Monitoring Capabilities Research Articles

Synthesis and evaluation of a novel ternary quaternary ammonium salts-fluorescent template copolymer: Integrating flocculation, sterilization, and monitoring functions

Developing flocculants with sterilization and monitoring functions is of practical significance for optimizing water treatment agents. In this research, a fluorescent monomer, EtFl, with double bonds was synthesized and copolymerized with acrylamide (AM) and 3-(methacryloyloxy)propyltrimethylammonium chloride (MAPTAC) using a linear anionic template agent. This synthesis successfully produced a multifunctional template copolymer, TP(AM-MAP-EtFl), with flocculation, sterilization and monitoring capabilities. The fluorescence intensity of TP(AM-MAP-EtFl) was measured using a fluorescence spectrophotometer, demonstrating strong performance even with an EtFl monomer mass fraction as low as 0.5 wt‰, resulting in a fluorescence intensity value of 1000 a.u., suitable for analytical detection. In flocculation experiments with simulated wastewater containing E. coli, the introduction of the fluorescent monomer did not affect E. coli flocculation. When flocculation conditions are optimized, the OD600 readings of the clear liquid remaining after treating with TP(AM-MAP-EtFl#0.5) and TP(AM-MAP-EtFl#5) are 0.0297 and 0.0457, respectively, illustrating superior sterilization effects. Interestingly, SEM and 3D-EEM results confirmed TP(AM-MAP-EtFl)’s bactericidal effect on E. coli, with the introduction of the fluorescent monomer not affecting the sterilization capability of quaternary ammonium. This work integrates flocculation, sterilization and fluorescence properties to provide sterilization-type flocculants with monitoring capabilities, enabling accurate calculation of polymer concentration by detecting solution fluorescence intensity, thereby offering potential for online detection and automatic control of water treatment systems, demonstrating good applicability.

Controllable Fabrication of Multifunctional Micro-thermocouples for Temperature Detection inside Single Suspended Droplets.

Microdroplets have recently emerged as an exciting technological platform for wide applications. In this work, we developed a controllable fabrication approach to novel tungsten-platinum micro-thermocouples that function not only as a sensitive temperature sensor but also as a flexible suspender for individual microdroplet studies. The controllable fabrication hinges on the formation of tungsten tip apex nodes to make junctions with platinum, which was achieved through a unique combinational strategy, involving gradient coating with a complete insulating layer and subsequent targeted removal by tip electroporation. Benefiting from its coaxial structure, microspherical contact node end, hydrophobic surface, and thermoelectric performance, the as-fabricated micro-thermocouple was successfully employed for the microdroplet suspension and in situ temperature detection throughout the droplet evaporation cycle. It was observed that the temperature inside the suspended microdroplets was lower than that of the external environment, and there existed temperature discontinuity during droplet evaporation. By integrating the capabilities of temperature monitoring and droplet manipulation into a single micro-thermocouple, this work demonstrates its versatility and promising applications in expanded sensing for single microdroplets and other microsystems.

A comprehensive engineering design analysis of Apple Watch as a smart wearable device

This paper presents a comprehensive engineering design analysis of the Apple Watch, a pioneering smart wearable device by Apple Inc. The analysis covers the device's hardware and software components, design principles, manufacturing processes, impact on user experience, and its influence on the wearable technology market. The Apple Watch integrates advanced technologies into a sleek and functional design, enhancing user interaction through its intuitive interface and robust health monitoring capabilities. By examining its engineering intricacies and market impact, this study highlights the Apple Watch's role in shaping the future of wearable technology.

Self-Indicating Polymer Prodrug Nanoparticles for pH-Responsive Drug Delivery in Cancer Cells and Real-Time Monitoring of Drug Release.

Amphiphilic self-indicating and responsive polymer-based prodrugs have generated much interest as potential stimuli-responsive intelligent drug delivery systems (DDS) due to their ability to selectively deliver drugs to the cancer cells and to monitor real-time cellular uptake of the drug by imaging technique(s). In this direction, we have synthesized a new pH-responsive N-vinyl-2-pyrrolidone and coumarin-based fluorescent self-indicating polymeric prodrug (SIPD), poly(NVP)-b-poly(FPA.DOX-r-FPA-r-CA). This block copolymer prodrug self-assembled into stable micellar nanoparticles under physiological conditions that reduced undesirable drug leakage to normal cells but resulted in the release of the anticancer drug doxorubicin (DOX) in cancer cells because of acidic pH-induced cleavage of imine bonds between DOX and the copolymer. While the polymer was found to be highly biocompatible with both normal (HEK-293) cells and cancer (MCF-7) cells even at high concentrations by MTT assay, the polymer prodrug nanoparticles showed toxicity even higher than that of free DOX in cancer cells. Phase contrast microscopy also depicted the cytotoxic effects of the nanoparticles on cancer cells. The coumarin units present in the polymer served as a fluorescence resonance energy transfer (FRET) pair with the covalently attached DOX molecules, which was established by steady-state and time-resolved fluorescence spectroscopy. Furthermore, confocal microscopy results confirmed the FRET phenomenon, as the fluorescence intensity of coumarin in the micellar nanoparticles remained quenched initially in MCF-7 cells but recovered with time as the DOX molecules were released and gradually shifted toward the targeted nucleus. All of these studies implied that the synthesized prodrug nanoparticles may provide another viable option for delivering chemotherapeutic drugs into cancer cells with a capability of real-time monitoring of drug release.

Probabilistic Programming for Transportable Source Characterization and Uncertainty Quantification of the North Korean Nuclear Tests 2006–2017

Abstract We introduce a transportable technique to determine the yield and depth of burial (DOB) from seismic source spectra of underground nuclear explosions. We demonstrate this technique on the six declared North Korean nuclear tests. This approach derives source spectra in absolute units from regional phase (Pg) amplitudes by correcting the observations for geometric spreading, attenuation, and site amplification. We couple the source spectra and explosion source models with a probabilistic programming framework that integrates deep learning techniques and Bayesian modeling. This approach permits the exchange of information across various data categories to quantify both the data and model uncertainty. This technique stands out as an innovative use of broad-area propagation models, making it transportable across various geologic settings. This method proves to be effective in scenarios with diverse and/or limited observational data, even when the source depth is unknown. We present new independent estimates of absolute yield and DOB that are consistent with the prior assessments, underscoring the potential of this method in enhancing transportable nuclear explosion monitoring capabilities.

Wet Spinning/UV Dual-Curing enabled 3D printable fiber for intelligent electronic devices

Conductive fibers are widely used in wearable sensors and implantable devices in real life due to their excellent properties of softness and lightweight. However, most conductive fibers obtained through the simple blending of polymer elastomers and inorganic conductive particles often leads to issues such as uneven conductor distribution and poor mechanical performance. Herein, the composite fiber consists of deep eutectic solvents (composed of choline chloride and 2-hydroxyethyl methacrylate) and thermoplastic polyurethane was prepared by wet spinning/ ultraviolet dual-curing method. The uniform distribution of deep eutectic solvents and the resilience of thermoplastic polyurethane ensure that the fiber material can maintain stable signal monitoring capability when subject to various strains over the long term. The composite fiber as motion sensor with an ultra-wide operating range (0–710 %), fast response time (186 ms), long lifetime (stable after 5 months), wide temperature range and excellent weather resistance. It is worth mention that the dual curing process combined with 3D printing technology, allows for the fabrication of flexible sensors in a variety of shapes. 3D printing sensors can be produced underwater to achieve different sensitivities, enabling customization to meet the requirements of applications. This innovative study holds tremendous potential for the next generation of flexible electronic products, such as human motion monitoring and information transmission.

An intelligent garment for long COVID-19 real-time monitoring

As monitoring and diagnostic tools for long COVID-19 cases, wearable systems and supervised learning-based medical image analysis have proven to be useful. Current research on these two technical roadmaps has various drawbacks, despite their respective benefits. Wearable systems allow only the real-time monitoring of physiological parameters (heart rate, temperature, blood oxygen saturation, or SpO2). Therefore, they are unable to conduct in-depth investigations or differentiate COVID-19 from other illnesses that share similar symptoms. Medical image analysis using supervised learning-based models can be used to conduct in-depth analyses and provide precise diagnostic decision support. However, these methods are rarely used for real-time monitoring. In this regard, we present an intelligent garment combining the precision of supervised learning-based models with real-time monitoring capabilities of wearable systems. Given the relevance of electrocardiogram (ECG) signals to long COVID-19 symptom severity, an explainable data fusion strategy based on multiple machine learning models uses heart rate, temperature, SpO2, and ECG signal analysis to accurately assess the patient's health status. Experiments show that the proposed intelligent garment achieves an accuracy of 97.5 %, outperforming most of the existing wearable systems. Furthermore, it was confirmed that the two physiological indicators most significantly affected by the presence of long COVID-19 were SpO2 and the ST intervals of ECG signals.

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.

The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve its predictive potential. This study aims to objectively review the literature on AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its strengths, weaknesses, opportunities, and threats. A scoping review on AI-assisted lung sound analysis in children with asthma was conducted across 4 major scientific databases (PubMed, MEDLINE Ovid, Embase, and Web of Science), supplemented by a gray literature search on Google Scholar, to identify relevant studies published from January 1, 2000, until May 23, 2023. The search strategy incorporated a combination of keywords related to AI, pulmonary auscultation, children, and asthma. The quality of eligible studies was assessed using the ChAMAI (Checklist for the Assessment of Medical Artificial Intelligence). The search identified 7 relevant studies out of 82 (9%) to be included through an academic literature search, while 11 of 250 (4.4%) studies from the gray literature search were considered but not included in the subsequent review and quality assessment. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Identified strengths were improved predictive accuracy of AI to allow for prompt and early diagnosis, personalized management strategies, and remote monitoring capabilities. Weaknesses were the heterogeneity between studies and the lack of standardization in data collection and interpretation. Opportunities were the potential of coordinated surveillance, growing data sets, and new ways of collaboratively learning from distributed data. Threats were both generic for the field of medical AI (loss of interpretability) but also specific to the use case, as clinicians might lose the skill of auscultation. To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning.

Mapping and monitoring dense non-aqueous phase liquid source zone by fused surface and cross-borehole electrical resistivity tomography

Effective characterization of dense non-aqueous phase liquid (DNAPL) source zones is crucial for remediating polluted sites. DNAPL often reside as residuals or pools within high-permeability lenses and above impermeable layers due to soil heterogeneity, gravity, and capillary barriers. Given the high cost of drilling, electrical resistivity tomography (ERT) techniques—including surface ERT and cross-borehole ERT, are commonly used for DNAPL source zone mapping and monitoring. However, the low spatial resolution of ERT increases uncertainty in source zone investigations. This study proposes a method for improving DNAPL mapping and monitoring by fusing surface and cross-borehole ERT data. Sandbox experiments were conducted to simulate a heterogeneous DNAPL source zone, employing both ERT methods for static mapping and dynamic monitoring. Reflective light imaging (RLM) was used to visualize DNAPL migration and provide saturation data, allowing for the quantification of ERT's effectiveness in characterizing DNAPL distribution. The results indicate that individual ERT methods face significant challenges in DNAPL source zone mapping due to background interference. Surface ERT alone tends to underestimate the extent of deeper DNAPL source zones. However, fusing surface and cross-borehole ERT results in a complementary enhancement of vertical spatial resolution, thereby improving the characterization of DNAPL source zones. The fusion of static and time-lapse ERT data substantially enhances DNAPL source zone mapping and monitoring capabilities. By calculating the ratio of the ERT-monitored area to the actual area using resistivity change contours (5 %, 10 %, 15 %), it was found that fusing surface and cross-borehole ERT data improved monitoring resolution by 50.48 % compared to surface ERT alone and by 22.95 % compared to cross-borehole ERT. Principal component analysis (PCA) was effective in fusing time-lapse data, while the weighted average method (WAM) outperformed PCA for static resistivity data fusion.

A method for estimating the rotation errors of BDS broadcast ephemeris using global satellite laser ranging data

Abstract The BeiDou-3 Navigation Satellite System (BDS-3) generates navigation messages from a regional ground tracking network and inter-satellite links, relying on predicted station coordinates and Earth rotation parameters (ERPs). These procedures, however, may lead to orientation errors in the broadcast ephemeris, termed constellation rotation errors. Traditionally, these errors are monitored through Helmert transformation analysis comparing broadcast and post-processed orbits, a method that highlights discrepancies between the global navigation satellite system (GNSS) terrestrial reference frame (TRF) and the International TRF (ITRF) but suffers from a two-week evaluation delay. This study proposes a near real-time approach for estimating constellation rotation errors using high-precision satellite laser ranging (SLR) data, representing the difference between GNSS TRF and ITRF through the International laser ranging service TRF. The impact of SLR data quantity and distribution on rotation estimation accuracy is investigated, and the effectiveness of this method on BDS-3 orbits with ERP-induced orientation deficiencies is validated. Results show that the root mean square of broadcast orbit errors and the orbit user ranging error reduce from 0.8/0.7/0.2 m to 0.1/0.1/0.1 m, respectively. This demonstrates the method’s capability for real-time monitoring and correcting orientation errors within BDS-3 navigation messages.

Assessment of Atmospheric Correction Algorithms for Correcting Sunglint Effects in Sentinel-2 MSI Imagery: A Case Study in Clean Lakes

The Sentinel-2 Multi-Spectral Instrument (MSI) is characterized by short revisit times (5 days), red-edge spectral bands (665 nm and 705 nm), and a high spatial resolution (10 m), making it highly suitable for monitoring water quality in both inland and coastal waters. Unlike SeaWiFS, which can adjust its viewing angles to minimize sunglint, the Sentinel-2 MSI operates with fixed near-nadir angles, which makes it more susceptible to sunglint. Additionally, the complex optical properties of water pose challenges in accurately determining its water-leaving reflectance. Therefore, we compared the effectiveness of six atmospheric correction (AC) algorithms (POLYMER, MUMM, DSF, C2RCC, BP, and GRS) in correcting sunglint using two typical lakes in Xinjiang, China, as examples. The results indicated that POLYMER achieved the highest overall evaluation score (1.61), followed by MUMM (1.21), while BP exhibited the lowest performance (0.62). Specifically, POLYMER showed robust performance at the 665 nm band with RMSE = 0.0012 sr−1, R2 = 0.74, and MAPE = 30.68%, as well as at the 705 nm band with RMSE = 0.0014 sr−1, R2 = 0.42, and MAPE = 38.44%. At the 443, 490, and 560 nm bands, MUMM showed better performance (RMSE ≤ 0.0026 sr−1, R2 ≥ 0.86, MAPE ≤ 28.20%). In terms of band ratios, POLYMER exhibited the highest accuracy (RMSE ≤ 0.093 and MAPE ≤ 22.2%), particularly for the ratio Rrs(490)/Rrs(560) (R2 = 0.71). In general, POLYMER is the best choice for the sunglint correction of Xinjiang’s clean lakes. This study assessed the capability of different AC algorithms for sunglint correction and enhanced the monitoring capability of MSI data in clean waters.

Enhancing the Capabilities of Continuous Glucose Monitoring With a Predictive App.

Despite abundant evidence demonstrating the benefits of continuous glucose monitoring (CGM) in diabetes management, a significant proportion of people using this technology still struggle to achieve glycemic targets. To address this challenge, we propose the Accu-Chek® SmartGuide Predict app, an innovative CGM digital companion that incorporates a suite of advanced glucose predictive functionalities aiming to inform users earlier about acute glycemic situations. The app's functionalities, powered by three machine learning models, include a two-hour glucose forecast, a 30-minute low glucose detection, and a nighttime low glucose prediction for bedtime interventions. Evaluation of the models' performance included three data sets, comprising subjects with T1D on MDI (n = 21), subjects with type 2 diabetes (T2D) on MDI (n = 59), and subjects with T1D on insulin pump therapy (n = 226). On an aggregated data set, the two-hour glucose prediction model, at a forecasting horizon of 30, 45, 60, and 120 minutes, achieved a percentage of data points in zones A and B of Consensus Error Grid of: 99.8%, 99.3%, 98.7%, and 96.3%, respectively. The 30-minute low glucose prediction model achieved an accuracy, sensitivity, specificity, mean lead time, and area under the receiver operating characteristic curve (ROC AUC) of: 98.9%, 95.2%, 98.9%, 16.2 minutes, and 0.958, respectively. The nighttime low glucose prediction model achieved an accuracy, sensitivity, specificity, and ROC AUC of: 86.5%, 55.3%, 91.6%, and 0.859, respectively. The consistency of the performance of the three predictive models when evaluated on different cohorts of subjects with T1D and T2D on different insulin therapies, including real-world data, offers reassurance for real-world efficacy.

Novel joint algorithm for state-of-charge estimation of rechargeable batteries based on the back propagation neural network combining ultrasonic inspection method

State of Charge (SoC) is an essential indicator for energy storage distribution in lithium-ion batteries, which prevents overcharge and over-discharge of the battery by integrating it into the Battery Management System. Common estimation approaches are Extended Kalman Filtering (EKF), Coulomb counting, open-circuit-voltage (OCV), and neural networks (NN). While these methods are able to estimate SoC more accurately, they lack real-time monitoring capability for internal and external battery damage such as air bubbles and foreign objects. This study is the first to propose a novel joint algorithm for real-time monitoring of battery SoC and health with high accuracy, integrating ultrasonic non-destructive testing (NDT). The proposed algorithm predicts the battery overcharge based on the ultrasonic signal and reveals structural changes in commercial batteries during charging/discharging. Experimental results demonstrate that ultrasonic inspection cannot only enhance the SoC estimation accuracy of the backpropagation (BP) neural network, but also detects damaged conditions of the battery.

Coupled and radiated microwave sensors for vital signs and lung water level monitoring

This paper presents a novel approach for non-invasive monitoring of vital signs (heart rate (HR), respiration rate (RR)) and lung water levels using ultra-wideband (UWB) microwave sensors. The primary contribution of this study is the development and testing of two different sensor technologies to enhance the penetration and monitoring capabilities of electromagnetic waves within the human body. The first sensor technology employs a radiated UWB microwave that can be integrated into textiles, operating within a frequency range of 1.5 to 10 GHz, providing flexibility and comfort for wearable health monitoring. The second sensor technology involves using body-coupled UWB non-radiating sensors with a wide operational bandwidth from 1.3 to 10 GHz and a stable feeding structure, ensuring efficient reflection and transmission of waves for accurate monitoring. The shapes of flexible and textile UWB electromagnetic microwave sensor radiators and couplers, along with the measurement procedure, are tested in human clinical studies. The sensor-specific absorption rate (SAR) is evaluated at two distinct resonant frequencies on tissue masses of 1 g and 10 g, aligning with the IEEE C95.3 standard to ensure adherence to standard thresholds of 1.6 W/kg and 2 W/kg, respectively. Additionally, a denoising scheme utilizing deep learning techniques is proposed to filter Base Line Wander (BLW) noise attributable to misplaced electrodes, skin impedance, poor skin preparation, patient movement, and breathing within the frequency range of 0.05 to 3 Hz. The postprocessing outcome demonstrates the superiority of couplers over radiating sensors.

Exposing Data Leakage in Wi-Fi CSI-Based Human Action Recognition: A Critical Analysis

Wi-Fi channel state information (CSI)-based human action recognition systems have garnered significant interest for their non-intrusive monitoring capabilities. However, the integrity of these systems can be compromised by data leakage, particularly when improper dataset partitioning strategies are employed. This paper investigates the presence and impact of data leakage in three published Wi-Fi CSI-based human action recognition methods that utilize deep learning techniques. The original studies achieve precision rates of 95% or higher, attributed to the lack of human-based dataset splitting. By re-evaluating these systems with proper subject-based partitioning, our analysis reveals a substantial decline in performance, underscoring the prevalence of data leakage. This study highlights the critical need for rigorous dataset management and evaluation protocols to ensure the development of robust and reliable human action recognition systems. Our findings advocate for standardized practices in dataset partitioning to mitigate data leakage and enhance the generalizability of Wi-Fi CSI-based models.

Long-term and Continuous Plasmonic Oligonucleotide Monitoring Enabled by Regeneration Approach.

The demand for continuous monitoring of biochemical markers for diagnostic purposes is increasing as it overcomes the limitations of traditional intermittent measurements. This study introduces a method for long-term, continuous plasmonic biosensing of oligonucleotides with high temporal resolution. Our method is based on a regeneration-based reversibility approach that ensures rapid reversibility in less than 1 minute, allowing the sensor to fully reset after each measurement. We investigated label-free and AuNP enhancements for different dynamic ranges and sensitivities, achieving a limit of detection down to pM levels. We developed a regeneration-based reversibility approach for continuous biosensing, optimizing buffer conditions using the Taguchi method to achieve rapid, consistent reversibility, ensuring reliable performance for long-term monitoring. We detected oligonucleotides in buffered and complex solutions, including undiluted and unfiltered human serum, for up to 100 sampling cycles in a day. Moreover, we showed the long-term stability of the sensor for monitoring capabilities in buffered solutions and human serum, with minimal signal value drift and excellent sensor reversibility for up to 9 days. Our method opens the door to new prospects in continuous biosensing by providing insights beyond intermittent measurements for numerous analytical and diagnostic applications.

Satellite-ground synchronous in-situ dataset of water optical parameters and surface temperature for typical lakes in China

Remote sensing technology has the potential to enhance the lake’s large-scale and long-term dynamic monitoring capabilities significantly. High-quality in-situ datasets are essential for improving the accuracy and reliability of remote sensing retrieval of lake ecosystems. This dataset provides satellite-ground synchronized in-situ data on water multi-parameters for typical lakes in China spanning the period between 2020 and 2023. It includes quality-checked water optical parameters (remote sensing reflectance (Rrs), chlorophyll-a (Chl-a), total suspended matter (TSM) and Secchi disk depth (SDD)), and water surface temperature (WST) data. It encompasses 586 sampling points across 18 lakes. The dataset exhibits two significant highlights: Firstly, synchronous observations from multiple satellites are coordinated during the data collection effectively supporting the retrieval and validation of water remote sensing products. Secondly, it encompasses diverse data types, collecting synchronous measurements of Rrs and various parameters. This dataset will continuously update, substantially enhancing regional and global lake monitoring capabilities through satellite remote sensing data.

Portable surface acoustic wave device platform coupled with a paper-based capillary fluidics for real-time biosensing applications

Surface acoustic wave (SAW) sensors have emerged as prominent real-time, label-free biosensors with diverse applications in immunoassays and nucleic acid detection. However, widespread adoption in diagnostics has been impeded by challenges such as miniaturization of instrumentation, microfluidics fabrication and high attenuation in liquid environments. In this study we address these limitations by presenting a portable platform capable of real-time monitoring of a SAW microarray chip comprising up to four sensing channels, combined with a novel paper-based capillary flow channel. For the fabrication of a portable, automated and versatile sensor platform, we integrated a microcontroller, RF components, and a micropump for flow control. Calibration procedures ensured accurate measurements for both amplitude and phase, with a low drift rate (0.72 mdB/h and 1.00 mdeg./h, respectively) and high resolution (2.10 mdB and 6.10 mdeg.), indicating stable operation with minimal interface electronics. The capillary flow channel, implemented by confining the fluid on the sensing surface using a nitrocellulose strip, facilitated laminar and continuous sample flow with minimum damping of the acoustic signal. The sensor system was evaluated with two SAW devices at 100 and 200 MHz using glycerol dilutions within the range of 1 % and 70 %, demonstrating real-time monitoring capabilities and linear correlations between amplitude and phase changes. To prove the biosensing and clinical validity of the SAW newly developed system, DNA adsorption on a poly(L-lysine)-graft-poly(ethylene glycol) (PLL-g-PEG) functionalized SAW-surface was demonstrated with a detection limit of 0.001 μg/mL. Our study demonstrates the feasibility of a portable SAW sensor system coupled with a low-cost and replaceable capillary flow channel for real-time monitoring and biomolecular detection.

A new method for the rapid identification of external water types in rainwater pipeline networks using UV–Vis absorption spectroscopy

Ultraviolet–visible (UV–Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV–Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks.

Portable Voltammetry: A Rapid and Efficient Technique for Determining UV Filters in Cosmetics: A Comparative Study with HPLC-PDA and HPLC-MS/MS.

This study addresses the widespread use of UV filters (UVFs) in cosmetic and solar products due to the negative effects of UV radiation, particularly in relation to melanoma risk. While these filters offer protection, their extensive application raises concerns about their environmental and health impacts. Organic UVFs, in particular, have been associated with endocrine disruption in aquatic species and coral reef damage. To mitigate these concerns, regulatory limits have been imposed on certain UVFs. Current analytical techniques for UVF determination, such as HPLC-PDA and HPLC-MS/MS, offer high accuracy but are expensive and lack on-site monitoring capabilities. In response, this research aims to develop a rapid and cost-effective method, utilizing voltammetry for organic UVF quantification in complex matrices like sunscreens. Additionally, HPLC-PDA and HPLC-MS/MS are employed for electrochemical methods and device validation. This approach not only addresses the need for efficient UVF analysis but also provides a basis for regulatory compliance and environmental stewardship in the cosmetics industry.