A Water Surface Contaminants Monitoring Method Based on Airborne Depth Reasoning

Water surface plastic pollution turns out to be a global issue, having aroused rising attention worldwide. How to monitor water surface plastic waste in real time and accurately collect and analyze the relevant numerical data has become a hotspot in water environment research. (1) Background: Over the past few years, unmanned aerial vehicles (UAVs) have been progressively adopted to conduct studies on the monitoring of water surface plastic waste. On the whole, the monitored data are stored in the UAVS to be subsequently retrieved and analyzed, thereby probably causing the loss of real-time information and hindering the whole monitoring process from being fully automated. (2) Methods: An investigation was conducted on the relationship, function and relevant mechanism between various types of plastic waste in the water surface system. On that basis, this study built a deep learning-based lightweight water surface plastic waste detection model, which was capable of automatically detecting and locating different water surface plastic waste. Moreover, a UAV platform-based edge computing architecture was built. (3) Results: The delay of return task data and UAV energy consumption were effectively reduced, and computing and network resources were optimally allocated. (4) Conclusions: The UAV platform based on airborne depth reasoning is expected to be the mainstream means of water environment monitoring in the future.

Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder

Continuous monitoring of blood pressure (BP) is essential for the prediction and the prevention of cardiovascular diseases. Cuffless BP methods based on non-invasive sensors integrated into wearable devices can translate blood pulsatile activity into continuous BP data. However, local blood pulsatile sensors from wearable devices suffer from inaccurate pulsatile activity measurement based on superficial capillaries, large form-factor devices and BP variation with sensor location which degrade the accuracy of BP estimation and the device wearability. This study presents a cuffless BP monitoring method based on a novel bio-impedance (Bio-Z) sensor array built in a flexible wristband with small-form factor that provides a robust blood pulsatile sensing and BP estimation without calibration methods for the sensing location. We use a convolutional neural network (CNN) autoencoder that reconstructs an accurate estimate of the arterial pulse signal independent of sensing location from a group of six Bio-Z sensors within the sensor array. We rely on an Adaptive Boosting regression model which maps the features of the estimated arterial pulse signal to systolic and diastolic BP readings. BP was accurately estimated with average error and correlation coefficient of 0.5 ± 5.0 mmHg and 0.80 for diastolic BP, and 0.2 ± 6.5 mmHg and 0.79 for systolic BP, respectively.

One Bat’s Waste is Another Man’s Treasure: A DNA Metabarcoding Approach for the Assessment of Biodiversity and Ecosystem Services Using Bat Faeces

Arthropod populations are constantly changing due to changes in climate and the globalisation of trade and travel. Effective and diverse monitoring techniques are required to understand these changes. DNA metabarcoding has facilitated the development of a broad monitoring method to sample arthropod diversity from environmental and faecal samples. In this study, we applied DNA metabarcoding to DNA extracted from bat faecal pellets of Rhinolophus hipposideros, the lesser horseshoe bat in Ireland, a highly protected bat species of conservation concern in Europe. From as few as 24 bat faecal pellets, we detected 161 arthropod species, spanning 11 orders, including 38 pest species of which five were determined to be priority pests, highlighting important ecosystem services. We also report the identification 14 species not previously reported in Ireland, but upon further investigation found that many of these are likely misidentified due to inadequacies in the genetic reference database. For the first time, we were able to use non-invasively collected bat samples to examine the role of sex in the diet of bats and found that the male and female diets did not differ significantly. However, sampling location did explain variation within the diet, highlighting how landscape features influence arthropod composition and diversity. We discuss the current limitations of the methodology in Ireland, how these can be overcome in future studies, and how this data can be used for biodiversity monitoring and informing conservation management of protected bat species.

A novel method for diagnosing rolling bearing surface damage by electric impedance analysis

This paper presents a novel condition monitoring method for rolling bearings, based on measuring the electric bearing impedance. The method can diagnose the presence of damage by frequency-domain analysis, and its extension along the raceway by time-domain analysis. The latter enables the assessment of the severity and the progression of bearing damage. A fatigue test shows that the occurrence of pittings in the bearing raceways causes characteristic peaks in the impedance signal, and that the duration of the peaks increases during damage progression. A second test series with artificial damage shows that the duration of the peaks depends on the bearing load and the length of the damage along the raceway and confirms the explanation hypothesis.

Micro-Bioreactors in Space: Case Study of a Yeast (Saccharomyces cerevisiae) Bioreactor With a Non-Invasive Monitoring Method

Bioreactors in space have applications from basic science to microbial factories. Monitoring bioreactors in microgravity has challenges with respect to fluidics, aeration, sensor size, sample volume and disturbance of medium and cultures. We present a case study of the development of small bioreactors and a non-invasive method to monitor dissolved oxygen, pH, and biomass of yeast cultures. Two different bioreactor configurations were tested for system volumes of 60 ml and 10.5 ml. For both configurations, the PreSens SFR vario, an optical sensor array, collected data autonomously. Oxygen and pH in the cultures were monitored using chemically doped spots, 7 mm in diameter, that were fixed to the bottom of sampling chambers. Spots emitted a fluorescent signal for DO and pH when reacted with oxygen molecules and hydrogen ions, respectively. Biomass was sensed using light reflectance at centered at 605 nm. The, optical array had three light detectors, one for each variable, that returned signals that were pre- and post-calibrated. For heterotrophic cultures requiring oxygen and respiring carbon dioxide, a hollow fiber filter, in-line with the optical array, oxygenated cells and remove carbon dioxide. This provided oxygen levels that were sufficient to maintain aerobic respiration for steady state conditions. Time series of yeast metabolism in the two bioreactors are compared and discussed. The bioreactor configurations can be easily be modified for autotrophic cultures such that carbon dioxide is enhanced and oxygen removed, which would be required for photosynthetic algal cultures.