Monitoring Technology Research Articles

Caracterización y Análisis de la Calidad del Agua en Entornos Urbanos mediante la implementación de un Sistema Embebido con Tecnología IoT

This study aims to characterize and analyze water quality in urban environments using an IoT embedded system, focusing on San Camilo’s community of Quevedo, Ecuador, where population growth has increased the demand for drinking water. An embedded system was designed to make use of a microcontroller and sensors to monitor critical water quality parameters in real time. The collected data is transmitted to a cloud platform for storage, analysis, and visualization. Spearman's correlation coefficient was preferred for statistical analysis of the data. This allowed for the identification of patterns and relationships between the variables examined. The data revealed significant associations in non-linear data, facilitating a decision regarding the development of predictive models for water management and treatment. The results demonstrate the feasibility and efficiency of using IoT technologies in the continuous monitoring of water quality, offering a scientific basis for the collection and processing of relevant data on the quality of drinking water in residential areas, providing a robust tool to monitor public health and the comfort of the inhabitants.

CUIDADOS DE ENFERMAGEM NA PREVENÇÃO DE INFECÇÕES HOSPITALARES: ESTRATÉGIAS E PROTOCOLOS

Objective: The study aimed to analyze the strategies and protocols adopted by nursing professionals in order to evaluate their effectiveness and propose improvements. Method: A bibliographic methodology was used, reviewing the current literature on hand hygiene protocols, use of Personal Protective Equipment (PPE), and prevention of infections associated with invasive devices. Results: The results indicated that the fundamental protocols are effective in reducing infections, but highlighted the need for continuous training and the potential use of monitoring technologies to improve adherence and evaluation of practices. The study objectives were achieved, recommending regular updating of protocols, intensification of training, and the integration of new technologies to improve strategies for preventing hospital infections. Conclusion: This article investigated nursing care in the prevention of hospital infections. The justification for the study lies in the importance of optimizing infection prevention, a continuous challenge in hospital settings that impacts patient safety and health costs. Keywords: Hospital infections. Nursing care. Prevention. Control strategies.

Time-lapse difference seismic inversion based on scattering theory

Time-lapse seismic monitoring technology is an effective tool for quantitatively estimating subsurface medium variations and plays a crucial role in resource and engineering explorations. Among existing time-lapse inversion methods, the difference inversion technique offers higher accuracy but requires linear inversion operators. During linearization, the background is often approximated as a homogeneous medium, resulting in the loss of effective information. Additionally, merely using reflection wave information is not conducive to improving inversion accuracy. However, the true structure of the subsurface is often complex and inhomogeneous, which can easily generate complex wavefields, such as scattering waves, thereby hindering the resolution of differences between time-lapse models. Therefore, incorporating more comprehensive wavefield information is essential for enhancing the accuracy of time-lapse seismic inversion. To address this issue, a wave-equation-based time-lapse difference inversion method is proposed to retrieve the production-induced property perturbation. In the proposed method, the forward operator is formulated through the scattering wave equation, the operator is linearized through the Born approximation, and the WKBJ method is used to approximate Green's function with high accuracy for the inhomogeneous background. In this study, time-lapse seismic parallel inversion and difference inversion are conducted using the proposed forward operator. Both numerical experiments and application of field seismic data indicate that the proposed time-lapse difference inversion method can realize a more accurate quantitative characterization of time-lapse variations.

CUIDADOS DE ENFERMAGEM NA PREVENÇÃO DE INFECÇÕES HOSPITALARES: ESTRATÉGIAS E PROTOCOLOS

Objective: The study aimed to analyze the strategies and protocols adopted by nursing professionals in order to evaluate their effectiveness and propose improvements. Method: A bibliographic methodology was used, reviewing the current literature on hand hygiene protocols, use of Personal Protective Equipment (PPE), and prevention of infections associated with invasive devices. Results: The results indicated that the fundamental protocols are effective in reducing infections, but highlighted the need for continuous training and the potential use of monitoring technologies to improve adherence and evaluation of practices. The study objectives were achieved, recommending regular updating of protocols, intensification of training, and the integration of new technologies to improve strategies for preventing hospital infections. Conclusion: This article investigated nursing care in the prevention of hospital infections. The justification for the study lies in the importance of optimizing infection prevention, a continuous challenge in hospital settings that impacts patient safety and health costs. Keywords: Hospital infections. Nursing care. Prevention. Control strategies.

Development and Validation of a Portable EIT System for Real-Time Respiratory Monitoring.

This work contributes to the improvement of novel medical technologies for the prevention and treatment of diseases. Electrical impedance tomography (EIT) has gained attention as a valuable tool for non-invasive monitoring providing real-time insights. The purpose of this work is to develop and validate a novel portable EIT system with a small form factor for respiratory monitoring. The device uses a 16-electrode architecture with adjacent stimulation and measurement patterns, an integrated circuit current source and a single high-speed ADC operating with multiplexers to stimulate and measure across all electrodes. Tests were conducted on 25 healthy subjects who performed a pulmonary function test with a flowmeter while using the EIT device. The results showed a good performance of the device, which was able to recognize all respirations correctly, and from the EIT signals and images, correlations of 96.7% were obtained for instantaneous respiratory rate and 96.1% for tidal volume prediction. These results validate the preliminary technical feasibility of the EIT system and demonstrates its potential as a reliable tool for non-invasive respiratory assessment. The significance of this work lies in its potential to democratize advanced respiratory monitoring technologies, making them accessible to a wider population, including those in remote or underserved areas.

Dual-Feature Fusion Learning: An Acoustic Signal Recognition Method for Marine Mammals

Marine mammal acoustic signal recognition is a key technology for species conservation and ecological environment monitoring. Aiming at the complex and changing marine environment, and because the traditional recognition method based on a single feature input has the problems of poor environmental adaptability and low recognition accuracy, this paper proposes a dual-feature fusion learning method. First, dual-domain feature extraction is performed on marine mammal acoustic signals to overcome the limitations of single feature input methods by interacting feature information between the time-frequency domain and the Delay-Doppler domain. Second, this paper constructs a dual-feature fusion learning target recognition model, which improves the generalization ability and robustness of mammal acoustic signal recognition in complex marine environments. Finally, the feasibility and effectiveness of the dual-feature fusion learning target recognition model are verified in this study by using the acoustic datasets of three marine mammals, namely, the Fraser’s Dolphin, the Spinner Dolphin, and the Long-Finned Pilot Whale. The dual-feature fusion learning target recognition model improved the accuracy of the training set by 3% to 6% and 20% to 23%, and the accuracy of the test set by 1% to 3% and 25% to 38%, respectively, compared to the model that used the time-frequency domain features and the Delay-Doppler domain features alone for recognition.

Emerging innovations in neonatal monitoring: a comprehensive review of progress and potential for non-contact technologies.

Continuous monitoring of high-risk neonates is essential for the timely management of medical conditions. However, the current reliance on wearable or contact sensor technologies for vital sign monitoring often leads to complications including discomfort, skin damage, and infections which can impede medical management, nursing care, and parental bonding. Moreover, the dependence on multiple devices is problematic since they are not interconnected or time-synchronized, use a variety of different wires and probes/sensors, and are designed based on adult specifications. Therefore, there is an urgent unmet need to enable development of wireless, non- or minimal-contact, and non-adhesive technologies capable of integrating multiple signals into a single platform, specifically designed for neonates. This paper summarizes the limitations of existing wearable devices for neonates, discusses advancements in non-contact sensor technologies, and proposes directions for future research and development.

Deep Learning‐Coupled Metabolic Heat Integrated Sensing System for Noninvasive Continuous Monitoring of Blood Glucose

The dynamic and continuous monitoring of blood glucose (BG) concentration is crucial for the health management of diabetic patients. Despite its importance, significant challenges remain in the development of effective BG monitoring technologies. Metabolic heat conformation (MHC) offers a promising solution due to its noninvasiveness and reliability. However, progress in MHC technology is hindered by the complexities of multisensor integration and the intricate correlation between MHC and BG. Herein, a wearable BG continuous monitoring device based on metabolic heat integrated sensing (MHIS) is developed, combined with a deep learning network to enable continuous BG detection using MHC principles. The MHIS device integrates a miniaturized circuit and intelligent secondary signal processing, allowing for the simultaneous acquisition and integrated operation of various physiological parameters, including metabolic heat production. By integrating a gate recurrent unit neural network, a model is established to facilitate continuous BG monitoring. The wearable device has a certain accuracy, and when analyzed in comparison with commercial noninvasive glucose meters, the mean absolute relative error meets international standards. The deep learning‐enhanced MHIS system proposed in this work enables noninvasive BG monitoring, paving the way for advancements in personalized healthcare management and offering new opportunities in digital healthcare consultation.

Integration of Non-Destructive Testing Technologies for Effective Monitoring and Evaluation of Road Pavements

Integration of Non-Destructive Testing Technologies for Effective Monitoring and Evaluation of Road Pavements

Advanced Structural Monitoring Technologies in Assessing the Performance of Retrofitted Reinforced Concrete Elements

Advanced Structural Monitoring Technologies in Assessing the Performance of Retrofitted Reinforced Concrete Elements

Application of Aggregation-Induced Emission (AIE) Technology in Monitoring of the Preparation of Spinning Solution for Electrospinning

Application of Aggregation-Induced Emission (AIE) Technology in Monitoring of the Preparation of Spinning Solution for Electrospinning

Technologies and Solutions for Cattle Tracking: A Review of the State of the Art.

This article presents a systematic literature review of technologies and solutions for cattle tracking and monitoring based on a comprehensive analysis of scientific articles published since 2017. The main objective of this review is to identify the current state of the art and the trends in this field, as well as to provide a guide for selecting the most suitable solution according to the user's needs and preferences. This review covers various aspects of cattle tracking, such as the devices, sensors, power supply, wireless communication protocols, and software used to collect, process, and visualize the data. The review also compares the advantages and disadvantages of different solutions, such as collars, cameras, and drones, in terms of cost, scalability, precision, and invasiveness. The results show that there is a growing interest and innovation in livestock localization and tracking, with a focus on integrating and adapting various technologies for effective and reliable monitoring in real-world environments.

Intelligent control of pressure based on ureteral access sheaths: renal pelvic pressure during flexible ureteroscope perfusion at different sites.

This study aimed to evaluate the accuracy and effectiveness of renal pelvic pressure (RPP) control during flexible ureteroscopic perfusion at various sites. Flexible ureteroscopy for the intelligent control of RPP was based on a ureteral access sheath (UAS) that integrated pressure measurement and suction functions (with automatic adjustment). Eleven consecutive patients with indurating nephrostomy tubes were enrolled. The nephrostomy tube was connected to a disposable invasive blood pressure sensor. The RPP was zeroed after the pressure stabilized. The flow rate and control value were set at 100ml/min and - 5 mmHg, respectively. An 8.5 Fr flexible ureteroscope was irrigated at the renal pelvis, as well as the upper, middle, and lower calyces of the kidney for 1min. All 11 patients with upper urinary tract calculi underwent successful UAS placement. Pressures measured by the sheath and fistula during perfusion were - 5.07 ± 1.41 and - 4.89 ± 1.07 mmHg at the renal pelvis, -5.16 ± 1.36 and - 5.12 ± 1.32 mmHg at the upper calyces, -4.98 ± 0.87 and - 5.39 ± 1.01 mmHg at the middle calyces, as well as -4.95 ± 1.56 and - 5.64 ± 1.24 mmHg at the lower calyces, respectively. There were no significant differences in pressure between the sheath and fistula groups or in sheath and fistula pressures among parts. The RPP fluctuated; however, all values were within the safe limit of 20 mmHg. The UAS-based pressure monitoring technology can accurately and reliably monitor and control RPP within a set range.

Recent Advancements in Nanomaterials for Near‐Infrared to Long‐Wave Infrared Photodetectors

AbstractNanomaterials have superior electronic, optical, and mechanical properties making them highly suitable for a range of applications in optoelectronics, biomedical fields, and photonics. Nanomaterials‐based IR detectors are rapidly growing due to enhanced sensitivity, wide spectral range, and device miniaturization compared to commercial photodetectors. This review paper focuses on the significant role of nanomaterials in infrared detection, an area critical for enhancing night vision and health monitoring technologies. The latest advancements in IR photodetectors that employ various nanomaterials and their hybrids are discussed. The manuscript covers the operational mechanisms, device designing, performance optimization strategies, and material challenges. This review aims to provide a comprehensive overview of the current developments in nanomaterial‐based IR photodetectors and to identify key directions for future research and technological advancements.

Investigation of Rheological Properties of Molten Materials for Dripping Pills Based on Imaging Monitoring.

Rheological properties, as critical material attributes (CMAs) of solid dispersion drugs such as dripping pills, affect the melting, dispersion, and solidification. Therefore, characterization and assessments of rheological properties in the pharmaceutical process are important in enhancing drug stability and bioavailability. The study aimed to develop a method for analyzing the rheology of molten materials, assessing their consistency and how rheological properties affect the dripping process and pills quality. The rheological behavior of molten materials composed of Ginkgo biloba leaf extract (GBE) and polyethylene glycol (PEG) 4000 was characterized. Batch consistency of molten materials was evaluated. Image monitoring technology was utilized to capture and process images of the droplet formation process. We established the relationship between the rheological properties of molten materials and various attributes. The quality consistency of molten materials was evaluated, with 12 batches showing similarity above 0.8. The MLR models showed strong correlations (R2 > 0.80) between rheological properties and evaluation attributes. The rheological properties, including consistency coefficient, flow index, and viscosity at 80°C, were identified as critical rheological properties of the molten materials. Rheological property differences of molten materials have an impact on the morphology of droplet and quality performance. A rheological method was established, enabling quality consistency evaluation of molten materials in dripping pills. This study revealed the influence of rheological properties on droplet formation process and dripping pills quality, providing a reference for researches on material attributes control of other traditional Chinese medicine dripping pills.

Gas sensitivity evaluation of decomposition gases in environmentally friendly insulating devices (C4F7N/CO2) (Chromium cluster-modified BNNTs surface interface at the atomic scale)

Under the global trend of green development, the research and development of environmentally friendly gas-insulated electrical equipment and the associated technical issues in the power systems industry have become important research topics. This study investigates the monitoring technology for decomposition gases (CO, C2F6, and COF2) under insulation defect conditions in environmentally friendly GIS equipment based on the green insulating gas mixture C4F7N/CO2. Initially, chromium clusters (Crn, n = 1–4) were used to modify one-dimensional boron nitride nanotubes (BNNTs), describing the changes in semiconductor electronic properties and magnetism at the microscopic interface. Interestingly, the electronic properties of the system changed significantly after modification, with increased electron mobility and enhanced conductivity. Subsequently, adsorption tests were conducted on three potential target gases, revealing that Cr-BNNT exhibited excellent adsorption for COF2, with an adsorption energy reaching -9.555 eV. This indicates its potential as a clean material for addressing COF2 (toxic) and CO leaks. Lastly, and most importantly, in the assessment of sensing performance, it has been discovered that Cr-BNNT holds promise as a gas sensor for C2F6. Furthermore, to meet diverse sensing requirements and environmental conditions, Cr3-BNNT and Cr4-BNNT have demonstrated potential as sensor array materials for the detection of CO (300 K, 0.145 μs) and COF2 (500 K, 3 min) gas concentrations, respectively. Our study contributes to the advancement of novel eco-friendly insulating gases and explores the use of new materials for monitoring the insulation performance and condition assessment of environmentally friendly GIS equipment.

The efficiency of CUSUM schemes for monitoring the multivariate coefficient of variation in short runs process

Current monitoring technologies emphasize and address the issue of monitoring high-volume production processes. The high flexibility and diversity of current industrial production processes make monitoring technology for small batch processes even more important. In multivariate process monitoring, a broader applicability exists in multivariate coefficients of variation (MCV) based monitoring schemes due to the lower restriction of the process. In view of the effectiveness of MCV monitoring and with the aim to achieve further performance improvement of current MCV monitoring schemes in a finite horizon production, we additionally introduce two one-sided cumulative sum (CUSUM) MCV schemes. In the case of deterministic and random shifts, the design parameters of the proposed schemes are obtained via an optimization procedure designed by the Markov chain method and the corresponding performance is analysed based on different run length (RL) characteristics, including the mean and the standard deviation. Simulation comparisons with existing exponentially weighted moving average (EWMA) MCV schemes show that the proposed CUSUM MCV schemes are more efficient in monitoring most of the shifts, including the deterministic and random shifts. Finally, to demonstrate the benefits of the new monitoring schemes, a comprehensive case study on monitoring a steel sleeve manufacturing process is conducted.

Invisible Bridges: Unveiling the Role and Prospects of Tunneling Nanotubes in Cancer Therapy.

Tunneling nanotubes (TNTs) are essential intercellular communication channels that significantly impact cancer pathophysiology, affecting tumor progression and resistance. This review methodically examines the mechanisms of TNTs formation, their structural characteristics, and their functional roles in material and signal transmission between cells. Highlighting their regulatory functions within the tumor microenvironment, TNTs are crucial for modulating cell survival, proliferation, drug resistance, and immune evasion. The review critically evaluates the therapeutic potential of TNTs, focusing on their applications in targeted drug delivery and gene therapy. It also proposes future research directions to thoroughly understand TNTs biogenesis, identify cell-specific molecular targets, and develop advanced technologies for the real-time monitoring of TNTs. By integrating insights from molecular biology, nanotechnology, and immunology, this review highlights the transformative potential of TNTs in advancing cancer treatment strategies.

Establishment of a semi-continuous nano-production line using the Microfluidizer® technology for the fabrication of lipid-based nanoparticles part 1: Screening of critical parameters and design of experiment optimization studies

A variety of strategies for producing high-quality nanoparticles have been reported in recent years. Batch-based bottom-up and top-down technologies are generally the most efficient methods, but present a number of challenges, particularly in terms of variability, safety, sustainability and large-scale production. In this study, a scalable, semi-continuous production line was built by connecting individual processing units, including a high shear mixing device, the Microfluidizer® technology and a cooling system. Each unit was equipped with an adequate temperature control to allow solvent-free production of solid lipid nanoparticles (consisting of Precirol® ATO 5 or Gelucire® 43/01) and nanostructured lipid carriers (additionally comprising Labrafac™ lipophile WL 1349). Subsequently, critical formulation parameters and critical process parameters (CPPs) of the individual processing units and their effects on particle size (i.e., critical quality attribute (CQA)) were investigated to identify appropriate input parameters for the subsequent Design of Experiment (DoE) studies conducted after linking the process units to a semi-continuous production line. For particle size monitoring, spatially resolved dynamic light scattering (SR-DLS) measurements were conducted and compared to standard DLS measurements to evaluate the applicability of SR-DLS as an inline monitoring tool. It was found that matrix composition, emulsifier concentration, pressure and number of cycles when processing through Microfluidizer® processor were the most influencing parameters. By optimizing these parameters, five-times higher throughputs could be achieved by the semi-continuous manufacturing line. In addition, the particle size measurements with SR-DLS confirmed the feasibility of implementing this technology for real-time particle size monitoring as an important safety factor in quality control.

Advances in Detection and Monitoring of Coal Spontaneous Combustion: Techniques, Challenges, and Future Directions

Coal spontaneous combustion (CSC) is a multifaceted research domain that has been widely explored in the literature, ranging from analytical and numerical modeling to the development of fire suppression materials and methods. A comprehensive review of the literature has revealed several distinct research trajectories, or “roadmaps”, identified through criteria such as the volume of studies addressing each theme, the presence of review papers dedicated to a specific roadmap, and the explicit mention of coal spontaneous combustion in the title or keywords. This classification framework has outlined six primary roadmaps: (1) spread, quantification, and impact; (2) mechanisms, models, factors, and parameters; (3) experimental studies and models; (4) detection, monitoring, and prediction; (5) prevention and control; and (6) applications. While interconnections exist between these roadmaps, and all ultimately converge towards roadmap 5 (prevention and control), each roadmap constitutes a distinct research cluster. The focus of this review is on roadmap 4, specifically addressing the methods and technologies for detection, monitoring, and prediction of CSC events. This review encompasses studies published from 2010 to the present, providing a thorough examination of the various detection techniques employed, with particular emphasis on their limitations and the strategies proposed to overcome these challenges. A critical analysis highlights the key advantages and disadvantages of each category of techniques, offering insights into their practical applications and the potential for future advancements in this field. The present review aims to contribute to the refinement of detection and monitoring methods for CSC, with the goal of enhancing early detection capabilities and improving fire management strategies.