Search Results (23)

The increasing complexity of the new generation of production lines necessitates the development of intelligent, autonomous, and adaptable systems that are capable of self-diagnosis and recovery from failures and errors. A “self-healing production line” refers to a production system that integrates artificial intelligence (AI), the Internet of Things (IoT), and advanced mathematical models to identify anomalies, forecast potential failures that can occur, and implement corrective measures with minimal or no human oversight. This manuscript offers a comprehensive examination of self-healing mechanisms, encompassing IoT-enabled sensors, AI-driven predictive maintenance, and Markov Decision Processes (MDPs) for the optimization of decision-making. Also, it includes an exploration of practical implementation strategies and an automotive case study that illustrates significant enhancements in operational uptime and cost-effectiveness. Full article

The subway power supply system, as a critical component of urban rail transit infrastructure, plays a pivotal role in ensuring operational efficiency and safety. However, current systems remain heavily dependent on manual interventions for fault diagnosis and recovery, limiting their ability to meet the growing demand for automation and efficiency in modern urban environments. While the concept of “self-healing” has been successfully implemented in power grids and distribution networks, adapting these technologies to subway power systems presents distinct challenges. This review introduces an innovative approach by integrating multi-agent systems (MASs) with advanced artificial intelligence (AI) algorithms, focusing on their potential to create fully autonomous self-healing control architectures for subway power networks. The novel contribution of this review lies in its hybrid model, which combines MASs with the IEC 61850 communication standard to develop fault diagnosis, isolation, and recovery mechanisms specifically tailored for subway systems. Unlike traditional methods, which rely on centralized control, the proposed approach leverages distributed decision-making capabilities within MASs, enhancing fault detection accuracy, speed, and system resilience. Through a thorough review of the state of the art in self-healing technologies, this work demonstrates the unique benefits of applying MASs and AI to address the specific challenges of subway power systems, offering significant advancement over existing methodologies in the field. Full article

To address the issue of insufficient reliability of fiber optic sensing networks in complex environments, this study proposes a self-diagnosis and self-healing method based on intelligent algorithms. This method integrates redundant fiber paths and a fault detection mechanism, enabling rapid data transmission recovery through redundant paths during network faults, ensuring the stable operation of the monitoring system. Unlike traditional self-diagnosis techniques that rely on an optical time domain reflectometer, the proposed self-diagnosis algorithm utilizes data structure analysis, significantly reducing dependence on costly equipment and improving self-diagnosis efficiency. On the hardware front, a light switch driving device that does not require an external power source has been developed, expanding the application scenarios of optical switches and enhancing system adaptability and ease of operation. In the experiments, three fiber optic sensing network topologies—redundant ring structure, redundant dual-ring structure, and redundant mesh structure—are constructed for testing. The results show that the average self-diagnosis time is 0.1257 s, and the self-healing time is 0.5364 s, validating the efficiency and practicality of the proposed method. Furthermore, this study also proposes a robustness evaluation model based on sensor perception ability and coverage uniformity indicators, providing a theoretical basis for the self-healing capability of fiber optic sensing networks. This model aids in network topology optimization and fault recovery strategy design, contributing to the improvement of the stability and reliability of fiber optic sensing networks in practical applications. Full article

Interfaces between implantable bioelectrodes and tissues provide critical insights into the biological and pathological conditions of targeted organs, aiding diagnosis and treatment. While conventional bioelectronics, made from rigid materials like metals and silicon, have been essential for recording signals and delivering electric stimulation, they face limitations due to the mechanical mismatch between rigid devices and soft tissues. Recently, focus has shifted toward soft conductive materials, such as conductive hydrogels and hydrogel nanocomposites, known for their tissue-like softness, biocompatibility, and potential for functionalization. This review introduces these materials and provides an overview of recent advances in soft hydrogel nanocomposites for implantable electronics. It covers material strategies for conductive hydrogels, including both intrinsically conductive hydrogels and hydrogel nanocomposites, and explores key functionalization techniques like biodegradation, bioadhesiveness, injectability, and self-healing. Practical applications of these materials in implantable electronics are also highlighted, showcasing their effectiveness in real-world scenarios. Finally, we discuss emerging technologies and future needs for chronically implantable bioelectronics, offering insights into the evolving landscape of this field. Full article

The phenomenon of child abuse/maltreatment is underestimated and often represents a difficult challenge for healthcare professionals and forensic pathologists who must proceed with the differential diagnosis with accidental or self-induced events, or with lesions due to pathologies that overlap with that of mistreatment, defined as “Mimics”. This study presents a case series with the aim of discussing lesions that may mimic signs of physical abuse in children but are due to a different etiology to raise awareness and train healthcare professionals and forensic pathologists on possible confounding factors in order to avoid diagnostic errors. Six cases of “Mimics” out of 418 cases of suspected mistreatment (1.43% of cases) were identified, presenting skin lesions initially classified as injuries of abuse due to their location and type and, in particular, sexual abuse for three cases. Then, the lesions and the subjects, in particular the anamnestic history, were examined by a multidisciplinary team and the diagnosis of genital lichen sclerosus et atrophicus in three cases, and the results of popular healing techniques (i.e., “cupping”) in the other three cases were ascertained. These situations require specific skills and a forensic background from healthcare professionals to conduct a correct differential diagnosis and the intervention of a multidisciplinary team to investigate every possible pathology or alternative therapeutic practice that could simulate child abuse. In particular, when “mimics” are due to alternative medicine, it should not strictly be considered child abuse, but professionals must be aware of the hypothesis of mistreatment in case of non-medical indication or potential personal injuries from other crimes, such as illegal practice of the medicine. This awareness is also crucial to direct the child toward appropriate medical care, and it is essential to recognize that these conditions can coexist within the same clinical presentation. Full article

Background: Wound healing competence is implied in the nursing profession, but there is no standardized content regulation for wound care in university curricula. The primary objective of this study was to identify the barriers to the acquisition of knowledge about skin integrity impairment. Methods: A quasi-experimental pre-test and post-test study with an ad hoc questionnaire involved 304 students (control: 165; intervention: 139) from June to July 2023. A 10-h educational intervention focused on skin integrity assessment and treatment was conducted. Results: The control group, scoring 17 ± 0.22 out of a maximum of 61, achieved a significantly lower final test score (p < 0.001) compared to the wound care educational intervention group, with the pre-test group scoring 30 ± 0.76 and the post-test group scoring 43 ± 0.61. The educational intervention in wound care program improved nursing students’ knowledge of prevention, assessment/diagnosis, treatment, lower limb wounds, and wound bed preparation by replacing the number of "Don’t know" answers in the post-test group with correct answers. Conclusions: The barriers identified to the acquisition of knowledge about skin integrity impairment in nursing studies are the following: the transversality of teaching, the teaching and evaluation system, and the variability in the training of professionals and teachers in charge of their education. The educational intervention can be used to consolidate knowledge and to enhance students’ self-confidence in caring for patients with wounds. Full article

Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of $91.25\%$, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry. Full article

Keratoacanthoma (KA) is a fast-growing skin tumor subtype that can be observed as a solitary lesion or rarely as multiple lesions in the context of rare genetic syndromes. Syndromes with multiple keratoacanthoma-like lesions have been documented as multiple self-healing squamous epithelioma (Ferguson–Smith syndrome), eruptive keratoacanthoma of Grzybowski, multiple familial keratoacanthoma of Witten and Zak Muir–Torre syndrome, and incontinentia pigmenti. The treatment approach of those entities is challenging due to the numerous lesions, the lesions’ undefined nature, and the co-existence of other malignant skin tumors. Herein, we report a case of a 40-year-old woman who developed multiple treatment-resistant Ferguson–Smith-like keratoacanthomas with a co-existing large and ulcerated invasive squamous cell carcinoma and microcystic adnexal carcinoma on the scalp. Multiple keratoacanthomas on her extremities were successfully treated with oral acitretin (0.5 mg/kg/day) in combination with topical Fluorouracil (5-FU) 5%, while excision and plastic surgery restoration were performed to treat the ulcerated cancer lesion on her scalp. Due to the interesting nature of this rare syndrome, we performed a literature review including case reports and case series on multiple-KA-like lesions syndromes and focusing on diagnosis and therapy approaches. We also conducted a comparison of patient reports, which included assessing the clinical appearance of the lesions and evaluating the success and progress or the failure of various treatment approaches that were implemented. Full article

Wound management represents a well-known continuous challenge and concern of the global healthcare systems worldwide. The challenge is on the one hand related to the accurate diagnosis, and on the other hand to establishing an effective treatment plan and choosing appropriate wound care products in order to maximize the healing outcome and minimize the financial cost. The market of wound dressings is a dynamic field which grows and evolves continuously as a result of extensive research on developing versatile formulations with innovative properties. Hydrogels are one of the most attractive wound care products which, in many aspects, are considered ideal for wound treatment and are widely exploited for extension of their advantages in healing process. Smart hydrogels (SHs) offer the opportunities of the modulation physico-chemical properties of hydrogels in response to external stimuli (light, pressure, pH variations, magnetic/electric field, etc.) in order to achieve innovative behavior of their three-dimensional matrix (gel–sol transitions, self-healing and self-adapting abilities, controlled release of drugs). The SHs response to different triggers depends on their composition, cross-linking method, and manufacturing process approach. Both native or functionalized natural and synthetic polymers may be used to develop stimuli-responsive matrices, while the mandatory characteristics of hydrogels (biocompatibility, water permeability, bioadhesion) are preserved. In this review, we briefly present the physiopathology and healing mechanisms of chronic wounds, as well as current therapeutic approaches. The rational of using traditional hydrogels and SHs in wound healing, as well as the current research directions for developing SHs with innovative features, are addressed and discussed along with their limitations and perspectives in industrial-scale manufacturing. Full article

The diagnosis of malnutrition should be one of the pillars of comprehensive patient care, especially in the case of patients with large wounds, prolonged healing, or comorbidities. The condition for a reliable and accurate nutritional diagnosis is to link it with the parameters of nutritional status assessment at the basic level (anthropometric measurements and clinical assessment) and in depth (biochemical tests and bioelectrical impedance). A prospective study included a sample of 60 patients with coexisting chronic wounds (venous ulcers, diabetic foot syndrome, pressure injury) treated at the Wound Treatment Clinic of Fr. B. Markiewicz Podkarpackie Oncology Center (Poland). The method of estimation and diagnostic survey was used; the research tool was a scientific research protocol consisting of four parts. Self-care capacity was assessed based on the Barthel scale, nutritional status using blood biochemical parameters, and electrical bioimpedance. Wounds were classified according to the extent, depth of tissue structures, and potential infection. Subjects with pressure ulcers had statistically significantly lower fat-free mass component indices compared to those with diabetic foot syndrome and venous ulceration. The subjects with pressure ulcers had significantly lower values of body composition components compared to those with diabetic foot syndrome and venous ulcers. In the group of patients with pressure ulcers, the lowest values of albumin (3.20 g/dL), hemoglobin (10.81 g/dL), and nutritional risk index (NRI) (88.13 pts.) scores were confirmed. Subjects with pressure ulcers with limited self-care presented a non-physiological nutritional status, indicating a risk of malnutrition. Local actions related to wound treatment should be preceded by a general examination, considering the state of augmented nutrition with the use of electrical bioimpedance. Full article

Several factors affect existing electric power systems and negatively impact power quality (PQ): the high penetration of renewable and distributed sources that are based on power converters with or without energy storage, non-linear and unbalanced loads, and the deployment of electric vehicles. In addition, the power grid needs more improvement in the performances of real-time PQ monitoring, fault diagnosis, information technology, and advanced control and communication techniques. To overcome these challenges, it is imperative to re-evaluate power quality and requirements to build a smart, self-healing power grid. This will enable early detection of power system disturbances, maximize productivity, and minimize power system downtime. This paper provides an overview of the state-of-the-art signal processing- (SP) and pattern recognition-based power quality disturbances (PQDs) characterization techniques for monitoring purposes. Full article

Background: Cancer is very disruptive in adolescence and hospitalizations interfere with this development stage in becoming independent, developing social relationships, and making plans for the future. A major challenge in the care of adolescents with cancer is being able to enhance their quality of life. The aim of this project is to increase our understanding of how adventure therapy influenced quality of life for adolescents with cancer. Methods: Bambino Gesù Children’s Hospital, in collaboration with the Tender to Nave Italia Foundation (TTNI), has been conducting a unique project, located on a beautiful brigantine of the Italian Navy. Adventure therapy is a form of experiential therapy that consists of various types of adventure, in particular outdoor and sailing activities. Ninety teenagers have been the protagonists of this project to date and filled out two questionnaires about quality of life and self-esteem, before and after the sailing experience. Results: The adventure provides the opportunity for the participants to build interpersonal relationships and develop life skills that they can benefit from in the future experiences. All participants report a significant improvement in their quality of life and self-esteem at the end of this experience. Conclusion: This collaborative adventure project is a great way to learn and practice new behaviors, improve interpersonal skills, heal painful emotions, overcome personal obstacles and challenges, and help the teenagers to resume their developmental path after an onco-hematological diagnosis. Full article

Handling faults in a running cellular network can impair the performance and dissatisfy the end users. It is important to design an automatic self-healing procedure to not only detect the active faults, but also to diagnosis them automatically. Although fault detection has been well studied in the literature, fewer studies have targeted the more complicated task of diagnosing. Our presented method aims to tackle fault detection and diagnosis using two sets of data collected by the network: performance support system data and drive test data. Although performance support system data is collected automatically by the network, drive test data are collected manually in three mode call scenarios: short, long and idle. The short call can identify faults in a call setup, the long call is designed to identify handover failures and call interruption, and, finally, the idle mode is designed to understand the characteristics of the standard signal in the network. We have applied unsupervised learning, along with various classified algorithms, on performance support system data. Congestion and failures in TCH assignments are a few examples of the detected and diagnosed faults with our method. In addition, we present a framework to identify the need for handovers. The Silhouette coefficient is used to evaluate the quality of the unsupervised learning approach. We achieved an accuracy of 96.86% with the dynamic neural network method. Full article

To solve the global shortage of land and offshore resources, the development of deep-sea resources has become a popular topic in recent decades. Deep-sea composites are widely used materials in abyssal resources extraction, and corresponding marine exploration vehicles and monitoring devices for deep-sea engineering. This article firstly reviews the existing research results and limitations of marine composites and equipment or devices used for resource extraction. By combining the research progress of smart composites, deep-sea smart composite materials with the three characteristics of self-diagnosis, self-healing, and self-powered are proposed and relevant studies are summarized. Finally, the review summarizes research challenges for the materials, and looks forward to the development of new composites and their practical application in conjunction with the progress of composites disciplines and AI techniques. Full article

To ensure the implementation of the marine electric propulsion self-healing strategy after faults, it is necessary to diagnose and accurately classify the faults. Considering the characteristics of the residual network (ResNet) and bidirectional long short-term memory (BiLSTM), the Res-BiLSTM deep learning algorithm is used to establish a fault diagnosis model to distinguish the types of electric drive faults. First, the powerful fault feature extraction ability of the residual network is used to deeply mine the fault features in the signals. Then, perform time-series learning through a bidirectional long short-term memory network, and further excavate the transient time-series features in the fault features so as to achieve the accurate classification of drive inverter faults. The effectiveness of the method is verified using noise-free fault data, and the robustness of the method is verified using data with varying degrees of noise. The results show that compared with conventional deep learning algorithms, Res-BiLSTM has the fastest and most stable training process, the diagnostic performance is improved, and the accuracy can be maintained over 95% under 25–19 dB. It has certain robustness and can be applied to marine electric propulsion systems drive inverter fault diagnosis, and its results can provide data support for the implementation of self-healing control strategies. Full article