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Background: Acute deteriorations of health status are common in hospitalized patients and are often preceded by changes in their vital signs. Events such as heart attacks, death or admission to the intensive care unit can be averted by early detection, therefore so-called Early Warning Scores (EWS) such as the National Early Warning Score 2 (NEWS2), including basic vital parameters such as heart rate, blood pressure, respiratory rate, temperature and level of consciousness, have been developed for a systematic approach. Although studies have shown that EWS have a positive impact on patient outcomes, they are often limited by issues such as calculation errors, time constraints, and a shortage of human resources. Therefore, development of tools for automatic calculation of EWS could help improve quality of EWS calculation and may improve patient outcomes. The aim of this study is to analyze the feasibility of wearable devices for the automatic calculation of NEWS2 compared to conventional calculation using vital signs measured by health care professionals. Methods: We conducted a prospective trial at a large tertiary hospital in Switzerland. Patients were given a wristband with a photoplethysmogram (PPG) sensor that continuously recorded their heart rate and respiratory rate for 3 consecutive days. Combined with data from the electronic health record (EHR), NEWS2-score was calculated and compared to NEWS2 score calculated from vital parameters in the EHR measured by medical staff. The main objective of our study was to assess the agreement between NEWS2 scores calculated using both methods. This analysis was conducted using Cohen's Kappa and Bland–Altman analysis. Secondary endpoints were compliance concerning the medical device, patient acceptance, data quality analysis and data availability and signal quality for all time stamps needed for accurate calculation. Results: Of 210 patients enrolled in our study, NEWS2 was calculated in 904 cases, with 191 cases being directly compared to conventional measurements. Thirty-three of these measurements resulted in a NEWS2 ≥ 5, 158 in a NEWS2 < 5. Comparing all 191 measurements, accordance was substantial (K = 0.76) between conventional and automated NEWS2. No adverse effects due to the device were recorded. Patient acceptance was high. Conclusions: In conclusion, the study found strong agreement between automated and conventional NEWS2 calculations using wearable devices, with high patient acceptance despite some data quality challenges. To maximize the potential of continuous monitoring, further research into fully automated EWS calculations without relying on spot measurements is suggested, as this could provide a reliable alternative to traditional methods. Trial registration: January 26, 2023, NCT05699967. [ABSTRACT FROM AUTHOR]

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases. [ABSTRACT FROM AUTHOR]

[Display omitted] The Sudokwon landfill (SL) in the Seoul metropolitan area, South Korea, is among the world's largest landfills, striving to curtail landfill gas (LFG) emissions and achieve carbon neutrality by 2050. Since 2005, the SL Management Corporation (SLC) has measured LFG emissions (i.e., methane (CH 4) and carbon dioxide (CO 2)) using a dynamic flux chamber proposed by the US EPA. However, uncertainty prevails in validating the reduction of LFG emissions due to the limited spatiotemporal data coverage. In 2020, an eddy covariance (EC) system was installed to enhance measurements, revealing highly fluctuating LFG emissions driven by waste layer LFG production, LFG collection, and atmospheric pressure changes. During the study period, the annual CH 4 emission increased slightly from 465.0 ± 4.2 to 485.5 ± 6.4 g C m−2, while that of CO 2 decreased by 2/3 (from 408.7 ± 16.5 to 270.6 ± 18.8 g C m−2), primarily due to the doubled CO 2 uptake by the vegetated topsoil. Our first long-term (March 2020 to February 2022) quasi-continuous monitoring using EC (with a gap-filling and partitioning technique based on Random Forest) emphasizes the difficulty of temporal upscaling of discontinuously observed surface emissions to quantify the LFG inventory and the need for continuous observations or suitable proxies (e.g., atmospheric CH 4 concentration). [ABSTRACT FROM AUTHOR]

A LOx-based electrochemical biosensor for high-level lactate determination was developed. For the construction of the biosensor, chitosan and Nafion layers were integrated by using a spin coating procedure, leading to less porous surfaces in comparison with those recorded after a drop casting procedure. The analytical performance of the resulting biosensor for lactate determination was evaluated in batch and flow regime, displaying satisfactory results in both modes ranging from 0.5 to 20 mM concentration range for assessing the lactic acidosis. Finally, the lactate levels in raw serum samples were estimated using the biosensor developed and verified with a blood gas analyzer. Based on these results, the biosensor developed is promising for its use in healthcare environment, after its proper miniaturization. A pH probe based on common polyaniline-based electrochemical sensor was also developed to assist the biosensor for the lactic acidosis monitoring, leading to excellent results in stock solutions ranging from 6.0 to 8.0 mM and raw plasma samples. The results were confirmed by using two different approaches, blood gas analyzer and pH-meter. Consequently, the lactic acidosis monitoring could be achieved in continuous flow regime using both (bio)sensors. [ABSTRACT FROM AUTHOR]

Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient η using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast η values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter η , achieving an R 2 of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast η with an R 2 of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring. [ABSTRACT FROM AUTHOR]

In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3). [ABSTRACT FROM AUTHOR]

Freshwater pollution is a global challenge that citizens recognise as unacceptable, despite professional efforts to monitor, manage, and regulate it. Water quality is difficult to observe at high spatial and temporal resolutions; it is costly and typically requires trained specialists in the field and in laboratories. However, the rise in citizen science monitoring has generated opportunities to overcome many barriers and fill data gaps. Citizens want access to actionable water quality information that can provide early warnings and drive change. Our bibliographic analysis emphasises that citizen science is rarely paired with the use of continuous sensors, and many monitoring schemes involving the public are unable to offer the detail required. This study has explored the practicalities and competencies associated with community-led (near) continuous water quality monitoring (CWQM), and has generated an extensive checklist containing technical, social, economic, and wider responsibilities that stakeholders should consider. The "UpStream" project has provided a testbed for this exploratory work and the development and deployment of the "WaterBox" CWQM device. Case studies from the UK and Taiwan, where community-led CWQM programmes have been piloted, have provided novel methodological insights. Lessons learnt have enabled researchers to determine whether, and to what extent, communityled CWQM is achievable in practice. Results confirm that 75% of the practicalities observed fall under the "technical" category, despite offering a social and inclusive methodology. Through observational work, it was found that only 34% of the 104 practicalities were led by community groups and community champions. Communities require significant support owing to the sheer number of technical barriers, although some of this could be provided by skilled community champions. These findings have surfaced despite the WaterBox equipment being open, accessible, cost-effective, and adaptable. Unless support is in place and/or technology significantly evolves to reduce the number of challenges, it is unlikely that communities will lead and take full ownership of CWQM programmes. Despite this, the generation of site-specific CWQM data are welcomed by citizens, who can co-design schemes, interpret trends and offer valuable local information that trained scientists cannot achieve alone. These transferable findings are relevant to monitoring initiatives across the environmental sector. [ABSTRACT FROM AUTHOR]

This study introduces a lightweight storage system for wearable devices, aiming to optimize energy efficiency in long-term and continuous monitoring applications. Utilizing Direct Memory Access and the Serial Peripheral Interface protocol, the system ensures efficient data transfer, significantly reduces energy consumption, and enhances the device autonomy. Data organization into Time Block Data (TBD) units, rather than files, significantly diminishes control overhead, facilitating the streamlined management of periodic data recordings in wearable devices. A comparative analysis revealed marked improvements in energy efficiency and write speed over existing file systems, validating the proposed system as an effective solution for boosting wearable device performance in health monitoring and various long-term data acquisition scenarios. [ABSTRACT FROM AUTHOR]

Background: Patients undergoing major oncological abdominal surgery are prone to postoperative complications, making early recognition crucial. Clinical deterioration is often preceded by changes in vital signs, which are typically measured thrice a day by a nurse. However, intermittent measurements may delay recognizing clinical deterioration. Continuous vital parameter monitoring may lead to earlier recognition and management of complications and reduce nursing workload. Objective: To compare vital parameter measurements between ward nurses and a wireless continuous monitoring system (Sensium® wireless patch) and assess whether this patch can detect clinical deterioration earlier in patients with complications in the first postoperative week. Methods: Vital parameters (heart rate, respiratory rate, and temperature) were collected in patients undergoing an oncological resection of the liver, colorectal, or pancreas. Sensium® patch measurements were compared to nurses' measurements to assess the percentages of discordant measurements. In patients with complications in the first postoperative week, time discrepancies between nurses and Sensium® patch measurements were identified in cases of clinical deterioration (respiratory rate ≥15/min, heart rate ≥100/min, and temperature ≥38°C). Results: Among 227 patients, 22% of the patients experienced complications. Nurse and Sensium® measurements were discrepant in 586/2272 measurements (26%). In 506/586 discrepancies (86%), this was due to the respiratory rate (difference ≥4/min). Compared to nurses, the Sensium® patch detected an elevated respiratory rate 14 h earlier and heart rate 2 h earlier within complications in the first postoperative week. For temperature, no difference was observed. Conclusion: Continuous monitoring with the Sensium® wireless patch holds promise for earlier recognition of complications in patients who underwent major oncological abdominal surgery. [ABSTRACT FROM AUTHOR]

Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal. We used data collected during the observational, prospective PRODIGY trial, in which patients receiving parenteral opioids underwent continuous capnography and pulse oximetry monitoring while on the general care floor [1]. Abnormal ventilation segments in the data stream were reviewed by nine experts and inter-rater agreement was assessed. Abnormal segments were defined as the time series 60s before and 30s after an abnormal pattern was detected. Normal segments (90s continuous monitoring) were randomly sampled and filtered to discard sequences with missing values. Five ML models were trained on extracted features and optimized towards an Fβ score with β = 2. The results show a high inter-rater agreement (> 87%), allowing 7,858 sequences (2,944 abnormal) to be used for model development. Data were divided into 80% training and 20% test sequences. The XGBoost model had the highest Fβ score of 0.94 (with β = 2), showcasing an impressive recall of 0.98 against a precision of 0.83. This study presents a promising advancement in respiratory monitoring, focusing on reducing false alarms and enhancing accuracy of alarm systems. Our algorithm reliably distinguishes normal from abnormal waveforms. More research is needed to define patterns to distinguish abnormal ventilation from artifacts. Trial Registration: clinicaltrials.gov: NCT02811302, registered June 23, 2016. [ABSTRACT FROM AUTHOR]

The study presents the development of a structural monitoring system installed in a 45-m-high steel wind tower located in Italy. The installed monitoring system was composed by 16 strain gauges placed in the tower wall, in a pattern of four Wheatstone bridges at 45°, together with thermal couples, at 21 m from the ground (half-height of the tower). Moreover, several accelerometers were placed along the tower height (with one of them located next to the strain gauges). The wind velocity and directions were obtained directly from the turbine own monitoring system. Such a monitoring system was designed because, due to the decrement of the total height from the original design, the tower suffers from resonance problems. In fact, the investigated tower was originally designed with 65 m of height but then, to comply with local regulations, the height was decreased to the actual size. Therefore, to allow safe operation and avoid excessive fatigue due to the increased displacements, the velocity of the rotor has been manually limited causing an important reduction in the energy production. The results of the study show the importance of monitoring the resonance issue. The differences between the damage indexes obtained with two different working conditions are discussed: tower working with limited operational capacity and tower working at its maximum capacity (in resonance). [ABSTRACT FROM AUTHOR]

Abstract Background Acute deteriorations of health status are common in hospitalized patients and are often preceded by changes in their vital signs. Events such as heart attacks, death or admission to the intensive care unit can be averted by early detection, therefore so-called Early Warning Scores (EWS) such as the National Early Warning Score 2 (NEWS2), including basic vital parameters such as heart rate, blood pressure, respiratory rate, temperature and level of consciousness, have been developed for a systematic approach. Although studies have shown that EWS have a positive impact on patient outcomes, they are often limited by issues such as calculation errors, time constraints, and a shortage of human resources. Therefore, development of tools for automatic calculation of EWS could help improve quality of EWS calculation and may improve patient outcomes. The aim of this study is to analyze the feasibility of wearable devices for the automatic calculation of NEWS2 compared to conventional calculation using vital signs measured by health care professionals. Methods We conducted a prospective trial at a large tertiary hospital in Switzerland. Patients were given a wristband with a photoplethysmogram (PPG) sensor that continuously recorded their heart rate and respiratory rate for 3 consecutive days. Combined with data from the electronic health record (EHR), NEWS2-score was calculated and compared to NEWS2 score calculated from vital parameters in the EHR measured by medical staff. The main objective of our study was to assess the agreement between NEWS2 scores calculated using both methods. This analysis was conducted using Cohen's Kappa and Bland–Altman analysis. Secondary endpoints were compliance concerning the medical device, patient acceptance, data quality analysis and data availability and signal quality for all time stamps needed for accurate calculation. Results Of 210 patients enrolled in our study, NEWS2 was calculated in 904 cases, with 191 cases being directly compared to conventional measurements. Thirty-three of these measurements resulted in a NEWS2 ≥ 5, 158 in a NEWS2

To promote the development and application of continuous monitoring technologies for CO2 emissions from thermal power plants in China and support the establishment of a carbon emission statistics and accounting system in the country, this study analyzes the international experience in the continuous monitoring of CO2 emissions from thermal power plants using literature research, expert discussion, and technical interpretation, with a focus on the United States and the European Union. The analysis covers eight aspects: types of units applying the technology, formulation of implementation standards, measures for dealing with failures, selection of monitoring technologies, research on key technologies, support for the low-carbon development of thermal power plants, verification of monitoring reports, and evaluation of measurement accuracy. Drawing on international beneficial experience and considering the current status of continuous monitoring of CO2 emissions from thermal power plants in China, the study proposes the following suggestions: (1) formulating supporting policies and regulations while considering the characteristics of technology application and the actual situation of thermal power plants in China, (2) accelerating the improvement in technical implementation standards and specifications to enhance the guiding and supporting role of standards, (3) strengthening the statistical analysis of the measurements using existing flue gas flow meters to provide references for relevant work, (4) enhancing research on flue gas flow measurement in thermal power plants to improve the technical level of continuous monitoring of CO2 emissions, (5) promoting the application of digital technologies in the verification of monitoring reports to support closed-loop management of continuous monitoring of CO2 emissions, and (6) improving the evaluation standards system for measurement accuracy of continuous emission monitoring systems to enhance the international recognition of China’s carbon emission data.

This article reports the process that allowed to check the condition of a steel railway bridge having a strategic position within the network. The framework representing the actions needed for the service life extension of the bridge is first described, then the conducted inspections are illustrated, together with the developed numerical analyses and the conception of specific tests on site. The conclusion, downstream of all these analyses and with the comparison of the real-life experiments, was to allow a limited extension of the useful life of the bridge, even with some reductions in the safety factor, respecting a reduction in the speed of railway trains and prescribing continuous monitoring of the bridge. In this context, all the test evaluations, combined with the surveillance/monitoring, and joined with an important numerical modelling activity, lead to the concept of "structural augmentation" of the bridge for service life extension purposes. The specific case of the examined bridge is supposed to be an example of good practice useful for infrastructure authorities. [ABSTRACT FROM AUTHOR]

[Display omitted] As lyophilization continues to be a critical step in the manufacturing of sensitive biopharmaceuticals, challenges often arise during the scale up to commercial scale or the transfer from one manufacturing site to another. While data from the small-scale development of the lyophilization cycle is abundant it is typically much more difficult to extract important information from commercial scale cycles, due to the lack of process analytical technologies available on the commercial line. There is often a reluctance to include wireless temperature or pressure probes during GMP operations due to the additional contamination risk, and retrofitting equipment such as the TDLAS can be prohibitively expensive. Further, as products become more advanced, the cost of consuming the product or even the availability of material may limit the opportunities to run commercial scale trials. This paper presents two novel methods to garner critical cycle information to allow for the evaluation of cycle performance without the need for expensive analytical equipment, costly revalidation and line downtime. Critically, this can be achieved using commonly available temperature and capacitance probes on existing commercial scale equipment. The first method is a calorimetric method, based on quantifying the differences in heat transfer liquid temperature between the shelf inlet and shelf outlet. This change in temperature results from the on-going sublimation, an endo-thermic reaction occurring during lyophilization. The second method uses the differential pressure between the chamber and condenser resulting from the vapor flow from vial to condenser during primary drying. As stated by the authors both methods align well and provide valuable cycle characterization data. [ABSTRACT FROM AUTHOR]

Microneedles, the miniaturized needles, which can pierce the skin with minimal invasiveness open up new possibilities for constructing personalized Point-of-Care (POC) diagnostic platforms. Recent advances in microneedle-based POC diagnostic systems, especially their successful implementation with wearable technologies, enable biochemical detection and physiological recordings in a user-friendly manner. This review presents an overview of the current advances in microneedle-based sensor devices, with emphasis on the biological basis of transdermal sensing, fabrication, and application of different types of microneedles, and a summary of microneedle devices based on various sensing strategies. It concludes with the challenges and future prospects of this swiftly growing field. The aim is to present a critical and thorough analysis of the state-of-the-art development of transdermal diagnostics and sensing devices based on microneedles, and to bridge the gap between microneedle technology and pragmatic applications. [ABSTRACT FROM AUTHOR]

Noninvasive continuous blood pressure monitoring has the potential to improve patient treatment in the hospital setting. Such noninvasive devices can be applied earlier in the treatment process to empower nurses and clinicians to react more quickly to patient deterioration with the added benefit of eliminating the risks associated with invasive monitoring. However, emerging technologies must be capable of reproducing current clinical measures for medical decision making. This study aimed to determine the usability and willingness of nurses to implement a noninvasive continuous blood pressure monitoring device. The secondary aim directly compared the systolic blood pressure, diastolic blood pressure, and mean arterial pressure values recorded by the device (VitalStream; CareTaker Medical LLC, Charlottesville, VA) with the "gold standard" brachial cuff and arterial line measures recorded in the emergency department and intensive care unit settings. VitalStream was similarly received by nurses in the emergency department and intensive care setting, but ultimately had greater promotion from emergency nurses. Despite some statistical similarity between measurement methodologies, all direct comparisons were found to not meet the Association for the Advancement of Medical Instrumentation 2008 and Association for the Advancement of Medical Instrumentation / European Society of Hypertension / International Organization for Standardization 2019 consensus statement criteria for acceptable blood pressure measure differences between the VitalStream and "gold standard" clinical measures. In all instances, the standard deviation of the Bland-Altman bias exceeded 8 mm Hg with less than 85% of paired differences falling within 10 mm Hg of the "gold standard." Taken together, the tested device requires additional postprocessing for medical decision making in trauma or emergent care. [ABSTRACT FROM AUTHOR]

[Display omitted] • The deviation of N 2 O emission factor (EF) from short-term monitoring can reach 20%. • The N 2 O EF for sludge incineration in China may be smaller than the default value. • The N 2 O EF decreases as the temperature in the furnace increases. • The N 2 O EF of the sludge incinerators with smaller heat loads fluctuates more. • The fluctuation range of the N 2 O EF narrows with the increase of temperature. The N 2 O emissions resulting from sludge incineration are estimated using the default values published by the Intergovernmental Panel on Climate Change (IPCC), which may differ significantly from the actual emissions. In this investigation, N 2 O emissions from four sludge incineration lines in two plants were monitored for varying durations. The variation in N 2 O emission factors (EFs) between incineration lines of the same plant was much smaller than the difference between different plants. Data on N 2 O EFs obtained from brief monitoring may contain variabilities of up to 30%. N 2 O EFs were more sensitive to temperature changes at low temperatures, necessitating extended monitoring periods to improve the reliability of N 2 O monitoring outcomes in cases of low furnace temperatures. Excessive use of the SNCR system to reduce NOx emissions resulted in concentrations of N 2 O and NH 3 in the exhaust gases exceeding NOx levels. In the case of furnace temperature control and advanced reburning technology, it is advisable to utilize actual monitoring data or the smaller default values provided by the IPCC in China. Otherwise, the estimated N 2 O emissions may exceed the actual emissions. [ABSTRACT FROM AUTHOR]

Waste treatment plants (WTPs) often generate odours that may cause nuisance to citizens living nearby. In general, people are becoming more sensitive to environmental issues, and particularly to odour pollution. Instrumental Odour Monitoring Systems (IOMSs) represent an emerging tool for continuous odour measurement and real-time identification of odour peaks, which can provide useful information about the process operation and indicate the occurrence of anomalous conditions likely to cause odour events in the surrounding territories. This paper describes the implementation of two IOMSs at the fenceline of a WTP, focusing on the definition of a specific experimental protocol and data processing procedure for dealing with the interferences of humidity and temperature affecting sensors' responses. Different approaches for data processing were compared and the optimal one was selected based on field performance testing. The humidity compensation model developed proved to be effective, bringing the IOMS classification accuracy above 95%. Also, the adoption of a class-specific regression model compared to a global regression model resulted in an odour quantification capability comparable with those of the reference method (i.e., dynamic olfactometry). Lastly, the validated models were used to process the monitoring data over a period of about one year. [ABSTRACT FROM AUTHOR]

Among the various types of enzyme-based biosensors, sensors utilizing enzymes capable of direct electron transfer (DET) are recognized as the most ideal. However, only a limited number of redox enzymes are capable of DET with electrodes, that is, dehydrogenases harboring a subunit or domain that functions specifically to accept electrons from the redox cofactor of the catalytic site and transfer the electrons to the external electron acceptor. Such subunits or domains act as built-in mediators for electron transfer between enzymes and electrodes; consequently, such enzymes enable direct electron transfer to electrodes and are designated as DET-type enzymes. DET-type enzymes fall into several categories, including redox cofactors of catalytic reactions, built-in mediators for DET with electrodes and by their protein hierarchic structures, DET-type oxidoreductases with oligomeric structures harboring electron transfer subunits, and monomeric DET-type oxidoreductases harboring electron transfer domains. In this review, we cover the science of DET-type oxidoreductases and their biomedical applications. First, we introduce the structural biology and current understanding of DET-type enzyme reactions. Next, we describe recent technological developments based on DET-type enzymes for biomedical applications, such as biosensors and biochemical energy harvesting for self-powered medical devices. Finally, after discussing how to further engineer and create DET-type enzymes, we address the future prospects for DET-type enzymes in biomedical engineering. [ABSTRACT FROM AUTHOR]

Historically, orthopaedic implants required little clinical evidence before market introduction, either in the USA or the European Union (EU). There have been major incidents, with faulty designs or manufacturing techniques resulting in patient harm. The new EU Medical Device Regulations (MDR) have significantly changed the requirements around the introduction to the market of new medical devices in the EU. Initiatives like CORE-MD are founded to improve this process. EUDAMED will provide a mandatory European implant safety register. Real-world data, such as implant registries, have a major role to play in the continuous monitoring of implants, but this depends on them having high completeness (>95%) as well as coverage. Registries can also be used for post-market surveillance. Although implant registries use endpoints such as revision, as well as patient-reported outcomes, long-term follow-up is still needed to detect mediocre implants. For new designs, this often takes too long and potentially exposes patients to mediocre implants. This can be prevented by using more accurate, highly predictive methods at early follow-up (at 1 year), such as implant migration studies. Furthermore, registry-based or nested randomized controlled trials can be used to evaluate new implant designs and surgical techniques. Monitoring implants through registries remains vital in order to detect early or late unexpected failures related to the implant, surgical technique or indication, enhancing both implant development as well as patient safety. [ABSTRACT FROM AUTHOR]

This article presents a wireless in situ sensor designed to continuously monitor profiles of parameters in porous media, such as soil moisture, salinity, and temperature. A review of existing in situ soil sensors reveals that it is the only device capable of measuring the complex permittivity of the medium, allowing for conversions into moisture and salinity that are independent of the instrument. Flow perturbation and invasiveness have also been minimized to maintain good representativeness. Plans include autonomous networks of such sensors, facilitated by the use of the recent radio mode LoRaWAN and cost optimizations for series production. Costs were reduced through electronic simplification and integration, and the use of low-cost modular sensing parts in soil, while still maintaining high measurement quality. A complete set of sensor data recorded during a three-month trial is also presented and interpreted. [ABSTRACT FROM AUTHOR]

Competitive functions based on political, social, cultural, and economic changes become more complex as social expectations and approaches change. Analysis methods are determined based on the Delphi objective, round structure, question type, and participant number. A just-in-time production system focuses on producing as little inventory as possible rather than keeping a large stock in the warehouse. An organization should design a unique system and maintain close relationships with its suppliers to ensure that materials and parts are provided on time when they are needed. Other past production management methods that have less integration into processes have lost their effectiveness because of the characteristics of new production environments and the nature of customers. Today, companies need to have integrity in all production processes. [ABSTRACT FROM AUTHOR]

The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor glucose levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development and application of this novel fNIRS system, emphasizing its potential to revolutionize diabetes management by providing a non-invasive, real-time monitoring solution. Fifteen healthy university students participated in a controlled study, where we monitored their PFC activity and blood glucose levels under fasting and glucose-loaded conditions. Our findings reveal a significant correlation between PFC activity, as measured by our fNIRS system, and blood glucose levels, suggesting the feasibility of fNIRS technology for CGM. The portable nature of our system overcomes the mobility limitations of traditional setups, enabling continuous, real-time monitoring in everyday settings. We identified 10 critical features related to blood glucose levels from extensive fNIRS data and successfully correlated PFC function with blood glucose levels by constructing predictive models. Results show a positive association between fNIRS data and blood glucose levels, with the PFC exhibiting a clear response to blood glucose. Furthermore, the improved regressive rule principal component analysis (PCA) method outperforms traditional PCA in model prediction. We propose a model validation approach based on leave-one-out cross-validation, demonstrating the unique advantages of K-nearest neighbor (KNN) models. Comparative analysis with existing CGM methods reveals that our paper's KNN model exhibits lower RMSE and MARD at 0.11 and 8.96%, respectively, and the fNIRS data were highly significant positive correlation with actual blood glucose levels (r = 0.995, p <0.000). This study provides valuable insights into the relationship between metabolic states and brain activity, laying the foundation for innovative CGM solutions. Our portable wearable fNIRS system represents a significant advancement in effective diabetes management, offering a promising alternative to current technologies and paving the way for future advancements in health monitoring and personalized medicine. [ABSTRACT FROM AUTHOR]

Recent advancements in polymer-assisted layer-by-layer (LbL) fabrication have revolutionized the development of wearable sensors for health monitoring. LbL self-assembly has emerged as a powerful and versatile technique for creating conformal, flexible, and multi-functional films on various substrates, making it particularly suitable for fabricating wearable sensors. The incorporation of polymers, both natural and synthetic, has played a crucial role in enhancing the performance, stability, and biocompatibility of these sensors. This review provides a comprehensive overview of the principles of LbL self-assembly, the role of polymers in sensor fabrication, and the various types of LbL-fabricated wearable sensors for physical, chemical, and biological sensing. The applications of these sensors in continuous health monitoring, disease diagnosis, and management are discussed in detail, highlighting their potential to revolutionize personalized healthcare. Despite significant progress, challenges related to long-term stability, biocompatibility, data acquisition, and large-scale manufacturing are still to be addressed, providing insights into future research directions. With continued advancements in polymer-assisted LbL fabrication and related fields, wearable sensors are poised to improve the quality of life for individuals worldwide. [ABSTRACT FROM AUTHOR]

In today's health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors. [ABSTRACT FROM AUTHOR]

Continuous glucose monitoring is valuable for people with diabetes but faces limitations due to enzyme–electrode interactions and biofouling from biological samples that reduce sensor sensitivity and the monitoring performance. We created an enzyme-based electrochemical system with a unique nanocomposite coating that incorporates the redox molecule, aminoferrocene (NH2-Fc). This coating enhances stability via electroactivity and reduces nonspecific binding, as demonstrated through cyclic voltammetry. Our approach enables real-time glucose detection via chronoamperometry with a calculated linear range of 0.5 to 20 mM and a 1 mM detection limit. Validated with plasma and saliva, this platform shows promise for robust metabolite detection in clinical and research contexts. This versatile platform can be applied to accurately monitor a wide range of metabolites in various biological matrices, improving patient outcomes. [ABSTRACT FROM AUTHOR]

Urea and creatinine concentrations in plasma are used to guide hemodialysis (HD) in patients with end-stage renal disease (ESRD). To support individualized HD treatment in a home situation, there is a clinical need for a non-invasive and continuous alternative to plasma for biomarker monitoring during and between cycles of HD. In this observational study, we therefore established the correlation of urea and creatinine concentrations between sweat, saliva and plasma in a cohort of ESRD patients on HD. Forty HD patients were recruited at the Dialysis Department of the Catharina Hospital Eindhoven. Sweat and salivary urea and creatinine concentrations were analyzed at the start and at the end of one HD cycle and compared to the corresponding plasma concentrations. A decrease of urea concentrations during HD was observed in sweat, from 27.86 mmol/L to 12.60 mmol/L, and saliva, from 24.70 mmol/L to 5.64 mmol/L. Urea concentrations in sweat and saliva strongly correlated with the concentrations in plasma (ρ 0.92 [p<0.001] and 0.94 [p<0.001], respectively). Creatinine concentrations also decreased in sweat from 43.39 μmol/L to 19.69 μmol/L, and saliva, from 59.00 μmol/L to 13.70 μmol/L. However, for creatinine, correlation coefficients were lower than for urea for both sweat and saliva compared to plasma (ρ: 0.58 [p<0.001] and 0.77 [p<0.001], respectively). The results illustrate a proof of principle of urea measurements in sweat and saliva to monitor HD adequacy in a non-invasive and continuous manner. Biosensors enabling urea monitoring in sweat or saliva could fill in a clinical need to enable at-home HD for more patients and thereby decrease patient burden. [ABSTRACT FROM AUTHOR]

In the realm of intensive care medicine, wearable electronic devices have emerged as a highly promising field, driven by advancements in mobile, intelligent, and personalized healthcare. They are defined as devices that can be worn directly on the body, offering portable services by actively recording physiological parameters and metabolic status, providing index monitoring, clinical diagnosis, and disease treatment. This review specifically highlights the utilization of wearable devices in intensive care units within the field of intensive care medicine, anticipating their future applications.

This study evaluated multiple commercially available continuous monitoring (CM) point sensor network (PSN) solutions under single-blind controlled release testing conducted at operational upstream and midstream oil and natural gas (O&G) sites. During releases, PSNs reported site-level emission rate estimates of 0 kg/h between 38 and 86% of the time. When non-zero site-level emission rate estimates were provided, no linear correlation between the release rate and the reported emission rate estimate was observed. The average, aggregated across all PSN solutions during releases, shows 5% of the mixing ratio readings at downwind sensors were greater than the site's baseline plus two standard deviations. Four of seven total PSN solutions tested during this field campaign provided site-level emission rate estimates with the site average relative error ranging from −100% to 24% for solution D, −100% to −43% for solution E, −25% for solution F (solution F was only at one site), and −99% to 430% for solution G, with an overall average of −29% across all sites and solutions. Of all the individual site-level emission rate estimates, only 11% were within ±2.5 kg/h of the study team's best estimate of site-level emissions at the time of the releases. [ABSTRACT FROM AUTHOR]

The measurement of partial pressures of oxygen (O2) and carbon dioxide (CO2) is fundamental for evaluating a patient's conditions in clinical practice. There are many ways to retrieve O2/CO2 partial pressures and concentrations. Arterial blood gas (ABG) analysis is the gold standard technique for such a purpose, but it is invasive, intermittent, and potentially painful. Among all the alternative methods for gas monitoring, non-invasive transcutaneous O2 and CO2 monitoring has been emerging since the 1970s, being able to overcome the main drawbacks of ABG analysis. Clark and Severinghaus electrodes enabled the breakthrough for transcutaneous O2 and CO2 monitoring, respectively, and in the last twenty years, many innovations have been introduced as alternatives to overcome their limitations. This review reports the most recent solutions for transcutaneous O2 and CO2 monitoring, with a particular consideration for wearable measurement systems. Luminescence-based electronic paramagnetic resonance and photoacoustic sensors are investigated. Optical sensors appear to be the most promising, giving fast and accurate measurements without the need for frequent calibrations and being suitable for integration into wearable measurement systems. [ABSTRACT FROM AUTHOR]

In sports science, the use of wearable technology has facilitated the development of new approaches for tracking and assessing athletes' performance. This narrative review rigorously explores the evolution and contemporary state of wearable devices specifically engineered for continuously monitoring lactate levels in sweat, an essential biomarker for appraising endurance performance. Lactate threshold tests have traditionally been integral in tailoring training intensity for athletes, but these tests have relied on invasive blood tests that are impractical outside a laboratory setting. The transition to noninvasive, realtime monitoring through wearable technology introduces an innovative approach, facilitating continuous assessment without the constraints inherent in traditional methodologies. We selected 34 products from a pool of 246 articles found through a meticulous search of articles published up to January 2024 in renowned databases: PubMed, Web of Science, and ScienceDirect. We used keywords such as "sweat lactate monitoring," "continuous lactate monitoring," and "wearable devices." The findings underscore the capabilities of noninvasive sweat lactate monitoring technologies to conduct long-term assessments over a broad range of 0-100 mM, providing a safer alternative with minimal infection risks. By enabling real-time evaluations of the lactate threshold (LT) and maximal lactate steady state (MLSS), these technologies offer athletes various device options tailored to their specific sports and preferences. This review explores the mechanisms of currently available lactate monitoring technologies, focusing on electrochemical sensors that have undergone extensive research and show promise for commercialization. These sensors employ amperometric reactions to quantify lactate levels and detect changes resulting from enzymatic activities. In contrast, colorimetric sensors offer a more straightforward and user-friendly approach by displaying lactate concentrations through color alterations. Despite significant advancements, the relationship between sweat lactate and blood lactate levels remains intricate owing to various factors such as environmental conditions and the lag between exercise initiation and sweating. Furthermore, there is a marked gap in research on sweat lactate compared to blood lactate across various sports disciplines. This review highlights the need for further research to address these shortcomings and substantiate the performance of lactate sweat monitoring technologies in a broader spectrum of sports environments. The tremendous potential of these technologies to supplant invasive blood lactate tests and pioneer new avenues for athlete management and performance optimization in real-world settings heralds a promising future for integrating sports science and wearable technology. [ABSTRACT FROM AUTHOR]

Background: Wearable devices based on the PPG algorithm can detect atrial fibrillation (AF) effectively. However, further investigation of its application on long‐term, continuous monitoring of AF burden is warranted. Method: The performance of a smartwatch with continuous photoplethysmography (PPG) and PPG‐based algorithms for AF burden estimation was evaluated in a prospective study enrolling AF patients admitted to Beijing Anzhen Hospital for catheter ablation from September to November 2022. A continuous Electrocardiograph patch (ECG) was used as the reference device to validate algorithm performance for AF detection in 30‐s intervals. Results: A total of 578669 non‐overlapping 30‐s intervals for PPG and ECG each from 245 eligible patients were generated. An interval‐level sensitivity of PPG was 96.3% (95% CI 96.2%–96.4%), and specificity was 99.5% (95% CI 99.5%–99.6%) for the estimation of AF burden. AF burden estimation by PPG was highly correlated with AF burden calculated by ECG via Pearson correlation coefficient (R2 = 0.996) with a mean difference of ‐0.59 (95% limits of agreement, ‐7.9% to 6.7%). The subgroup study showed the robust performance of the algorithm in different subgroups, including heart rate and different hours of the day. Conclusion: Our results showed the smartwatch with an algorithm‐based PPG monitor has good accuracy and stability in continuously monitoring AF burden compared with ECG patch monitors, indicating its potential for diagnosing and managing AF. [ABSTRACT FROM AUTHOR]

Continuous monitoring of multiple physiological parameters, such as glucose levels, temperature, and heart rate variability (HRV) is crucial for effective diabetes management and mitigating the risks associated with hypoglycemic events. These events often occur without apparent symptoms, posing a challenge for diabetic patients in managing their condition. Therefore, a non‐invasive wearable device capable of continuously measuring multiple body signals to predict hypoglycemic events would be highly beneficial. In this study, a wearable patch that continuously measures glucose, temperature, and HRV is presented. The device uses a novel power harvesting system to convert radiofrequency (RF) signals with the frequency of 2.45 GHz to direct current (DC) signals to extend the battery life for further continuous monitoring. The patch is small and has a conformal structure that can easily fit onto different body parts. The screen‐printed glucose sensor demonstrates a sensitivity of 10.3 nA cm−2 µM−1, a limit of detection (LOD) of 8.9 µM, and a limit of quantification (LOQ) of 27 µM. The device employs a photoplethysmography (PPG) module with a peak‐finding algorithm to calculate the HRV values. In vivo experiments demonstrate the validation of the device's proper operation in glucose, HRV, and temperature measurement. [ABSTRACT FROM AUTHOR]

Background: There is growing interest in the possibility of discontinuing oral anticoagulation following successful catheter ablation of atrial fibrillation (AF). However, it remains unknown whether patients can accurately detect arrhythmia recurrences following ablation. We therefore sought to characterize the accuracy of pulse checking and arrhythmia symptoms for the identification of AF following ablation. Methods: This prospective cohort study included patients at the Hospital of the University of Pennsylvania with an insertable cardiac monitor (ICM) treated with catheter ablation for AF who recorded the results from minimum twice daily pulse checks and additionally with arrhythmia symptoms into a diary for 2 months following their procedure. Accuracy of this self-assessment protocol was determined by comparison to ICM-detected AF. Results: A total of 55 patients (age 69 ± 8 years, 30 (55%) male, CHA2DS2VASc score 3.2 ± 1. 5) were included. Patients recorded a total of 5911 pulse checks, and there were 280 episodes of ICM-documented AF among 26 patients with an average duration of 2.5 ± 3.3 h. Among 362 episodes of patient-suspected AF, 134 correlated with ICM-identified AF (37% true positive rate). Of the 5549 pulse checks that did not identify AF, 196 correlated with ICM-identified AF (4% false negative rate). Twice daily pulse checking had a sensitivity of 47% and a specificity of 96% for identifying each episode of AF. Conclusions: Our data indicate that a strategy of pulse checks and symptom assessment is insufficient to identify all episodes of AF in many patients following catheter ablation. [ABSTRACT FROM AUTHOR]

This research shows a complete security design for Internet of Things (IoT) devices. It improves security by using five methods that work together. At the beginning of the process, a machine learning-based method for ranking changes is used. Then, architectures are put in place for scalable patch distribution, anomaly detection, dynamic risk assessment, and integrating threat data. Using five connected algorithms, the purpose of this research is to create a complete security framework for Internet of Things devices. Dynamic risk assessment, scalable patch delivery, integration with threat intelligence, and anomaly detection for zero-day vulnerabilities are among its characteristics. It also identifies zero-day vulnerabilities. Furthermore, it prioritises repairs using machine learning data. Every solution seeks to address a specific component of IoT security, such as dynamic risk assessments, effective patch distribution, and patch prioritisation based on vulnerability data. It is critical to maintain the Internet of Things ecosystem's safety, flexibility, and efficiency. An integrated approach provides a strong defence against cyberattacks, which is crucial for ecosystem preservation. With this system, you can get better accuracy, flexibility, and resource use than with other methods. To help explain how the methods work, charts and flowcharts are used. The ablation study indicates that each method is important because it shows how they all help keep IoT devices safe. The suggested design considers how cyber risks are always changing to protect connected devices in a lot of different places from hackers. [ABSTRACT FROM AUTHOR]

Continuous monitoring plays a crucial role in diagnosing hypertension, characterized by the increase in Arterial Blood Pressure (ABP). The gold-standard method for obtaining ABP involves the uncomfortable and invasive technique of cannulation. Conversely, ABP can be acquired non-invasively by using Photoplethysmography (PPG). This non-invasive approach offers the advantage of continuous BP monitoring outside a hospital setting and can be implemented in cost-effective wearable devices. PPG and ABP signals differ in scale values, which creates a non-linear relationship, opening avenues for the utilization of algorithms capable of detecting non-linear associations. In this study, we introduce Neural Model of Blood Pressure (NeuBP), which estimates systolic and diastolic values from PPG signals. The problem is treated as a binary classification task, distinguishing between Normotensive and Hypertensive categories. Furthermore, our research investigates NeuBP's performance in classifying different BP categories, including Normotensive, Prehypertensive, Grade 1 Hypertensive, and Grade 2 Hypertensive cases. We evaluate our proposed method by using data from the publicly available MIMIC-III database. The experimental results demonstrate that NeuBP achieves results comparable to more complex models with fewer parameters. The mean absolute errors for systolic and diastolic values are 5.02 mmHg and 3.11 mmHg, respectively. [ABSTRACT FROM AUTHOR]

Soil carbon fluxes are pivotal indicators of climate impacts, yet field-level monitoring remains challenging. This study puts forth an innovative integrated framework coupling support vector machine (SVM) and XGBoost algorithms to enable automated, precise tracking of peat soil carbon dioxide emissions. The core methodology handles a multi-dimensional dataset encompassing 72-h flux measurements from 360 intact tropical peat cores under controlled moisture conditions spanning 30–85% water-filled pore space across intact, logged, and oil palm converted sites. Rigorous preprocessing via outlier elimination and missing value imputation coupled with a tenfold cross-validation approach lays the robust analytical foundation. SVM first applies nonlinear transformation through Gaussian radial basis functions to classify complex soil respiration patterns. An optimized hyperplane decision boundary discretizes the high-dimensional space to separate classes. XGBoost subsequently constructs an ensemble of weighted decision trees targeting residual errors to incrementally boost predictions over 500 iterations. The integrated framework combines SVM and XGBoost outputs using performance-based weighting. This allows efficiently mapping intricate moisture, temperature, oxygen availability, microbial activity, and land use effects on peat soil carbon dioxide production and emission dynamics. Integrated predictions leverage complementary strengths. Peaking at 94.4% accuracy, 92% precision, 91% recall and 0.3 RMSE, SVM with XGBoost decisively surpasses neural networks, LSTM, gradient boosting and regression trees, proving optimized encoding of intricate moisture, texture and land use effects on soil respiration. Clustered data representations confirm feasibility of mapping complex emission behaviors across intact and drained sites. Overall, the dual framework delivers a precise, automated system to unlock new frontiers in responsive soil carbon monitoring and modeling at scale. Next phases should focus on expanding multivariate input data and assessing generalizability across soil and vegetation types. [ABSTRACT FROM AUTHOR]

Networks of fixed-point continuous monitoring systems are becoming widely used in the detection and quantification of methane emissions from oil and gas facilities in the United States. Regulatory agencies and operators are developing performance metrics for these systems, such as minimum detection limits. Performance characteristics, such as minimum detection limits, would ideally be expressed in emission rate units; however, performance parameters such as detection limits for a continuous monitoring system (CMS) will depend on meteorological conditions, the characteristics of emissions at the site where the CMS is deployed, the positioning of CMS devices in relation to the emission sources, and the amount of time allowed for the CMS to detect an emission source. This means that certifying the performance of a CMS will require test protocols with well-defined emission rates and durations; initial protocols are now being used in field tests. Field testing results will vary, however, depending on meteorological conditions and the time allowed for detection. This work demonstrates methods for evaluating CMS performance characteristics using dispersion modeling and defines an approach for normalizing test results to standard meteorological conditions using dispersion modeling. [ABSTRACT FROM AUTHOR]

Variations in the CO2 dissolved in water springs have long been observed near the epicenters of moderate and strong earthquakes. In a recent work focused on data collected during the 2017–2021 period from a monitoring site in the Northern Apennines, Italy, we noticed a significant correlation between CO2 anomalies and moderate-to-weak seismic activity. Here, we extended this analysis by focusing on data collected from the same site during a different period (2010–2013) and by integrating the CENSUS method with an artificial neural network (ANN) in the already-tested protocol. As in our previous work, a fit of the computed residual CO2 distributions allowed us to evidence statistically relevant CO2 anomalies. Thus, we extended a test of the linear dependence of these anomalies to seismic events over a longer period by means of binary correlations. This new analysis also included strong seismic events. Depending on the method applied, we observed different time lags. Specifically, using the CENSUS methodology, we detected a CO2 anomaly one day ahead of the earthquake and another anomaly eleven days ahead. However, no anomaly was observed with the ANN methodology. We also investigated possible correlations between CO2 concentrations and rain events and between rain events and earthquakes, highlighting the occurrence of a CO2 anomaly one day after a rain event of at least 10 mm and no linear dependence of seismic and rain events. Similar to our previous work, we achieved a probability gain of around 4, which is the probably of earthquake increases after CO2 anomaly observations. [ABSTRACT FROM AUTHOR]