Quality Monitoring Applications Research Articles

Electrochemical activity of self-supporting nitrogen-doped graphene for the degradation and in-situ determination of methylene blue

Environmentally friendly wastewater treatment and online pollution monitoring systems, important for water quality control, are yet to be developed. Electrochemical strategies combining the electrocatalytic degradation and electroanalysis of organic pollutants are expected to achieve this key technology. This study employed a chemical vapor deposition method using N₂ gas as the nitrogen source and applying an alternating electric field to prepare self-supporting nitrogen-doped graphene (NG) tubes with a wall thickness of up to 480 μm comprising few-layer NG nanosheets. The correlation between the properties of these materials and the electrocatalytic activity for methylene blue (MB) was investigated. The feasibility of the electrochemical strategy for online wastewater treatment and detection was validated using a combination of UV–visible spectroscopy and differential pulse voltammetry. The NG tube with the N-C bond distribution regulated by adjusting the methane flow rate and growth time exhibited high electrocatalytic activity, rapidly degrading 50 mg L⁻¹ of MB within 36 min and tracking changes in MB concentration. The NG tubes were assembled in an electrocatalytic meter pen for water treatment, achieving a degradation efficiency of 99.4 % and a detection limit of 0.3 mg L−1 for the MB solution, quite satisfactory for emerging simultaneous degradation and monitoring processes. The developed self-supporting NG tubes are promising materials for electrocatalytic degradation anodes and sensing electrodes in water treatment. Dual-functional electrocatalytic systems have potential applications in water quality monitoring and improvement.

Application of microbial fuel cell‐based biosensor in environmental monitoring – A critical review

AbstractMicrobial Fuel Cells (MFCs) represent an innovative approach for transforming biomass energy directly into electricity, which showed great promise in various applications beyond energy generation and wastewater treatment. The use of MFCs as biosensors for in‐situ and online monitoring has garnered increasing interest. These biosensors stand out for their compactness, ease of operation, affordability, and portability. They have proven effectively in the detection of various water quality indicators, including organic matter, nitrogen, heavy metals, pH levels, and dissolved oxygen. This comprehensive review aims to provide a critical analysis of the current research landscape and the latest advancements in MFC technology, with special emphasis on the challenges encountered in its application for wastewater and water quality monitoring. Moreover, strategies for performance improvement, such as the adoption of miniaturized structures, the exploration of innovative materials, and the application of mathematical modelling for analysis, are also discussed. The review also explores potential avenues for future research, especially in the realm of detecting mixed pollutants. Thus, it provides insightful perspectives on the evolving field of biosensor technology based on MFCs.

Research on Flight Safety Indicators Based on Confidence and Principal Component Analysis

Modern technology protects flight safety, especially in the specific research and application of flight quality monitoring, and similar studies are currently focusing on large-scale reading of flight data, and storing and analysing it. In this paper, the data from switching on the autopilot is extracted and the reliability is firstly investigated using the Cronbach's alpha reliability coefficient method. Considering the various types of data as the questions of the questionnaire, and the result of Min-Max normalisation of the data as the score of the questionnaire, the size of the reliability coefficients of the data for the eight flight segments was finally derived, and the reliability of the data was quantitatively described based on this. In order to filter the importance of factors reflecting flight safety, seven indicators related to flight safety were then extracted. Then, through principal component analysis, the importance of the indicators was analysed by the size of the coefficients, and the degrees of importance, from the largest to the smallest, were as follows: airspeed, ground speed, descent rate, altitude, and attitude.

Ultrasensitive Indium Phosphide Nanomembrane Wearable Gas Sensors

Air quality is deteriorating due to continuing urbanization and industrialization. In particular, nitrogen dioxide (NO2) is a biologically and environmentally hazardous byproduct from fuel combustion that is ubiquitous in urban life. To address this issue, we report a high‐performance flexible indium phosphide nanomembrane NO2 sensor for real‐time air quality monitoring. An ultralow limit of detection of ~200 ppt and a fast response have been achieved with this device by optimizing the film thickness and doping concentration during indium phosphide epitaxy. By varying the film thickness, a dynamic range of values for NO2 detection from parts per trillion (ppt) to parts per million (ppm) level have also been demonstrated under low bias voltage and at room temperature without additional light activation. Flexibility measurements show an adequately stable response after repeated bending. On‐site testing of the sensor in a residential kitchen shows that NO2 concentration from the gas stove emission could exceed the NO2 Time Weighted Average limit, i.e., 200 ppb, highlighting the significance of real‐time monitoring. Critically, the indium phosphide nanomembrane sensor element cost is estimated at <0.1 US$ due to the miniatured size, nanoscale thickness, and ease of fabrication. With these superior performance characteristics, low cost, and real‐world applicability, our indium phosphide nanomembrane sensors offer a promising solution for a variety of air quality monitoring applications.

A novel electrochemical sensor for the detection of metronidazole residues in food samples

The widespread use and misuse of antibiotics in pharmaceuticals and animal farming has resulted in their accumulation in food sources and the environment, posing significant threats to human health, the environment, and the global economy. In this study, we have developed a hypersensitive, and ultra-selective electrochemical sensor, the first of its kind, by integrating a thermally annealed gold-silver alloy nanoporous matrix (TA-Au-Ag-ANpM) with reduced graphene oxide (r-GO) and poly(glycine) at the surface of a glassy carbon electrode (GCE). This sensor aims to detect life-threatening metronidazole (MTZ) residues in food samples. TA-Au-Ag-ANpM/r-GO/poly(glycine)/GCE was thoroughly characterized using a range of analytical techniques, including UV–Vis, FT-IR, XRD, SEM, and EDX. Furthermore, its electrochemical properties were investigated by cyclic voltammetry (CV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS). The sensor exhibited outstanding performance, with a broad linear range of 2.0 pM–410 μM. The limits of detection (LOD) and quantification (LOQ) were determined to be 0.0312 pM and 0.104 pM, respectively. The TA-Au-Ag-ANpM/r-GO/poly(glycine)/GCE exhibited exceptional reproducibility, repeatability, stability, and resistance to interferences. Moreover, the sensor demonstrated outstanding performance in detecting MTZ residues in milk powder, pork, and chicken meat samples, achieving very good recoveries (96.9%–101.4%) with a relative standard deviation (RSD) below 5%. This performance highlights the potential for practical applications in food safety and quality monitoring. Therefore, the developed sensor contributes to the advancement of electrochemical sensing technology and its application in ensuring food safety and integrity by combating antibiotic residues.

Programmable multi-stimulus-responsive whirligig beetle inspired soft robot with multifunctionality based on composite materials

In nature, surfaces are evolutionarily designed to allow species to adapt to their environments. Nowadays, more and more researchers have paid attention to soft robots based on superhydrophobic surfaces. Inspired by the swimming characteristics of whirligig beetles on the surface of water, we propose in this study a multi-stimulus-responsive soft robot that can be driven by light, magnetism and chemistry to achieve fast straight-line motions, turns and rotations on the surface of water. The soft robot is made of a composite material based on graphene oxide (GO) and polydimethylsiloxane (PDMS), which has high hydrophobicity and excellent photothermal properties. With a multi-stimulus-responsive driving mechanism, the soft robot can be used in applications such as oil recovery, cargo transportation, obstacle avoidance, and material dispersion. It is envisioned that this study can guide the development of multi-stimulus-responsive soft robots and offer some useful reference for their applications in water quality monitoring and oil recovery.

A Ratiometric Fluorescent Detection Platform Using G‐CDs@[Ru(bpy)3]2+ for the Specific Detection of Hypochlorite and Live Cell Imaging

AbstractHypochlorite (ClO−), an essential reactive oxygen species (ROS) in physiological processes, is identified to be closely connected with oxidative stress and related diseases. Meanwhile, ClO− is a commonly‐used disinfector for water treatment, and in public places, under acidic conditions, it's easily decomposed into hypertoxic chlorine gas. Since the strong oxidizing property of ClO−, many oxidizing agents may disturb the ClO− detection. Specific and accurate detection of ClO− with superior sensitivity is a challenge. In this work, a sensing platform for rapid, sensitive, and specific ClO− detection is constructed using green fluorescent carbon dots (G‐CDs), with a linear detection range of 0.5–11 µm and a detection limit of 0.233 µm. Moreover, introducing a red fluorescent tripyridinium ruthenium ([Ru(bpy)3]2+) as a reference, a ratiometric fluorescence nanoprobe G‐CDs@[Ru(bpy)3]2+ is prepared and shows favorable intracellular imaging of exogenous and endogenous ClO−. With G‐CDs@[Ru(bpy)3]2+‐based test paper microarrays and a color recognition APP, a smartphone‐based sensing system for point‐of‐care testing of ClO− is also fabricated. In summary, this work proposed a versatile and economical smartphone‐based sensing system that featured reliability and simplicity, and suggested its potential applications in environmental water quality monitoring and live cell imaging.

Calibration and validation of the HydroColor and Citclops smartphone applications for water quality monitoring

Water quality monitoring, essential for safeguarding ecosystems and human health, has gained increasing significance as societies worldwide prioritize environmental awareness and sustainable practices. Therefore, this study evaluates the performance of two smartphone applications (APPs), HydroColor and Citclops (now EyeOnWater), in estimating water quality parameters such as turbidity, the concentration of suspended particulate matter ([SPM]), and colour. By comparing laboratory and hyperspectral sensors measurements with water quality parameters estimated from smartphone images, the study assessed the accuracy and efficiency of the transfer functions employed by these APPs. The study findings revealed varying degrees of accuracy, with HydroColor R2 values of 0.36 and 0.83 for turbidity and [SPM], respectively, while Citclops achieved an R2 value of 0.7 for colour estimation. The study identified limitations in both APPs, particularly in their applicability to different water systems. These insights underscore the importance of proper calibration and validation procedures for smartphone-based water quality monitoring APPs. Also, the findings underscore the growing significance of smartphone APPs in enabling accessible and real-time monitoring of water quality, highlighting their potential to revolutionize the democratization of environmental monitoring practices through citizen science. Ultimately, this research contributes to the advancement of smartphone-based monitoring initiatives to inform decision-making processes in environmental management, and enhancing our understanding of water quality dynamics in diverse environments.

Photoacoustic Spectroscopy Using a Quantum Cascade Laser for Analysis of Ammonia in Water Solutions.

Ammonia (NH3) toxicity, stemming from nitrification, can adversely affect aquatic life and influence the taste and odor of drinking water. This underscores the necessity for highly responsive and accurate sensors to continuously monitor NH3 levels in water, especially in complex environments, where reliable sensors have been lacking until this point. Herein, we detail the development of a sensor comprising a compact and selective analyzer with low gas consumption and a timely response based on photoacoustic spectroscopy. This, combined with an automated liquid sampling system, enables the precise detection of ammonia traces in water. The sensor system incorporates a state-of-the art quantum cascade laser as the excitation source emitting at 9 μm in resonance with the absorption line of NH3 located at 1103.46 cm-1. Our instrument demonstrated detection sensitivity at a low ppm level for the ammonia molecule with response times of less than 60 s. For the sampling system, an ammonia stripping solution was designed, resulting in a prompt full measurement cycle (6.35 min). A further evaluation of the sensor within a pilot study showed good reliability and agreement with the reference method for real water samples, confirming the potential of our NH3 analyzer for water quality monitoring applications.

Recent Progress in Intelligent Packaging for Seafood and Meat Quality Monitoring

AbstractFood waste plays a crucial role in environmental and human health issues. To address these issues, intelligent packaging systems are proposed for the accurate assessment of the quality of food products. Intelligent packaging is a technology that integrates sensing systems with conventional packaging to provide smart sensing communication functions for real‐time food quality monitoring. The most important parts of this technology are generally classified as quantitative sensors (sensors) and qualitative sensors (indicators). Since seafood and meat products can spoil easily if not stored properly, applying intelligent packaging for real‐time monitoring of the safety of these products is beneficial. In this review, the spoiling process and essential performance characteristics are indicators of the quality of these food products are first discussed. Then, The characteristics and importance of various sensors and indicators that can be used for seafood and meat quality monitoring are presented. While discussing these topics, an updated review of recent scientific studies, preconditions, materials, advantages, and limitations are provided. Furthermore, the future need for improvements in intelligent packaging systems for real‐time quality and safety monitoring of food products is discussed. Finally, several important research examples of the challenges and perspectives of intelligent packaging applications for meat and seafood quality monitoring are presented.

Machine learning enabled microneedle-based colorimetric pH sensing patch for wound health monitoring and meat spoilage detection

Since pH can alter the biological functions, level of nutrients, wound healing process, and the behavior of chemicals, various healthcare and food industries are showing increased interest in manufacturing low-cost optical pH sensors for meat spoilage detection and wound health monitoring. To meet this demand, we have developed a simple and low-cost machine learning-enabled microneedle-based colorimetric pH sensing patch that can be used for food quality and wound health monitoring applications. The 3D–printed ultrasharp open side channel microneedle array facilitated the autonomous fluid extraction and transportation via surface tension for colorimetric pH sensing. Further, to predict the exact pH value against the obtained color on the pH-test strip, a machine learning model was prepared using experimentally collected different color images obtained from a known pH solution. Furthermore, to make the device user-friendly for older individuals and color-blind individuals, a simple and smartphone-enabled web application was prepared using the developed machine learning model. The proof-of-concept study of the developed patch was demonstrated by determining the pH of real meat samples before and after spoilage and detecting pH in two different skin-mimicking in vitro models (phantom gel and parafilm tape) using a smartphone. The analytical results demonstrated that the developed machine learning-enabled microneedle-based colorimetric pH sensing patch has excellent potential for wound health and food safety applications.

Combined fabrication of zeolitic imidazolate framework-8 and lanthanide towards coordination polymers: A dual-signal fluorescent probe for sensing Cu2+ based on synergistic effect of aggregation-induced emission and antenna effect

Copper ion (Cu2+) detection remains an important task for monitoring water quality because of its specific toxicity. Herein, a new dual-signal fluorescent probe was developed by combining zeolitic imidazolate framework-8 (ZIF-8) and lanthanide for the detection of Cu2+ for the first time. The lanthanide coordination polymer (guanosine monophosphate and Eu3+, GMP/Eu) was initially incorporated into ZIF-8 to yield ZIF-8/GMP/Eu nanomaterials with extremely weak single emission fluorescence at 618 nm. It was found that the resulted nanomaterials could display a dual emission fluorescence at 515 nm and 618 nm after the introduction of tetracycline (TC) due to the synergistic effect of aggregation-induced emission effect (AIE, TC induced by ZIF-8) and antenna effect (AE, between TC and GMP/Eu). Interestingly, in the presence of Cu2+, the AIE of TC was destroyed because of the interaction of Cu2+ with ZIF-8 and TC. The AE between TC and GMP/Eu disappeared due to the formation of complex between TC and Cu2+. A dual-signal fluorescent probe of ZIF-8/GMP/Eu/TC was thereby established for sensing Cu2+ in the range of 0.5–100 μM. Such a dual-signal response strategy that intelligently utilized the “ON”/“OFF” of AIE and AE can not only eliminate the background interference, but also ensure the improved selectivity of Cu2+ sensing. Subsequently, the dual-signal fluorimetric strategy was applied for the detection of Cu2+ in environmental water samples, indicating the potential feasibility of applications for water quality monitoring.

Highly sensitive and reversible MXene-based micro quartz tuning fork gas sensors with tunable selectivity

Due to their distinctive morphology, significant surface-to-volume ratio, and metal-like electrical conductivity, MXenes have emerged as highly promising gas-sensing materials. Traditional MXene-based gas sensors predominantly rely on the electrical conductivity of MXenes for signal transduction. However, it is crucial to explore alternative signal transduction mechanisms to fully unlock the potential of MXenes in gas sensing applications. In this study, we have successfully showcased the development of a mass-transduction-based MXene gas sensor, utilizing MXenes as the adaptable receptor and MQTF as the transducer. The interaction between the gas analyte and MXenes induces a change in mass, resulting in a resonant frequency shift of the MQTF. This signal transduction mechanism eliminates the dependency on the electrical conductivity of MXenes, offering a broader range of possibilities for chemical modification of MXenes without concerns about compromising their conductivity. By engineering Ti3C2Tx surfaces, we have demonstrated high sensitivity and selectivity tuning of MXene-MQTF gas sensors for detecting CO, SO2, and NH3. This antisymmetric mass-transduction-based (low-cost, stable, sensitive, and practical tuning fork-based) MXene gas sensor demonstrated exceptional sensing performance, customizable selectivity, and high cost-effectiveness. This study paves the way for designing high-performance MXene-based chemical sensors and expands the scope of potential applications in air quality monitoring, wearable devices, the Internet of Things (IoT), and robotics.

A BIM-enabled digital twin framework for real-time indoor environment monitoring and visualization by integrating autonomous robotics, LiDAR-based 3D mobile mapping, IoT sensing, and indoor positioning technologies

Existing indoor environment monitoring methods often fall short due to the lack of using standardized evaluation methods, missing spatial information, or the absence of user-friendly visualization interfaces. To address these limitations, this paper proposes a novel digital twin framework for real-time indoor environment monitoring and visualization by integrating Building Information Modeling (BIM), Internet of Things, autonomous robot-based mobile mapping and sensing, and indoor positioning technologies. The goal is to autonomously monitor indoor environmental conditions and provide real-time visualization using a standardized assessment index with estimated spatial information in BIM software. The proposed framework was demonstrated and validated in two indoor scenarios for air quality monitoring and visualization-related applications. Implementation results revealed that: the framework effectively captured spatial characteristics of air quality in small and medium-sized indoor spaces and intuitively visualized it using a BIM digital twin model; and, the ultra-wideband-based indoor positioning system achieved a nearly centimeter-level positioning precision. Practical interpretations of the results demonstrated that the proposed framework can not only detect and localize the potential sources of air pollutants but can also identify the possible causes of low indoor air quality. This study ultimately contributes to the body of knowledge by offering a novel and generic digital twin framework that seamlessly bridges various popular yet previously isolated technologies for better informing building facility management-related decisions. This paper is the first research work that integrates several emerging technologies (i.e., BIM, digital twins, IoT sensing, indoor localization/positioning/tracking, and robotics) for advancing real-time monitoring and visualization of indoor environmental conditions. While the proposed framework was applied for indoor air quality monitoring, it could be extended and used for various indoor environmental assessment and visualization-related applications.

Smart nanocomposites: Harnessing magnetically recoverable MWCNT-CF for efficient organic dyes reduction in water quality monitoring applications

The accelerating use of organic dyes in various industries has led to a surge in water pollution, especially from non-biodegradable dye effluents discharged into water resources. This study addresses the critical issue of catalyzing the reduction of two prevalent dyes, methylene blue (MB) and rhodamine-B (RhB), using a multiwalled carbon nanotube-cobalt ferrite (MWCNT-CF) nanocomposite. The synthesized nanocomposite demonstrates exceptional catalytic activity, stability, and recyclability. Conventional methods for treating dye-containing wastewater often prove expensive. This study explores the efficacy of catalytic reduction, a relatively fast process facilitated by semiconductor nanoparticles. Structural analyses using X-ray diffraction and high-resolution transmission electron microscopy (HRTEM) confirm the formation of the nanocomposite, revealing unsaturated surface bonds and chains conducive to adsorption. The nanocomposite exhibits a remarkable reduction in both dyes, with easy recyclability for multiple cycles. Magnetization studies confirm the ferrimagnetic nature of the nanocomposite, facilitating its efficient separation from the reaction mixture using a magnet. The study delves into the kinetics of the catalytic reduction following pseudo-first-order kinetics. The surface modifications of the nanocomposite, as revealed by TEM, contribute to enhanced adsorption and catalytic efficiency. Notably, the MWCNT-CF nanocomposite demonstrates negligible loss of catalytic activity during recycling, highlighting its potential for cost-effective and sustainable applications in dye reduction across various industries.

Revolutionizing food quality assessment: Unleashing the potential of artificial intelligence for enhancing honey physicochemical, biochemical, and melissopalynological insights

In the pursuit of advancing food quality assessment, this study employs sophisticated data-driven techniques to delve into the complex realm of honey analysis. With the aim of unraveling the multifaceted nature of honey quality, Self-Organizing Maps (SOMs) and Principal Component Analysis (PCA) were employed to scrutinize the interplay of physicochemical, biochemical, and melissopalynological attributes in honey samples collected from the diverse drylands of Algeria. The dataset comprised 62 honey samples and eight crucial parameters. These parameters span climate zones (arid vs. desertic), honeybee breeds (Tellian, Saharan, and hybrid), honey extraction methods (manual pressing vs. electric centrifugation), and beekeeping systems (modern vs. traditional). Using SOMs, honey samples were categorized into distinct clusters that reflect variations across these four honey-related variables. Additionally, SOM heatmaps offer granular insights into individual parameters' distribution across the SOM map. Our analysis revealed nuanced distinctions in honey quality across North African regions, with specific parameters playing a pivotal role in defining honey quality. On average, the honey samples exhibited the following characteristics: a water content of 15.14 %, an electrical conductivity of 0.5 µS/cm, a pH of 4.20, a total sugar content of 83 %, a reducing sugar content of 63.83 %, a proline concentration of 382.7 mg/kg of honey, an hydroxymethylfurfural level of 77.4 mg/kg, and an average pollen grain density of 4.56 × 105 grains per 10 g of honey. Notably, the study identified clear demarcations in honey quality linked to beekeeping systems and revealed characteristics associated with bee breeds and extraction techniques. The results underscored the significance of selected honey parameters as key indicators of quality. This analytical approach not only offered a comprehensive assessment of honey quality but also holds potential for broader applications within the food industry. The findings invite further exploration into the ecological and genetic dimensions of beekeeping practices in North Africa to deepen our understanding of honey's multifaceted attributes. This study showcased the efficacy of SOMs and PCA in unraveling the complex fabric of honey quality assessment. These data-driven techniques, complemented by the structured dataset and analytical approach used, provided valuable insights that contributed to enhancing the scientific understanding of honey quality. By elucidating the complex relationships between physicochemical, biochemical, and melissopalynological parameters and honey quality, this research paves the way for future studies in this field and holds promise for broader applications in food quality assessment and monitoring.

Proposition of optimal self-curing method in horizontal-floating gate FET-type gas sensors for reliability improvement

Gas sensing technology has seen rapid advancements in recent years, finding applications in air quality monitoring, food freshness inspection, and medical diagnosis. However, to successfully integrate gas sensors into these applications, it is crucial to ensure their stability and long-term reliability. While previous studies have primarily focused on improving response, sensitivity, and selectivity, there has been limited research on enhancing their reliability. This study aims to address this gap by proposing a method to improve the reliability of horizontal floating-gate FET-type gas sensors using a self-curing approach. When the sensor is exposed to the repetitive program/erase operation, the FET transducer used as a sensor platform is damaged by the Fowler-Nordheim tunneling-induced stress. A significant increase in the trap density inside the gate oxide is observed after the P/E cycling. The parasitic bipolar junction (PBJT) inherent to the FET transducer flows a large current via the entire channel, generating a Joule heat. The trap is decreased by the annealing effects of the Joule heat, and the damaged gate oxide is fully recovered by the curing. The proposed PBJT curing can effectively improve the reliability of the sensor using the internal operation of the FET transducer without using bulky external equipment.

Integration of carbon dioxide sensor with GNSS receiver for dynamic air quality monitoring applications

Air pollution is a significant problem in big cities due to the rapid increase of anthropogenic activities and severe traffic congestion. Therefore, real-time and micro tools for air monitoring are urgently necessary for fast and better policy decision-making. The current city air monitoring tool is typically static and serves a macro area. This study introduces technology development to integrate the air quality sensor with the satellite-based navigation receiver. This study used a carbon dioxide (CO2) MH-Z19C sensor and real-time kinematic global navigation satellite system (RTK GNSS) U-Blox F9P with GNSS Trimble NetR9 receiver. The field air quality monitoring (CO2 observed in ppm) and the movement velocity (vehicle speed observed in km/h) were recorded on two main roads of Jakarta by using a survey vehicle. The study compares the observation results of the non-integrated system (NIS) and integrated technology system (IS). The two systems generated the CSV database (CO2 and vehicle speed); however, IS generated the automatic synchronized and error-free data output. The statistical regression analysis of CSV data (CO2 and vehicle speed) between the NIS and IS reported significant results, which means both are reliable. Still, the NIS did not require manual synchronization, with some possibility of error. The R square values show a significant gap (speed 0.99 over CO2 0.144), indicating that IS needs further development as the CO2 data varies due to technicality. The finding presents that integrating the CO2 sensor and GNSS receiver generates a more effective time synchronization process and a reliable error removal technique in developing the CSV data. This finding is a significant reference in developing the integrated satellite-based receiver system with external environmental sensors.

Triggerless data acquisition pipeline for Machine Learning based statistical anomaly detection

This work describes an online processing pipeline designed to identify anomalies in a continuous stream of data collected without external triggers from a particle detector. The processing pipeline begins with a local reconstruction algorithm, employing neural networks on an FPGA as its first stage. Subsequent data preparation and anomaly detection stages are accelerated using GPGPUs. As a practical demonstration of anomaly detection, we have developed a data quality monitoring application using a cosmic muon detector. Its primary objective is to detect deviations from the expected operational conditions of the detector. This serves as a proof-of-concept for a system that can be adapted for use in large particle physics experiments, enabling anomaly detection on datasets with reduced bias.

Remote Sensing Estimation of CDOM and DOC with the Environmental Implications for Lake Khanka

Chromophoric dissolved organic matter (CDOM) is a significant contributor to the biogeochemical cycle and energy dynamics within aquatic ecosystems. Hence, the implementation of a systematic and comprehensive monitoring and governance framework for the CDOM in inland waters holds significant importance. This study conducted the retrieval of CDOM in Lake Khanka. Specifically, we use the GBDT (R2 = 0.84) algorithm which performed best in retrieving CDOM levels and an empirical relationship based on the situ data between CDOM and dissolved organic carbon (DOC) to indicate the distribution of DOC indirectly. The performance of the CDOM-DOC retrieval scheme was reasonably good, achieving an R2 value of 0.69. The empirical algorithms were utilized for the analysis of Sentinel-3 datasets from the period 2016 to 2020 in Lake Khanka. The potential factors that contributed to the sources of DOM were also analyzed with the humification index (HIX). The significant relationship between CDOM and DOC (HIX and chemical oxygen demand (COD)) indicated the potential remote sensing application of water quality monitoring for water management. An analysis of our findings suggests that the water quality of the Great Khanka is superior to that of the Small Khanka. Moreover, the distribution of diverse organic matter exhibits a pattern where concentrations are generally higher along the shoreline compared to the center of the lake. Efficient measures should be promptly implemented to safeguard the water resources in international boundary lakes such as Lake Khanka and comprehensive monitoring systems including DOM distribution, DOM sources, and water quality management would be essential for water resource protection and government management.