搜索结果：healing

Mental health issues are worsening in today's competitive society, such as depression and anxiety. Traditional healings like counseling and chatbots fail to engage effectively, they often provide generic responses lacking emotional depth. Although large language models (LLMs) have the potential to create more human-like interactions, they still struggle to capture subtle emotions. This requires LLMs to be equipped with human-like adaptability and warmth. To fill this gap, we propose the MIND (Multi-agent INner Dialogue), a novel paradigm that provides more immersive psychological healing environments. Considering the strong generative and role-playing ability of LLM agents, we predefine an interactive healing framework and assign LLM agents different roles within the framework to engage in interactive inner dialogues with users, thereby providing an immersive healing experience. We conduct extensive human experiments in various real-world healing dimensions, and find that MIND provides a more user-friendly experience than traditional paradigms. This demonstrates that MIND effectively leverages the significant potential of LLMs in psychological healing.

Hospitalization of patients is one of the major factors for high wound care costs. Most patients do not acquire a wound which needs immediate hospitalization. However, due to factors such as delay in treatment, patient's non-compliance or existing co-morbid conditions, an injury can deteriorate and ultimately lead to patient hospitalization. In this paper, we propose a deep multi-modal method to predict the patient's risk of hospitalization. Our goal is to predict the risk confidently by collectively using the wound variables and wound images of the patient. Existing works in this domain have mainly focused on healing trajectories based on distinct wound types. We developed a transfer learning-based wound assessment solution, which can predict both wound variables from wound images and their healing trajectories, which is our primary contribution. We argue that the development of a novel model can help in early detection of the complexities in the wound, which might affect the healing process and also reduce the time spent by a clinician to diagnose the wound.

Pruning is a process designed to reduce the number of weights in a large neural network. This can substantially speed up inference but might cause a considerable reduction in the model's accuracy, and thus it is usually followed by a healing process that regains some of the lost accuracy. In this paper, we propose a new healing method, STARFISH, that can recover (most of) the accuracy of any pruned network efficiently. The main idea of STARFISH is to optimize the pruned network to align with the original network's internal state representations using a tiny calibration set of unlabeled examples. For the common case of removing 50% of the weights, STARFISH healing improves the recovered accuracy by up to 22% over the state-of-the-art methods on ViT-based networks. Its advantage is even more pronounced under aggressive pruning. For example, after eliminating 75% of the weights in a DeiT-B network for ImageNet, STARFISH uses only 0.4% of the number of training images as a calibration set and recovers 82% of the original dense accuracy, whereas competing recovery techniques reach only 40% of the dense model accuracy.

The stability and performance of halide perovskite photovoltaic devices are critically limited by progressive defect generation and associated local non-radiative losses during operation. Self-healing of defects provides a promising pathway to prolong device functionality, yet the underlying microscopic mechanisms remain poorly understood, particularly the role of interfacial chemistry on trap dynamics and healing kinetics. Here, we elucidate self-healing and defect evolution in triple-cation mixed halide (TCMH) perovskite films and their device-relevant charge transport layer heterostructures subjected to photo-induced damage. Using correlation clustering imaging (CLIM), our recently developed local functional imaging tool, we map spatiotemporal photoluminescence heterogeneity to track defect dynamics in pristine and heterostructure films. The defect healing follows bi-phasic kinetics, with an initial electronic relaxation (tens of minutes) and a subsequent slower phase (~ hours) associated to ionic and lattice rearrangement. Most importantly, our results demonstrate that the chemical nature of charge-transport layers modulates trap activity, healing kinetics, and halide redistrib

Defects in single-walled carbon nanotubes (SWCNTs) degrade their mechanical,electrical, and thermal properties, limiting their potential applications. To realize the diverse applications of SWCNTs, it is essential to enhance their crystallinity through effective defect healing. However, traditional thermal treatments typically require temperatures above 1800°C, which can alter the nanotube structure. Previously, defect healing of SWCNTs was achieved at a relatively low temperature of 1100°C, using C$_{2}$H$_{2}$ assistance, but the efficiency was limited. In this study, we developed a C$_{2}$H$_{2}$-assisted multiple-cycle process at an even lower temperature of 1000°C combined with air exposure, achieving highly efficient defect healing while preserving the nanotube structure. The combination of multiple-cycle treatment and air exposure between cycles was found to promote defect activation, suppress the formation of amorphous carbon, and enhance the effectiveness of defect healing. Additionally, we successfully healed commercially available bulk-scale SWCNTs (super-growth SWCNTs), noting that their healing behavior differed from lab-grown SWCNTs with smaller diameters synthesized

A time-dependent modeling framework for autogenous self-healing concrete that couples moisture diffusion with damage evolution was developed. Water transport follows Fick's second law with a damage-dependent diffusivity obtained by power-law interpolation between intact concrete and crack space. Healing reduces damage in proportion to the product of local moisture and a smoothed cement availability field computed via a novel Helmholtz filtering approach that models the spatial extent over which cement clinker can travel and form crystals. Two finite element variants were implemented in FEniCSx: a Crack Diffusion Model (CDM) with standard diffusion and a Crack Membrane Model (CMM) that introduces a novel threshold-based gating mechanism to control cross-crack water transport until a critical moisture threshold is reached. Key control parameters are the initial crack orientation and size, the diffusion coefficients of intact and cracked concrete, the healing rate constant, and the cement availability smoothing parameter. Simulations show that healing time varies non-monotonically with crack orientation, peaking near $45^\circ$ and $135^\circ$ and minimizing near $90^\circ$. The depen

As modern software systems grow in complexity and scale, their ability to autonomously detect, diagnose, and recover from failures becomes increasingly vital. Drawing inspiration from biological healing - where the human body detects damage, signals the brain, and activates targeted recovery - this paper explores the concept of self-healing software driven by artificial intelligence. We propose a novel framework that mimics this biological model system observability tools serve as sensory inputs, AI models function as the cognitive core for diagnosis and repair, and healing agents apply targeted code and test modifications. By combining log analysis, static code inspection, and AI-driven generation of patches or test updates, our approach aims to reduce downtime, accelerate debugging, and enhance software resilience. We evaluate the effectiveness of this model through case studies and simulations, comparing it against traditional manual debugging and recovery workflows. This work paves the way toward intelligent, adaptive and self-reliant software systems capable of continuous healing, akin to living organisms.

Wound healing is a complex process involving changes in collagen fibers. Accurate monitoring of these changes is crucial for assessing the progress of wound healing and has significant implications for guiding clinical treatment strategies and drug screening. However, traditional quantitative analysis methods focus on spatial characteristics such as collagen fiber alignment and variance, lacking threshold standards to differentiate between different stages of wound healing. To address this issue, we propose an innovative approach based on deep learning to predict the progression of wound healing by analyzing collagen fiber features in histological images of wound tissue. Leveraging the unique learning capabilities of deep learning models, our approach captures the feature variations of collagen fibers in histological images from different categories and classifies them into various stages of wound healing. To overcome the limited availability of histological image data, we employ a transfer learning strategy. Specifically, we fine-tune a VGG16 model pretrained on the ImageNet dataset to adapt it to the classification task of histological images of wounds. Through this process, our

This study develops sustainable materials using hydrogel as the matrix and explores the transition from sustainable materials to user-centered sustainability, with a particular focus on achieving art healing through material experience. The findings reveal that "Aesthetic" property exert the greatest influence on art healing in the context of multimodal material experiences involving visual, tactile, and smell, followed by "Intrinsic" property, whereas "Physical" property have a comparatively limited effect. Furthermore, the study proposes a material experience framework that enables designers to systematically and holistically understanding material characteristics. It highlights the importance of considering users' psychological perceptions and emotional needs in the material design process.

Guided by experiments contrasting electrically accelerated recovery with natural healing, this study formulates a model to investigate the importance of electroactive differential growth and morphological changes in tissue repair. It underscores the clinical potential of leveraging electroactive differential growth for improved healing outcomes. The study reveals that voltage stimulation significantly enhances the healing and growth of biological tissues, accelerating the regeneration process across various growth modalities and steering towards isotropic growth conditions that do not favor any specific growth pathways. Enhancing the electroelastic coupling parameters improves the efficacy of bioelectric devices, initiating contraction and fortification of biological tissues in alignment with the electric field. This process facilitates swift cell migration and proliferation, as well as oriented growth of tissue. In instances of strain stiffening at elevated strains, the extreme critical growth ratio aligns with the predictions of neo-Hookean models. Conversely, for tissues experiencing strain stiffening under moderate to very low strain conditions, the strain stiffening effect sub

We uncover the mechanism behind the enhancement of photoluminescence yield in monolayer WS$_2$ through oleic acid treatment, a promising scalable strategy for defect healing. By inducing sulfur vacancies through thermal treatment and monitoring the changes in photoluminescence yield and emission spectra, we demonstrate that oleic acid heals the sulfur vacancy by providing substitutional oxygen. Using density functional theory calculations, we provide insight into the underlying mechanism governing the oleic acid-mediated sulfur vacancy healing process. Our findings suggest that effective defect passivation by oxygen doping can be achieved through chemical treatment, opening a pathway for oxygen doping in transition metal dichalcogenides. However, we also highlight the limitations of chemical treatment, which may only lead to small increases in photoluminescence yield beyond a certain point.

This study presents a mathematical model formulated as a system of first-order non-linear ordinary differential equations, aimed at examining the effects of different factors, classified as local and systemic factors on a wound healing process. Specifically, the model incorporates pathogens, inflammatory cells, fibroblast cells, collagen, and the initial wound size. The variables utilized in the model lack specific units since they represent combined responses of different cell types. As such, these variables serve as indicators of the relative behaviors of these cellular populations. The feasibility of the model solution is examined through the demonstration of its positivity. A threshold, denoted $R_w$, is established to determine the conditions necessary for the existence of a wound-free equilibrium. Sensitivity analysis is carried out to determine the contribution of parameters to the behavior of fibroblast cells and $R_w$, which are crucial to the wound healing process. Through numerical simulations, it is demonstrated that factors such as oxygen levels, pathogen activity, inflammatory cell activity, age, smoking, alcoholism, and stress exert considerable influence on the beha

Embryonic healing in epithelial tissues is distinct from adult wound healing, as it lacks inflammatory responses or immune cell recruitments, making it ideal to test models of wound healing driven primarily by epithelial dynamics. Many models have been developed to describe this process, ranging from simple mechanistic models to more elaborate multiscale simulations. We review different classes of physical models, from discrete to continuum models, and how they address key questions about the mechanics, signaling, and coordination of cells during wound closure. We highlight tensions between model complexity and interpretability and discuss recent efforts to bridge gaps across scales. Finally, we identify directions for hybrid modeling and model-experiment integration that could push forward our understanding of epithelial repair in development and disease.

Quasi-brittle materials such as concrete suffer from cracks during their life cycle, requiring great cost for conventional maintenance or replacement. In the last decades, self-healing materials are developed which are capable of filling and healing the cracks and regaining part of the stiffness and strength automatically after getting damaged, bringing the possibility of maintenance-free materials and structures. In this paper, a time dependent softening-healing law for self-healing quasi-brittle materials is presented by introducing limited material parameters with clear physical background. Strong Discontinuity embedded Approach (SDA) is adopted for evaluating the reliability of the model. In the numerical studies, values of healing parameters are firstly obtained by back analysis of experimental results of self-healing beams. Then numerical models regarding concrete members and structures built with self-healing and non-healing materials are simulated and compared for showing the capability of the self-healing material.

In this paper, we propose a hybrid framework that heals corrupted finite semigroups, combining deterministic repair strategies with Machine Learning using a Random Forest Classifier. Corruption in these tables breaks associativity and invalidates the algebraic structure. Deterministic methods work for small cardinality n and low corruption but degrade rapidly. Our experiments, carried out on Mace4-generated data sets, demonstrate that our hybrid framework achieves higher healing rates than deterministic-only and ML-only baselines. At a corruption percentage of p=15%, our framework healed 95% of semigroups up to cardinality n=6 and 60% at n=10.

Self-healing systems have long been a focus of research, aiming to enable software to recover from unexpected runtime errors without human intervention. Traditional approaches rely on predefined heuristic rules, such as reusing error handlers or rolling back to checkpoints, but these methods struggle to adapt to the diverse range of runtime errors. The emergence of Large Language Models offers a new opportunity to address this challenge. Leveraging their ability to understand and generate code and natural language, we propose using LLMs to dynamically generate error-handling strategies in real time, tailored to specific runtime contexts such as error messages and program states. We demonstrate the feasibility of this approach by designing such a framework, Healer, and empirically showing that it can handle runtime errors with a high success rate. When an unanticipated runtime error occurs, Healer leverages its internal LLM to generate bespoke error-handling code. The generated healing code is then executed to produce a corrected program state, allowing the program to continue execution with minimal disruption. We evaluate Healer across four code datasets and three state-of-the-art

Long-term bone healing/adaptation after a dental implant treatment starts with diffusion of mesenchymal stem cells to the fracture callus and their subsequent differentiation. The healing phase is followed by the bone-remodeling phase. In this work, a mechano-regulatory cellular differentiation model was used to simulate tissue healing around an immediately loaded dental implant. All tissue types were modeled as poroelastic in the healing phase. Material properties of the healing region were updated after each loading cycle for 30 cycles (days). The tissue distribution in the healed state was then used as the initial condition for the remodeling phase during which regions healed into bone adapt their internal density with respect to a homeostatic remodeling stimulus. The short- and long-term effects of micro-motion on bone healing and remodeling were studied. Development of soft tissue was observed both in the coronal region due to high fluid velocity, and on the vertical sides of the healing-callus due to high shear stress. In cases with small implant micromotion, tissue between the implant threads differentiated into bone during the healing phase, but resorbed during remodeling.

We study a system that experiences damaging external shocks at stochastic intervals, continuous degradation, and self-healing. The motivation for such a system comes from real-life applications based on micro-electro-mechanical systems (MEMS). The system fails if the cumulative damage exceeds a time-dependent threshold. We develop a preventive maintenance policy to replace the system such that its lifetime is prudently utilized. Further, three variations on the healing pattern have been considered: (i) shocks heal for a fixed duration $τ$; (ii) a fixed proportion of shocks are non-healable (that is, $τ=0$); (iii) there are two types of shocks -- self healable shocks heal for a finite duration, and nonhealable shocks inflict a random system degradation. We implement a proposed preventive maintenance policy and compare the optimal replacement times in these new cases to that of the original case where all shocks heal indefinitely and thereby enable the system manager to take necessary decisions in generalized system set-ups.

Wounds in epithelial tissues compromise their vital role in homeostasis. A rapid and efficient wound healing encompasses different mechanisms, which includes the formation of a contractile actin-myosin cable around its edge, known as the purse-string mechanism. We combine mean-field calculations and numerical simulations of the Vertex model to study the interplay between tissue properties and the purse-string mechanism and its impact on the healing process. We find different regimes, where the wound opens, closes partially or completely. We also derive an analytic expression for the closure time which is validated by numerical simulations. This study establishes under which conditions the purse-string mechanism suffices for closure, providing an analytical mean-field expression for the respective thresholds.