Implicit Dynamics Research Articles

The Integrated Model: Dynamic Changes in Mother–Child Relationship in the Context of Intimate Partner Violence

Abstract Purpose This study aimed to examine the explicit and implicit aspects of mother–child relationship to explain changes in the relationship led by changes in the context of intimate partner violence (IPV) over time. The explicit mother–child relationship involves verbal and conscious communications, while the implicit relationship consists of nonverbal and nonconscious interactions. Method Grounded Theory was employed to assert the participant perspectives as most important. Participants included 33 Chinese mother–child survivors (13 mothers and 20 children) who were residents and ex-residents of a shelter for abused women and their children in Hong Kong. Data on their explicit relationship was collected from qualitative individual interviews. An art-based method, Joint Painting Procedure (JPP), was applied to obtain dyadic mother–child data on their implicit relationship. Results The integrated model, “Dynamic changes in mother–child relationship in the context of IPV”, was grounded in three main parts of findings. First, the participants’ intersecting social identities and backgrounds. Second, the changes in their explicit relational dynamics led by the transitions in the context of IPV. Third, their bidirectional and reciprocal implicit relational dynamics elicited through their JPP and post-painting discussions. Conclusion The integrated model has significant implications for professional interventions at the post-separation stage. First, IPV is an intersectional social problem requiring social and policy changes, and consideration of diversities among mother–child survivors. Second, the mother–child relationship and the context of IPV are dynamic and changing instead of static. Third, the implicit relationship provides a new dimension for professional interventions to strengthen the mother–child relationship.

Domain decomposition methods and acceleration techniques for the phase field fracture staggered solver

AbstractThe phase field modeling of fracture is able to simulate the nucleation and the propagation of complex crack patterns. However, the relatively small internal lengths that are required usually lead to very fine meshes and high computational costs, especially for three‐dimensional applications. In the present work, additional cost also comes from the implicit dynamics regularization of unstable crack propagations which potentially leads to a large variation of time steps when switching from quasi‐static to dynamic regimes. To reduce the time to solution in this context, this study proposes a domain decomposition framework and acceleration techniques for the phase field fracture staggered solver. The displacement subproblem and the phase field one are solved with parallel domain decomposition solvers. Dual domain decomposition methods provide low cost preconditioner well adapted to the phase field subproblem. For displacement subproblems undergoing unstable crack propagations, primal domain decomposition methods are preferred to be less sensitive to the treatment of floating substructures. Preconditioners performances are assessed and scalability studies over academic test cases, up to 324 subdomains, are presented. Finally, the robustness of the approach is illustrated on two semi‐industrial simulations.

Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning

Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of problem specifications. In factored state spaces, one approach towards achieving both goals is to learn state abstractions, which only keep the necessary variables for learning the tasks at hand. This paper introduces Causal Bisimulation Modeling (CBM), a method that learns the causal relationships in the dynamics and reward functions for each task to derive a minimal, task-specific abstraction. CBM leverages and improves implicit modeling to train a high-fidelity causal dynamics model that can be reused for all tasks in the same environment. Empirical validation on two manipulation environments and four tasks reveals that CBM's learned implicit dynamics models identify the underlying causal relationships and state abstractions more accurately than explicit ones. Furthermore, the derived state abstractions allow a task learner to achieve near-oracle levels of sample efficiency and outperform baselines on all tasks.

Structure preserving and energy dissipative contact approaches for implicit dynamics

AbstractIn this work, several structure preserving and energy dissipative contact approaches are proposed and evaluated. The time integration schemes considered are general with regard to the version of constraint type, but here the emphasis was on mortar contact. The proposed mortar contact approach conserves both linear and angular momentum for mortar contact in a novel way. The proposed time integration scheme can conserve energy or provide strict contact dissipation. In addition, the proposed scheme enforces both gap constraints and gap velocity constraints (i.e., persistency). Using a midstep time integrator often causes energy dissipation during initial impact, here this energy can be recovered in a novel way. Enforcing the gap velocity constraint mitigates the contact chatter of the contact pressure and nodes in many problems. Whereas some approaches enforce the persistency condition and gap constraints simultaneously during the solution of the equations of motion (EOM) requiring multipliers for both constraints included in the equation set, here the gap constraint is solved through the equations of motion and the persistency condition is satisfied in the time integration scheme by the velocity update after the equations of motion. It is shown that this approach is strictly dissipative in that a plastic contact condition can be achieved. Analogous to a coefficient of restitution for rigid bodies, any dissipated energy can then be returned upon release if energy conservation is desired. Structure preserving methods are good for long‐time dynamics simulations and energy conserving and strictly dissipative methods can overcome stability issues associated with standard time integration algorithms such as the Newmark method.

Heterogeneity in Disulfide Bond Reduction in IgG1 Antibodies Is Governed by Solvent Accessibility of the Cysteines.

We studied unpaired cysteine levels and disulfide bond susceptibility in four different γ-immunoglobulin antibodies using liquid chromatography-mass spectrometry. Our choice of differential alkylating agents ensures that the differential peaks are non-overlapping, thus allowing us to accurately quantify free cysteine levels. For each cysteine residue, we observed no more than 5% to be unpaired, and the free cysteine levels across antibodies were slightly higher in those containing lambda light chains. Interchain and hinge residues were highly susceptible to reducing stresses and showed a 100-1000-fold higher rate of reduction compared to intrachain cysteines. Estimations of the solvent-accessible surface for individual cysteines in IgG1, using an implicit all-atom molecular dynamics simulation, show that interchain and hinge cysteines have >1000-fold higher solvent accessibility compared to intrachain cysteines. Further analyses show that solvent accessibility and the rate of reduction are linearly correlated. Our work clearly establishes the fact that a cysteine's accessibility to the surrounding solvent is one of the primary determinants of its disulfide bond stability.

Disentangling sensory precision and prior expectation of change in autism during tactile discrimination

Predictive coding theories suggest that core symptoms in autism spectrum disorders (ASD) may stem from atypical mechanisms of perceptual inference (i.e., inferring the hidden causes of sensations). Specifically, there would be an imbalance in the precision or weight ascribed to sensory inputs relative to prior expectations. Using three tactile behavioral tasks and computational modeling, we specifically targeted the implicit dynamics of sensory adaptation and perceptual learning in ASD. Participants were neurotypical and autistic adults without intellectual disability. In Experiment I, tactile detection thresholds and adaptation effects were measured to assess sensory precision. Experiments II and III relied on two-alternative forced choice tasks designed to elicit a time-order effect, where prior knowledge biases perceptual decisions. Our results suggest a subtler explanation than a simple imbalance in the prior/sensory weights, having to do with the dynamic nature of perception, that is the adjustment of precision weights to context. Compared to neurotypicals, autistic adults showed no difference in average performance and sensory sensitivity. Both groups managed to implicitly learn and adjust a prior that biased their perception. However, depending on the context, autistic participants showed no, normal or slower adaptation, a phenomenon that computational modeling of trial-to-trial responses helped us to associate with a higher expectation for sameness in ASD, and to dissociate from another observed robust difference in terms of response bias. These results point to atypical perceptual learning rather than altered perceptual inference per se, calling for further empirical and computational studies to refine the current predictive coding theories of ASD.

At home in the field, in the field at home? Reflections on power and fieldwork in familiar settings

Critical epistemologies and methodologies have over time challenged the static and mono-dimensional approaches to fieldwork, allowing researchers to contemplate and conduct their research in spaces of in-betweenness. Despite this important shift, the essentialist idea that both ‘the field’ and ‘home’ in a fieldwork setting must be actual places persists. In this article, we challenge the conceptualization and operationalization of ‘home’ not only as the juxtaposition to ‘the field’, but also as the embodiment of a place in a specific temporality. We argue that the postulation of ‘home’ as a constant disregards the non-predetermined and unpredictable nature of fieldwork relationships that are often complicated by implicit and explicit power dynamics, especially in places researchers identify as ‘home’. We demonstrate that unequal power relations, especially (1) between the Global North and Global South, (2) between majority and minoritized groups, (3) among genders, and (4) between elites and non-elites, require us to envisage ‘the field’ and ‘home’ in relative terms. We propose the reconceptualization of fieldwork place as a hybridized space that conjoins ‘the field’ and ‘home’ as ‘field-home’, particularly at a time when research mobility is restricted by the COVID-19 pandemic. In this way, we extend the literature on issues related to power, positionality and reflexivity in qualitative research, and provide practical insights for those preparing for fieldwork.

Investigating the aftermath of the Türkiye 2023 earthquake: exploring post-disaster uncertainty among Syrian migrants using social network analysis with public health approach.

On February 6th, 2023, a doublet earthquake struck Türkiye, impacting more than 15 million people including migrants, and resulting in over 50,000 deaths. The Syrian migrants experience multiple uncertainties in their daily lives which are further compounded by multifaceted challenges of the post-disaster environment. Social media was used intensively and with impunity in this environment and thereby provides a window into the explicit and implicit dynamics of daily life after a disaster. We aimed to explore how a post-disaster environment potentially generates new uncertainties or exacerbating pre-existing ones for migrants through social media analysis with an indirect perspective, in the context of 2023-Earthquake in Türkiye and Syrian migrants. Social network analysis was used to analyze Twitter-data with the hashtags 'Syrian' and 'earthquake' during a 10-day period beginning on March 22nd, 2023. We calculated network metrics, including degree-values and betweenness-centrality and clustered the network to understand groups. We analyzed a combination of 27 tweets with summative content analysis using a text analysis tool, to identify the most frequently used words. We identified the main points of each tweet and assessed these as possible contributors to post-disaster uncertainty among migrants by using inductive reasoning. There were 1918 Twitter users, 274 tweets, 124 replies and 1726 mentions. Discussions about Syrian migrants and earthquakes were established across various groups (ngroups(edges > 15) = 16). Certain users had a greater influence on the overall network. The nine most frequently used words were included under uncertainty-related category (nmost_frequently_used_words = 20); 'aid, vote, house, citizen, Afghan, illegal, children, border, and leave'. Nine main points were identified as possible post-disaster uncertainties among migrants. The post-disaster environment has the potential to exacerbate existing uncertainties, such as being an undocumented migrant, concerns about deportation and housing, being or having a child, inequality of rights between being a citizen and non-citizen, being in minority within minority, political climate of the host nation and access to education or to generate new ones such equitable distribution of aid, which can lead to poor health outcomes. Recognizing the possible post-disaster uncertainties among migrants and addressing probable underlying factors might help to build more resilient and healthy communities.

Implicit contact dynamics and Hamilton-Jacobi theory

In this paper, we introduce implicit Hamiltonian dynamics in the framework of contact geometry in two different ways: first, we introduce classical implicit Hamiltonian dynamics on a contact manifold, followed by evolution Hamiltonian dynamics. In the first case, implicit contact Hamiltonian dynamics is defined as a Legendrian submanifold of a tangent contact space, whilst the implicit evolution dynamic is understood as a Lagrangian submanifold of a certain symplectic space embedded into the tangent contact space. To conclude, we propose a geometric Hamilton-Jacobi theory for both of these formulations.

Sparse identification of Lagrangian for nonlinear dynamical systems via proximal gradient method

The autonomous distillation of physical laws only from data is of great interest in many scientific fields. Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. However, SINDy faces certain difficulties when the dynamics contain rational functions. The Lagrangian is substantially more concise than the actual equations of motion, especially for complex systems, and it does not usually contain rational functions for mechanical systems. Few proposed methods proposed to date, such as Lagrangian-SINDy we have proposed recently, can extract the true form of the Lagrangian of dynamical systems from data; however, these methods are easily affected by noise as a fact. In this study, we developed an extended version of Lagrangian-SINDy (xL-SINDy) to obtain the Lagrangian of dynamical systems from noisy measurement data. We incorporated the concept of SINDy and used the proximal gradient method to obtain sparse Lagrangian expressions. Further, we demonstrated the effectiveness of xL-SINDy against different noise levels using four mechanical systems. In addition, we compared its performance with SINDy-PI (parallel, implicit) which is a latest robust variant of SINDy that can handle implicit dynamics and rational nonlinearities. The experimental results reveal that xL-SINDy is much more robust than the existing methods for extracting the governing equations of nonlinear mechanical systems from data with noise. We believe this contribution is significant toward noise-tolerant computational method for explicit dynamics law extraction from data.

Safety of Reinforced Concrete Columns: Effect of Initial Imperfections and Material Deterioration under Emergency Actions

The effect of (1) initial imperfections and (2) material degradation of reinforced concrete columns on their safety in emergency situations was investigated. The research was limited to low- and medium-flexibility columns. Numerical modeling and proven regulatory methods of analysis were applied to determine the ultimate bearing capacity, taking into account supplementary dynamic loading by a longitudinal force and a bending moment in case of emergency. The numerical model, describing the column structure, has 3D elements simulating concrete, and rebars simulating reinforcement frames (cages). Imperfections are simulated by (1) the physical loss of elements, (2) unzip of nodal elements, and (3) unzip and further zip using nonlinear elements simulating gaps and cohesion between concrete and reinforcement. Implicit dynamics and an incremental method were employed to make computations. Within the framework of this computational scheme, a nonlinear problem was solved using the Newton–Raphson method with nodal forces convergence. The effect of imperfections, such as geometrical deviations and deterioration of mechanical characteristics, on the bearing capacity of compressed bending elements was identified under emergency actions. Risks of mechanical safety loss were analyzed to find that columns in the frame structures of highly hazardous, technically complex, and unique buildings and structures, subjected to supplementary loading, need an additional safety margin in the range of 3–21%. Rectangular cross-sections of columns are the most effective in terms of the safety criterion.

Development of Calculation Technique for FEM Thermal Simulation Using Virtual Fluid Elements

In recent years, CAE (Computer-aided engineering) using FEM (Finite element method) simulations were generally used in the design field. FEM simulations were classified as static implicit and explicit dynamics methods. Particularly FEM simulations using the static implicit method were very generally and usefully used in the industrial world. The static implicit method FEM consists of static, buckling, thermal, vibration analyses, and so on. This FEM thermal analysis can't calculate the phenomena of heat transfer and internal forced cooling in some enclosures of a machine tool. On the other hand, when the temperature distributions in a structure such as that are calculated, FVM fluid simulation was used for that. However, this simulation can't exactly calculate a complex structure in a machine tool, needs very long calculation times and the calculation accuracy is very poor. Therefore, in this research, the calculation technique regarding FEM thermal simulation using 4 virtual fluid elements was developed and evaluated for the phenomena of heat build-up and internal forced cooling in some enclosures of a machine tool. The algorithms and the calculation models regarding 4 virtual fluid elements were developed, then the proposed method was evaluated in the experiment. It is concluded from the results that; (1) the 4 virtual fluid elements were developed for FEM thermal simulation instead of FVM fluid simulation, (2) FEM thermal simulation with the developed 4 virtual fluid elements has high calculation accuracy and a short calculation time, (3) the proposed method was very effective in the design.

DLC1 as Druggable Target for Specific Subsets of Gastric Cancer: An RNA-seq-Based Study.

Background: Gastric cancer has been ranked the third leading cause of cancer death worldwide. Its detection at the early stage is difficult because patients mostly experience vague and non-specific symptoms in the early stages. Methods: The RNA-seq datasets of both gastric cancer and normal samples were considered and processed. The obtained differentially expressed genes were then subjected to functional enrichment analysis and pathway analysis. An implicit atomistic molecular dynamics simulation was executed on the selected protein receptor for 50 ns. The electrostatics, surface potential, radius of gyration, and macromolecular energy frustration landscape were computed. Results: We obtained a large number of DEGs; most of them were down-regulated, while few were up-regulated. A DAVID analysis showed that most of the genes were prominent in the KEGG and Reactome pathways. The most prominent GAD disease classes were cancer, metabolic, chemdependency, and infection. After an implicit atomistic molecular dynamics simulation, we observed that DLC1 is electrostatically optimized, stable, and has a reliable energy frustration landscape, with only a few maximum energy frustrations in the loop regions. It has a good functional and binding affinity mechanism. Conclusions: Our study revealed that DLC1 could be used as a potential druggable target for specific subsets of gastric cancer.

Identification of implicit dynamics of supercritical CO2 invasion in sub-regions of bench micromodels

The ambiguous dynamics of invading carbon dioxide in subcritical and supercritical states, as well as the response of pore-scale resident fluids, play a key role in understanding CO2 capture and storage (CCS) and the corresponding phase equilibrium mechanisms. This paper reveals the implicit dynamics of invading supercritical carbon dioxide (sCO2) in deionized water (DIW)-saturated micromodels using a variant of Lattice-Boltzmann Color Fluid model and descriptive experimental data. The breakthrough time is evaluated by characterizing the displacement velocity, the capillary to pressure-difference ratio, and the transient saturation at a series of micromodels with scaling pore-throats. The recorded sub-regimes are remarkably categorized as oscillatory while the interfacial velocity of sCO2/DIW is jumping into oscillatory magnitudes. Hence, the transient saturation would be significantly accelerated with decreasing pore-throats, demonstrating increased invasion efficiency. Accordingly, a renovated model is established to account for the transient dynamics of invading sCO2 towards efficient geological sequestration.

Analyzing the binding affinity of potential homodimers produced by the C55 portion of the amyloid precursor protein, a molecular dynamics study.

Alzheimer's disease is a neurodegenerative disease that affects millions of people in the United States alone. Agglomeration of amyloid beta (Aβ) peptides is a potential cause of the disease and the loss of mental acuity it induces. Aβ is created via the cleavage of the amyloid precursor protein (APP). When APP is cleaved, part of it—approximately the C99 fragment—remains in the lipid membrane, with its N-terminus being the Aβ peptide. The dimerization of C99 is theorized to limit its further cleavage and thereby limit the production of Aβ. However, this has yet to be proven experimentally. The first 55 residues of the C99 fragment (called C55) contains both the transmembrane domain and Aβ. In this work, we explored multiple potential conformations of C55 dimers (starting from the PDB ID 2LP1) and calculated their binding affinity to better understand the viability of homodimerization. The homodimer conformations were explored using both implicit and explicit water Molecular Dynamics simulations. We used an Extended Adaptive Biasing Force method to calculate the binding affinity of these dimers in a POPC lipid membrane. This methodology is characterized by the use of a continuously updated biasing force that cancels all force along a specified reaction coordinate and, in this way, enables uniform sampling on a flat free-energy surface. Applying this methodology using collective variables for root-mean-square deviation, spin, tilt, and Euclidean distance for both the bulk (monomer) and bound (dimer) states, we compute a binding affinity of roughly −4 kcal/mol for one of our dimer conformations. This lends in silica credence to the hypothesis that the dimerization of C55 limits the formation of Aβ, as the dimer appears energetically viable.

Treating Polarization Effects in Charged and Polar Bio-Molecules Through Variable Electrostatic Parameters.

Polarization plays important roles in charged and hydrogen bonding containing systems. Much effort ranging from the construction of physics-based models to quantum mechanism (QM)-based and machine learning (ML)-assisted models have been devoted to incorporating the polarization effect into the conventional force fields at different levels, such as atomic and coarse grained (CG). The application of polarizable force fields or polarization models was limited by two aspects, namely, computational cost and transferability. Different from physics-based models, no predetermining parameters were required in the QM-based approaches. Taking advantage of both the accuracy of QM calculations and efficiency of molecular mechanism (MM) and ML, polarization effects could be treated more efficiently while maintaining the QM accuracy. The computational cost could be reduced with variable electrostatic parameters, such as the charge, dipole, and electronic dielectric constant with the help of linear scaling fragmentation-based QM calculations and ML models. Polarization and entropy effects on the prediction of partition coefficient of druglike molecules are demonstrated by using both explicit or implicit all-atom molecular dynamics simulations and machine learning-assisted models. Directions and challenges for future development are also envisioned.

Optimality equivalence and computational advantages of free-floating base dynamics compared to full-body dynamics

Several transcription methods exist for predictive simulations of free-floating base motions like athlete performing twisting somersaults. Following a brief overview of the literature, we proposed two original direct multiple shooting optimal control problems: (1) with a reduced set of equations motion (free-floating base dynamics) and generalized accelerations as controls; and (2) with the dynamics enforced implicitly through algebraic constraints and generalized jerks as controls. To assess their performance (computational time, dynamic consistency, cost), we compared full-body vs free-floating base dynamics and explicit vs implicit formulations of an optimal control problem of a double straight somersault with three twists. We found that free-floating dynamics is relevant when bounds on joint torques are not required since it hastens the convergence up to 10 times and the optimal solutions are similar. Implicit dynamics also speeds up optimization compared to the explicit formulation, but dynamic consistency may be affected. Using joint jerks instead of accelerations as controls mitigates this limitation, especially when the mesh grid densifies.

On locating interculturalism and somatics: Looseness, holding on and swimming

This article addresses how improvisation and somatics are methods of movement research that permit the overlap of Jewish and African diasporic practices and coalitions outside of the bounds of language. Improvisation and somatics are queried as ways of making dance and as shifting spaces of coalition, of standing with others, and standing also for myself as a Jewish woman. I articulate a viewpoint on overlapping diasporas between Jewish and African diasporic populations which is asserted in tandem with analyses of the ways in which diasporas, and interculturalisms, make present for me implicit power dynamics through improvisation, technique and somatic practices. This analysis of my own practices permits a deeper rendering of the plurality of diasporic Jewish female identity as it relates to my body, interculturalism, spirituality, anti-racism and decolonization implicit in somatics, technique and improvisation.

Hidden Degrees of Freedom in Implicit Vortex Filaments

This paper presents a new representation of curve dynamics, with applications to vortex filaments in fluid dynamics. Instead of representing these filaments with explicit curve geometry and Lagrangian equations of motion, we represent curves implicitly with a new co-dimensional 2 level set description. Our implicit representation admits several redundant mathematical degrees of freedom in both the configuration and the dynamics of the curves, which can be tailored specifically to improve numerical robustness, in contrast to naive approaches for implicit curve dynamics that suffer from overwhelming numerical stability problems. Furthermore, we note how these hidden degrees of freedom perfectly map to a Clebsch representation in fluid dynamics. Motivated by these observations, we introduce untwisted level set functions and non-swirling dynamics which successfully regularize sources of numerical instability, particularly in the twisting modes around curve filaments. A consequence is a novel simulation method which produces stable dynamics for large numbers of interacting vortex filaments and effortlessly handles topological changes and re-connection events.

Engaging with the client’s existential concerns: the impact on therapists and counselors

ABSTRACT This study examined which themes therapists identify as existential and explored what happens when therapists engage with their clients’ existential concerns. Over two simultaneous studies, 19 participants were interviewed about working with existential themes. The first study consisted of experienced person-centered and existential psychotherapists and the second study of counselors or therapists working in a setting that would evoke existential themes. The data were first analyzed separately for both groups using Consensual Qualitative Research. Subsequently, a general cross analysis was conducted to achieve a meta-synthesis. Four existential themes were identified: Death and boundaries of life; Freedom, choice and responsibility; Connection and isolation; and Meaning and meaninglessness. In addition to providing a descriptive model of these themes, this study suggests that existential training might help therapists notice more implicit existential dynamics in the therapeutic process. Furthermore, engaging with clients’ existential concerns might make therapists more aware of their own existential reality which would help them to relate to existential themes differently. Experienced therapists can identify existential themes in their clients’ stories. However, specific existential training and personal reflection can deepen therapists’ openness towards existential issues and expand insight in existential processes.