Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition.

Early-warning signals of delicate design are used to predict critical transitions in complex systems, which makes it possible to render the systems far away from the catastrophic state by introducing timely interventions. Traditional signals including the dynamical network biomarker (DNB), based on statistical properties such as variance and autocorrelation of nodal dynamics, overlook directional interactions and thus have limitations in capturing underlying mechanisms and simultaneously sustaining robustness against noise perturbations. This study therefore introduces a framework of causal network markers (CNMs) by incorporating causality indicators, which reflect the directional influence between variables.

CPG-based neural control of peristaltic planar locomotion in an earthworm-like robot: evaluation of nonlinear oscillators.

Earthworm-like robots have excellent locomotion capability in confined environments. Central pattern generator (CPG) based controllers utilize the dynamics of coupled nonlinear oscillators to spontaneously generate actuation signals for all segments, which offer significant merits over conventional locomotion control strategies. There are a number of oscillators that can be exploited for CPG control, while their performance in controlling peristaltic locomotion has not been systematically evaluated.

Identifying Research Hotspots and Trends in Psoriasis Literature: Autotuned Topic Modeling with Agent.

The rapid expansion of psoriasis research presents challenges in efficient analysis and trend identification, necessitating advanced approaches. We propose AgenTopic, an interactive topic modeling framework that integrates Bidirectional Encoder Representations from Transformers embeddings, dimensionality reduction, clustering, and a language model feedback loop to analyze the psoriasis research literature from 2000 to 2023. Applied to PubMed articles, AgenTopic extracted 158 psoriasis-related topics across 8 categories, outperforming traditional methods in handling complex medical literature.

Long-term care plan recommendation for older adults with disabilities: a bipartite graph transformer and self-supervised approach.

Background: With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirements of these individuals make developing effective long-term care plans heavily reliant on professional nursing staff, and even experienced caregivers may make mistakes or face confusion during the care plan development process. Consequently, there is a rigid demand for intelligent systems that can recommend comprehensive long-term care plans for older adults with disabilities who have stable clinical conditions.

Exploring geriatric syndromes among physically disabled older adults: A network analysis.

Objective: To explore the network structure of common geriatric syndromes and conditions in physically disabled older adults.

Methods: We chose fourteen common geriatric syndromes and conditions from the dataset and estimated networks with the partial correlation network method. We tested the stability and accuracy of the network using the package "bootnet" in R software.

Patterns of Comorbidity, Disability, and Home Health Care Service for Older Adults With Physical Disability: A Latent Class Analysis and Visualized Illustration.

Understanding the relationships between comorbidity, disability, and home health-care services aids in user-centered care design. This study identifies patterns of these factors among older adults with physical disability living at home and explores their associations. This cross-sectional study included community-dwelling older adults assessed for Long-term Care Insurance from September 1 to December 31, 2018, in Yiwu, Zhejiang, China.

Mechanical Property, Efficacy, and User Experience of an Innovative Wearable Device in Preventing Fall-Induced Injuries.

Background And Objectives: With the global population aging at an unprecedented pace, the imminent surge in falls and fall-induced injuries necessitates urgent attention. Innovative assistive technologies are crucial in addressing this daunting challenge. This study aimed to evaluate the mechanical properties, efficacy, safety, and user experience of the Intelligent Bone Protection Vest (IBPV), a novel, reusable, non-airbag wearable device.

Reinforcement learning-based pinning control for synchronization suppression in complex networks.

Synchronization in complex networks is a ubiquitous and important phenomenon with implications in various fields. Excessive synchronization may lead to undesired consequences, making desynchronization techniques essential. Exploiting the Proximal Policy Optimization algorithm, this work studies reinforcement learning-based pinning control strategies for synchronization suppression in global coupling networks and two types of irregular coupling networks: the Watts-Strogatz small-world networks and the Barabási-Albert scale-free networks.

Coiled Carbon Nanotube Fibers Sheathed by a Reinforced Liquid Crystal Elastomer for Strong and Programmable Artificial Muscles.

Fibers of liquid crystal elastomers (LCEs) as promising artificial muscle show ultralarge and reversible contractile strokes. However, the contractile force is limited by the poor mechanical properties of the LCE fibers. Herein, we report high-strength LCE fibers by introducing a secondary network into the single-network LCE.

Origami-Enhanced Mechanical Properties for Worm-Like Robot.

In recent years, the exploration of worm-like robots has garnered much attention for their adaptability in confined environments. However, current designs face challenges in fully utilizing the mechanical properties of structures/materials to replicate the superior performance of real worms. In this article, we propose an approach to address this limitation based on the stacked Miura origami structure, achieving the seamless integration of structural design, mechanical properties, and robotic functionalities, that is, the mechanical properties originate from the geometric design of the origami structure and at the same time serve the locomotion capability of the robot.

Topic Modeling Analysis of Chinese Medicine Literature on Gastroesophageal Reflux Disease: Insights into Potential Treatment.

Objective: To analyze Chinese medicine (CM) prescriptions for gastroesophageal reflux disease (GERD), we model topics on GERD-related classical CM literature, providing insights into the potential treatment.

Methods: Clinical guidelines were used to identify symptom terms for GERD, and CM literature from the database "Imedbooks" was retrieved for related prescriptions and their corresponding sources, indications, and other information. BERTopic was applied to identify the main topics and visualize the data.

High-Sensitivity Flexible Capacitive Pressure Sensors Based on Biomimetic Hibiscus Flower Microstructures.

The integration of low-dimensional nanomaterials with microscale architectures in flexible pressure sensors has garnered significant interest due to their outstanding performance in healthcare monitoring. However, achieving high sensitivity across different magnitudes of external pressure remains a critical challenge. Herein, we present a high-performance flexible pressure sensor crafted from biomimetic hibiscus flower microstructures coated with silver nanowires.

Design and fabrication of wearable electronic textiles using twisted fiber-based threads.

Mono-dimensional fiber-based electronics can effectively address the growing demand for improved wearable electronic devices because of their exceptional flexibility and stretchability. For practical applications, functional fiber electronic devices need to be integrated into more powerful and versatile systems to execute complex tasks that cannot be completed by single-fiber devices. Existing techniques, such as printing and sintering, reduce the flexibility and cause low connection strength of fiber-based electronic devices because of the high curvature of the fiber.

A Protocol for Digitalized Collection of Traditional Chinese Medicine (TCM) Pulse Information Using Bionic Pulse Diagnosis Equipment.

Pulse diagnosis equipment used in Traditional Chinese Medicine (TCM) has long been developed for collecting pulse information and in TCM research. However, it is still difficult to implement pulse taking automatically or efficiently in clinical practice. Here, we present a digital protocol for TCM pulse information collection based on bionic pulse diagnosis equipment, which ensures high efficiency, reliability and data integrity of pulse diagnosis information.

Cellular Automata Inspired Multistable Origami Metamaterials for Mechanical Learning.

Recent advances in multistable metamaterials reveal a link between structural configuration transition and Boolean logic, heralding a new generation of computationally capable intelligent materials. To enable higher-level computation, existing computational frameworks require the integration of large-scale networked logic gates, which places demanding requirements on the fabrication of materials counterparts and the propagation of signals. Inspired by cellular automata, a novel computational framework based on multistable origami metamaterials by incorporating reservoir computing is proposed, which can accomplish high-level computation tasks without the need to construct a logic gate network.

Distributed Consensus Algorithms in Sensor Networks with Higher-Order Topology.

Information aggregation in distributed sensor networks has received significant attention from researchers in various disciplines. Distributed consensus algorithms are broadly developed to accelerate the convergence to consensus under different communication and/or energy limitations. Non-Bayesian social learning strategies are representative algorithms for distributed agents to learn progressively an underlying state of nature by information communications and evolutions.

Super-resolution multicolor fluorescence microscopy enabled by an apochromatic super-oscillatory lens with extended depth-of-focus.

Planar super-oscillatory lens (SOL), a far-field subwavelength-focusing diffractive device, holds great potential for achieving sub-diffraction-limit imaging at multiple wavelengths. However, conventional SOL devices suffer from a numerical-aperture-related intrinsic tradeoff among the depth of focus (DoF), chromatic dispersion and focusing spot size. Here, we apply a multi-objective genetic algorithm (GA) optimization approach to design an apochromatic binary-phase SOL having a prolonged DoF, customized working distance (WD), minimized main-lobe size, and suppressed side-lobe intensity.

Conditional cross-map-based technique: From pairwise dynamical causality to causal network reconstruction.

Causality detection methods based on mutual cross mapping have been fruitfully developed and applied to data originating from nonlinear dynamical systems, where the causes and effects are non-separable. However, these pairwise methods still have shortcomings in discriminating typical network structures, including common drivers, indirect dependencies, and facing the curse of dimensionality, when they are stepping to causal network reconstruction. A few endeavors have been devoted to conquer these shortcomings.

Reviving the Dynamics of Attacked Reservoir Computers.

Physically implemented neural networks are subject to external perturbations and internal variations. Existing works focus on the adversarial attacks but seldom consider attack on the network structure and the corresponding recovery method. Inspired by the biological neural compensation mechanism and the neuromodulation technique in clinical practice, we propose a novel framework of reviving attacked reservoir computers, consisting of several strategies direct at different types of attacks on structure by adjusting only a minor fraction of edges in the reservoir.

Resting-state SEEG-based brain network analysis for the detection of epileptic area.

Background: Most epilepsy research is based on interictal or ictal functional connectivity. However, prolonged electrode implantation may affect patients' health and the accuracy of epileptic zone identification. Brief resting-state SEEG recordings reduce the observation of epileptic discharges by reducing electrode implantation and other seizure-inducing interventions.

A CPG-Based Versatile Control Framework for Metameric Earthworm-Like Robotic Locomotion.

Annelids such as earthworms are considered to have central pattern generators (CPGs) that generate rhythms in neural circuits to coordinate the deformation of body segments for effective locomotion. At present, the states of earthworm-like robot segments are often assigned holistically and artificially by mimicking the earthworms' retrograde peristalsis wave, which is unable to adapt their gaits for variable environments and tasks. This motivates the authors to extend the bioinspired research from morphology to neurobiology by mimicking the CPG to build a versatile framework for spontaneous motion control.

Cold Laser Micro-Machining of PDMS as an Encapsulation Layer for Soft Implantable Neural Interface.

PDMS (polydimethylsiloxane) is an important soft biocompatible material, which has various applications such as an implantable neural interface, a microfluidic chip, a wearable brain-computer interface, etc. However, the selective removal of the PDMS encapsulation layer is still a big challenge due to its chemical inertness and soft mechanical properties. Here, we use an excimer laser as a cold micro-machining tool for the precise removal of the PDMS encapsulation layer which can expose the electrode sites in an implantable neural interface.

Parylene C as an Insulating Polymer for Implantable Neural Interfaces: Acute Electrochemical Impedance Behaviors in Saline and Pig Brain In Vitro.

Parylene is used as encapsulating material for medical devices due to its excellent biocompatibility and insulativity. Its performance as the insulating polymer of implantable neural interfaces has been studied in electrolyte solutions and in vivo. Biological tissue in vitro, as a potential environment for characterization and application, is convenient to access in the fabrication lab of polymer and neural electrodes, but there has been little study investigating the behaviors of Parylene in the tissue in vitro.