Sequence Of Transitions Research Articles

Emergence and Stability of Hierarchical Structures under Cylindrical Confinement

AbstractFocusing on the formation of hierarchical structure under cylindrical confinement, the self‐assembly of A(BC)2B multiblock copolymer of chain length N in a nanopore with size R is studied using the self‐consistent field theory. The hierarchical concentric ring (HCk), hierarchical perforated cylinder (HPk), hierarchical helix (HHk), and even hierarchical disk (HDk) is obtained with different number of mid‐thin layers k via a proposed design principle. The results show that large pore size and χAB favor the hierarchical structure with more k, while χBC prefers hierarchical structure with less k, consistent with the results of hierarchical structure in bulk. By investigating the effect of the volume fraction of the tail A block (fA), the phase transition sequence, HCk → HPk → HHk → HDk is explored, which shares the same transition of multiblock copolymer in bulk with Lk → Gk → Ck → Sk. Finally, the phase diagram with respect to the fA and R is explored, where the stability regime of these hierarchical structures is well understood. The results provide a compelling panacea for the fabrication of hierarchical 3D nanostructures under confinement.

Robust SSRL analysis framework for intervention strategy construction in CSCL environment

While the importance of socially shared regulatory of learning (SSRL) in computer-supported collaborative learning (CSCL) environments has increasingly been emphasized, a surge of research has been conducted to identify socially shared regulation activities and their transition sequences. However, little research has been carried out on constructing a systematic framework in which significant regulation activities and transition sequences can be mined automatically with high reliability. Moreover, though efforts have been made, the current SSRL analysis neither serves the construction of downstream teaching intervention strategy nor explores how SSRL analysis results can be utilized conversely for refining the intervention strategy. Based on advanced machine learning techniques, this work proposes a robust framework on SSRL analysis, aiming to find the optimal teaching intervention strategy to improve learners’ performance in CSCL by analyzing the SSRL process. In particular, our framework can automatically identify significant SSRL regulation activities along with high-contribution activity transition sequences. The proposed Ensemble Learning-based classification model with four distilled additional regulation activities can ensure the high reliability of our framework. The framework serves to construct a downstream teaching intervention strategy, while the strategy is updated and verified based on empirical and experimental statistical results within five rounds of iterative experiments. Extensive theoretical analysis and experimental results both confirm the effectiveness of our framework. Meanwhile, the attempt to leverage advanced machine learning algorithms to enhance SSRL analysis in this work can provide a nontrivial contribution to the literature.

The winding road toward transcriptional repression

Enhancers are known for their role in mediating transcriptional activation. In this issue, Vermunt etal.1 report the unexpected finding that genes can undergo a sequential transition between distinct enhancers to mediate progressive downregulation of expression.

Complexity, Crash and Collapse of Chaos: Clues for Designing Sustainable Systems, with Focus on Grassland-Based Systems

Terms such as system crash, collapse of chaos and complexity can help one understand change, also in biological, socio-economic and technical systems. These terms need, however, explanation for fruitful dialogue on design of sustainable systems. We start this paper on Grass Based (GB) systems, therefore, dwelling on these terms and notions as review for the insiders and to help interested ‘outsiders’. We also stress the need to use additional and/or new paradigms for understanding of the nature of nature. However, we show that many such ‘new’ paradigms were known for long time around the globe among philosophers and common men, giving reason to include quotes and examples from other cultures and eras. In the past few centuries, those paradigms have become hidden, perhaps, under impressive but short-term successes of more linear paradigms. Therefore, we list hang-ups on paradigms of those past few centuries. We then outline what is meant by ‘GB systems’, which exist in multiple forms/‘scapes’. Coping with such variation is perhaps the most central aspect of complexity. To help cope with this variation, the different (GB) systems can be arranged on spatial, temporal, and other scales in such a way that the arrangements form logical sequences (evolutions) of stable states and transitions of Complex Adaptive Systems (CAS). Together with other ways to handle complexity, we give examples of such arrangements to illustrate how one can (re-)imagine, (re-)cognize and manage initial chaotic behaviors and eventual ‘collapse’ of chaos into design and/or emergence of new systems. Then, we list known system behaviors, such as predator–prey cycles, adaptive cycles, lock-in, specialization and even tendency to higher (or lower) entropy. All this is needed to understand changes in management of evolving GB into multi-scapes. Integration of disciplines and paradigms indicates that a win-win is likely to be exception rather than rule. With the rules given in this paper, one can reset teaching, research, rural development, and policy agendas in GB-systems and other areas of life.

SanMove: next location recommendation via self-attention network

PurposeThis paper aims to address the following issues: (1) most existing methods are based on recurrent network, which is time-consuming to train long sequences due to not allowing for full parallelism; (2) personalized preference generally are not considered reasonably; (3) existing methods rarely systematically studied how to efficiently utilize various auxiliary information (e.g. user ID and time stamp) in trajectory data and the spatiotemporal relations among nonconsecutive locations.Design/methodology/approachThe authors propose a novel self-attention network–based model named SanMove to predict the next location via capturing the long- and short-term mobility patterns of users. Specifically, SanMove uses a self-attention module to capture each user's long-term preference, which can represent her personalized location preference. Meanwhile, the authors use a spatial-temporal guided noninvasive self-attention (STNOVA) module to exploit auxiliary information in the trajectory data to learn the user's short-term preference.FindingsThe authors evaluate SanMove on two real-world datasets. The experimental results demonstrate that SanMove is not only faster than the state-of-the-art recurrent neural network (RNN) based predict model but also outperforms the baselines for next location prediction.Originality/valueThe authors propose a self-attention-based sequential model named SanMove to predict the user's trajectory, which comprised long-term and short-term preference learning modules. SanMove allows full parallel processing of trajectories to improve processing efficiency. They propose an STNOVA module to capture the sequential transitions of current trajectories. Moreover, the self-attention module is used to process historical trajectory sequences in order to capture the personalized location preference of each user. The authors conduct extensive experiments on two check-in datasets. The experimental results demonstrate that the model has a fast training speed and excellent performance compared with the existing RNN-based methods for next location prediction.

Hierarchy of Lifshitz Transitions in the Surface Electronic Structure of Sr_{2}RuO_{4} under Uniaxial Compression.

We report the evolution of the electronic structure at the surface of the layered perovskite Sr_{2}RuO_{4} under large in-plane uniaxial compression, leading to anisotropic B_{1g} strains of ϵ_{xx}-ϵ_{yy}=-0.9±0.1%. From angle-resolved photoemission, we show how this drives a sequence of Lifshitz transitions, reshaping the low-energy electronic structure and the rich spectrum of van Hove singularities that the surface layer of Sr_{2}RuO_{4} hosts. From comparison to tight-binding modeling, we find that the strain is accommodated predominantly by bond-length changes rather than modifications of octahedral tilt and rotation angles. Our study sheds new light on the nature of structural distortions at oxide surfaces, and how targeted control of these can be used to tune density of state singularities to the Fermi level, in turn paving the way to the possible realization of rich collective states at the Sr_{2}RuO_{4} surface.

Conversational dynamics of joint attention and shared emotion predict outcomes in interpersonal influence situations: an interaction ritual perspective

Abstract This article addresses conversational dynamics in interpersonal influence situations. Drawing on interaction ritual theories and the research of interaction processes and patterns, we argue that sequential transition patterns of task and social–emotional acts can capture essences of conversational interaction ritual.A successful ritual then generates emergent solidarity and induces desired outcomes of interpersonal influence. In two experimental situations of online text chats between strangers (advice-giving, Nconversation = 336 with a student sample and compliance-gaining, Nconversation = 737 with a MTurk sample), we found certain transition patterns consistently predicted influence outcomes and interpersonal perceptions. Aligning with our theoretical expectations, these transitions indicate the moment-to-moment emergence of joint attention and shared emotion, which are essential ingredients of a successful interaction ritual.

Oscillations in the magnetothermal conductivity of α−RuCl3 : Evidence of transition anomalies

The two-dimensional layered insulator $\ensuremath{\alpha}\text{\ensuremath{-}}{\mathrm{RuCl}}_{3}$ is a candidate material for a quantum spin-liquid state, which may be realized when a magnetic field suppresses the antiferromagnetic order present at low temperature. Oscillations in the field dependence of the thermal conductivity, observed for an in-plane magnetic field $B$ up to a critical field ${B}^{★}$, have been attributed to exotic charge-neutral fermions, viewed as evidence of a quantum spin-liquid state between the critical field ${B}_{c}\ensuremath{\simeq}7$ T at which the antiferromagnetic phase ends and ${B}^{★}$. Here we report measurements of the thermal conductivity of $\ensuremath{\alpha}\text{\ensuremath{-}}{\mathrm{RuCl}}_{3}$ as a function of magnetic field up to 15 T applied in two distinct in-plane directions: parallel and perpendicular to the Ru-Ru bond. We find that the number of oscillations between ${B}_{c}$ and ${B}^{★}$ is the same for the two field directions even though the field interval between ${B}_{c}$ and ${B}^{★}$ is different. In other words, the period of the oscillations is controlled by the transition fields ${B}_{c}$ and ${B}^{★}$. We conclude that these are not true oscillations---coming from putative fermions in a spin-liquid state---but anomalies associated with a sequence of magnetic transitions.

Programming metastable transition sequences in digital mechanical materials

The realization of unconventional computing methods in soft matter has inspired the creation of mechanological systems capable of processing information. These systems often utilize bistable mechanisms that serve as mechanically abstracted bits to perform digital logic operations. Yet, the input of such operations often requires manual control of complex cyclic loading to reach a desired configuration. This research presents a method to design digital mechanical materials that can enter a programmable sequence of metastable configurations through simple displacement-controlled inputs. Interactions between serially connected bistable units enable the transition and reset behavior of mechanical bits. An analytical model using the principle of minimum total potential energy of a single bit is presented to articulate systematic ways to tune the critical force thresholds and displacements of metastable transition sequences. This work elucidates how the application of a prescribed displacement allows for novel resetting behavior that enables simultaneous transitions of multiple bits. By combining the principles of deterministic transition sequences and mechanical computing, a mechanical analog to digital conversion (ADC) material system is introduced that digitizes a continuous force stimulus into binary configurations that perform computations within the mechanical material based on the applied load. The mechanical ADC establishes a foundation for the integration of environmental sensing, information processing, and response in a soft material system.

Relativistic Hybrid Stars with Sequential First-order Phase Transitions in Light of Multimessenger Constraints

In this work, we consider the properties of compact stars in which quark matter has low- and high-density phases that are separated by a first-order phase transition. Thus, unlike the commonly considered case of a single phase transition from hadronic to quark matter, our models of hybrid stars contain sequential phase transitions from hadronic matter to low- and then to high-density quark matter phases. We extend our previous study of the parameter space of hybrid stars with a single phase transition to those with sequential phase transitions, taking into account the constraints on the mass and radius of neutron stars from the NICER experiment, the experimental inferences of the neutron skin thickness of the lead nucleus by the PREX-II experiment, and constraints on the tidal deformability from the gravitational-wave event GW170817. We determine the range of the masses for which both twin and triplet configurations, i.e., identical-mass stars with two and three different values of radii, arise.

Multi-level combinatoriality in magpie non-song vocalizations.

Comparative studies conducted over the past few decades have provided important insights into the capacity for animals to combine vocal segments at either one of two levels: within- or between-calls. There remains, however, a distinct gap in knowledge as to whether animal combinatoriality can extend beyond one level. Investigating this requires a comprehensive analysis of the combinatorial features characterizing a species' vocal system. Here, we used a nonlinear dimensionality reduction analysis and sequential transition analysis to quantitatively describe the non-song combinatorial repertoire of the Western Australian magpie (Gymnorhina tibicen dorsalis). We found that (i) magpies recombine four distinct acoustic segments to create a larger number of calls, and (ii) the resultant calls are further combined into larger call combinations. Our work demonstrates two levels in the combining of magpie vocal units. These results are incongruous with the notion that a capacity for multi-level combinatoriality is unique to human language, wherein the combining of meaningless sounds and meaningful words interactively occurs across different combinatorial levels. Our study thus provides novel insights into the combinatorial capacities of a non-human species, adding to the growing evidence of analogues of language-specific traits present in the animal kingdom.

Turbulent Taylor-Couette flow of dilute polymeric solutions: a10-year retrospective.

This retrospective aims to present a coherent history of important findings in direct numerical simulations and experiments in turbulent Taylor-Couette (TC) flow of dilute polymeric solutions in the last decade. Specifically, the sequence of flow transitions due to a continuous increase of fluid elasticity from classical Newtonian, to inertially and in turn to elastically dominated, and finally to the inertialess purely elastic turbulence, is presented. In each elastically modified flow state, the drag modification, coherent flow structures, velocity and elastic stress statistics, mechanism of turbulent kinetic energy production, spectral features as well as the self-sustaining cycles of turbulence, are discussed. Finally, to provide a broader perspective, an overview of important similarities and differences between elastically induced turbulence in prototypical curvilinear and rectilinear shear flows including the curvature-free limit of TC flow, namely, the spanwise-rotating plane Couette flow, is presented. This article is part of the theme issue 'Taylor-Couette and related flows on the centennial of Taylor's seminal Philosophical Transactions paper (part 1)'.

Parameterization of the Stochastic Model for Evaluating Variable Small Data in the Shannon Entropy Basis

The article analytically summarizes the idea of applying Shannon's principle of entropy maximization to sets that represent the results of observations of the "input" and "output" entities of the stochastic model for evaluating variable small data. To formalize this idea, a sequential transition from the likelihood function to the likelihood functional and the Shannon entropy functional is analytically described. Shannon's entropy characterizes the uncertainty caused not only by the probabilistic nature of the parameters of the stochastic data evaluation model but also by interferences that distort the results of the measurements of the values of these parameters. Accordingly, based on the Shannon entropy, it is possible to determine the best estimates of the values of these parameters for maximally uncertain (per entropy unit) distortions that cause measurement variability. This postulate is organically transferred to the statement that the estimates of the density of the probability distribution of the parameters of the stochastic model of small data obtained as a result of Shannon entropy maximization will also take into account the fact of the variability of the process of their measurements. In the article, this principle is developed into the information technology of the parametric and non-parametric evaluation on the basis of Shannon entropy of small data measured under the influence of interferences. The article analytically formalizes three key elements: -instances of the class of parameterized stochastic models for evaluating variable small data; -methods of estimating the probability density function of their parameters, represented by normalized or interval probabilities; -approaches to generating an ensemble of random vectors of initial parameters.

The role of a ciliary GTPase in the regulation of neuronal maturation of olfactory sensory neurons.

Olfactory sensory neurons (OSNs) form embryonically and mature perinatally, innervating glomeruli and extending dendrites with multiple cilia. This process and its timing are crucial for odor detection and perception and continues throughout life. In the olfactory epithelium (OE), differentiated OSNs proceed from an immature (iOSN) to a mature (mOSN) state through well-defined sequential morphological and molecular transitions, but the precise mechanisms controlling OSN maturation remain largely unknown. We have identified that a GTPase, ARL13B, has a transient and maturation state-dependent expression in OSNs marking the emergence of a primary cilium. Utilizing an iOSN-specific Arl13b-null murine model, we examined the role of ARL13B in the maturation of OSNs. The loss of Arl13b in iOSNs caused a profound dysregulation of the cellular homeostasis and development of the OE. Importantly, Arl13b null OSNs demonstrated a delay in the timing of their maturation. Finally, the loss of Arl13b resulted in severe deformation in the structure and innervation of glomeruli. Our findings demonstrate a previously unknown role of ARL13B in the maturation of OSNs and development of the OE.

Optimized electric-energy storage in BiFeO3–BaTiO3 ceramics via tailoring microstructure and nanocluster

This study demonstrates the high energy-storage performance using 0.1 wt% MnO2–added 0.7(Bi1−xSmxFeO3)− 0.3(BaTiO3) (x = 0–0.3) ceramics through tailoring microstructures and polar order. Sequential structure transitions were identified from a co-occurrence of nonpolar pseudo-cubic Pm-3m and ferroelectric rhombohedral R3c symmetries to antipolar orthorhombic Pbam and nonpolar orthorhombic Pnma symmetries as Sm substitution increases. Recoverable energy densities (Wrec) of 4.5 J/cm3 and 4.1 J/cm3 with efficiencies (η) of 62.1% and 78.1% were achieved respectively for x = 0.15 and 0.2 at a field of 220 kV/cm. The improved energy storage is associated with microstructure modification and complex grain matrix, consisting of grain boundaries, nanocluster/nanomosaic structures, core-shell structures, and polar nanoregions. The nanocluster/nanomosaic structures may act as barriers to suppress polar order and enhance dielectric breakdown strength. This work provides an efficient route to utilize binary BiFeO3-BaTiO3 ceramics for electrical energy storage.

Каскадно-циркулярна модель інноваційної трансформації регіональних господарських комплексів: фундаментальні принципи, пріоритети і воєнні та поствоєнні виклики

It is substantiated that the cascade-circular model of innovative transformation of regional economic complexes should be based on a combination of the cascade principle of attracting material resources in the reproduction process and the principle of maximizing closed cycles in product chains. It is determined that the cascade principle is a sequential transition from one phase of production to another, based on the priority of formation of value chains and features of extracting the resource value of primary and secondary raw materials; the principle of maximizing closed cycles in product chains is a principle that provides for the minimization of unit costs and losses of the material substance of raw materials on the basis of the integrated use of primary raw materials through the use of waste-free and low-waste technologies and the most complete utilization of secondary raw materials in order to maximize the value added in food chains. It is identified that the key point in the formation of the cascade-circular model of innovative transformation of regional economic complexes is the formation of the structure-establishing innovation core (a complex of types of production and economic activity with appropriate supporting subsystems), which will be differentiated in the context of economic regions of Ukraine, based on: the existing production and technical base of material production; the level of infrastructural arrangement; available and projected reserves of natural raw materials; scientific and scientific-technical potential; opportunities for establishing inter-regional cooperative relations; participation of the region in the national division of labor and in ensuring national security. It is substantiated that the cascade-circular model of innovative transformation of the economy of the regions of Ukraine should be aimed at more effective use of secondary material resources in the reproduction process and at increasing the assimilation potential as a basic prerequisite for the transition to the rails of low-carbon development.

Investigating structural properties and reaction mechanism of non-stoichiometric spinel LNMO via solid state NMR

By applying solid-state 6,7Li NMR, we reveal a redox reaction sequence of solid solution and biphasic transition and highlight the existence of intermediate species in non-stoichiometric transition metal-ordered high-voltage LNMO cathode reaction vs. Li.

Modular learning technology based on a subject-activity approach

The article discusses the essence, purpose, and objectives of modular learning, which is one of the types of personality-oriented learning technology. The sequence of transition to modular learning, the technology of the subject-activity approach, with the use of which continuity, intensification, individualization of learning is ensured, is presented.

Advances in Computational Intelligence Techniques-Based Multi-Intersection Querying Theory for Efficient QoS in the Next Generation Internet of Things.

An environment of physically linked, technologically networked things that can be found online is known as the "Internet of Things." With the use of various devices connected to a network that allows data transfer between these devices, this includes the creation of intelligent communications and computational environments, such as intelligent homes, smart transportation systems, and intelligent FinTech. A variety of learning and optimization methods form the foundation of computational intelligence. Therefore, including new learning techniques such as opposition-based learning, optimization strategies, and reinforcement learning is the key growing trend for the next generation of IoT applications. In this study, a collaborative control system based on multiagent reinforcement learning with intelligent sensors for variable-guidance sections at various junctions is proposed. In the future generation of Internet of Things (IoT) applications, this study provides a multi-intersection variable steering lane-appropriate control approach that uses intelligent sensors to reduce traffic congestion at many junctions. Since the multi-intersection scene's complicated traffic flow cannot be accommodated by the conventional variable steering lane management approach. The priority experience replay algorithm is also included to improve the efficiency of the transition sequence's use in the experience replay pool and speed up the algorithm's convergence for effective quality of service in the upcoming IoT applications. The experimental investigation demonstrates that the multi-intersection variable steering lane with intelligent sensors is an appropriate control mechanism, successfully reducing queue length and delay time. The effectiveness of waiting times and other indicators is superior to that of other control methods, which efficiently coordinate the strategy switching of variable steerable lanes and enhance the traffic capacity of the road network under multiple intersections for effective quality of service in the upcoming IoT applications.

Transformer-based Siamese and Triplet Networks for Facial Expression Intensity Estimation

Recognizing facial expressions and estimating their corresponding action units’ intensities have achieved many milestones. However, such estimating is still challenging due to subtle action units’ variations during emotional arousal. The latest approaches are confined to the probabilistic models’ characteristics that model action units’ relationships. Considering ordinal relationships across an emotional transition sequence, we propose two metric learning approaches with self-attention-based triplet and Siamese networks to estimate emotional intensities. Our emotion expert branches use shifted-window SWIN-transformer which restricts self-attention computation to adjacent windows while also allowing for cross-window connection. This offers flexible modeling at various scales of action units with high performance. We evaluated our network’s spatial and time-based feature localization on CK+, KDEF-dyn, AFEW, SAMM, and CASME-II datasets. They outperform deep learning state-of-the-art methods in micro-expression detection on the latter two datasets with 2.4% and 2.6% UAR respectively. Ablation studies highlight the strength of our design with a thorough analysis.