Real-time Continuous Monitoring Research Articles

High-performance specific detection of NO2 based on PCN-222-Mn doped with Bi electron mediator

Continuous real-time monitoring of hazardous gases is essential for safeguarding public health and environmental integrity, necessitating the utilization of highly selective and sensitive gas sensors. Herein, the chemiresistive sensor for NO2 was fabricated based on Bi nanoparticles (NPs) doped Mn-porphyrin MOF PCN-222-Mn (Bi@PCN-222-Mn) through a straightforward in situ reduction process. Bi NPs serve as electron donors, facilitating the migration of electrons towards the PCN-222-Mn through the formation of a Schottky barrier caused by electrostatic interaction. Besides the Mn-porphyrin as detecting site in PCN-222-Mn, the Bi NPs in Bi@PCN-222-Mn act as supportive sites, enabling NO2 adsorption and electron transfer to NO2 simultaneously, thus enhancing the response signal amplification. Consequently, Bi@PCN-222-Mn exhibited the specific sensing for NO2 with 5 times higher sensitivity (238.6 % ppm−1) in the range of 0.05–2 ppm and a lower limit of detection (LOD) (13 ppb) compared to PCN-222-Mn. Moreover, the Bi@PCN-222-Mn-based chemiresistive sensor also exhibited outstanding long-term stability and moisture resistance, applicable for the practical detection of NO2. These results prove the feasibility of improvement of gas detection performance by introducing metal NPs as electron mediator and an idea for the application of MOFs in gas sensing.

Development and integration of a hierarchical Pd/WO3 acetone-sensing device for real-time exhaled breath monitoring with disposable face mask

This study introduces an inventive acetone-sensing device seamlessly integrated into a disposable face mask, enabling real-time continuous breath monitoring. The sensor demonstrates exceptional sensitivity, registering a response of 8.22 at 1 ppm and an impressive sensor response of 57.33 at 100 ppm of acetone concentration. Particularly noteworthy is the remarkable lower limit of detection (LOD) of 0.076 ppm within the concentration range of 0.1–0.8 ppm, underscored by a robust R2 value of 0.994. To validate practicality, the Pd/WO3 sensor was fabricated onto cellulose paper and utilized for real-time breath analysis, yielding a substantial sensor response of 1.70 at 8 vol% (equivalent to a single exhale breath volume). The unique design incorporates a built-in disposable face mask, facilitating dependable and convenient real-time breath analysis. Additionally, this research explores the profound impact of introducing acetone and Pd atoms on the energy levels and dipole moments. The species elucidated through density functional theory (DFT) investigations encompassing WO3, WO3-acetone, Pd-WO3, and Pd-WO3-acetone species. This work presents an innovative and cost-effective approach for developing a portable, non-invasive, and highly sensitive acetone-sensing device, effectively integrated into a disposable face mask for real-time breath analysis. This pioneering technology holds immense potential for various applications in healthcare and beyond.

Deep learning and IoT enabled digital twin framework for monitoring open-pit coal mines

Early detection of cracks enables timely mitigation and maintenance actions, ensuring the safety of personnel and equipment within the open-pit coal mine. Monitoring open-pit coal mines and cracks is essential for the safety of workers and for saving national assets. Digital twins (DTs) can be crucial in open-pit coal mine crack detection. DTs enable continuous real-time monitoring of the open-pit mine, including its structures and surrounding environment. Various sensors and internet-of-things devices can be deployed to collect data on factors such as ground movement and strain. Integrating this data into the DT makes it possible to identify and analyze anomalous behavior or changes that may indicate crack formation or propagation. Deep learning-based networks are a crucial factor in detecting open-pit coal mine cracks. In this work, we propose a deep learning-based densely connected lightweight network incorporated into the DT-based framework for detecting cracks and taking predictive maintenance-based decisions by combining historical data, real-time sensor data, and predictive models. The proposed DT-based framework provides insights into the potential crack formation, allowing for proactive maintenance and mitigation measures. We compare the performance of our proposed network on different evaluation measures such as precision, recall, overall accuracy, mean average precision, F1-score, and kappa coefficient, where our proposed lightweight multiscale feature fusion-based network outperformed all other state-of-the-art deep neural networks. We also achieved the best performance on mean average precision by surpassing all other models. Additionally, we also compared the performance of our proposed network with U-Net and recurrent neural network on model training and prediction time benchmarks by outperforming those cutting-edge models.

Highly Breathable, Stretchable, and Tailorable TPU Foam for Flexible Gas Sensors.

Continuous real-time monitoring of air quality is of great significance in the realms of environmental monitoring, personal safety, and healthcare. Recently, flexible gas sensors have gained great popularity for their potential to be integrated into various smart wearable electronics and display devices. However, the development of gas sensors with superior sensitivity, breathability, and stretchability remains a challenge. Here, a new high porosity thermoplastic polyurethane (HP-TPU) foam was reported for gas sensors, which exhibited large three-dimensional network structures and excellent mechanical properties. The HP-TPU foam was achieved by using a simple steam-induced method, which was suitable for mass production. The unique structure endowed this foam with 77.5% porosity, 260% strain ability, and 0.45 MPa Young's modulus, which improved 35, 31, and 80%, respectively, compared to previously reported traditional TPU foam (T-TPU) prepared by the drying method. In addition, the foam presented high gas permeability (312 g/m-2, 24 h) and excellent stability, and it remained undamaged even after 2000 cycles at 70% strain. The sensing material was coated on a PET flexible interdigital electrode and sandwiched between two HP-TPU foam layers for a gas sensitivity test. Due to the easy diffusion of gas between the pores and contact with the sensing materials, the HP-TPU foam exhibited a significant reduction of 85% in average response time and 46% in average recovery time, compared to the T-TPU foam. A wearable sensing device, comprising sensing, data processing, and wireless transmission modules, was successfully developed to enable outdoor testing and achieved a detection range at the ppb level. Finally, the cytotoxicity test results confirmed that this flexible gas sensor did not harm human health. These results proved that this HP-TPU foam was an ideal matrix for the flexible gas sensor, exhibiting great application potential in the fields of seamless human-machine integration.

Detection and mapping of water and chlorophyll-a spread using Sentinel-2 satellite imagery for water quality assessment of inland water bodies.

Water quality monitoring of reservoirs is currently a significant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal efforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modified Normalized Difference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifically at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the findings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby offering a robust scientific basis for effective reservoir management.

Real-Time Monitoring of Antibiotics in the Critically Ill Using Biosensors.

By ensuring optimal dosing, therapeutic drug monitoring (TDM) improves outcomes in critically ill patients by maximizing effectiveness while minimizing toxicity. Current methods for measuring plasma drug concentrations, however, can be challenging, time-consuming, and slow to return an answer, limiting the extent to which TDM is used to optimize drug exposure. A potentially promising solution to this dilemma is provided by biosensors, molecular sensing devices that employ biorecognition elements to recognize and quantify their target molecules rapidly and in a single step. This paper reviews the current state of the art for biosensors regarding their application to TDM of antibiotics in the critically ill, both as ex vivo point-of-care devices supporting single timepoint measurements and in vivo devices supporting continuous real-time monitoring in situ in the body. This paper also discusses the clinical development of biosensors for TDM, including regulatory challenges and the need for standardized performance evaluation. We conclude by arguing that, through precise and real-time monitoring of antibiotics, the application of biosensors in TDM holds great promise for enhancing the optimization of drug exposure in critically ill patients, offering the potential for improved outcomes.

Ti3C2Tx-rGO-chitosan-based microcatheter sensor for real-time continuous monitoring of propofol: toward improved anesthetic management.

We are aiming to develop an electrochemical microcatheter sensor for the detection and real-time continuous monitoring of propofol (PPF), which is an anesthetic drug majorly used during medical treatment. This proposed microcatheter-based sensing strategy meets the challenge of real-time periodic and continuous monitoring of propofol by using d-Ti3C2Tx-rGO-chi-modified carbon paste microcatheter sensor transducer. The sensing methodology relies on voltammetry and chronoamperometry transduction methods. The reusable microcatheter sensor was fabricated by embedding the three electrodes into a few millimeters-wide Teflon tube. The nanocomposite was characterized using advanced analytical instruments such as XRD, FE-SEM, EDX, Raman spectroscopy, and XPS. Further, electrode interfacial properties were characterized with voltammetry and electrochemical impedance spectroscopy. The electroanalytical performance of the modified microcatheter sensor was tested for the detection of PPF in phosphate buffer by using chronoamperometry with a wide linear range of 5 to 110µM (at an applied potential of 0.3V vs. Ag/AgCl). The sensor's practical potency was confirmed in human serum with a dynamic linear range of 10 to 130µM. The sensor exhibited a good limit of detection values in phosphate buffer (2µM) and natural human plasma (4µM). The new sensor displays different dimensions of information while displaying high sensitivity, selectivity, and long-term stability. The outstandinganalytical performance of the developed sensor holds considerable promise for the continuous monitoring of propofol, its effective management, and optimization of the doses in the patient's body.

Optical Nanosensors for the Detection of Quaternary Ammonium Compounds

Quaternary ammonium compounds (QACs) are common active ingredients in chemical disinfectants, household cleaners, cosmetics, pesticides, etc. Recently QACs have been detected in human blood and breast milk during the COVID-19 pandemic. Humans can be exposed to QACs through dermal adsorption during application, hand-to-mouth ingestion of disinfectant residues, and inhalation of indoor air upon treatment with disinfectant spray. Exposure to these chemicals can cause respiratory illness such as asthma and chronic obstructive pulmonary disease, increase inflammatory cytokines, decrease mitochondrial function, and disrupt cholesterol biosynthesis. However, the complete biological fate of QACs is currently unknown. Therefore, a real-time continuous monitoring system in complex biological environments is crucial to learn the long-term effects of QACs on human health.Photoluminescent single-walled carbon nanotubes (SWCNTs) are great candidates for developing optical probes to monitor biological systems due to their outstanding photophysical properties in the tissue-transparent near-infrared spectral region, high sensitivity, and high stability. Furthermore, nanotube-based sensors exhibit a great potential for an implantable, real-time, wireless optical detection technology. Herein, we developed a carbon nanotube-based optical sensor for the detection of QACs in complex solution. Our findings from this research could provide a basis for engineering an optical detection technology to gain insight on the long-term effects of QACs to human health.

Smart cushion-based non-invasive mental fatigue assessment of construction equipment operators: A feasible study

Mental fatigue has a considerable influence on the completion of work as well as the safety and health of construction equipment operators who have been exposed to human–machine interaction for an extended period of time. Existing techniques of monitoring and measuring mental fatigue among construction equipment operators are either invasive or rely on subjective judgements, limiting their applicability on construction sites. Therefore, this paper developed a non-invasive smart cushion to evaluate the mental fatigue of construction equipment operators through real-time continuous monitoring of the heartbeat and respiration signals. A laboratory experiment was designed, and twelve participants were recruited to conduct the simulated excavator operation for data acquisition. First, wearable sensors were used as a reference to verify the monitoring accuracy of heartbeat and respiration signals. Excellent correlations were realized between the results of the two monitoring methods. Second, the relevant time domain and frequency domain features were extracted to construct a random forest classification model to discover the relationship between vital signs and subjectively reported mental fatigue status. The results demonstrated that the random forest model had a classification accuracy of 92%, better than other commonly used machine learning algorithms. A combination of heartbeat and respiration features is more effective than a single feature in identifying and classifying mental fatigue among construction equipment operators. The study's findings indicate that it is feasible to apply the smart cushion to identify and classify the mental fatigue of construction equipment operators, therefore contributing to the improvement of their health and safety.

IMPLEMENTASI PEMANTAUAN SUHU, KELEMBABAN DAN KADAR GAS AMONIA BERBASIS ANDROID PADA PETERNAKAN KAMBING SAPERA MITRA AGRO ABADI FARM

The partner of this program is a dairy goat breeder Mitra Agro Abadi Farm which is located in Barukan Village, Semarang Regency. The fundamental problem of dairy goat farmers in Barukan Village is related to continuous real time monitoring of temperature, humidity and ammonia gas levels in goat cages. Breeders still use the manual method by estimating changes in temperature and humidity and changes in ammonia gas making the goat cage susceptible to disease in goats so that it affects the production of goat milk produced. Therefore, the PKM team offers a solution to create a system that can monitor temperature, humidity and ammonia gas levels in an Android-based goat pen that can be monitored via the farmer's cellphone, then the monitoring data is sent via the internet and then provides notifications to farmers remotely in real time. via mobile phones so that farmers can provide immediate handling or action if the cage conditions are not in accordance with ideal conditions. The goal to be achieved from this activity is that the Barukan Village goat breeders are IT literate and it is easier to control the condition of the goat cages in a measurable manner. Thus, the quality of goat's milk production also increases

Development of Soil Microbial Fuel Cell for Continuous Real-time Monitoring of Soil Data

The planet is getting hotter due to human activities that release greenhouse gases. Promoting renewable energy, which doesn’t emit carbon, can help mitigate global warming. Farmland, which covers a large portion of the Earth’s land area, can also be used as an energy source through the help of indigenous microorganisms in the soil that transform chemical energy into electrical energy with electrolysis. Real-time monitoring of soil nutrients is necessary to obtain an accurate yield from crops. This is achieved with the use of sensors that are powered by the energy generated from the soil. Zinc and copper electrodes are used to generate power, and a combination of 15x6 cells is used to maximize voltage and current generation. The output powers soil sensors like FC-28 and NPK sensors, and the data is sent to the AWS cloud for real-time monitoring of soil parameters. The experimental results show that a single SMFC cell generates 0.6V, while a series-parallel combination generates approximately 9V. By using a current booster circuit, the efficiency of the setup can be further improved. This technology is not yet widely used in the agricultural industry, but it has the potential to help farmers conveniently monitor soil conditions by utilizing the power generated from the soil itself.

Time Series Prediction Methodology and Ensemble Model Using Real-World Data

Time series data analysis and forecasting have recently received considerable attention, supporting new technology development trends for predicting load fluctuations or uncertainty conditions in many domains. In particular, when the load is small, such as a building, the effect of load fluctuation on the total load is relatively large compared to the power system, except for specific factors, and the amount is very difficult to quantify. Recently, accurate power consumption prediction has become an important issue in the Internet of Things (IoT) environment. In this paper, a traditional time series prediction method was applied and a new model and scientific approach were used for power prediction in IoT and big data environments. To this end, to obtain data used in real life, the power consumption of commercial refrigerators was continuously collected at 15 min intervals, and prediction results were obtained by applying time series prediction methods (e.g., RNN, LSTM, and GRU). At this time, the seasonality and periodicity of electricity use were also analyzed. In this paper, we propose a method to improve the performance of the model by classifying power consumption into three classes: weekday, Saturday, and Sunday. Finally, we propose a method for predicting power consumption using a new type of ensemble model combined with three time series methods. Experimental results confirmed the accuracy of RNN (i.e., 96.1%), LSTM (i.e., 96.9%), and GRU (i.e., 96.4%). In addition, it was confirmed that the ensemble model combining the three time series models showed 98.43% accuracy in predicting power consumption. Through these experiments and approaches, scientific achievements for time series data analysis through real data were accomplished, which provided an opportunity to once again identify the need for continuous real-time power consumption monitoring.

589-P: Impact of Sedentary Behavior on Glycemic Profiles by Real-Time Continuous Glycemic Monitoring in People with Type 1 Diabetes

The purpose of this study was to examine the association between sedentary behavior (SB) and glycemic profiles by real-time continuous glycemic monitoring (rt-CGM) in people with type 1 diabetes (T1D). Fifty-eight Japanese adults with T1D were assessed and analyzed in this study (8 men and 50 women, age: 43.5 [32.0-54.0], HbA1c: 7.1 [6.7-7.8], median [25%-75% quartiles]). The type of physical activity (PA) measured using a triaxial accelerometer was defined according to intensity: SB (≤1.5 metabolic equivalents [METs], light-intensity PA (1.6-2.9 METs), and moderate-to-vigorous-intensity PA (≥3.0 METs). Total SB per day was 506.4 (442.2-575.9) min. A longer prolonged (≥30 min) SB and fewer steps per day were associated with higher mean 24-h sensor glucose values, higher time above range, and lower time in range (prolonged SB: ρ = 0.662 [p < 0.01], 0.627 [p < 0.01], and −0.591 [p < 0.01]; steps: ρ = −0.405 [p < 0.01], −0.401 [p < 0.01], and 0.327 [p < 0.05], respectively) (Figure). Conversely, total SB and other PA had no association with glycemic profiles. These results implied that more time spent on prolonged SB and fewer steps might contribute to the deterioration of glycemic profiles by rt-CGM in people with T1D. It is important to consider these characteristics when managing T1D. Disclosure H.Honda: None. N.Hashimoto: None. M.Zenibayashi: None. T.Takeuchi: None. A.Yamamoto: None. Y.Hirota: Other Relationship; Lilly, Sanofi K.K., Terumo Corporation, Sumitomo Dainippon Pharma Co., Ltd., Research Support; Sumitomo Dainippon Pharma Co., Ltd.

Coastal acoustic tomography system for monitoring transect suspended sediment discharge of Yangtze river

Suspended sediment discharge (SSD) in the Yangtze River (YR) plays a crucial role in transporting nutrients and sediment to the YR estuaries and the East China Sea. In order to gather information on suspended sediment concentration (SSC) and SSD across an entire river cross-section, we investigated a novel method supported by Coastal Acoustic Tomography (CAT). Consequently, two CAT systems were employed to estimate the cross-section averaged SSC and SSD at the Datong (DT) station of the YR over approximately 2.5 years. Low (0.02 kg/m3), high (0.44 kg/m3), and mean (0.10 kg/m3) SSC values were successfully estimated. The SSD was primarily driven by water discharge, exhibiting low values (204 kg/s) during the dry season and a maximum of 36,299 kg/s during flood events, with the mean value of 3,903 kg/s throughout the observation period. The water sample and CAT results demonstrated high consistency, with correlation coefficients (R2) of 0.92 for SSC between the water sample and CAT results, and 0.96 for SSD between the DT result and CAT results. This study established the ability to monitor transect SSC and SSD in the YR using the CAT sound attenuation approach, offering the advantage of continuous real-time monitoring of transect river flow, SSC, and SSD, even during extreme flood events. For the success of this method, factors such as particle size, sound frequency selection, and the measurable distance between instruments must be meticulously considered. This work highlights the potential of CAT for monitoring riverine transect SSC and SSD.

Automatic Detection of Transformer Health Based on Bayesian Network Model

Abstract In order to effectively reduce the increasing operation and maintenance costs of aging power systems and infrastructure, the authors propose a real-time monitoring method of transformer operation state based on dynamic Bayesian network modeling and prediction uncertainty. The transformer fault mode, fault mechanism, different standards and codes, as well as the current transformer operation status are converted into component status, and then these statuses are transmitted to the real-time monitoring system of transformer operation status, the overall risk probability of the transformer or the subsystem risk probability of focus can be calculated according to the Bayesian network, and the elements in the transformer that may cause system failure or have operational risk can be supplemented through appropriate data processing and interpretation. In addition, on the basis of Bayesian network framework, continuous time steps can be added for continuous real-time monitoring of operation status, and a real-time monitoring system of transformer operation status based on dynamic Bayesian network can be built.

Heart rate variability according to electrolyte changes during hemodialysis in patients with chronic kidney disease

Abstract Funding Acknowledgements Type of funding sources: None. Background Autonomic neuropathy commonly arises as a long-term complication in chronic kidney disease (CKD) and can be diagnosed from heart rate variability (HRV), calculated from electrocardiogram (ECG) recordings. Purpose There is limited data about HRV using real-time ECG according to electrolyte levels during hemodialysis (HD) in patients with CKD. Methods Total of 50 patients (62.1±10.7 years) with CKD underwent continuous real-time ECG monitor (237.4±15.3 mins) during HD for HRV using remote monitoring system and checked electrolyte levels before/after HD. And we compared HRV according to electrolyte levels. Results During the monitor, we checked total 2374 times of ECG and electrolyte level simultaneously of all patients. Both time and frequency domain HRV were higher when the patients had lower K+ level change before/after HD (<0.5mEq/L) as compared with those with higher K+ level change (>0.5mEq/L) in Table. In addition, there were higher incidence of arrhythmic events including atrial/ventricular premature complexes, even though no difference of mean heart rate in patients with lower K+ level change group (P<0.001). Conclusions Poor controlled K+ level during hemodialysis was independently associated with higher HRV in patients with CKD. This is further substantiated by independent continuous associations between real-time measures of K+ level and higher HRV. These data strongly suggest that cardiac autonomic dysfunction can be caused by lower change before/after hemodialysis alone.

Real-Time Continuous Monitoring of Oral Soft Tissue Pressure with a Wireless Mouthguard Device for Assessing Tongue Thrusting Habits.

In orthodontics, understanding the pressure of oral soft tissues on teeth is important to elucidate the cause and establish treatment methods. We developed a small wireless mouthguard (MG)-type device that continuously and unrestrainedly measures pressure, which had previously been unachieved, and evaluated its feasibility in human subjects. First, the optimal device components were considered. Next, the devices were compared with wired-type systems. Subsequently, the devices were fabricated for human testing to measure tongue pressure during swallowing. The highest sensitivity (51-510 g/cm2) with minimum error (CV < 5%) was obtained using an MG device with polyethylene terephthalate glycol and ethylene vinyl acetate for the lower and upper layers, respectively, and with a 4 mm PMMA plate. A high correlation coefficient (0.969) was observed between the wired and wireless devices. In the measurements of tongue pressure on teeth during swallowing, 132.14 ± 21.37 g/cm2 for normal and 201.17 ± 38.12 g/cm2 for simulated tongue thrust were found to be significantly different using a t-test (n = 50, p = 6.2 × 10-19), which is consistent with the results of a previous study. This device can contribute to assessing tongue thrusting habits. In the future, this device is expected to measure changes in the pressure exerted on teeth during daily life.

The application of Near-Infrared Spatially Resolved Spectroscopy in scope of achieving continuous real-time quality monitoring and control of tablets with challenging dimensions

In scope of achieving real-time release of tablets, quality attributes need to be monitored and controlled through Process Analytical Technology tools such as near-infrared spectroscopy (NIRS). The authors evaluated the suitability of NIR-Spatially Resolved Spectroscopy (NIR-SRS) for continuous real-time monitoring and control of content uniformity, hardness and homogeneity of tablets with challenging dimensions. A novel user-friendly research and development inspection unit was used as standalone equipment for the analysis of small oblong tablets with deep-cut break lines. A total of 66 tablets varying in hardness and Active Pharmaceutical Ingredient (API) content were inspected, with each tablet being analysed five times and measurements repeated on three different days. Partial Least Squares (PLS) models were developed to assess content uniformity and hardness, of which the former showed higher accuracy. The authors attempted to visualize tablet homogeneity through NIR-SRS spectra by regressing all spectra obtained during a single measurement using a content uniformity PLS model. The NIR-SRS probe demonstrated its potential towards real-time release testing through its ability to quickly monitor content uniformity, hardness and visualize homogeneity, even for tablets with challenging dimensions.

A Cost-Effective GNSS Solution for Continuous Monitoring of Landslides

The development of low-cost dual-frequency global navigation satellite system (GNSS) receivers in recent years has enabled the use of these devices in numerous applications. In the monitoring of natural hazards, such as landslides, these devices can be considered suitable sensors. In this work, dual-frequency GNSS receivers and antennas were used for setting up near-real-time continuous low-cost GNSS monitoring systems (LGMSs) under field conditions. The SimpleRTK2B board, which integrates the u-blox ZED-F9P dual-frequency GNSS chip and the survey-calibrated GNSS antenna are the main components of the GNSS system. The LGMS was installed and tested for six months in the Laze landslide located in the northwestern part of Slovenia. A total of four GNSS systems were deployed, three of which were located in pillars in the landslide itself and one in a stable area. Open-source software was used to postprocess the acquired data, providing daily coordinates in static relative and precise point positioning (PPP) positioning modes. The results of six months of near-real-time monitoring showed that the Laze landslide was stable during this period, with only minor changes in the vertical component. The trend of decreasing ellipsoid height was evident at all stations, although it was in the range of a few millimeters. To validate the results in static relative positioning mode, the coordinate differences between low-cost and high-end geodetic GNSS instruments were estimated and found to be in the range of 5 mm or less, while the difference between horizontal and spatial positions was less than 7 mm for all stations. The same data were processed in PPP, vertical displacements were not detected as in the static relative positioning mode due to the lower accuracy of the method itself. Considering the six-month performance of a low-cost GNSS system under field conditions, it can be emphasized that these devices are capable of performing near real-time continuous monitoring of slow movements with high accuracy and decreased costs. In addition, an experimental test was performed to identify the size of detected displacements in real-time kinematic (RTK). Based on the achieved results, it was concluded that 20 mm spatial displacements are detectable with LGMSs in RTK considering only 15 s of observations.

Continuous real-time monitoring of carbon dioxide emitted from human skin by quartz-enhanced photoacoustic spectroscopy

In this study, a skin gas detection system based on quartz enhanced photoacoustic spectroscopy (QEPAS) with a constant temperature collection chamber and an automatic frequency adjustment function was used to collect and monitor carbon dioxide (CO2) emissions from human skin. The detection element of the system is an on-beam structure assembled by a 30.72 kHz quartz tuning fork (QTF). A laser with a wavelength of 4991.26 cm−1 is emitted (with a wavelength adjustment range of 10 cm−1) to excite the QTF. When the integration time is 365 s, the system can achieve a minimum detection limit (MDL) of 2.6 ppmv. The sensitivity of the system is 636.9 ppmv/V. The gas detection system is used to monitor the concentration of CO2 emissions from different parts of the skin and the same part covered by different cosmetics. The CO2 emission rate is defined as the ratio of the skin gas monitoring time of 25 min to the CO2 concentration variable in the gas chamber (volume of 8 mL). The results were collected from three healthy volunteers. Among the six different parts, the cheeks emitted the fastest rate (the average rate was 365.5 ppmv/min) of CO2, and the thighs emitted the slowest rate (the average rate was 56.4 ppmv/min) of CO2. Comparing the experimental results of the six sites at different times, the order of the CO2 emission rate is identical for all six sites. In the experiments with the three cosmetic products (experimental site: forearm), comparing the CO2 emission rate from clean skin with the CO2 emission rate from cosmetic-covered skin shows that sunscreen is the most breathable, followed by barrier cream, and foundation is the least breathable.