Conventional Monitoring Methods Research Articles

Safeguarding Vascular Health: Unleashing the Potential of Smartphone Early Warning Systems to Elevate Phlebitis Prevention in IV Infusion Therapy

Intravenous (IV) infusion is a pervasive medical intervention, administered to approximately 90% of hospitalized patients. Phlebitis, characterized by inflammation of the veins resulting from infusion, stands as a prevalent complication, ranking fourth among hospitalacquired infections globally. This research investigates the efficacy of a Smartphone Early Warning System (EWS) display in mitigating the incidence of phlebitis within the Safa treatment room at Aisyiyah Hospital. Employing a pre-experimental research design with a Static-group Comparison approach, 16 respondents were allocated to treatment and control groups. The Mann-Whitney Test, a statistical analysis, unveiled a significant difference (P Value = 0.001 < 0.05) in phlebitis incidence between the treatment group, utilizing the Smartphone EWS display, and the control group, which relied on conventional monitoring methods. Notably, the average rank of phlebitis incidence in the control group (21.12) exceeded that in the treatment group (9.78). This study sheds light on the potential of the Smartphone EWS display to curtail phlebitis during infusion, emphasizing its role in advancing nursing care quality through real-time monitoring and early prevention strategies.

Answering the key stakeholder questions about the impact of offshore wind farms on marine life using hypothesis testing to inform targeted monitoring

Abstract Stakeholders need scientific advice on the environmental impacts of offshore wind (OW) before the facilities are installed. The utility of conventional environmental monitoring methods as a basis for forecasting OW impacts is limited because they do not explain the causes of the observed effects. We propose a multistep approach, based on process-oriented hypothesis testing, targeted monitoring and numerical modeling, to answer key stakeholder questions about planning an OW facility: Q1—Where do we place future OW farms so that impacts on the ecosystem are minimized? Q2—Which species and ecosystem processes will be impacted and to what degree? Q3—Can we mitigate impacts and, if so, how? and Q4—What are the risks of placing an OW facility in one location vs. another? Hypothesis testing can be used to assess impacts of OW facilities on target species-ecological process. This knowledge is transferable and is broadly applicable, a priori, to assess suitable locations for OW (Q1). Hypothesis testing can be combined with monitoring methods to guide targeted monitoring. The knowledge generated can identify the species/habitats at risk (Q2), help selecting/developing mitigation measures (Q3), and be used as input parameters for models to forecast OW impacts at a large spatial scale (Q1; Q4).

Clarity tubes as effective citizen science tools for monitoring wastewater treatment works and rivers.

Improved freshwater resource management requires the implementation of widespread, effective, and timely water quality monitoring. Conventional monitoring methods are often inhibited by financial, infrastructural, and human capacity limitations, especially in developing regions. This study aimed to validate the citizen-scientist-operated transparency or clarity tube (hereafter "clarity tube") for measuring water clarity as a proxy for total suspended solids (TSS) concentration, a critical quality metric in river systems and wastewater treatment works (WWTW) effluent in Southern Africa. Clarity tubes provided a relatively accurate and precise proxy for TSS in riverine lotic systems and WWTW effluent, revealing significant inverse log-linear relationships between clarity and TSS with r2 = 0.715 and 0.503, respectively. We demonstrate that clarity-derived estimates of TSS concentration (TSScde) can be used to estimate WWTW compliance with WWTW effluent TSS concentration regulations. The measurements can then be used to engage with WWTW management, potentially affecting WWTW performance. Overall, these findings demonstrate the usefulness of clarity tubes as low-cost, accessible, and easy-to-use citizen science tools for high spatial and temporal resolution water quality monitoring, not only in rivers in Southern Africa but also in WWTW effluent for estimating compliance, with strong global relevance to the sustainable development goals (SDGs). Integr Environ Assess Manag 2024;00:1-10. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

A blockchain-machine learning ecosystem for IoT-Based remote health monitoring of diabetic patients

Diabetes poses a global health challenge, demanding continuous monitoring and expert care for effective management. Conventional monitoring methods lack real-time insights and secure data-sharing capabilities, necessitating innovative solutions that leverage emerging technologies. Existing centralized monitoring systems often entail risks such as data breaches and single points of failure, emphasizing the necessity for a secure, decentralized approach that integrates the Internet of Things (IoT), blockchain, and machine learning for efficient and secure diabetes management. This paper introduces a decentralized, blockchain-based framework for remote diabetes monitoring, IoT sensors, machine learning models, and decentralized applications (DApps). The proposed framework comprises five layers: the IoT Sensor Layer, which collects real-time health data from patients; the Blockchain Layer, leveraging smart contracts on the Ethereum blockchain for secure data sharing and transactions; the machine learning Layer, analyzing patient data to detect diabetes; and the DApps Layer, facilitating interactions between patients, doctors, and hospitals. For intelligent decision-making regarding diabetes based on data collected from different sensors, nine machine learning algorithms, including logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, Random Forest, AdaBoost, stochastic gradient boosting (SGD), and Naive Bayes, were trained and tested on the PIMA dataset. Based on the performance evaluation parameters such as accuracy, recall, F1-score, and the area under the curve (AUC), it was found that the AdaBoost model achieved the highest predictive accuracy of 92.64%, followed by the Decision Tree with an accuracy of 92.21% in diabetes classification.

DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

Combined 2D- and 3D ERT monitoring as a geophysical tool for investigating spatial and temporal soil moisture fluctuations in a pine-beech forest

As climate change continues, forests are increasingly suffering from drought stress, which is leading to widespread forest dieback, but also to increased mortality of individual trees. In this regard, the impact of small-scale differences in water availability on individual trees has not yet been sufficiently studied to determine possible responses of different tree species to future droughts. Since conventional soil moisture monitoring and sampling methods only consider single points or small volumes, Electrical Resistivity Tomography (ERT) is becoming increasingly important to cover a larger survey area and to detect small-scale heterogeneities with regard to soil properties and soil moisture.The current study describes the application of a combined two- and three-dimensional geoelectrical monitoring approach with daily measurements over two years (May 2021 – April 2023) in a forest ecosystem in Lower Franconia (northwestern Bavaria, Germany), which is strongly affected by climate change. Soil water content, soil matric potential, throughfall, and stem flow are also measured at the forest site as well as precipitation at an adjacent forest clearing.The seasonally (long-term) and precipitation-driven (short-term) temporal change of soil resistivity is correlated strongly with the measured soil water content and matric potential. The applied 3D-ERT approach also allowed a first three-dimensional monitoring of the subsurface below a European beech located in the middle of the measuring grid with a daily resolution. The corresponding results also provide first indications that besides soil moisture changes also chemical processes in the subsurface may influence temporal resistivity changes in the soil of forest sites.The results of this study show that daily 3D-ERT is very suitable for investigating small-scale as well as short- and long-term variations in soil moisture, which is becoming increasingly important for understanding the causal relationships considering tree mortality and the subsurface.

Automated monitoring innovations for efficient and safe construction practices

As construction projects increase in complexity, there are growing challenges with conventional monitoring methods in terms of efficiency, safety, and competitiveness. Traditional supervision techniques are labour-intensive, intermittent, and prone to errors. Hence, this study statistically evaluates the potential advantages of photogrammetry, sensors, and algorithms to enable continuous automated monitoring. The results of this survey were analysed to compare manual and automated monitoring systems. The outcome shows that the Malaysian construction industry is aware of Automated Monitoring Innovations for Efficient and Safe Construction Practices. The top ranked factor was Photogrammetry which had a relative importance index (RII) of 0.821 for straightforward site monitoring and 0.812 for accelerated 3D BIM modelling. The RII for sensors to track labourers, apparatus, and progress in real time was 0.82, while the RII for hazard anticipation was 0.796. Automation achieved a reduction in fatigue by 0.784, labour intensity by 0.792, and time demands by 0.768, as measured by the RII. A conceptual framework was developed that incorporates measurable improvements in schedules, safety, and quality control. Automated solutions, as opposed to human examinations which are prone to error, provided exhaustive geographical data and ongoing surveillance notwithstanding obstacles pertaining to cost, cybersecurity, privacy, and integration. Construction monitoring must incorporate new technology, strategic change management, data investments, and supporting regulations to increase profitability, safety, and efficiency as competition and complexity increase.

Human Eye Activity Monitoring Using Continuous Wave Doppler Radar: A Feasibility Study.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

A novel transformer-enhanced and acoustic-based approach for wind turbine blade fault detection with integrated system implementation

Rising global wind power capacity urgently requires effective real-time turbine monitoring. Complex installations, high maintenance costs, and the occasional need for turbine shutdown hinder conventional monitoring methods. Acoustic-based techniques, while cost-efficient for health assessments, currently face challenges with detection precision and signal processing capabilities in complex acoustic environments. To overcome these challenges, this study proposes a low-cost, AI-driven acoustic-based health monitoring system framework for wind turbine blades, featuring enhanced detection accuracy and signal processing capabilities in complex sound environments. Firstly, a microphone array is employed to capture blade sounds with rich spatial attributes, while beamforming algorithms with a fixed orientation integrate prior spatial information. Furthermore, to further strengthen signal processing and fault detection capabilities, the Transformer model based on self-attention mechanisms is employed for feature extraction and fault diagnosis. An actual wind turbine blade health monitoring system is designed and developed to validate the usability of the proposed framework. Experimental results based on a scaled-down wind turbine model validate the proposed algorithm's effectiveness and practicality, presenting an effective approach in the wind turbine blade health monitoring domain.

Empowering coffee farming using counterfactual recommendation based RNN driven IoT integrated soil quality command system

Soil health is essential for whirling stale soil into rich coffee-growing land. By keeping healthy soil, coffee producers may improve plant growth, leaf health, buds, cherry and bean quality, and yield. Traditional soil monitoring is tedious, time-consuming, and error-prone. Enhancing the monitoring system using AI-based IoT technologies for quick and precise changes. Integrated soil fertility control system to optimize soil health, maximize efficiency, promote sustainability, and prevent crop threads using real-time data analysis to turn infertile land into fertile land. The RNN-IoT approach uses IoT sensors in the coffee plantation to collect real-time data on soil temperature, moisture, pH, nutrient levels, weather, CO2 levels, EC, TDS, and historical data. Data transmission using a wireless cloud platform. Testing and training using recurrent neural networks (RNNs) and gated recurrent units gathered data for predicting soil conditions and crop hazards. Researchers are carrying out detailed qualitative testing to evaluate the proposed RNN-IoT approach. Utilize counterfactual recommendations for developing alternative strategies for irrigation, fertilization, fertilizer regulation, and crop management, taking into account the existing soil conditions, forecasts, and historical data. The accuracy is evaluated by comparing it to other deep learning algorithms. The utilization of the RNN-IoT methodology for soil health monitoring enhances both efficiency and accuracy in comparison to conventional soil monitoring methods. Minimized the ecological impact by minimizing water and fertilizer utilization. Enhanced farmer decision-making and data accessibility with a mobile application that provides real-time data, AI-generated suggestions, and the ability to detect possible crop hazards for swift action.

Scalable semantic 3D mapping of coral reefs with deep learning

Abstract Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existentially threatened by global climate change and local anthropogenic pressures. To better understand the dynamics underlying deterioration of reefs, monitoring at high spatial and temporal resolution is key. However, conventional monitoring methods for quantifying coral cover and species abundance are limited in scale due to the extensive manual labor required. Although computer vision tools have been employed to aid in this process, in particular structure‐from‐motion (SfM) photogrammetry for 3D mapping and deep neural networks for image segmentation, analysis of the data products creates a bottleneck, effectively limiting their scalability. This paper presents a new paradigm for mapping underwater environments from ego‐motion video, unifying 3D mapping systems that use machine learning to adapt to challenging conditions under water, combined with a modern approach for semantic segmentation of images. The method is exemplified on coral reefs in the northern Gulf of Aqaba, Red Sea, demonstrating high‐precision 3D semantic mapping at unprecedented scale with significantly reduced required labor costs: given a trained model, a 100 m video transect acquired within 5 min of diving with a cheap consumer‐grade camera can be fully automatically transformed into a semantic point cloud within 5 min. We demonstrate the spatial accuracy of our method and the semantic segmentation performance (of at least 80% total accuracy), and publish a large dataset of ego‐motion videos from the northern Gulf of Aqaba, along with a dataset of video frames annotated for dense semantic segmentation of benthic classes. Our approach significantly scales up coral reef monitoring by taking a leap towards fully automatic analysis of video transects. The method advances coral reef transects by reducing the labor, equipment, logistics, and computing cost. This can help to inform conservation policies more efficiently. The underlying computational method of learning‐based Structure‐from‐Motion has broad implications for fast low‐cost mapping of underwater environments other than coral reefs.

On-line condition monitoring for rotor systems based on nonlinear data-driven modelling and model frequency analysis

This paper proposes a novel on-line rotor system condition monitoring approach using nonlinear data-driven modelling and model frequency analysis. First, the dynamic process model of the vibration transmission path between the vibration measurement points of two fulcrum structures is established by utilizing nonlinear data-driven modelling. Then, the unique frequency properties are extracted from the established model to reveal, in real time, the health condition of the rotor system. Finally, using the frequency properties as features, the unsupervised learning technology is applied to the on-line monitoring of the rotor system. Compared to conventional condition monitoring methods, the proposed approach can output an early warning 26 min before a shaft fracture occurs, without generating false alarms. Consequently, this approach can greatly enhance diagnostic accuracy, demonstrating its potential to contribute to the advancement of rotor system condition monitoring techniques.

Image detection model construction of Apolygus lucorum and Empoasca spp. based on improved YOLOv5.

The polyphagous mirid bug Apolygus lucorum (Meyer-Dür) and the green leafhopper Empoasca spp. Walsh are small pests that are widely distributed and important pests of many economically important crops, especially kiwis. Conventional monitoring methods are expensive, laborious and error-prone. Currently, deep learning methods are ineffective at recognizing them. This study proposes a new deep-learning-based YOLOv5s_HSSE model to automatically detect and count them on sticky card traps. Based on a database of 1502 images, all images were collected from kiwi orchards at multiple locations and times. We trained the YOLOv5s model to detect and count them and then changed the activation function to Hard swish in YOLOv5s, introduced the SIoU Loss function, and added the squeeze-and-excitation attention mechanism to form a new YOLOv5s_HSSE model. Mean average precision of this model in the test dataset was 95.9%, the recall rate was 93.9% and the frames per second was 155, which are higher than those of other single-stage deep-learning models, such as SSD, YOLOv3 and YOLOv4. The proposed YOLOv5s_HSSE model can be used to identify and count A. lucorum and Empoasca spp., and it is a new, efficient and accurate monitoring method. Pest detection will benefit from the broader applications of deep learning. © 2024 Society of Chemical Industry.

Measuring Dam Deformation of Long-Distance Water Transfer Using Multi-Temporal Synthetic Aperture Radar Interferometry: A Case Study in South-to-North Water Diversion Project, China

Long-distance water transfer is a critical engineering measure to rectify disparities in water resource distribution across regions. The effective operation and safety of such projects are paramount to their success, as localized issues can have cascading consequences, potentially disrupting the entire network. Conventional ground-based monitoring methods have limitations in measuring the deformation of large-scale structures. In this paper, InSAR is employed to monitor the deformation of the Shuangwangcheng (SWC) Reservoir, which features a long embankment dam as part of the South-to-North Water Diversion Project in China. We utilize data from both Sentinel-1 and TerraSAR-X satellites to derive 7-year deformation. Results reveal that the entire dam experiences continuous subsidence, with the maximum deformation in the line-of-sight direction measuring ~160 mm. While minor differential settlements are noted in different sections of the dam, the gradient is not significant due to the dam’s substantial length. The InSAR deformation results from multiple geometries demonstrate good consistency, with the highest correlation observed between the Sentinel-1 ascending and descending datasets, exceeding 0.9. Validation against the GNSS observations of the three sites on the SWC Dam shows the accuracy of InSAR displacements is ~8 mm. Water level changes do impact deformation, but consolidation settlement appears to be the primary controlling factor during the monitoring period. This study underscores the potential of InSAR in long-distance water transfer projects and highlights that spatially continuous deformation is the most significant advantage.

Progress in Research on Marine Litter-Related Monitoring Technologies

The problem of marine litter pollution is becoming increasingly serious and has become one of the important global environmental issues. From the perspective of source and sink, floating riverine litter (FRL) is the main source of floating marine litter (FML), while beach litter (BL) and FML are the source and sink of each other. It is necessary to research the transformation mechanism and collaborative governance policy among these three kinds of marine litter, based on accurate monitoring data. However, conventional monitoring methods have problems such as high labor costs, insufficient representativeness and poor comparability. Hence, developing efficient and intelligent monitoring technology is the focus of recent future. Since 2018, monitoring technique of marine litter relevant research literature has grown rapidly, showing this technique a hot spot. In this paper, the relevant research literature is divided into three litter types (BL, FML, and FRL) and two ways (fixed monitoring and mobile monitoring). Similarities and differences in research focus, platform selection, result accuracy and result application are summarized and reasoned, with a view to accelerating the research progress and better supporting BL, FML, and FRL control in China.

Monitoring Electrochemical Signals from Sacrificial Coatings for Pipeline Materials with Membrane-Based Sensors

Monitoring internal corrosion within pipelines is essential to the safe management of gas transportation infrastructures. Membrane-based electrochemical sensors are a promising new method of monitoring corrosion risk, as they can operate in humidified gas environments which were previously inaccessible to conventional electrochemical monitoring methods. In parallel, new sacrificial coatings and coating application methods are being explored to extend the lifetime of existing pipeline structures and minimize repair costs. Herein, we extend the use of membrane-based electrochemical sensors to examine the response of electrodes with sacrificial coatings for a series of fluids with varying extents of corrosivity applicable to natural gas pipelines. Multiple sensors with X65 steel working electrodes were coated then exposed to humidified gases with differing relative humidities (RH) (5 , 50 , and 95 %) and condensed phases with differing salt content (deionized water and 0.1 mol kg-1 NaCl solution). Three electrochemical techniques (potentiometry, impedance spectroscopy and potentiodynamic scans) were used to monitor the corrosive environment and probe the extent of coating coverage. For the conditions studied, impedance values were extremely sensitive to changes in water content through changes in the membrane resistance (from ~1 kΩ in deionize water to ~10 MΩ with 5 % RH). Polarization resistances (Rp) consistently decreased with increasing corrosivity (~10 kΩ in deionized water to 1 MΩ with 5 % RH). Of the methods used, open circuit potential (OCP) values were the most sensitive to the presence of a sacrificial coating. Fully coated samples typically had OCP values 0.2 V lower than uncoated and partially coated samples in humidified environments, suggesting this sensing method made provide a means to monitor coating lifetime.

Methane Exchange Flux Monitoring between Potential Source Sewage Inspection Wells and the Atmosphere Based on Laser Spectroscopy Method

Greenhouse gas emissions are changing the climate and affecting human activities. In cities, the anaerobic environment in sewage inspection wells produces CH4, which is exchanged with the atmosphere and causes pollution. Moreover, although the number of inspection wells has increased with the development of cities, people have not paid enough attention to this potential source of CH4 and ignored it in the statistics of greenhouse gas inventories. Conventional gas monitoring methods like gas chromatography are complex and expensive. Based on the portable TDLAS CH4 sensor developed by our team, combined with a gas velocity analyser, we realised in situ continuous flux monitoring. We corrected the effect of temperature on the results according to the theory of infrared thermometry. We showed that the measurement results of the sensor fluctuate within the range of ±0.1 ppm of the measured standard gas concentration. We also verified its repeatability and ensured its reliability in field applications by comparing its results with the results of gas chromatography analysis. In addition to flux monitoring, based on the monitoring data of 56 inspection wells in the study area, the average concentration was estimated using the Bootstrap method, and combined with the average value of gas velocity, the average flux was estimated to be 2.19 × 10−6 mol/s, and the daily exchange mass was 3.03 g CH4 d−1. Combined with information such as the length of sewage pipes, we estimate that the annual CH4 exchange mass in the city is about 5.49 × 105 kg CH4 yr−1. This monitoring method will help us to understand climate change and improve greenhouse gas inventories.

RESEARCH ON THE DIRECTIONS OF MONITORING THE USE OF REAL ESTATE IN SETTLEMENTS

Monitoring real estate assets in settlements is pivotal in effective territorial management. Accurate information on the status and utilisation of these assets is essential for informed decision-making, resource allocation, and the sustainable development of lands within settlements. This research delves into the multifaceted methodologies used to achieve these objectives. The driving force behind this research is the demand for data-driven, geolocation-based decision-making. The precise evaluation of real estate assets within settlements is indispensable for efficient land use planning, infrastructure development, and the prudent management of natural resources. In the ever-evolving urban landscape, comprehending the dynamics of real estate assets is strategically imperative, facilitating optimal resource allocation and sustainable growth. Populated areas often present intricate, multi-layered geographical landscapes. Conventional real estate monitoring methods, such as manual inspections and paper documentation, often fall short of the required timeliness and accuracy for effective management. It is here that modern technologies step in, offering innovative solutions to augment the monitoring process. Geographic Information Systems (GIS) are at the forefront of this transformation. They enable the collection, analysis, and visualisation of spatial data, offering a multidimensional perspective on real estate assets. Through GIS, real-time insights into asset usage, condition, and their impact on the environment become attainable. Utilising GIS empowers decision-makers and stakeholders, aiding informed choices that facilitate sustainable urban planning and development. Furthermore, integrating remote sensing technology elevates the monitoring of real estate assets in settlements. Satellite imagery and aerial photography provide high-resolution data to detect changes and trends. That is particularly valuable for assessing urban expansion, environmental shifts, and the allocation of lands for agriculture, industry, or residential purposes. Ensuring data accuracy, accessibility, and confidentiality is a paramount aspect of efficient real estate monitoring. Advanced data analytics, machine learning algorithms, and blockchain technology can be leveraged to safeguard data integrity, ensure privacy, and enhance the overall quality of information. In summary, research on monitoring the use of real estate assets in settlements stands as a critical field with wide-reaching implications. The amalgamation of GIS, remote sensing technology, and advanced data analytics is reshaping our approach to comprehending, managing, and planning land usage in urban and rural contexts. By harnessing the capabilities of modern technology and data-driven insights, we pave the way for more sustainable and efficient territorial management. Keywords: spatial provision, monitoring, geoinformation system, land use, cadastral data, modelling.

Organisms as sensors in biohybrid entities as a novel tool for in-field aquatic monitoring

Rapidly intensifying global warming and water pollution calls for more efficient and continuous environmental monitoring methods. Biohybrid systems connect mechatronic components to living organisms and this approach can be used to extract data from the organisms. Compared to conventional monitoring methods, they allow for a broader data collection over long periods, minimizing the need for sampling processes and human labour. We aim to develop a methodology for creating various bioinspired entities, here referred to as ‘biohybrids’, designed for long-term aquatic monitoring. Here, we test several aspects of the development of the biohybrid entity: autonomous power source, lifeform integration and partial biodegradability. An autonomous power source was supplied by microbial fuel cells which exploit electron flows from microbial metabolic processes in the sediments. Here, we show that by stacking multiple cells, sufficient power can be supplied. We integrated lifeforms into the developed bioinspired entity which includes organisms such as the zebra mussel Dreissena polymorpha and water flea Daphnia spp. The setups developed allowed for observing their stress behaviours. Through this, we can monitor changes in the environment in a continuous manner. The further development of this approach will allow for extensive, long-term aquatic data collection and create an early-warning monitoring system.

Seasonal activity of Trechnites insidiosus (Hymenoptera: Encyrtidae) and its host Cacopsylla pyricola (Hemiptera: Psyllidae) in pear.

Cacopsylla pyricola (Förster) (Hemiptera: Psyllidae) is the most expensive and challenging insect pest of commercial pear trees in the Pacific Northwest. Integrated pest management (IPM) programs are working toward relying more heavily on natural enemies to reduce insecticide use. Trechnites insidiosus (Crawford) (Hymenoptera: Encyrtidae) is the main parasitoid of C. pyricola, but little is known about its biology in the region. Developing sampling tools is important for the deployment of IPM programs, including monitoring of natural enemies. In this study, we examined 2 conventional monitoring methods: beat trays and yellow sticky cards, in addition to screened sticky cards and 3D-printed cylinder traps. Additionally, we tested an overwintering trap for the collection of parasitized C. pyricola. The trapping methods were tested in orchards in Oregon and Washington. Unscreened cards caught the most T. insidiosus and C. pyricola, followed by screened cards, cylinder traps, and then beat trays. Beat trays sometimes failed to catch any T. insidiosus, even when it was found in abundance via other methods. Screened cards and cylinder traps reduced bycatch and increased ease of identifying T. insidiosus. Specimens from the cylinder traps were also more suitable for use in molecular analysis. The overwintering traps were effective at capturing parasitized C. pyricola, but were highly variable year to year. The ideal trapping method will vary based on research needs (e.g., DNA preservation, reducing bycatch, catching higher numbers), but both screened sticky cards and cylinder traps were viable methods for monitoring T. insidiosus and its host.