Hierarchical State Research Articles

Fractal Biology — Evolution from Molecular to Cognitive, and Psychological Dimensions

Biological and artificial intelligence (BI and AI) share the fundamental principles of space-time information processing based on symmetry transformation. Therefore, cognitive-science-inspired AI represents a promising area of exploration. A convincing example are the fractal structure of human languages and protein assembly. Biological processes’ temporal and spatial plasticity links them to basic laws of physics. Continuous advances in fundamental physical theories allow understanding of all aspects of space-time symmetry (STS) natively intertwined with the principle of relativity and causality. Spatial aspects of symmetry represented by three sub-domains such as chirality, fractality, and topology, are widely studied in biology. The role of chirality in biology has been analyzed in several recent reviews. However, the fractals and topological states of biological structures is a relatively new and fast-developing branch of science. Here, we trace publications exploring the role of fractal symmetry in all hierarchical states of biological organization, including at the molecular, cellular, morphological, physiological, perceptual, cognitive, and psychological levels. The coverage of the above-listed areas in current studies is sharply unequal and unsystematic. A broad view of biological fractality opens a unique opportunity to discriminate between a healthy state and a wide range of disease conditions. Psychiatric, neurological, and immune disorders are associated with aberrant molecular assembly and morphological changes in neural circuits, suggesting that the chain of chirality/fractality transfer through all levels of physiological organization deserves persistent attention.

A Brain-Inspired Decision-Making method for upper limb exoskeleton based on Multi-Brain-Region structure and multimodal information fusion

For the challenges faced by upper limb exoskeleton in meeting human motion intention, this article proposes a novel brain-inspired decision-making model based on multi-brain-region information transmission mechanism and multimodal information fusion method to provide accurate prediction of motion trajectory. Firstly, a multimodal information perception and fusion method is proposed. Based on surface electromyographic (sEMG) signals and angle signals collected by sensors. Then a random forest algorithm is applied to screen the key features of sEMG signal for trajectory prediction. A multimodal information hierarchical fusion method based on dual-period mechanism is designed to solve the problem of fusion defect of information with different frequency. Finally, a multi-brain-region decision-making model based on hybrid hierarchical liquid state machine (HHLSM) is established to predict motion trajectory for the exoskeleton to meet human motion intention. Experiments show that the established model has a high computational efficiency and improves the effectiveness of exoskeleton assistance. The brain-inspired decision-making model based on HHLSM established in this paper is of great significance for further research on brain-inspired control and improving the human-robot interaction effect of upper limb exoskeleton.

Thermal comfort driven space heating control via hierarchical state machine strategy interacting with multiphysics simulation

Improving energy efficiency of space heating systems is as important as adopting emerging clean energy technologies for decarbonising heating. While exploring strategies for reducing energy consumption, the space occupants’ expectations to thermal comfort should not be sacrificed. Conventional space heating control systems are often developed under the hypothesis of uniform temperature distribution in a bounded space and the heating system controller is primarily operated based on the information from the temperature sensor at a fixed position. That is, the measured temperature does not represent the temperature surrounding the occupant’s location. This paper explores a new control method; the heating system control is led by the occupant’s thermal experience, expressed by the temperature surrounding the occupant. A new hierarchical state machine control strategy interacting with a Multiphysics simulation model to dynamically estimate the temperature at the point of the occupant is developed. The accuracy of the Multiphysics simulation model is verified based on the corresponding experimental test room data. A co-simulation platform is built to allow COMSOL and SIMULINK to communicate in real-time for control strategy realisation. The experimental results show that the maximum and average mean square error between the measured and simulated temperature under different operating modes is less than 0.21℃ and 0.06℃, respectively. The simulation results indicate that energy consumption could be potentially reduced around 20% with an improved thermal comfort of the occupant by 30%, compared with the conventional fixed sensor control method.

Hierarchical bound states in heat transport

Higher-order topological phases in non-Hermitian photonics revolutionize the understanding of wave propagation and modulation, which lead to hierarchical states in open systems. However, intrinsic insulating properties endorsed by the lattice symmetry of photonic crystals fundamentally confine the robust transport only at explicit system boundaries, letting alone the flexible reconfiguration in hierarchical states at arbitrary positions. Here, we report a dynamic topological platform for creating the reconfigurable hierarchical bound states in heat transport systems and observe the robust and nonlocalized higher-order states in both the real- and imaginary-valued bands. Our experiments showcase that the hierarchical features of zero-dimension corner and nontrivial edge modes occur at tailored positions within the system bulk states instead of the explicit system boundaries. Our findings uncover the mechanism of non-localized hierarchical non-trivial topological states and offer distinct paradigms for diffusive transport field management.

Exploring heterogeneity in coxarthrosis medication use patterns before total hip replacement: a State Sequence Analysis

ObjectiveEvidence of geographical variation in total hip replacement (THR) and deviations from treatment guidelines persists. In this exploratory study, we aim to gain an in-depth understanding of patients’ healthcare trajectories...

Authenticated hierarchical quantum state sharing based on non-maximally entangled states

Hierarchical quantum state sharing (HQSTS) provides a way for the quantum state from one party to another among multiple parties asymmetrically. In the process, it is necessary to ensure the legitimacy and authenticity of participants to defend against attacks caused by neglecting authentication. Hence, we propose a three-phase probabilistic HQSTS protocol with identity authentication. Firstly, the legitimacy of participants is verified in the identity authentication phase, which effectively prevents impersonation and deception. Secondly, the sender Alice sends the target quantum state to three agents asymmetrically, which implies that there is a hierarchy of agents regarding their ability to recover the secret state. The high authority agent can recover the state without the cooperation of all agents, while the low authority agent has to recover the state with the cooperation of all agents. Thirdly, based on the non-maximally entangled cluster states, the sharing of the arbitrary three-qubit states is realized, which increases the amount of information transmitted. Using the Qiskit framework, the quantum circuit and simulation results for a particular case are given to verify the feasibility and correctness of our protocol. Moreover, the security of the protocol is analyzed from the perspective of both internal and external attacks.

Advancements in Cooperative Mobile Robots Control Strategies for Large-Scale Material Transport

This paper explores groundbreaking advancements in control strategies for cooperative mobile robots used in large-scale material transport, a critical aspect of modern industrial, manufacturing, logistics, and construction sectors. It delves into the development of sophisticated systems that enable seamless coordination among multiple mobile robot systems. The research presents a novel hierarchical finite state automaton for dynamic mission adaptation and a null space-based control scheme for precise task execution and enhanced system resilience. The introduction of Mecanum wheels facilitates flexible movement and manipulation of materials, thereby increasing operational efficiency and safety. Cutting-edge sensory technology, including LiDAR, and the implementation of the Robot Operating System are highlighted for their roles in enhancing autonomous navigation and intelligent operation. Additionally, the paper discusses the impact of centralized and decentralized control methods in ensuring safe, cooperative object transport. The findings contribute to the vision of Industry 4.0 by promoting the integration of automation and robotic cooperation in complex environments and present a foundational blueprint for further research. Challenges for future work such as scalability, communication efficiency, collision avoidance, and energy efficiency are also considered, underscoring the need for ongoing development of robust and scalable robotic systems to address modern transport challenges.

Sound, Touch, Form, Taste, and Smell as Pathways for Meditation – A Review of Classical and Neuroscience Texts

Meditation has emerged as a useful tool to address various mental health issues and hence has become a fertile area of interdisciplinary research. Contemporary research is focused on evaluating the effects of meditation using different neural correlates and also on developing theoretical models to explain different meditative states. The value of these neuroscientific studies can be enhanced by integrating them with time-tested philosophical framework of classical meditation practices. This work attempts to review and summarize philosophical underpinnings of meditation with the help of hierarchical meditative states discussed in Patañjali’s Yoga darśana and an information flow model proposed based on Sāṅkhya and Yoga darśana. Sense perception as a vehicle to achieve higher meditative states is the focus of this review. Usage of gross sense objects via sense pathways of sound, touch, form, smell and taste to establish in Vitarka state and then moving to Vicāra state by replacing them with subtle sense object has been discussed in detail. A brief discussion on fundamentals of sense transduction has been discussed, which explains how sense stimulation in gross world gets converted to subtle neuro-electrical signals via multiple ionic processes. A summary of various ayurvedic, classical and contemporary yogic practices related to each sense pathway has also been presented.

Personalised Multi-modal Interactive Recommendation with Hierarchical State Representations

Multi-modal interactive recommender systems (MMIRS) can effectively guide users towards their desired items through multi-turn interactions by leveraging the users’ real-time feedback (in the form of natural-language critiques) on previously recommended items (such as images of fashion products). In this scenario, the users’ preferences can be expressed by both the users’ past interests from their historical interactions and their current needs from the real-time interactions. However, it is typically challenging to make satisfactory personalised recommendations across multi-turn interactions due to the difficulty in balancing the users’ past interests and the current needs for generating the users’ state (i.e., current preferences) representations over time. However, hierarchical reinforcement learning has been successfully applied in various fields by decomposing a complex task into a hierarchy of more easily addressed subtasks. In this journal article, we propose a novel personalised multi-modal interactive recommendation model (PMMIR) using hierarchical reinforcement learning to more effectively incorporate the users’ preferences from both their past and real-time interactions. In particular, PMMIR decomposes the personalised interactive recommendation process into a sequence of two subtasks with hierarchical state representations: a first subtask where a history encoder learns the users’ past interests with the hidden states of history for providing personalised initial recommendations and a second subtask where a state tracker estimates the current needs with the real-time estimated states for updating the subsequent recommendations. The history encoder and the state tracker are jointly optimised with a single objective by maximising the users’ future satisfaction with the recommendations. Following previous work, we train and evaluate our PMMIR model using a user simulator that can generate natural-language critiques about the recommendations as a surrogate for real human users. Experiments conducted on two derived fashion datasets from two well-known public datasets demonstrate that our proposed PMMIR model yields significant improvements in comparison to the existing state-of-the-art baseline models. The datasets and code are publicly available at: https://github.com/yashonwu/pmmir

Delocalization and higher-order topology in a nonlinear elastic lattice

Topological elastic waves provide novel and robust ways for manipulating mechanical energy transfer and information transmission, with potential applications in vibration control, analog computation, and more. Recently discovered higher-order topological insulators (HOTIs) with multidimensional and hierarchical edge states can further expand the capabilities of topological elastic waves. However, the effects of nonlinearity on elastic HOTIs remain elusive. In this paper, we propose a nonlinear elastic higher-order topological Kagome lattice. After briefly reviewing its linear properties, we explore the effects of nonlinearity on the higher-order band topology and topological states. To do this, we have developed a method to calculate approximate nonlinear modes in order to identify the bulk polarization and probe the higher-order topological phase in the nonlinear lattice. We find that nonlinearity induces unusual delocalization of topological corner states, band crossing, and higher-order topological phase transition. The delocalization reveals that intracell hardening nonlinearity leads to direct delocalization of topological corner states while intracell softening nonlinearity first enhances and then reduces localization. The nonlinear higher-order topological phase is amplitude dependent, and we demonstrate a transition from a trivial to a non-trivial phase, enabling amplitude induced topological corner and edge states. Additionally, this phase transition corresponds to the closing and reopening of the bandgap, accompanied by an unusual band crossing. By examining the band topology before and after the band crossing, we find that the bulk polarization becomes quantized with respect to amplitude and can predict higher-order topological phases in nonlinear lattices. The obtained results are expected to be beneficial for the development of tunable and robust elastic wave devices.

SEMIPARAMETRIC BIVARIATE HIERARCHICAL STATE SPACE MODEL WITH APPLICATION TO HORMONE CIRCADIAN RELATIONSHIP.

The adrenocorticotropic hormone and cortisol play critical roles in stress regulation and the sleep-wake cycle. Most research has been focused on how the two hormones regulate each other in terms of short-term pulses. Few studies have been conducted on the circadian relationship between the two hormones and how it differs between normal and abnormal groups. The circadian patterns are difficult to model as parametric functions. Directly extending univariate functional mixed effects models would result in a large dimensional problem and a challenging nonparametric inference. In this article, we propose a semi-parametric bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with nonparametric population-average and subject-specific components. The bivariate relationship is constructed by concatenating two latent independent subject-specific random functions specified by a design matrix, leading to a parametric inference on the correlation. We propose a computationally efficient state-space EM algorithm for estimation and inference. We apply the proposed method to a study of chronic fatigue syndrome and fibromyalgia and discover an erratic regulation pattern in the patient group in contrast to a circadian regulation pattern conforming to the day-night cycle in the control group.

Spatial evolution of the proton-coupled Mott transition in correlated oxides for neuromorphic computing.

The proton-electron coupling effect induces rich spectrums of electronic states in correlated oxides, opening tempting opportunities for exploring novel devices with multifunctions. Here, via modest Pt-aided hydrogen spillover at room temperature, amounts of protons are introduced into SmNiO3-based devices. In situ structural characterizations together with first-principles calculation reveal that the local Mott transition is reversibly driven by migration and redistribution of the predoped protons. The accompanying giant resistance change results in excellent memristive behaviors under ultralow electric fields. Hierarchical tree-like memory states, an instinct displayed in bio-synapses, are further realized in the devices by spatially varying the proton concentration with electric pulses, showing great promise in artificial neural networks for solving intricate problems. Our research demonstrates the direct and effective control of proton evolution using extremely low electric field, offering an alternative pathway for modifying the functionalities of correlated oxides and constructing low-power consumption intelligent devices and neural network circuits.

Dynamic hierarchical state space forecasting.

In this paper, we aim to both borrow information from existing units and incorporate the target unit's history data in time series forecasting. We consider a situation when we have time series data from multiple units that share similar patterns when aligned in terms of an internal time. The internal time is defined as an index according to evolving features of interest. When mapped back to the calendar time, these time series can span different time intervals that can include the future calendar time of the targeted unit, over which we can borrow the information from other units in forecasting the targeted unit. We first build a hierarchical state space model for the multiple time series data in terms of the internal time, where the shared components capture the similarities among different units while allowing for unit-specific deviations. A conditional state space model is then constructed to incorporate the information of existing units as the prior information in forecasting the targeted unit. By running the Kalman filtering based on the conditional state space model on the targeted unit, we incorporate both the information from the other units and the history of the targeted unit. The forecasts are then transformed from internal time back into calendar time for ease of interpretation. A simulation study is conducted to evaluate the finite sample performance. Forecasting state-level new COVID-19 cases in United States is used for illustration.

In situ anchored ternary hierarchical hybrid nickel@cobaltous sulfide on poly(3,4-ethylenedioxythiophene)-reduced graphene oxide for highly efficient non-enzymatic glucose sensing.

A ternary hierarchical hybrid Ni@CoxSy/poly(3,4-ethylenedioxythiophene)-reduced graphene oxide (Ni@CoxSy/PEDOT-rGO) is rationally designed and in situ facilely synthesized as electrocatalyst to construct a binder-free sensing platform for non-enzymatic glucose monitoring through traditional electrodeposition procedure. The as-prepared Ni@CoxSy/PEDOT-rGO presents unique hierarchical structure and multiple valence states as well as strong and robust adhesion between Ni@CoxSy/PEDOT-rGO and GCE. Profiting from the aforementioned merits, the sensing platform constructed under optimal conditions achieved a wide detection range (0.2μM ~ 2.0mM) with high sensitivity (1546.32 μA cm-2mM-1), a rapid response time (5s), an ultralow detection limit (0.094μM), superior anti-interference performance, excellent reproducibility and considerable stability. Furthermore, the sensor demonstrates an acceptable accuracy and appreciable recoveries ranging from 90.0 to 102.0% with less than 3.98% RSDin human blood serum samples, indicating the prospect of the sensor for the real samples analysis. It will provide a strategy to rationally design and fabricate ternary hierarchical hybrid as nanozyme for glucose assay.

Deterministic hierarchical joint remote state preparation via a non-maximally entangled state

Deterministic hierarchical joint remote state preparation via a non-maximally entangled state

Hermitian and non-hermitian higher-order topological states in mechanical metamaterials

Higher-order topological insulators exhibit clear hierarchical boundary states, providing an amazing platform for robust wave manipulations in mechanical or acoustic systems. Recently, this theory has been extended to non-Hermiticity-induced higher-order topology, which enables actively controllable topological transport. Nevertheless, most related studies focus on the non-Hermitian quadrupole topology, associated with the coexistence of negative and positive couplings to constitute a π-flux lattice, which hindered the realizations in classical wave systems. Here, we propose a novel tight-binding model without negative couplings, which supports non-Hermiticity-induced higher-order topological states. In the Hermitian case, the middle band gaps host the vanishing bulk polarization and the associated topology is dependent only upon the associated edge polarization. By introducing the external loss as non-Hermitian components, we reveal that therein the trivial structures can also host topological corner states, featured by the quantized nonzero edge polarizations in the biorthogonal basis. Furthermore, such a topological phase transition induced by non-Hermiticity can be realized for almost all of our lattices, except for the isotropic case where the middle band gaps never open. Basing on the theoretical design, we map the lattice models into the mechanical metamaterials to access the analogous higher-order topology in the elastic wave systems for both Hermitian and non-Hermitian physics. The simulated band structures match well with theoretical solutions. And the robust higher-order topological states are also identified. Our work paves the way to implement the non-Hermitian higher-order topology and offers the possibility for wave manipulations in non-Hermitian systems.

Using process model to define the legislative framework of electronic prescription in the Czech Republic.

Defining legislation for electronic prescription systems (EPS) is inherently challenging due to conflicting interests and requirements. The study aimed to develop a comprehensive EPS within the Czech healthcare framework, integrating legislative, process, and technical aspects to ensure security, user acceptability, and compliance with health regulations. A process modeling tool based on hierarchical state machines was employed to create a detailed process architecture for the EPS. Key participants, scenarios, and state transitions were identified and incorporated into a process model using the Craft.CASE based on the BORM methodology. The final process architecture model facilitated interdisciplinary communication and consensus-building among stakeholders, including healthcare professionals, IT specialists, and legislators. The model served as a foundation for the legislative framework and was included in the explanatory memorandum for the draft amendment to the Pharmaceuticals Act. The use of hierarchical state machines and process modeling tools in developing healthcare legislation effectively reduced misunderstandings and ensured precise implementation. This method can be applied to other complex legislative and system design projects, enhancing stakeholder communication and project success.

Excavation of Hushed Voices: A Spatio-temporal Imaginary Discourse on Socio-ethics in Kazuo Ishiguro’s The Remains of the Day

The concept of the ‘Great Chain of Being’, which influences the narrative style of the Renaissance Period of English Literature, depicts the hierarchical state of the old English Society. Suggestively, it’s a society driven by class/status and its attendant structures of inequality, discrimination and oppression. Hence, literary narratives of this period revolve round the travails of Kings and nobles, while subjecting the common men to the background. Subsequently, the esoteric state of the society, which was usually captured from the third person point of view, helps to question the place of truth in these literary narratives, as they could be liken to glossary reports from spectators, devoid of participant’ direct contribution(s). However, with the writing(s) of Kazuo Ishiguro, an Asian-British, there comes a direct feel of participant’s contribution in his narratives as he deconstructs the style of the old traditional English narratives in his novel, The Remains of the Day. Consequently, with the application of the first-person point of view in his novel, readers could explore the socio-cultural dispositions of every society and individual within the purview of ethics, politics and development. In furtherance, this study gives a historicized spatial alternate or temporal reality of an Old English society system - a system anchored on classism, professionalism and superficial orderliness devoid of ‘social interactions’ and freedom, especially for the workmen or common men. The emergence of an American Lord after the death of the English Lord gives a contrastive alternative pattern against that of the English. Hence, this alternate reflection of the American presence further deconstructs every long-held tradition in Darlington’s Hall as the author reveals that perceptions are influenced by cultural ingenuity which borders on the socio-cultural/socio-political formation of every society and individuals.

A differential game model for sponsored content

We consider a communication platform distinguished for its high-quality content, where advertising can take two different forms: traditional and sponsored (also known as native advertising in the marketing literature). Native advertising is a widely used marketing tool that aims to mimic the regular topics of the platform on which it is placed. Due to this striking resemblance, native advertising may be very effective, but at the same time, it may negatively influence the perceived credibility of the media outlet. In our model, a firm allocates investments to both traditional and native advertising on such a platform. Meanwhile, the media outlet must grapple with the trade-off between the profit accrued from publishing native advertising and the ensuing decline in credibility. We formalise this problem as a hierarchical infinite-time horizon linear state differential game, played à la Stackelberg, where the media outlet acts as the leader while the firm is the follower. Finally, we characterise a time-consistent open-loop equilibrium and obtain the conditions that make it optimal for the media outlet to accept native advertising.

Distributed Dynamic State Estimation of Power Systems Considering Dynamic State Constraints based on Phasor Measurements Units

Background: Dynamic state estimation can provide detailed information about power systems. However, there is no clear, dynamic equation for the state transition of bus voltages in power systems. Currently, smoothing methods based on historical data are commonly used, but they cannot ensure accurate state prediction in power systems with a large amount of renewable energy. Moreover, the fast sampling rate of phasor measurement units generates a vast amount of real-time data for the dispatch center, making it challenging for centralized state estimation to meet real-time demands. Objective: This paper proposes a distributed power system state estimation considering dynamic state constraints to address the above issues. Methods: By incorporating the constraints between the dynamic states of the system dynamic components and the bus voltage phasors, the state transition equations for bus voltage phasors are constructed based on the predicted dynamic states and the nodal injection power equations. This allows taking the dynamic model constraints into account when predicting bus voltage phasors. Then, based on the principle of hierarchical coordination and distributed state estimation, the method of estimation-coordination-correction is adopted to acquire system state information quickly and accurately. method: By incorporating the constraints between the dynamic states of the system dynamic components and the bus voltage phasors, the state transition equations for bus voltage phasors are constructed based on the predicted dynamic states and the nodal injection power equations. This allows taking the dynamic model constraints into account when predicting bus voltage phasors. Then, based on the principle of hierarchical coordination and distributed state estimation, the method of estimation-coordination-correction is adopted to acquire system state information quickly and accurately. Results: Furthermore, an IEEE 9-bus system and an IEEE 39-bus system are used to validate the proposed method. The proposed method is compared with other algorithms to prove its superiority. result: Furthermore, an IEEE 9-bus system and an IEEE 39-bus system are used to validate the proposed method. And the proposed method is compared with other algorithms to prove its superiority. Conclusion: The simulation results show that the proposed method can effectively improve the accuracy of the state estimation results of power systems under dynamic conditions. conclusion: The simulation results show that the proposed method can effectively improve the accuracy of the state estimation results of power systems under dynamic conditions. other: None