Model Of Transitions Research Articles

Resilient social workers’ perspective on their professional work during the COVID-19 pandemic. An interpretative phenomenological analysis

ABSTRACT This study aimed to an in-depth understanding and meaning making of the resilience processes of resilient social workers providing services during the COVID-19 pandemic, over a period of two years from the outbreak. A sample of 23 resilient social workers covering a range of different social services organisations in Romania participated in three focus groups. The method of interpretative phenomenological analysis has led to a matrix of four themes: resilience process, resilience factors, the meaning of the pandemic, the focus group experience. Findings showed that resilience processes evolved over three phases interpreted through the lenses of Bridges transitions model: firstly (March 2020–May 2020) ‘shock and chaos', secondly (June 2020–February 2021) ‘identifying order in chaos'; thirdly, (March 2021–March 2022) ‘integration and growth’. Findings indicate that individual resilience factors and team factors prevailed over organisational factors, even though supportive leadership and organisational support acted as resilience enhancers. Within a socio-ecological approach, a focus on individual resilience factors and relationships must not distract from the contribution that structural and social policy changes may have on resilience processes. Lessons learned - can contribute to the development of a resilience-informed approach to social work education and practice at both national and European level.

Active learning-based automated construction of Hamiltonian for structural phase transitions: A case study on BaTiO3.

The effective Hamiltonians have been widely applied to simulate the phase transitions in polarizable materials, with coefficients obtained by fitting to accurate first-principles calculations. However, it is tedious to generate distorted structures with symmetry constraints, in particular when high-ordered terms are considered. In this work, we implement and apply a Bayesian optimization-based approach to sample the potential energy surface, automating the Hamiltonian construction by selecting&#xD;distorted structures via active learning. Taking BaTiO3 (BTO) as an example, we demonstrate that the Hamiltonian can be obtained using fewer than 30 distorted structures. Follow-up Monte Carlo simulations can reproduce the structural phase&#xD;transition temperatures of BTO, comparable to experimental values with an error < 10%. Our approach can be straightforwardly applied on other polarizable materials and paves the way for quantitative atomistic modelling of diffusionless phase transitions.

Phenomenological models of ferroelastics with a fully symmetric order parameter: classification by methods of equivariant catastrophe theory

Using the methods of the catastrophe theory, which take into account the symmetry of the thermodynamic system, phenomenological models of phase transitions in proper and pseudo-proper ferroelastics have been constructed for interacting order parameters, one of which is fully symmetric, invariant with respect to all symmetry transformations of the crystal. A classification of models based on the number of control parameters, which depend on external thermodynamic conditions, has been carried out. A phase diagram of one of the models has been constructed, and the theoretical temperature dependence of the heat capacity has been calculated. The comparison of theoretical and experimental data showed satisfactory qualitative agreement.

Unveiling the Genome-Wide Consequences of Range Expansion and Mating System Transitions in Primula vulgaris.

Genetic diversity is heterogeneously distributed among populations of the same species, due to the joint effects of multiple demographic processes, including range contractions and expansions, and mating systems shifts. Here, we ask how both processes shape genomic diversity in space and time in the classical Primula vulgaris model. This perennial herb originated in the Caucasus region and was hypothesized to have expanded westward following glacial retreat in the Quaternary. Moreover, this species is a long-standing model for mating system transitions, exemplified by shifts from heterostyly to homostyly. Leveraging a high-quality reference genome of the closely related Primula veris and whole-genome resequencing data from both heterostylous and homostylous individuals from populations encompassing a wide distribution of P. vulgaris, we reconstructed the demographic history of P. vulgaris. Results are compatible with the previously proposed hypothesis of range expansion from the Caucasus region approximately 79,000 years ago and suggest later shifts to homostyly following rather than preceding postglacial colonization of England. Furthermore, in accordance with population genetic theoretical predictions, both processes are associated with reduced genetic diversity, increased linkage disequilibrium, and reduced efficacy of purifying selection. A novel result concerns the contrasting effects of range expansion versus shift to homostyly on transposable elements, for the former, process is associated with changes in transposable element genomic content, while the latter is not. Jointly, our results elucidate how the interactions among range expansion, transitions to selfing, and Quaternary climatic oscillations shape plant evolution.

MicroRNAs provide negative feedback and stability in gene regulatory network models of cell-state transitions

The development of multicellular organisms occurs through a series of cell state transitions controlled by gene regulatory networks. Central to these networks are transcription factors (TFs) which bind enhancers and activate the expression of other genes, some of which are also TFs. Gene regulatory networks (GRN) connect TFs and enhancers in a nonlinear circuit capable of producing complex behavior such as bifurcations between stable cell states. Our dynamic network modelling of the Embryonic Stem Cell (ESC) to Definitive Endoderm (DE) transition requires an as yet unknown negative feedback mechanism for stability. Here, we show that cell state specific microRNAs (miRNAs) can provide this negative feedback by inactivating other cell lineage determining TFs (ESC or DE) during the transition. Our model provides a mechanism to maintain stable cell states without requiring a large set of cell-type-specific repressive TFs, of which there are fewer known examples than activators. In support of this model, we use computational models and analyze gene and miRNA expression and chromatin accessibility data from human cell lines to detect enhancers activating the miRNAs consistent with our network model. Our analysis highlights the interplay between TFs and miRNAs during ESC to DE transition and proposes a novel model for gene regulation.

The Logistic Function in Glass Transition Models of Amorphous Polymers: A Theoretical Framework for Isobaric Cooling Processes

AbstractStudying the macroscopic‐phenomenological behavior of amorphous polymers at glass transition is often subject to limitations because the ordinary differential equations that describe the material behavior require numerical solution. To avoid these limitations, ad‐hoc‐formulated models of the glass transition have been proposed. However, their scope of application is expected to be limited due to insufficient theoretical foundation. This work establishes a theoretical framework for models that use the logistic function to approximate the macroscopic‐phenomenological behavior of amorphous polymers at glass transition. For this purpose, an exactly‐solvable Riccati equation is derived within thermodynamics with internal state variables. A closed‐form expression in terms of mathematical functions for the temperature derivative of a single internal state variable is the result. This closed‐form expression contains the logistic function thus featuring a continuous transition region centered around a pressure and cooling‐rate dependent transition temperature. Based on comparison of existing models with the exact solution derived from the Riccati equation, generalized models that approximate the thermal expansion coefficient and heat capacity at glass transition are proposed. This work thus demonstrates the validity of the logistic function in glass transition models of amorphous polymers and provides suggestions as to how existing models can be extended in their applicability.

Dynamic formation of the protein-lipid prefusion complex

Synaptic vesicles (SVs) fuse with the presynaptic membrane (PM) to release neuronal transmitters. The SV protein synaptotagmin 1 (Syt1) serves as a Ca2+ sensor for evoked fusion. Syt1 is thought to trigger fusion by penetrating the PM upon Ca2+ binding; however, the mechanistic detail of this process is still debated. Syt1 interacts with the SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) complex, a coiled-coil four-helical bundle that enables the SV-PM attachment. The SNARE-associated protein complexin (Cpx) promotes Ca2+-dependent fusion, possibly interacting with Syt1. We employed all-atom molecular dynamics to investigate the formation of the Syt1-SNARE-Cpx complex interacting with the lipid bilayers of the PM and SVs. Our simulations demonstrated that the PM-Syt1-SNARE-Cpx complex can transition to a “dead-end” state, wherein Syt1 attaches tightly to the PM but does not immerse into it, as opposed to a prefusion state, which has the tips of the Ca2+-bound C2 domains of Syt1 inserted into the PM. Our simulations unraveled the sequence of Syt1 conformational transitions, including the simultaneous docking of Syt1 to the SNARE-Cpx bundle and the PM, followed by Ca2+ chelation and the penetration of the tips of Syt1 domains into the PM, leading to the prefusion state of the protein-lipid complex. Importantly, we found that direct Syt1-Cpx interactions are required to promote these transitions. Thus, we developed the all-atom dynamic model of the conformational transitions that lead to the formation of the prefusion PM-Syt1-SNARE-Cpx complex. Our simulations also revealed an alternative dead-end state of the protein-lipid complex that can be formed if this pathway is disrupted.

Integrating Sustainability and Digitalization in Business Model Innovation. A Bibliometric Study

Abstract The aim of the study is to get a comprehensive understanding of the research landscape at the nexus between business model innovation, sustainability and/or digitalization, in order to determine key research patterns and trends. Accordingly, the following objectives have guided the development of the paper: (a) to examine the dynamics and structure of the research output (articles and citations) on the topic; (b) to explore the network of the most impactful articles; (c) to trace the origins of the research on the topic; (d) to decipher current developments in the research on the topic; and (e) to search for emerging trends in the research on the topic. An advanced search of the SCOPUS database was conducted to identify the relevant literature available; 1195 articles (published between 2014 and 2023) were selected and exported in VOSviewer for bibliometric analysis. The results suggest the emergence of a niche research area in the broad strategic management field, bringing together four related (sub)topics: (1) circular economy and sustainable business models; (2) tools, frameworks and processes for sustainable business model innovation; (3) digitalization and business model transformation; (4) business models for societal and sustainability transitions. For this research area to crystallize, the identified emerging research trends – on digital innovation and technology; strategic management and analytical methods; supply chain and manufacturing; policy, governance and sustainability – need to be further explored, both individually (in search of refinement and clarity) and collectively (in search of consistency and synergy).

A Review of the Relaxation Models for Phase Transition Flows Centered on the Topological Aspects of the Nonequilibrium Mass Transfer Modelling

A Review of the Relaxation Models for Phase Transition Flows Centered on the Topological Aspects of the Nonequilibrium Mass Transfer Modelling

Care coordination models for transition and long-term follow-up among childhood cancer survivors: a scoping review

ObjectivesChildhood cancer survivors may experience complex health issues during transition and long-term follow-up (LTFU); therefore, high-quality healthcare is warranted. Care coordination is one of the essential concepts in advanced healthcare....

Theoretical Model of Structural Phase Transitions in Al-Cu Solid Solutions under Dynamic Loading Using Machine Learning

Theoretical Model of Structural Phase Transitions in Al-Cu Solid Solutions under Dynamic Loading Using Machine Learning

Phenomenological Models of Phase Transitions with Multicomponent Interacting Order Parameters: Construction and Classification by Methods of Singularity Theoryy

Phenomenological models of phase transitions for two interacting order parameters, one–component and two-component, are constructed. The construction was carried out based on the number of control parameters depending on external thermodynamic conditions by methods of the equivariant catastrophe theory (singularity theory), taking into account the symmetry of the order parameters. The classification of phenomenological models according to the number of control parameters is carried out. Phase diagrams of the models are constructed and the theoretical temperature dependence of the heat capacity is calculated, which shows a satisfactory qualitative correspondence with the experimental dependence in the Rb2KInF6 elpasolite crystal

Dark radiation isocurvature from cosmological phase transitions

Cosmological first order phase transitions are typically associated with physics beyond the Standard Model, and thus of great theoretical and observational interest. Models of phase transitions where the energy is mostly converted to dark radiation can be constrained through limits on the dark radiation energy density (parameterized by ΔN eff). However, the current constraint (ΔN eff < 0.3) assumes the perturbations are adiabatic. We point out that a broad class of non-thermal first order phase transitions that start during inflation but do not complete until after reheating leave a distinct imprint in the scalar field from bubble nucleation. Dark radiation inherits the perturbation from the scalar field when the phase transition completes, leading to large-scale isocurvature that would be observable in the CMB. We perform a detailed calculation of the isocurvature power spectrum and derive constraints on ΔN eff based on CMB+BAO data. For a reheating temperature of T rh and a nucleation temperature T *, the constraint is approximately ΔN eff ≲ 10-5 (T */T rh)-4, which can be much stronger than the adiabatic result. We also point out that since perturbations of dark radiation have a non-Gaussian origin, searches for non-Gaussianity in the CMB could place a stringent bound on ΔN eff as well.

ZEROS OF PARTITION FUNCTION FOR Z5-SYMMETRIC MODEL ON SQUARE LATTICE-PARTICULAR CASE

We study a statistical mechanics model of multiple-phase transitions called the ZQ-symmetric model on the square lattice for five possible spin directions (Q = 5). This study aims to investigate the existence of phase transition(s) and discuss the distribution of partition function zeros on the complex-temperature plane. Some cases of energy list χ are considered where they are written in an arbitrary arrangement of integer numbers (usually in decreasing value due to the angle of separation). The partition functions are computed using a transfer matrix approach, and their zeros are found numerically using the NewtonRaphson method. A Monte-Carlo Metropolis simulation is then performed to study the critical point of phase transition. The results are compared to the zeros distribution of the partition functions for the considered cases. Studying this generalised model is vital to mimic the system in the real world and to help experimental works use a more sustainable approach.

One-dimensional models for supercritical and subcritical transitions in rotating convection

One-dimensional models for supercritical and subcritical transitions in rotating convection

Integrating distributive justice in system dynamics models of sustainability transitions

AbstractSocial‐ecological‐technological transitions for decarbonisation and sustainable development goals can perpetuate or introduce environmental and socio‐economic injustices. The just transitions literature provides useful perspectives for assessing transition pathways. This paper explores integrating distributive justice into system dynamics modelling for sustainability transitions. For this purpose, we adapt existing requirements to enable the evaluation of distributive justice in model‐based support for climate planning to the specific case of system dynamics modelling. We test the requirements by using a previously developed system dynamics model to (1) gain insights into the application of the requirements and (2) reflect on their further development. Our findings underscore the importance of transparency in model assumptions and results. They suggest exploring the behaviour space of models to account for uncertainty and plurality of stakeholder values. Furthermore, they emphasise the need to discuss the implications of model results and the plurality of stakeholder values to foster deliberation and inclusive decision‐making.

OneSC: A computational platform for recapitulating cell state transitions.

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.

Phenomenological Models of Phase Transitions with Multicomponent Interacting Order Parameters: Construction and Classification by Methods of Singularity Theory

Phenomenological Models of Phase Transitions with Multicomponent Interacting Order Parameters: Construction and Classification by Methods of Singularity Theory

Comparative Analysis of Algebraic Models of Laminar-Turbulent Transition

Comparative Analysis of Algebraic Models of Laminar-Turbulent Transition

Manifestation of molecular vibrations of polymers in thermoluminescence of nanocomposites

An experimental and theoretical study of the thermoluminescence (TL) of a PDHS/MCM-41 nanocomposite with the introduction of a polymer (poly-di-n-hexylsilane) (PDHS) into MCM nanopores has been carried out in the temperature range 5–150K. TL curve consists of a very narrow intense band and wider less intense band. In the nananocomposite there is a strong shift of the curve toward low temperatures compared to the curve of the film and a significant narrowing of the bands. The calculations used the model of non-adiabatic transitions. Results show a significant role of molecular vibrations in the formation of the TL curve of a nanocomposite.