Capability Of System Research Articles

Integrating gait and speech dynamics methodologies for enhanced stuttering detection across diverse datasets

<span lang="EN-US">Stuttering manifests as involuntary interruptions in the fluency of speech, often involving repetitions, prolongations, or blocks of sounds or syllables. These disruptions can significantly impact effective communication and psychosocial well-being. This research introduces a comprehensive system for speech impairment detection and gait analysis. Speech impairment, with a primary focus on stammer recognition, presents a multifaceted challenge in the field of speech processing. Stammers can manifest in various forms and detecting them accurately is a complex task. Our proposed methodology revolves around the development of StEnsembleNet, a neural network designed to learn spectral features at the frame level, enabling precise and efficient identification of speech impediments. Additionally, we extend our system's capabilities to the domain of gait analysis, leveraging a novel adaptive graph topology convolution network (AGT-ConvNet) for skeletal motion and visually enhanced topological learning to adapt to diverse visual environments and enhance the recognition of gait patterns. This research not only contributes to the field of speech therapy but also offers potential applications in healthcare and motion analysis.</span>

Surface engineering-based S, N co-doped biochar for improved anaerobic digestion: Enhancing microbial-pollutant and inter-microbial electron transfer synergistic EPS protection

Enhancing extracellular electron transfer (EET) efficiency is crucial for improving the anaerobic digestion (AD) system's capability to treat recalcitrant wastewater. In this study, a novel S, N co-doped biochar (S-N-BC) was prepared through surface engineering to optimize EET within AD systems. The addition of S-N-BC significantly enhanced the performance of a mesophilic AD system treating Congo red wastewater, increasing the decolorization rate by 78%, COD degradation rate by 82%, and methane yield by 87% compared to the control. Additionally, the shock resistance of anaerobic granular sludge was improved, as evidenced by the formation of the protective extracellular polymeric substances (EPS) barrier and the enhanced activities of the electron transport system. Mechanistic analysis revealed that adding S-N-BC did not alter the Congo red decolorization pathway but significantly enriched various electrochemically active bacteria and established EET pathways between microbial-pollutant and inter-microbial. This significantly accelerated EET efficiency within the AD system, ensuring stable and efficient operation under challenging conditions. This study proposed a novel approach using S-N-BC to simultaneously enhance "dual-pathway EET" between microbial-pollutant and inter-microbial while constructing an EPS protective barrier, addressing the issues of low efficiency and fragile stability of AD systems for treating recalcitrant wastewater.

Optimal Dispatch of Coal-fired Power Units with Carbon Capture Considering Peak Shaving and Ladder-type Carbon Trading

In the context of the low-carbon transformation of the power system, carbon markets, and carbon capture technologies have become important means to promote energy conservation and emission reduction. The rapid development of renewable energy poses a challenge to the regulation capability of the power system, and energy storage technology is currently an important means to enhance the regulation capability of the power grid. To reduce the carbon emission level of the power system, improve the system economy and the ability to consume renewable energy, this paper proposes a multi-energy complementary system (MECS) with carbon capture power plant (IFCCPP) considering ladder-type carbon trading (LCT) and energy storage. The optimal dispatch model was developed with the objective of minimizing the overall operating cost. The results indicated that the integration of IFCCPP and LCT has better economic and environmental benefits than separate applications. The integration of the two modes can reduce the total cost by 504.33 k$ and carbon emission by 39232.63 t, which verifies the rationality of integrating the two modes. The LCT produces more significant emission reduction effects due to its higher benefits when carbon emissions are lower. The traditional carbon trading mechanism reduces 4779.88 t of carbon emissions, and LCT reduces 38574.45 t.

Enhancing image quality using super-resolution residual network for small, blurry images

<span lang="EN-US">In the background, when low-resolution images are utilized, image identification tasks are frequently hampered. By employing the residual network super-resolution framework, super-resolution techniques are used to enhance image quality, specifically in the detection and identification of small and blurry objects. Improving resolution, decreasing blur, and enhancing object detail are the main goals of the suggested approach. The novelty of this research resides in its application of the activation exponential linear unit (ELU) to the super-resolution residual network (SR-ResNet) framework, which has been demonstrated to enhance image sharpness. The experimental findings demonstrate a substantial enhancement in the quality of the images, as evidenced by the training data's structural similarity index (SSIM) of 0.9989 and peak signal-to-noise ratio (PSNR) of 91.8455. Furthermore, the validation data demonstrated SSIM 0.9990 and PSNR 92.5520. The results of this study indicate that the implementation of SR-ResNet significantly enhances the capability of the detection system to detect and classify diminutive and opaque entities precisely. The expected and projected enhancement in image quality significantly influences image processing, especially in situations where accuracy and object differentiation are vital.</span>

A Secure Network with Minimization of Energy for E-healthcare Application in IoMT

Aims: Protect patient healthcare records. Background: The adaptability of the digital healthcare system is a major factor in its recent rise in popularity. Utilizing the digital healthcare system has resulted in an ever-increasing number of healthcare apps. The Internet of Medical Things-(IoMT) is a newly emerging digital healthcare system using various biomedical sensors and the cutting-edge capabilities of wireless systems and cloud computing. Since IoMT can exchange data between various connecting nodes thanks to the combination of other technologies, security and energy consumption provide the greatest challenge to the IoMT infrastructure. Objective: Reduce the cost of communication in order to strengthen defenses against unauthorized access and increase energy efficiency. Method: This study provides a protocol for protecting patients; medical records called the requesttype- based energy-aware framework (Re-EAF) based on patent. The primary goal is to reduce the cost of communication in order to strengthen defences against unauthorized access and increase energy efficiency. An identifying unit called a request-type energy aware framework has been proposed. The proposed method avoids treating all requests the same by instead characterizing them based on the identified criteria and characteristics. Using Constrained Application Protocol (CoAP), remote patient monitoring can increase the safety of gathered data. Results: Using Constrained Application Protocol (CoAP), remote patient monitoring can increase the safety of gathered data. Using a software-defined networking (SDN) framework, our research ensures that data and requests are sent and received as effectively and efficiently as possible while conserving energy. Conclusion: In this research, the transmitted healthcare data is encrypted via cipher Block-chaining. The experimental study demonstrates that the suggested Re-EAF consumes less energy while producing a higher throughput than conventional methods.

Flexible regulation of airport air-conditioning systems: Impact of cooling load variations on temperature stability

The integration of renewable energy sources has led to notable supply-demand imbalances due to their intermittent nature. Air-conditioning systems, as significant energy consumers with considerable flexibility, play a crucial role in integrating renewable energy and regulating power systems. This paper delves into the capability of air-conditioning systems in airports to flexibly adjust, particularly examining how varying cooling loads impact indoor temperatures. The energy flexibility of the air-conditioning system is evaluated based on three fundamental cooling load variations (incremental, translational, and discrete), and several evaluation parameters are defined. For areas with smaller cooling load fluctuations, there is a greater adjustment margin, but long adjustment periods should be avoided. For areas with larger fluctuations, the original cooling load curve should be considered in the adjustment strategy. The simulation results show that advancing or delaying the supply by 1 hour during peak cooling load periods can ensure indoor temperature stability, and step-type cooling load curves do not adversely affect indoor comfort levels. Practical case studies further demonstrate that utilizing water storage as an active regulation strategy can reduce operational costs by 26%, and with flexible adjustments, this reduction can increase to 35-54%. The study offers a framework to optimize air-conditioning flexibility in airports, facilitating better renewable energy integration and grid stability, which holds significant potential to reduce costs and improve energy efficiency, thus contributing to sustainable energy management.

The Integration of Artificial Intelligence in Secure Access Service Edge: Enhancing Network Security and Performance

Integrating Artificial Intelligence (AI) with Secure Access Service Edge (SASE) architecture represents a significant advancement in enterprise network security and management. This article examines the transformative impact of AI technologies on SASE implementations, focusing on enhanced threat detection capabilities, dynamic network optimization, and automated compliance management. The article comprehensively analyzes AI-driven security features, including real-time threat detection, zero-trust implementation, and privacy-preserving security measures. Through extensive evaluation of deployment scenarios and performance metrics, our research demonstrates significant improvements in security effectiveness and operational efficiency, including a 90% reduction in threat detection time, a 45% decrease in network latency, and a 65% reduction in manual configuration tasks. The article highlights the system's capability to maintain robust security while preserving data privacy through advanced encrypted traffic analysis techniques. The article addresses current integration challenges and provides insights into successful implementation strategies. This article contributes to the growing body of knowledge on AI-enhanced network security architectures and provides valuable insights for organizations seeking to modernize their security infrastructure while maintaining operational efficiency and compliance with evolving regulatory requirements.

Efficient Data Processing Pipeline for Event-Based Vision Datasets: Techniques and Insights

Abstract Event-based vision datasets have emerged as a critical asset in advancing the capabilities of real-time perception systems, particularly in fields such as robotics, autonomous vehicles, and human-computer interaction. These datasets enable low-latency processing by capturing asynchronous, pixel-level changes in the scene, providing a distinct advantage over traditional frame-based systems. However, the diverse formats and characteristics of event-based datasets pose significant challenges for efficient processing and analysis, hindering their broader adoption and integration. In this paper, we present a versatile and comprehensive data processing pipeline designed to address these challenges by supporting multiple event-data formats, including newer formats such as EVT2 and EVT3. This pipeline not only converts data into widely supported formats like AEDAT and NPZ, but also ensures that the unique characteristics of event-based data—such as temporal precision and sparse event representation—are preserved throughout the conversion process. By applying this pipeline to several open-source datasets, we establish a standardized, efficient methodology for dataset manipulation that enhances compatibility and reproducibility in event-based vision research. Additionally, we introduce a novel high-resolution event-based action dataset, converted into various formats using our pipeline, which opens new avenues for exploring event-based techniques in action recognition. This dataset and our pipeline serve as valuable resources for the research community, enabling advancements in real-time vision applications and fostering greater collaboration and standardization across studies.

Information and Analytical System Monitoring and Assessment of the Water Bodies State in the Mineral Resources Complex

Currently, one of the most pressing global issues is ensuring that human activities have access to water resources that meet essential quality standards. This challenge is addressed by implementing a series of organizational and technical measures aimed at preserving the ecology of water basins and reducing the level of harmful industrial emissions and other pollutants in the aquatic environment. To guarantee the necessary quality of water resources, monitoring is conducted based on selected parameters using various methods and means of technical quality control. From these results, suitable measures are formulated and applied to maintain water quality. Various scientific works extensively discuss different approaches to water quality management and compliance with specified requirements. Modern strategies for developing water monitoring systems leverage the capabilities of information systems that collect, process, store, and transmit information, enabling the resolution of issues in geographically distributed water bodies in real time. This paper proposes an approach that employs mathematical methods to identify the most significant factors determining water quality and to assess their interrelations using methods of a priori ranking, multivariate correlation regression analysis, and integral quantitative assessment. A hardware and software solution for the development of a unified integrated information and analytical system is proposed. This system enables continuous monitoring and assessment of water bodies based on a set of key parameters, addressing a range of critical tasks. This paper provides a detailed description of the software product, presents a demonstration using real-world data, and discusses the anticipated benefits of implementing such an information and analytical system.

Health Monitoring and Prediction Using Non-invasive Sensors in IoT Environment

Introduction: IoT's blend with non-invasive sensors has made health state prediction possible and constant real-time monitoring transformed healthcare. Using photoplethysmography (PPG) and electrocardiograms (ECG), this device evaluates important parameters including heart rate, blood oxygen saturation, and blood pressure. As healthcare systems face increasing demands, this technology provides a patient-centered and efficient method for long-term health tracking—needed for early intervention and management of chronic diseases. Objectives: The main goal is to evaluate using time-series data from PPG and ECG sensors the efficiency of an IoT-enabled health monitoring system coupled with machine learning models in predicting health conditions including arrhythmias and hypoxia. Methods: This system detects events based on temporal pattern recognition by use of Long Short-Term Memory (LSTM) networks and Random Forest classifiers. Noise reduction is preprocessed and wearable device data is labeled for training of machine learning models. The design combines wearable sensors, a smartphone app, and cloud-based analysis for continuous monitoring and real-time input. Results: Experimental data shows that the LSTM model attained a 95% accuracy while the Random Forest classifier attained a 92.5% accuracy. Both models displayed remarkable accuracy and recall even while the LSTM model excels in identifying anomalies in time-series data and the Random Forest classifier skillfully detects health events depending on certain criteria. Conclusions: This study demonstrates the capability of IoT-based health monitoring systems to enhance patient outcomes through continuous, non-invasive monitoring and predictive analytics. Despite its potential, difficulties persist, including data security, sensor reliability, and energy efficiency, necessitating additional research for broad use and clinical integration.

RBCKF-Based Vehicle State Estimation by Adaptive Weighted Fusion Strategy Considering Composite-State Tire Model

The acquisition of vehicle driving status information is a key function of vehicle dynamics systems, and research on high-precision and high-reliability estimation of key vehicle states has significant value. To improve the state observation effect, a vehicle sideslip angle estimation method adopting a robust bias compensation Kalman filter and adaptive weight fusion strategy is proposed. On the basis of the extended Kalman filter algorithm, and with the goals of estimation exactitude and robustness, considering the potential signal deviation, a vehicle state robust deviation compensation Kalman filter estimation algorithm considering bias compensation and residual covariance matrix weighting is proposed. Meanwhile, considering the adaptive and dynamic adjustment capabilities of the observation system in complex state-change scenarios, an estimation strategy based on adaptive weight fusion and a model-based estimator is proposed. The results confirm that the robust bias compensation Kalman filter can ensure estimation exactitude and robustness when the vehicle state fluctuates greatly, and the proposed fusion strategy can ensure that the vehicle maintains optimal estimation performance during operating condition switching.

Cyber attacks detection and mitigation in hybrid power system using type-2 fuzzy controller

Abstract This paper presents a novel control technique using Fuzzy Type-2 controller for regulating the frequency deviations in an independent Hybrid Power System (HPS). The HPS comprises of a wind system and a solar photovoltaic (PV) system as primary energy sources, along with Fuel Cells (FCs) as additional generation systems and Double Layer Capacitor (DLC) Banks as storage devices to meet the load demand. The frequency variations occur in the HPS due to the intermittency in power outputs from the primary sources, the load changes as well as any attacks in the system. The frequency regulation is achieved by effective coordination control of FC and DLC with the aid of a Fuzzy Type-2 controller and a high pass filter (HPF). The DLC technology adequately satisfies the residual power of hybrid systems resulting from the slow dynamics of FCs. The Fuzzy Type-2 controller is trained using an input-output data set of a PID controller whose gains are tuned to their optimal values using Monte-Carlo state estimators. The proposed system is simulated using real weather data to validate the capability of the Hybrid Power System (HPS) in supplying isolated loads while maintaining power frequency balance conditions. Also the HPS is subjected to attacks in daily load variation and the results are compared with the system without attacks. The simulation results show that the peak over shoots are reduced considerably but the settling time is 0.2 s in the system with no attacks where as the settling time is prolonged to 1.2 s with the attacks. This paper demonstrates the effectiveness of the Fuzzy Type-2 controller for the coordination control of DLC and FC to achieve generation-load balance condition in comparison with that of the PID controller for HPS system with and without attacks.

Exploration of a Novel Terpolymer Nanoparticle System for the Prevention of Alcohol-Induced Dose Dumping.

Alcohol-induced dose dumping (AIDD) remains a serious challenge in the controlled delivery of high potency drugs, such as opioids, which requires extensive investigation and innovative solutions. Current technologies rely on ethanol-insoluble excipients, such as guar gum and sodium alginate, to counteract the increased solubility of hydrophobic polymeric excipients in ethanol. However, these excipients pose several shortcomings, such as high viscosity of coating dispersion, high solution temperature, rapid gelation, and heterogeneity of resulted film. In this work, we explored the application of a cross-linked terpolymer nanoparticle (TPN) as an alcohol-resistant excipient in a water-insoluble controlled release film of ethylcellulose (EC) for the prevention of AIDD. Herein, we optimized the composition of TPN using a central composite design (CCD) to minimize swelling and weight loss of TPN-EC film in the presence of 20% ethanol. The optimized TPN showed a negligible effect on the viscosity of the coating dispersion, while guar gum increased the viscosity by 76-fold. Permeability studies in a pH 1.2 media containing 0% or 40% v/v ethanol revealed that cationic drugs (propranolol HCl, diltiazem HCl, and naloxone HCl (an opioid receptor-binding model drug)) exhibited significantly lower permeability ratios (P40%/P0%) than un-ionized drugs (theophylline and salicylic acid). FTIR analysis indicated an increase in ionic hydrogen bonding between TPN and the cationic drug in the presence of ethanol. These results suggest that drug-polymer-solvent interactions play an important role in alcohol-independent drug permeability through the TPN-EC film. By leveraging the drug permeability altering capability of the TPN-EC system, the release of cationic drugs in hydroethanolic media appeared to be suppressed, suggesting a promising new mechanism of alcohol resistance.

PUBLIC ANTI-CRISIS MANAGEMENT AND COMMUNICATIONS WHEN PROVIDING EMERGENCY MEDICAL AID IN THE MEDICAL INDUSTRY OF UKRAINE

In this article, the authors focus on several key problems that arise in the process of management and communication in crisis situations related to the provision of emergency medical care. The article examines approaches to managing crisis situations in the sphere of health care of Ukraine in conditions of increased uncertainty and instability. The authors emphasize the importance of effective communication between medical institutions, government structures and the public to ensure prompt response to emergency situations. The article examines various aspects of public crisis management, including planning, organization of resources, and coordination of actions during the provision of emergency medical care. The article analyzes the tools of public management, which allow to minimize various consequences of incoherence of actions between state institutions and medical institutions. Special attention is paid to the importance of prompt decision-making and establishment of communication links between state bodies, medical workers and the population. The possibilities of applying international experience in the field of anti-crisis management and its adaptation to Ukrainian realities are also discussed. The conclusions of the article emphasize the need to reform the health care system in the direction of strengthening its resilience to crises, improving communication mechanisms, and implementing innovative management solutions to increase the effectiveness of providing emergency medical care in emergency situations. The article emphasizes the need for quick decision-making, building effective communication chains between medical staff, patients and other stakeholders. Special attention is paid to the challenges associated with the lack of resources, the limited capabilities of the health care system in the context of crises and the role of leadership in implementing changes. The article also offers recommendations for improving communication processes and management approaches that will contribute to increasing the effectiveness of medical care during emergency situations, particularly in wartime.

Bio-Inspired Neuromorphic Sensory Systems from Intelligent Perception to Nervetronics.

Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificial neural networks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.

Neuro Fuzzy in Predicting the Characteristics of Some Nanomaterials

Unveiling the impressive capabilities of the Adaptive Neuro-Fuzzy Inference System (ANFIS), this study effectively predicts key properties of engineered nanomaterials, opening doors to innovative applications across various industries. We initially investigate the cytotoxic effects of TiO2 and ZnO nanoparticles on immortalized human lung epithelial cells, employing ANFIS to establish correlations between nanoparticle size and behaviour in different media and the resulting cellular membrane damage, quantified by lactate dehydrogenase release. Next, to predict the compressive strength of geopolymers, analysing over previous experimental datasets focused on critical chemical ratios. This model demonstrates its capability to optimize formulations for enhanced mechanical performance in sustainable construction materials. Additionally, we apply ANFIS to evaluate the size of silver nanoparticles in montmorillonite/starch bio nanocomposites, identifying significant factors such as AgNO3 concentration. The ANFIS models achieved high accuracy across all applications, underscoring their utility in predicting material behaviour and optimizing formulations for improved performance and safety. Collectively, these findings illustrate the potential of ANFIS as a robust tool in nanomaterial research and development.

Application and optimisation of intelligent control system for home robots

Abstract. As a significant part of the smart home, intelligent home robots can provide a variety of convenient home services such as automatic cleaning, elderly care, entertainment interaction, etc., which are highly valued and favoured by the majority of home users, and the market scale is expanding. However, the design and optimization of the control system represents a crucial technical challenge to truly realize the intelligence and autonomy of home robots. This paper presents a comprehensive analysis of the design and optimization of automatic control systems for intelligent home robots, which aims to improve the performance and user experience of intelligent home robots and better meet the needs of families. Through collection and analysis of the research on domestic robots and their control technology ,the core technical elements of the control system are summarized and refined, and the hardware structure and software framework of the control system are analyzed. The research results provide valuable insights for improving the performance and capabilities of home robot control systems to better meet the diverse needs and expectations of home users. These innovations provide a solid foundation for advancing the development of intelligent robots in the home. Future trends in control systems may include enhanced perceptual capabilities and decision-making intelligence to enable robots to better understand and meet the needs of home users.

The backpropagation algorithm implemented on spiking neuromorphic hardware

The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that most modern machine learning algorithms are not neurophysiologically plausible. In particular, the workhorse of modern deep learning, the backpropagation algorithm, has proven difficult to translate to neuromorphic hardware. This study presents a neuromorphic, spiking backpropagation algorithm based on synfire-gated dynamical information coordination and processing implemented on Intel’s Loihi neuromorphic research processor. We demonstrate a proof-of-principle three-layer circuit that learns to classify digits and clothing items from the MNIST and Fashion MNIST datasets. To our knowledge, this is the first work to show a Spiking Neural Network implementation of the exact backpropagation algorithm that is fully on-chip without a computer in the loop. It is competitive in accuracy with off-chip trained SNNs and achieves an energy-delay product suitable for edge computing. This implementation shows a path for using in-memory, massively parallel neuromorphic processors for low-power, low-latency implementation of modern deep learning applications.

On the effectiveness of virtual reality technology for relaxation of locomotive crew workers

Reducing the risks of occupational stress requires improving the methods of psychological support for employees of locomotive crews. Virtual reality technology offers restorative measures comparable to traditional techniques. The study aims to analyze the effectiveness of the impact of virtual reality technology on the basis of the analysis of the psycho-emotional state of employees of locomotive crews before and after rehabilitation measures; to evaluate the ratio of stress biomarkers before and after exposure to classical and technical techniques; to compare the effectiveness of psychological influence carried out by various methods. The scientists conducted a survey and diagnosis of the functional state of locomotive crew workers, followed by rehabilitation measures using various methods. The specialists performed a saliva analysis based on the concentration levels of cortisol and dehydroepiandrosterone. 120 people took part in the study, of which: 60 people according to the classical program and 60 people according to the virtual reality program, of which 15 people are at risk according to psychophysiological indicators. The session lasted 21 minutes. The average age of employees of locomotive crews participating in the study was 35.4 years. 88% of the subjects underwent functional state diagnostics and a full course of relaxation sessions in virtual reality. Of these, 73% have improved psychophysiological indicators: response accuracy, increased neuropsychiatric stability, and decreased anxiety levels. The authors noted calmness, improved mood and relief of muscle tension. 60% of the employees have completed a full course of rehabilitation measures using classical techniques. Of these, 70% have an improvement in their functional state according to certain parameters. There is a decrease in cortisol levels (unit of measurement — nanograms per milliliter) from 6.75 ng/ml to 6.28 ng/ml with a simultaneous increase in dehydroepiadrosterone levels (unit of measurement — micrograms per milliliter) from 0.53 mcg/100 ml to 1.77 mcg/100 ml, which corresponds to a decrease in the overall stress level. The analysis of the obtained results showed sufficient effectiveness of virtual methods comparable to classical approaches to relaxation. Immersive technologies more effectively activate natural compensatory mechanisms by expanding the individual regulatory capabilities of the nervous system. Scientists have identified areas for improving the latest methods of rehabilitation measures in order to correct the functional condition of locomotive crews. Limitations. The representativeness of the sample was ensured by the participation of 120 employees of locomotive crews in the study. The limitations are related to the gender homogeneity of the sample: only men are included, due to their professional status. Ethics. The study was performed in accordance with the rules of good clinical practice and the Helsinki Declaration of the World Medical Association, the conclusion of the ethics committee was not required.

Fully Wireless, In Vivo Assessment of Superimposed Physiological Vital Signs Using a Biodegradable Passive Tag Interrogated with a Wearable Reader Patch

AbstractState‐of‐the‐art biosignal monitoring systems strive to achieve a balance between biocompatibility, biodegradability, and miniaturized, unobtrusive signal acquisition, thus requiring further research for improved user safety, mobility, and comfort. Here, a fully wireless sensing system is presented for real‐time in vivo assessment of physiological vital signs, addressing these challenges to enhance performance and user experience. The system features a biodegradable passive tag with a nanoscale crack‐based strain gauge sensor connected to a coil antenna on a gelatin‐ionic liquid substrate (GIS). The thermal crosslinking in the GIS promotes adhesion, while the presence of ionic liquid decreases the self‐resonance frequency of the BCA to below 100 MHz, enabling the device to operate in detuned mode. A lightweight (1.547 g) wearable reader patch interrogates an implanted passive tag via near‐field inductive coupling and transmits sensory data to peripheral devices via Bluetooth. Thus, the system ensures minimal tissue absorption and extended transmission range while consuming ≈80 mW of power during operation. In vitro and in vivo studies, culminating in successful implementation within a rat model, validated the implanted tag's biocompatibility and the system's capability to wirelessly acquire and process superimposed physiological vital signs, highlighting its potential to enhance patient outcomes through improved diagnostic and monitoring practices.