Monitoring Capabilities Research Articles

Monitoring water quality parameters of freshwater aquaculture ponds using UAV-based multispectral images

Monitoring water quality is crucial for water exchange, precise feeding, and quality control of water products in freshwater aquaculture. In light of the issue of spatial heterogeneity in freshwater aquaculture pond waters and the constraints of conventional sensor detection techniques and traditional machine learning models. In this study, UAV multispectral images were combined with four machine learning algorithms (Ridge, XGBoost, CatBoost, RF) and the Stacking model to model the estimation of Chlorophyll a (Chl-a) and Turbidity and map their spatial distribution. The findings indicate that, in contrast to machine learning models, the Stacking model of water quality parameter performs better with higher accuracy. Meanwhile,for Chl-a and Turbidity the optimal sub-model combination in the Stacking model varies, with the most effective estimation model for Chl-a concentration identified as RF-XGB-Ridge (R2 = 0.84, RMSE=1.882 µg/L, MAE=3.433 µg/L and Slope = 0.791). As to Turbidity, the RF-CAB-Ridge model demonstrates superior performance, with macro-averaged precision (macro-p) of 93.3 %, macro-averaged recall (macro-R) of 88.8 %, macro-averaged F1-score (macro-F1) of 0.895, and Kappa coefficient of 0.813. Furthermore, the results of the joint analyses, which included measured samples and management measures at the test site, demonstrated that the spatial distribution maps of Chl-a and Turbidity were in alignment with the current status of water quality at the test site. This consistency was observed across both temporal and spatial scales. The results demonstrate that the integration of UAV multispectral images with the Stacking model can enhance the precision of water quality parameter models, facilitates the examination of the spatial and temporal distribution of water quality parameters and the underlying influencing factors, and advances the capability for dynamic monitoring of water quality parameters in freshwater aquaculture regions. Concurrently, it offers fundamental theoretical and methodological assistance for the precise regulation of water quality in freshwater aquaculture ponds and the formulation of optimal production management strategies.

Pro-Ictal EEG Scheduling Improves the Yield of Epilepsy Monitoring: Validating the Use of Multiday Seizure Cycles to Optimize Video-EEG Timing.

A significant challenge of video-electroencephalography (vEEG) in epilepsy diagnosis is timing monitoring sessions to capture epileptiform activity. In this study, we introduce and validate "pro-ictal EEG scheduling", a method to schedule vEEG monitoring to coincide with periods of increased seizure likelihood as a low-risk approach to enhance the diagnostic yield. A database of long-term ambulatory vEEG monitoring sessions (n = 5,038) of adults and children was examined. Data from linked electronic seizure diaries were extracted (minimum 10 self-reported events) to generate cycle-based estimates of seizure risk. In adults, vEEG monitoring sessions coinciding with periods of estimated high-risk were allocated to the high-risk group (n = 305) and compared to remaining studies (baseline: n = 3,586). Test of proportions and risk-ratios (RR) were applied to index differences in proportions and likelihood of capturing outcome measures (abnormal report, confirmed seizure, and diary event) during monitoring. The impact of clinical and demographic factors (age, sex, epilepsy-type, and medication) was also explored. During vEEG monitoring, the high-risk group was significantly more likely to have an abnormal vEEG report (190/305:62% vs 1,790/3,586:50% [%change = 12%], RR = 1.25, 95% confidence interval [CI] = [1.137-1.370], p < 0.001), present with a confirmed seizure (56/305:18% vs 424/3,586:11% [%change = 7%], RR = 1.63, 95% CI = [1.265-2.101], p < 0.001) and report an event (153/305:50% vs 1,267/3,586:35% (%change = 15%), RR = 1.420, 95% CI = [1.259:1.602], p < 0.001). Similar effects were observed across clinical and demographic features. This study provides the first large-scale validation of pro-ictal EEG scheduling in improving the yield of vEEG. This innovative approach offers a pragmatic and low-risk strategy to enhance the diagnostic capabilities of vEEG monitoring, significantly impacting epilepsy management. ANN NEUROL 2024.

Pesca sustentável em São Tomé e Príncipe: uma miragem ou uma realidade?Uma análise sobre a pesca em São Tomé nas últimas décadas

Fishing plays a crucial role in the socioeconomic development of São Tomé and Príncipe, contributing significantly to employment, food security, and poverty reduction. This study aims to examine the state of sustainable fishing in São Tomé and Príncipe by analyzing fishing regulations and proposing specific policies to promote sustainability. The research employs various methodologies, including document analysis and reviews of reports from national and international institutions, as well as consultations with the Ministries of Natural Resources and the Environment. Despite numerous initiatives aimed at achieving sustainable fishing, São Tomé and Príncipe has yet to attain this goal due to insufficient monitoring and enforcement capabilities. Achieving sustainability in fishing requires: Enhancing the country’s monitoring and enforcement capacities; Assessing and improving domestic fisheries management; Strengthening fishing regulations; Ensuring their effective implementation.

Electrochemical biosensor based on copper sulfide/reduced graphene oxide/glucose oxidase construct for glucose detection

Due to the current increase in the number of people suffering from diabetes worldwide, how to monitor the blood glucose level in the human body has become an urgent problem to be solved nowadays. The electrochemical sensor method can be used for real-time glucose monitoring due to its advantages of real-time monitoring capability and high sensitivity. Reduced graphene oxide (rGO) has great potential for application in the field of sensors due to its advantages of large specific surface area, high stability, and good electrical and thermal conductivity. Meanwhile, the synergistic effect between two-dimensional transition metal sulfides and graphene can improve the electrochemical performance of materials due to their similar mechanical flexibility and strength. This article uses flake graphite, copper sulfate, and glucose oxidase (GOx) as raw materials to prepare CuS/rGO/GOx/GCE electrodes, and explores the performance of electrode electrocatalysis for glucose. The results showed that the prepared sensor was characterized by a low detection limit (1.75 nM) and a wide linear range (0.1-100 mM) for glucose detection, displaying a good overall detection performance, and its sensing mechanism and dynamic process were also investigated. In addition, the sensor has outstanding selectivity, anti-interference, repeatability, reproducibility and practicality.

A multi-perspective assessment of satellite surface Ozone products in China: Spatiotemporal variability, land cover impacts and pollution monitoring capability

Accurate Ozone concentration datasets are essential for comprehensively understanding air quality, climate change, and the health and societal impacts related to Ozone exposure. Currently, there is limited research comparing near-surface Ozone concentration products. In this study, we employed continuous evaluation metrics (R2, RMSE, RB) and classified statistical indicators (POD, FAR, CSI) to comprehensively compare and assess three near-surface Ozone concentration products (CHAP O3, LGAP O3, TAP O3) in China from 2015 to 2020. Our findings indicate that CHAP outperforms other products in estimating near-surface Ozone concentration and monitoring Ozone pollution in China. In terms of temporal scale, the three Ozone products exhibited a strong correlation in specific regions across multiple time scales (R2> 0.6). The RMSE values of the products were lower in CHAP, followed by TAP, and higher in LGAP. Notably, LGAP significantly underestimated Ozone concentrations over time. Regarding spatial scale, CHAP and TAP align closely with ground observations, whereas LGAP shows significant underestimation in latitude and longitude, with the discrepancy increasing gradually along the longitude. All three products exhibited strong correlations with measured data at various sites (R2> 0.6). The RMSE and | RB| distributions of CHAP and TAP were similar and superior to LGAP. CHAP and TAP perform better than LGAP under specific land cover types, especially in urban and vegetation-covered areas, while their performance is relatively poorer in farmland-covered areas. Concerning pollution events, CHAP and TAP exhibit strong detection capabilities for Ozone pollution events in the Beijing–Tianjin–Hebei (BTH) region and other majority areas, with POD values exceeding 0.6, and FAR values below 0.1, showcasing excellent CSI performance. In contrast, LGAP has weaker detection capabilities, with POD values primarily in the 0–0.2 range and lower CSI values. This comprehensive evaluation sheds light on the strengths and weaknesses of different near-surface Ozone concentration products and their implications for assessing Ozone pollution in China.

In-situ electrochemically synthesized “artificial” Gly m TI antibody for soybean allergen quantification in complex foods

Biosensors, especially those designed for detecting food allergens like Gly m TI in soybean, play a crucial role in safeguarding individuals who suffer from adverse food allergies, extending to both individual well-being and broader public health considerations. Furthermore, their integration into food production and monitoring processes aids in compliance with regulatory standards, reducing the incidence of allergen-related recalls and protecting vulnerable populations. Technological advancements in biosensor development, such as increased portability, real-time monitoring capabilities, and user-friendly interfaces, have expanded their practical applications, making them indispensable in various settings, including manufacturing plants, food service establishments, and even at-home use by consumers.For the first time, a biosensor targeting the Gly m TI allergen based on molecularly imprinted polymer (MIP) technology was developed to detect/quantify soybean in complex food matrices and effectively address the detection challenges of complex and processed foods. The Gly m TI-MIP underwent a thorough validation process using anti-Gly m TI IgG raised as a polyclonal response to the trypsin inhibitor. Gly m TI-MIP was successfully tested across a range of food matrices, including tree nuts (e.g., peanuts, walnuts, and hazelnuts) and legumes (e.g., lentils, beans, and lupine), presenting minimal cross-reactivity with lupine and walnut. The innovative approach provided a linear response in the 1 ag mL−1 - 10 μg mL−1 range, with a LOD<1 ag mL−1. Applying the Gly m TI-MIP sensor to complex model foods allowed to detect 0.1 mg kg−1 (0.00001 %) of soybean protein isolate in biscuits, ham, and sausages before and after the respective thermal treatments. The innovative biosensor can significantly improve food safety protocols by addressing the complexities of tracing allergens in processed and unprocessed food products. By ensuring rigorous allergen control, these biosensors may support global food trade compliance with international safety standards, boost consumer confidence, and promote transparency in food labeling, ultimately contributing to a safer food supply chain.

A multifunctional nanosystem catalyzed by cascading natural glucose oxidase and Fe3O4 nanozymes for synergistic chemodynamic and photodynamic cancer therapy

The significance of the tumor microenvironment (TME) in tumor initiation and progression is increasingly acknowledged. Conventional therapeutic approaches face limitations within the complex TME, including the restrictions imposed by hypoxia on photodynamic therapy (PDT) and the deficiency of endogenous H₂O₂ affecting chemodynamic therapy (CDT). In response to the TME's characteristics of high metabolism, hypoxia, and weak acidity, a multifunctional nanosystem MNPs/GOD@CS/IR820, which synergistically integrates CDT and PDT, has been developed. This system can actively accumulate at tumor sites under an external magnetic field and release active components in response to the weakly acidic TME. It mitigates the limitations imposed by hypoxia and endogenous H₂O₂ deficiency on PDT and CDT, respectively, thereby enabling synergistic treatment. Additionally, the system's multimodal imaging capabilities facilitate precise tumor localization and real-time, non-invasive in vivo assessment via fluorescence imaging and MRI. In vitro and in vivo evaluations demonstrate significant antitumor efficacy, effectively inhibiting tumor growth and improving survival rates. By comprehensively addressing the challenges posed by the complex TME and enhancing real-time monitoring capabilities, our nanosystem paves the way for personalized and precise cancer treatment. Statement of significanceThis study introduces an innovative MNPs/GOD@CS/IR820 nanosystem that represents a significant advancement in cancer nanomedicine by addressing critical limitations of conventional photodynamic therapy (PDT), particularly in hypoxic tumor microenvironments. By synergistically integrating chemodynamic therapy (CDT) with PDT and incorporating MRI and fluorescence dual-modal imaging capabilities, this multifunctional platform offers enhanced therapeutic efficacy and real-time monitoring. The system's ability to generate oxygen in situ overcomes hypoxia-induced limitations, while its multimodal mechanism of action induces tumor cell apoptosis through multiple pathways. In vitro and in vivo studies demonstrate remarkable antitumor efficacy across diverse cancer types, significantly inhibiting tumor growth and improving survival rates. This comprehensive approach to cancer diagnosis and treatment not only advances precision medicine for targeted, multimodal cancer management but also provides a promising foundation for future clinical applications, potentially transforming cancer treatment strategies and improving patient outcomes.

Effect of Ownership Structure on Performance of Quoted Financial Firms in Nigeria

This study investigates the relationship between ownership structure and firm performance of quoted Nigerian financial firms in Nigeria. This study employs an Ex post facto research design, utilizing data extracted from annual reports of the financial firms listed on the Nigerian Exchange Group (NGX) between the years 2014 to 2023. The population of interest consists of 48 listed financial firms. Secondary data from the firms' annual reports were collected and analyzed using fixed effect panel regression analysis as specified by Hausman test. The findings reveal significant positive effects of managerial ownership, institutional ownership, and ownership concentration on firm performance. These results align with established theories such as agency theory and stewardship theory, as well as previous empirical studies in the field of corporate governance and ownership structure. The study concludes that increasing managerial ownership can align the interests of managers with shareholders, leading to improved firm performance. Attracting institutional investors who bring expertise, monitoring capabilities, and long-term investment perspectives can also contribute to enhanced firm performance. Furthermore, careful management of ownership concentration can enhance monitoring and decision-making efficiency, align shareholder interests, and reduce agency costs. However, the study finds no significant impact of foreign ownership on firm performance in the Nigerian context. This suggests the need for further research to better understand the specific dynamics and potential implications of foreign ownership in the Nigerian financial industry, considering factors such as regulatory restrictions, cultural differences, and information asymmetry.

Evaluating the impact of emerging hydro-geophysical technologies on sustainable water resource management

Emerging hydrogeophysical technologies are revolutionizing the management of water resources, providing unprecedented insights and enhancing sustainability practices. This article examines recent advancements in hydrogeophysical methods, focusing on time-lapse electromagnetic techniques, unmanned aerial vehicles (UAVs), and artificial intelligence (AI). Through a comprehensive review of the literature and practical case studies, including notable implementations in Africa, the paper highlights how these innovations improve groundwater monitoring, resource management, and predictive capabilities. Time-lapse electromagnetic methods have achieved up to 1-meter resolutions, significantly enhancing groundwater mapping accuracy (Binley &amp; Kemna, 2005). With their high-resolution remote sensing capabilities, UAVs have facilitated detailed hydrological surveys, while AI-driven analyses optimize groundwater predictions and management strategies (Rennie et al., 2021). The study presents a cost-benefit analysis showing a 20-30% reduction in operational costs and a 40% increase in data accuracy. Challenges such as technical limitations and high initial costs are discussed, alongside potential solutions and prospects. The findings underscore the transformative potential of these technologies for achieving sustainable water resource management, offering actionable insights for policymakers, researchers, and practitioners in the field.

Driving Wi-Fi IoT Sensors by a Hybrid Magneto-Mechano-Electric Energy Generator Extracting a Power of over 50mW.

Energy harvesting technology is mainly used as a power source for driving Internet of Things (IoT) devices. However, the output power of conventional harvesting devices are limited, suitable only for low-power-consumption IoT sensors based on Bluetooth communication. In contrast to Bluetooth, wireless fidelity (Wi-Fi) communication offers superior real-time monitoring and transmission capabilities, but requires more power in the range of hundreds of milliwatts or higher. Therefore, the hybridization of three energy conversion devices, namely, piezoelectric magneto-mechano-electric (MME) generator, electromagnetic (EM) induction coil, and triboelectric nanogenerator (TENG) is proposed as a standalone power source for Wi-Fi communication sensors. By integrating these three mechanisms, the hybrid MME energy harvester can achieve an output power exceeding 50mW at the second harmonic resonance condition under the alternating current (AC) magnetic field of 10Oe. Furthermore, it can successfully drive the Wi-Fi sensor, enabling continuous real-time monitoring without the degradation of charged power in a supercapacitor. These results highlight that energy harvesting technology is not limited to low-power devices but can also be applied to Wi-Fi communication sensors and beyond.

Spatio-temporal variations and multi-scenario simulation of landscape ecological risk in the drylands of the Yellow River Basin.

Over the past decades, the drylands of the Yellow River Basin (YRBD) have undergone profound changes in landscape patterns and ecological dynamics, significantly impacting regional sustainable development. To assess the spatio-temporal variations of ecological risk in the YRBD and provide guidance for sustainable regional development, we constructed a coupled Land Use-Landscape Ecological Risk Model-Geographical Detector-PLUS framework for the assessment, analysis, and simulation of dryland landscape ecological risk (LER). The main findings are as follows: (1) Between 2000 and 2020, the area of built-up land, forest, grassland, and water in the YRBD increased, while the area of unused land and cropland decreased. (2) LER exhibited significant spatial heterogeneity, dominated by Sub-low and Low risks. High risk areas were primarily located in the western Inner Mongolia Plateau, whereas Low risk areas were prevalent in the Loess Plateau, with an overall decline in risk levels over the 20years. (3) Water resources, ecological status, and human activities are the main driving factors affecting LER, with the impact of human activities becoming increasingly significant over the past 20years. (4) Under three development scenarios in 2030, the LER is projected to further decrease, although the impact of these scenarios varies across different research sub-regions. Notably, the Ecological Priority Scenario emerges as more effective in mitigating regional LER. (5) Developing precise land use policies tailored to regional characteristics, continuously implementing ecological restoration projects, strengthening water resource management, and enhancing monitoring capabilities are effective ways to reduce LER in the YRBD. This study systematically quantified the impact of different development scenarios on LER in the YRBD, revealing its spatio-temporal characteristics, and emphasized the importance of planning guidance, ecological restoration, and risk monitoring to align regional development with ecological protection. The findings provide scientific evidence for ecological protection and sustainable development in the YRBD and other drylands, offering valuable insights for global dryland ecological risk management.

Data-Driven Water Monitoring System with Descriptive Analytics for Aquaculture Prawn Farms

This study aimed to develop a data-driven water monitoring system with descriptive analytics for aquaculture prawn farms. This system was designed to automate the monitoring of the water quality of the prawn farm so as to minimize labor and time spent for manually testing the water. Utilizing a rapid prototyping methodology, which is a rapid or quick creation of a prototype or a physical model of a device used in the development of a project, the said monitoring system was evaluated through a comparative analysis between the data gathered manually and the data collected by the monitoring system. To ensure reliability and validity of this monitoring system, it was used various times over several consecutive days. Also, a t-test was used to determine the significant differences between manual and system readings. Findings of the study showed that the designed water monitoring system provided accurate results, and the objectives of the study were met, particularly in terms of integrating a Raspberry Pi 3 microprocessor to automate the system in monitoring the water quality. It was also found out that the use of different sensors to test the water condition in terms of its temperature, pH level, salinity, dissolved oxygen, and turbidity was accurate. Furthermore, the system also met its objective of developing an Android mobile application to monitor the status remotely. With this, it is recommended for prawn farmers to utilize the system (with internet connectivity) for their efficient water quality management and to improve their remote monitoring capabilities.

In vitro and in vivo evaluation of a novel wired transmission pH-combined photographic catheter for ambulatory gastroesophageal reflux monitoring (with videos).

Twenty-four-hour pH-impedance monitoring is an important diagnostic approach for gastroesophageal reflux disease (GERD). Reflux monitoring results cannot be synchronized with ambulatory motility imaging of the esophageal sphincter. We have designed a novel wired transmission pH-combined photographic catheter (WT-CPC) for the synchronous acquisition of reflux image and pH. Different patterns of reflux events were simulated to perform in a porcine gastroesophageal reflux model in vitro. The live porcine model of gastroesophageal reflux was established in three Bama pigs. Monitoring was conducted with the WT-CPC and pH-impedance catheter simultaneously. Measurements included the number and proportion of reflux events, as well as acid exposure time (AET). The detection rates of WT-CPC for distal and horizontal acid reflux events were significantly higher compared to those of pH-impedance catheters (100% vs. 14.29%, 100% vs. 57.14%, P < 0.05). There was no significant difference between the two methods in proximal acid reflux events (P = 0.217). Regarding mixed reflux events, WT-CPC exhibited higher detection rates for distal events than pH-impedance catheter (100% vs. 42.86%, P < 0.05). However, there was no significant difference between the two methods for proximal reflux events (P > 0.05). Both methods showed similar results for horizontal reflux events. A porcine gastroesophageal reflux model was successfully established and utilized for reflux monitoring. A total of 28 episodes of reflux were detected within 6.5min. The detection rate achieved by WT-CPC for reflux events was significantly higher than that obtained by pH-impedance (100% vs. 78.57%, P = 0.023). The WT-CPC has demonstrated reflux monitoring capabilities in an isolated reflux organ model. It also showed good operability and performance in the porcine model. The WT-CPC holds promising potential to provide valuable diagnostic evidence for GERD.

Data Analysis and Algorithm Innovation in Power System IntelligentMonitoring and Early Warning Technology

Data analysis and algorithm innovation play a pivotal role in enhancing power system intelligent monitoring and early warning technology. With the increasing complexity of modern power grids, the integration of advanced data analytics enables real-time monitoring, fault detection, and predictive maintenance. By leveraging machine learning algorithms, anomaly detection techniques, and big data analytics, power systems can efficiently identify potential risks and failures before they escalate into serious issues. These innovations not only improve grid reliability and resilience but also optimize resource utilization. Early warning mechanisms based on intelligent algorithms provide timely alerts, allowing for preventive measures that ensure the stability and safety of the power network. This approach fosters a smarter, more adaptive power infrastructure capable of meeting growing energy demands while minimizing downtime and disruptions.This paper presents a comprehensive investigation into the development and efficacy of an intelligent early warning system for power systems. Leveraging machine learning algorithms, IoT sensors, and cloud computing frameworks, the system aims to enhance real-time monitoring capabilities and facilitate proactive intervention and maintenance. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. Specifically, the system achieves an average accuracy of 95%, precision of 92%, recall of 94%, and F1 score of 92% across multiple iterations. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems.

DESIGNING A MICROSERVICES BASED ENTERPRISE ARCHITECTURE USING TOGAF 10: A CASE STUDY APPROACH

PT Umbara Karya Sejati, operating in the home living and industrial product sectors, faced significant digital transformation challenges, marked by inefficiencies due to system integration issues and vendor dependencies that incurred cost penalties of 10-15% each month. Addressing these challenges, this research developed an Enterprise Architecture using TOGAF 10's Microservices Architecture (MSA) from the Preliminary Phase to Phase F: Migration Planning. This approach aimed to enhance system integration and operational efficiency, thereby improving modularity and scalability while reducing reliance on external vendors. A pivotal component of this architecture, an API gateway, provided robust monitoring capabilities for integration processes, enabling quick identification and prioritization of critical issues, which will assist the internal IT team's workflow. Furthermore, the research planned the establishment of a DevOps team, incorporating Agile methodologies, and scheduled IT governance and data security training to prepare for future policy development and strengthen internal controls. This strategic design equips the organization to navigate the volatility, uncertainty, complexity, and ambiguity (VUCA) of market demands with agile and effective responses, projecting a 10% increase in both operational and cost efficiency for PT Umbara Karya Sejati.

Self-consuming solar-powered campus monitoring drone

Drones are utilised for a variety of purposes and are a regular sight these days. Selfies, insecticide application, and military monitoring. The issue with surveillance and monitoring is that a lot of applications call for continuous surveillance. Although they have a major disadvantage, drones do offer a good view for surveillance and monitoring. This is the battery life of the drone. The main worry a drone operator has when operating a surveillance drone is that the battery might die and the drone might crash into a building, tree, or other unreachable place from which it cannot be recovered and hence cannot be charged. This is also true for drones used in military surveillance; the risk of a drone's battery dying and becoming unusable places restrictions on drone operators while they are conducting surveillance or monitoring. Here, we've developed a drone that solves these issues by using solar power to continuously charge the device, extending its flying time. It can also land anywhere and remotely recharge its battery so it can take off later. The development and deployment of a solar-powered, self-charging campus surveillance drone are suggested in this research. By offering continual monitoring capabilities without requiring manual charge, the drone seeks to answer the demand for increased security measures on campus. The drone can recharge itself during the day because to the solar panels in its construction, which capture energy from sunshine. The drone can recharge itself during the day because to the solar panels in its construction, which capture energy from sunshine. This maximizes security coverage while minimizing downtime and guarantees continuous operation. Drone capabilities are further enhanced by the incorporation of cutting-edge surveillance technology, which enable detection of threats and constant surveillance. With possible applications in educational institutions, corporate campuses, and large-scale facilities, the suggested method provides an effective and long-lasting answer to campus security concerns.

Spectro-environmental factors integrated ensemble learning for urban river network water quality remote sensing

Remote sensing water quality monitoring technology can effectively supplement the shortcomings of traditional water quality monitoring methods in spatiotemporal dynamic monitoring capabilities. At present, although the spectral feature-based remote sensing water quality inversion models have achieved many successes, there could still be a problem of insufficient generalization ability in monitoring the water quality of complex river networks in large cities. In this paper, we propose a spectro-environmental factors integrated ensemble learning model for urban river network water quality inversion. We analyzed the correlation between water quality parameters, spectral reflectance, and environmental factors based on an in-situ dataset collected in the northern part of Shanghai. Using the Hot Spot Analysis (Getis-Ord Gi*), we found that river network water quality parameters have different patterns in different urban functional zones. Furthermore, daily average temperature, total rainfall within the seven days, and several band combinations were also selected as the environmental and spectral features using factor analysis and Pearson correlation coefficient analysis. After the feature analysis, the spectro-environmental factors integrated ensemble learning model was trained. Compared with the spectral-based machine learning inversion models, the coefficients of determination R2 increased by about 0.50. Our model was also tested in three different test areas within and outside the in-situ sampling areas in Shanghai based on low-altitude multispectral remote sensing images. The R2 results for total phosphorus (TP), ammonia nitrogen (NH3-N), and chemical oxygen demand (COD) within the in-situ sampling areas were 0.52, 0.58, and 0.56 respectively. The mean absolute percentage error (MAPE) results were 53.36%, 63.95%, and 22.46% respectively. After adding the area outside the in-situ sampling areas, the R2 results for TP, NH3-N, and COD were 0.47, 0.47, and 0.53. The MAPE were 49.38%, 74.46%, and 20.49%. Our research provided a new remote sensing water quality inversion method to be utilized in complex urban river networks which exhibited solid accuracy and generalization ability.

Sensing volatile organic compounds in aquatic samples: a review

ABSTRACT Volatile organic compounds (VOCs) are toxic and harmful to human health; thus, their detection in water samples is critical for ensuring safe drinking water and protecting public health. The aim of this review was to explore the various techniques available in the literature for detecting VOCs in water samples and discuss their features, advantages, and limitations in facilitating fast and rapid VOC analysis. Established techniques such as gas chromatography and mass spectrometry offer high sensitivity and selectivity but are often hindered by complexity and cost. In contrast, emerging technologies like microfluidic gas sensors and cantilever-based sensors provide portable and real-time monitoring capabilities, although they may be susceptible to matrix effects. Overcoming these challenges requires advanced sample preparation techniques and method optimization. Additionally, the integration of machine learning algorithms and nanotechnology shows promise for enhancing sensitivity and precision in the detection of VOCs. By overcoming these limitations and leveraging technological advancements, researchers can improve water quality monitoring and help protect the environment.

MHealth Apps for Hypertension Self-Management: Interview Study Among Patient-Users.

Hypertension is a major risk factor for cardiovascular disease, affecting over a billion people worldwide. Mobile health (mHealth) apps have emerged as effective tools for managing hypertension, offering capabilities for monitoring blood pressure, fostering lifestyle changes, and improving treatment adherence. This study aimed to explore patient-users' perspectives on the hypertension care mHealth app Hypertension.APP, focusing on its accessibility, expected benefits, potential risks, and role in hypertension management in Germany. A qualitative study was conducted involving semistructured interviews with 20 patient-users of a hypertension care mHealth app, Hypertension.APP. Participants were recruited between January and June 2023 using purposive sampling. Verbatim transcripts were analyzed using qualitative content analysis. Participants primarily discovered the app independently, driven by recent hypertension diagnoses and insufficient information from health care professionals regarding effective self-management strategies for their blood pressure. They valued the app for its continuous monitoring and feedback capabilities, aiding in understanding their condition and making lifestyle adjustments. Risks were perceived as minimal, mainly concerning data privacy and potential overreliance on the app. The app became integral to patient-users' hypertension management by offering consistent information and support. The integration into formal health care was limited, as patient-users felt that health care professionals did not accept the use of the technology or might have even felt intimidated to use it. Among the sample studied, mHealth apps like Hypertension.APP were valued for their continuous monitoring and educational content, aiding in hypertension management. The findings suggest potential benefits of mHealth apps for effective hypertension care among patients who are health- and digitally literate as well as self-effective. There is a critical need for better integration of these apps into routine health care practices, as perceived by the app users. Given the small and specific sample of this qualitative study, further quantitative research with a broader and more varied participant group is necessary to validate these findings. Deutsches Register Klinischer Studien DRKS00029761; https://tinyurl.com/r33ru22s.

Sparse regularized graph pooling network optimal sensor placement method for diesel engine vibration fault perception system

Vibration sensor network optimization increases monitoring effectiveness and reduces sensor quantity and transmission burden. However, the traditional model-driven methods depend on precise finite element models, challenging for complex machinery. This paper presents a data-driven approach using the Sparse Regularized Graph Pooling Network (SRGPN), which conceptualizes sensor networks as graphs and uses graph pooling to identify optimal sensor combinations. A sparsity regularization term related to the number of sensors is included in the loss function, aiming for the minimal yet effective sensor combination. Additionally, a monitoring capability metric suited for diesel engines with multi-source impulse signals is proposed, reflecting the sensors’ monitoring capacity. Validated through simulations and tests on a diesel engine test bench, the results show that SRGPN optimally places sensors near excitation sources, balancing sensor count and monitoring needs. This approach shows potential for optimizing sensor placements in condition monitoring.