by solving two deterministic optimization problems, and a Legendre Polynomial Chaos Expansion (LPCE) based surrogate is addressed to reduce the computational cost in the optimization process. In the inverse interval analysis, the interval estimation of constitutive parameters can be formulated by two deterministic optimization problems when intervals of displacements are partially known, which are solved by the Levenberg Marquardt method (LMM) when the interval scale is small, and by the combination of PSO with LPCE when the interval scale is lager.The accuracy and efficiency of the proposed approaches are demonstrated via numerical examples. Forward/inverse interval problems with stress singularity and heterogeneity are emphatically analyzed, and the impacts of interval scales, step sizes, noisy data, and so forth are taken into account.

Assessing the effects of head modelling errors and measurement noise on EEG source localization accuracy in preterm newborns: A single-subject study.

The accuracy of electroencephalogram (EEG) source localization is compromised because of head modelling errors. In this study, we investigated the effect of inaccuracy in the conductivity of head tissues and head model structural deficiencies on the accuracy of EEG source analysis in premature neonates. A series of EEG forward and inverse simulations was performed by introducing structural deficiencies into the reference head models to generate test models, which were then used to investigate head modelling errors caused by cerebrospinal fluid (CSF) exclusion, lack of grey matter (GM)-white matter (WM) distinction, fontanel exclusion and inaccuracy in skull conductivity. The modelling errors were computed between forward and inverse solutions obtained using the reference and test models generated for each deficiency. Our results showed that the exclusion of CSF from the head model had a strong widespread effect on the accuracy of the EEG source localization with position errors lower than 4.17 mm. The GM and WM distinction also caused strong localization errors (up to 3.5mm). The exclusion of fontanels from the head model also strongly affected the accuracy of the EEG source localization for sources located beneath the fontanels with a maximum localization error of 4.37 mm. Similarly, inaccuracies in the skull conductivity caused errors in EEG forward and inverse modelling in sources beneath cranial bones. Our results indicate that the accuracy of EEG source imaging in premature neonates can be largely improved by using head models, which include not only the brain, skull and scalp but also the CSF, GM, WM and fontanels.

A general forward solver for 3D CSEMs with multitype sources and operating environments

To determine the electromagnetic (EM) fields of different three-dimensional (3D) controlled-source electromagnetic methods (CSEMs) using the same parameters of the forward solution, by explicitly considering the commonalities, we present a general 3D forward modeling solver for CSEMs with multitype sources and operating environments. The commonality of the solver is reflected in two aspects. First, the solver is based on a frequency-domain (FD) vector Helmholtz equation for determining the scattered electric field. The different types of sources are imposed on the right-hand term of the equation, expressed as background Green’s function. Second, sources of any CSEM can be composed of electric dipole (ED) or magnetic dipole (MD) superposition. Thus, the focus of the 3D forward modeling of CSEMs is reduced to determining the EM fields of ED or MD sources for the background medium. The quasi-minimal residual (QMR) method is used to solve the large sparse complex linear system. Once the FD EM fields have been calculated, the time-domain (TD) response can be obtained using the cosine/sine transformation. The numerical results show that the relative error is less than 5% between the 3D numerical and analytical solutions, which verifies the accuracy of the solver. We further study the difference between the real (bent) and theoretical (straight) wires. We suggest that the shape of the source must be considered for TD and FD CSEMs with a wire source during data processing and inversion. The last example investigated the characteristics of FD EM fields from a finite-length wire and TD EM fields from a rectangular fixed loop on the same conductive tilted disk model buried in resistive sediments. According to the numerical results, we recommend FD CSEMs with a wire source for detecting deep anomalies.

How An Established Health System Ventures Forth: Ochsner Invests in the Future.

The founding of Ochsner Ventures followed the natural evolution of more than a decade of growth and expansion of Ochsner Health's offerings and capabilities beyond traditional patient care. This growth has enabled the health system to bring critical services to underserved communities across the Gulf South. Ochsner Ventures supports promising companies both in and beyond the region while bringing forward new solutions to healthcare sector challenges and improving health outcomes, access, and equity. In a dynamic healthcare environment amid the persistent effects of the COVID-19 pandemic, Ochsner Health is executing a multiyear strategic plan to reinforce its mission and maintain a position of strength in the region. One aspect of the strategy focuses on the diversification and pursuit of new value by creating new revenue, additional savings, cost reductions, innovations, and amplifying existing assets and capabilities.

Evaluating the influence of anatomical accuracy and electrode positions on EEG forward solutions

Generating realistic volume conductor models for forward calculations in electroencephalography (EEG) is not trivial and several factors contribute to the accuracy of such models, two of which are its anatomical accuracy and the accuracy with which electrode positions are known. Here, we investigate effects of anatomical accuracy by comparing forward solutions from SimNIBS, a tool which allows state-of-the-art anatomical modeling, with well-established pipelines in MNE-Python and FieldTrip. We also compare different ways of specifying electrode locations when digitized positions are not available such as transformation of measured positions from standard space and transformation of a manufacturer layout.Substantial effects of anatomical accuracy were seen throughout the entire brain both in terms of field topography and magnitude with SimNIBS generally being more accurate than the pipelines in MNE-Python and FieldTrip. Topographic and magnitude effects were particularly pronounced for MNE-Python which uses a three-layer boundary element method (BEM) model. We attribute these mainly to the coarse representation of the anatomy used in this model, in particular differences in skull and cerebrospinal fluid (CSF). Effects of electrode specification method were evident in occipital and posterior areas when using a transformed manufacturer layout whereas transforming measured positions from standard space generally resulted in smaller errors.We suggest modeling the anatomy of the volume conductor as accurately possible and we hope to facilitate this by making it easy to export simulations from SimNIBS to MNE-Python and FieldTrip for further analysis. Likewise, if digitized electrode positions are not available, a set of measured positions on a standard head template may be preferable to those specified by the manufacturer.

Influence of the number of measurements on detecting internal defects using photothermal super resolution reconstruction with random pixel patterns

ABSTRACT In this work, the limits of using spatially structured heating combined with subsequent photothermal super resolution reconstruction for the thermographic detection and resolution of internal defects are investigated. The approach is based on the application of modern high-power laser projector technology, which is used to repeatedly project varying spatially structured 2D pixel patterns to photothermally heat the object under test. After processing the generated thermographic data using nonlinear convex optimisation in conjunction with exploiting the joint-sparse nature of the defect signals within the individual measurements, a high-resolution 2D-sparse defect/inhomogeneity map is obtained. The main focus of the investigation is set on the influence of the number of individual measurements on the achievable reconstruction quality. Using numerical simulations based on an analytical representation of the forward solution to the underlying inverse problem, the convergence rate over performed measurements of the achievable reconstruction quality is determined. Finally, all findings are experimentally validated by reconstructing a set of internal defects in an additively manufactured sample. In this work, it is shown that for a variety of different defect separation distances, the projection of 50 different pixel patterns allows for a good trade-off between experimental complexity and reconstruction quality.

Self-similar shrinking of supports and non-extinction for a nonlinear diffusion equation with spatially inhomogeneous strong absorption

We study the dynamics of the following porous medium equation with strong absorption [Formula: see text] posed for [Formula: see text], with [Formula: see text], [Formula: see text] and [Formula: see text]. Considering the Cauchy problem with non-negative initial condition [Formula: see text], instantaneous shrinking and localization of supports for the solution [Formula: see text] at any [Formula: see text] are established. With the help of this property, existence and uniqueness of a non-negative compactly supported and radially symmetric forward self-similar solution with algebraic decay in time are proven. Finally, it is shown that finite time extinction does not occur for a wide class of initial conditions and this unique self-similar solution is the pattern for large time behavior of these general solutions.

Restorative Justice as a missional call in South African intercultural context and beyond.

Restorative justice tends to be a controversial issue. Millions of marginalized people across the planet who are in the vicious cycle of being underprivileged, have already lost any hope and sense of dignity, as they see unfairness in the justice system as well as in the socio-economic and political sphere of life. The concept of equality and equity add confusion as they are often mixed up and confused in their attempt to seek justice. The question is: what is the nature and significance of restorative justice? The misconception about restorative justice is one of the reasons for the lack of fulfilling this missional call. The reflection is done using the reformation framework, namely the framework involving creation, fall, redemption, and future consummation. Before the conclusion and recommendation, this article is set to discuss the nature, significance, scope, and role of the concept of restorative justice. Contribution: This research deals with restorative justice as a missional call in the South African intercultural context and beyond. Both the lawmakers (and executors), and the underprivileged (and marginalized), need the way forward clarity and solution regarding the nature and the significance, scope, and role of the concept of restorative justice.

Is Regulatory Approval without Autonomous Operation for Natural Extract Manufacturing under Economic Competitiveness and Climate-Neutrality Demands Still Permissible?

Natural extracts are broadly utilized as remedies, nutrition additives, cosmetics or flavors as well as natural pesticides, fungicides or herbicides. Green manufacturing technologies are of added market value and are sustainable towards the climate neutrality politically demanded for 2045. The concept of digital twins involves experimentally distinct validated process models combined with process analytical technology that is to be adapted to the existing operations. This is a key technology for the autonomous operations in industry 4.0. This paper exemplifies this approach and evaluates the results of the application and implementation efforts of regulated industries. A conductivity sensor for the measurement of the dry residue content and/or Fourier-transformed infrared spectroscopy for marker/lead or reference substance concentration determination are the most feasible and straight forward solutions. Different process control concepts from simple PID controllers (proportional, integral and differential) to advanced process control using digital twin models are evaluated and discussed in terms of industrialization efforts and benefits. The global warming potential CO2 equivalent per kg of natural product could be decreased by a factor of 5–10 as well as the cost of goods, which makes the pay-out time for the industrialization investment less than 1 year and the approach highly competitive. The success rate of the extraction process under regulatory constraints can be raised to 100%, reducing waste, overall solvent consumption, personnel efforts and energy requirements to a minimum.

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data.

Introduction: Localization of premature ventricular contraction (PVC) origin to guide the radiofrequency ablation (RFA) procedure is one of the prominent clinical goals of non-invasive electrocardiographic imaging. However, the results reported in the literature vary significantly depending on the source model and the level of complexity in the forward model. This study aims to compare the paced and spontaneous PVC localization performances of dipole-based and potential-based source models and corresponding inverse methods using the same clinical data and to evaluate the effects of torso inhomogeneities on these performances. Methods: The publicly available EP solution data from the EDGAR data repository (BSPs from a maximum of 240 electrodes) with known pacing locations and the Bratislava data (BSPs in 128 leads) with spontaneous PVCs from patients who underwent successful RFA procedures were used. Homogeneous and inhomogeneous torso models and corresponding forward problem solutions were used to relate sources on the closed epicardial and epicardial-endocardial surfaces. The localization error (LE) between the true and estimated pacing site/PVC origin was evaluated. Results: For paced data, the median LE values were 25.2 and 13.9mm for the dipole-based and potential-based models, respectively. These median LE values were higher for the spontaneous PVC data: 30.2-33.0mm for the dipole-based model and 28.9-39.2mm for the potential-based model. The assumption of inhomogeneities in the torso model did not change the dipole-based solutions much, but using an inhomogeneous model improved the potential-based solutions on the epicardial-endocardial ventricular surface. Conclusion: For the specific task of localization of pacing site/PVC origin, the dipole-based source model is more stable and robust than the potential-based source model. The torso inhomogeneities affect the performances of PVC origin localization in each source model differently. Hence, care must be taken in generating patient-specific geometric and forward models depending on the source model representation used in electrocardiographic imaging (ECGI).

Comparing one-stage and two-stage learning strategy in object detection

Object detection plays a vital role in computer social perception and computer vision. It could be applied to computer navigation, video monitoring, industrial detection, and so on. It greatly reduces the human labours by automatically locate and identify objects. Nowadays, the mainstream methods of object detection could be separated into the one- and two-stage method. The one-stage method leverages Convolutional Neural Network (CNN) for obtaining features and directly locate the target objects and their corresponding category probabilities. Different from the two-stage solutions, its accuracy is lower and the recognition speed is higher. The two-stage method is a straight forward solution, which process is mainly completed through a complete CNN, so CNN features will be leveraged to extract the feature description of the target among candidate regions through a CNN. The accuracy of the two-step method has been greatly improved, but the running speed is much slower than the one-step method. While the one-step method is less accurate, it is much faster. In this work, representative works for object detection are conducted and compared. The results could further demonstrate their respective advantages.

Exploring how Professional Associations Influence Health System Transformation: The Case of Ontario Health Teams.

Health care system transformations that align with the principles of integrated care require the collaborative efforts of various macro-, meso- and micro-level stakeholders. Understanding the roles of various system actors can improve collaboration in ways that support purposeful health system change. Professional associations (PAs) have considerable influence, but little is known about the strategies they use to influence health system transformation. Using a qualitative descriptive approach, eight interviews with 11 senior level leaders from local PAs were conducted to learn about the strategies used to influence the province-wide reorganization of health care into Ontario Health Teams. During times of health system transformation, PAs balance: (1) supporting members, (2) negotiating with government, (3) collaborating with stakeholders, and (4) reflecting on their role. The enactment of these various functions demonstrates the strategic nature of PAs, and showcases their ability to evolve in ways that align with the dynamic nature of healthcare. PAs are highly connected groups, deeply engaged with their members and regularly engaged with other key stakeholders and decision-makers. PAs play a critical role in influencing health system transformations, by bringing forward practical solutions to government that reflect the needs of their members, often frontline clinicians. PAs strategically seek opportunities for collaboration with stakeholders that can amplify their message. Insights from this work could support health system leaders, policymakers, and researchers in leveraging the role of PAs in health system transformations via strategic collaboration.

Determining Homogenous Transformation Matrix from DH Parameter Table using Deep Learning Techniques

One of the most popular ways of representing any robotic model mathematically is through Denavit-Hartenberg (DH) parameter table. And the most common way of finding a forward kinematics solution to any robotic model is by finding its homogenous transformation matrix, which is obtained from the DH parameter table by a certain set of steps or algorithms. In this research work, we have tried solving this problem in just a single step by deep learning method and thus finding forward kinematics of almost any kind of manipulator. This research work shows not just this problem but many more such complex problems which require a certain set of steps or algorithms that can be solved by deep learning techniques in a single step. The results obtained are very close to accurate and show the ability of deep learning techniques for solving different kinds of such problems.