Investigating quantitative approach for microalgal biomass using deep convolutional neural networks and image recognition.

The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) and residual network (RES), is proposed. Suspension samples prepared from two types of dried microalgae powders, Rhodophyta (RH) and Spirulina (SP), were used to mimic real microalgae cultivation settings.

Potential Strategy to Control the Organic Components of Condensable Particulate Matter: A Critical Review.

More and more attention has been paid to condensable particulate matter (CPM) since its emissions have surpassed that of filterable particulate matter (FPM) with the large-scale application of ultralow-emission reform. CPM is a gaseous material in the flue stack but instantly turns into particles after leaving the stack. It is composed of inorganic and organic components.

Effects of solvent phase recycling on microalgae liquefaction in ethanol: Bio-oil production and nitrogen transformation.

Liquefaction of microalgae in ethanol offers an eco-friendly bio-oil alternative, but solvent recycling is crucial for sustainability due to extra costs. In this work, Chlorella vulgaris was liquefied in supercritical ethanol at 260 °C, and the solvent phase (SP) separated from bio-oil was recovered and reused. Five liquefaction cycles were performed at identical temperature and pressure conditions to investigate the effects on oil production and nitrogen transformation.

On the Reliability of Wearable Technology: A Tutorial on Measuring Heart Rate and Heart Rate Variability in the Wild.

Wearable sensors are quickly making their way into psychophysiological research, as they allow collecting data outside of a laboratory and for an extended period of time. The present tutorial considers fidelity of physiological measurement with wearable sensors, focusing on reliability. We elaborate on why ensuring reliability for wearables is important and offer statistical tools for assessing wearable reliability for between participants and within-participant designs.

Trace-Level Sensing of Phosphate for Natural Soils by a Nano-Screen-Printed Electrode.

Phosphate as one of the most essential components of living systems, robust analytical techniques available for phosphate sensing in natural waters and soils are essential for monitoring and predicting water quality and agronomic evaluation of phosphate. Using cyclic voltammetry, a point-of-use electrochemical sensor zirconium dioxide/zinc oxide/multiple-wall carbon nanotubes/ammonium molybdate tetrahydrate/screen printed electrode (ZrO/ZnO/MWCNTs/AMT/SPE) was applied to explore the electro-redox reaction of phosphomolybdate complexes on the surface of electrode, which produced a quantitative electrochemical response of phosphate anions. The modification of the electrode surface with ZrO/ZnO/MWCNTs nanocomposites is able to generate the electroactive species via chemical reaction between molybdenum (Mo(VI)) and the targeted phosphate anions, leading to a sensitive detection technique for trace phosphate with a lower detection limit (LOD = 2.

Performance of CuAl-LDH/Gr Nanocomposite-Based Electrochemical Sensor with Regard to Trace Glyphosate Detection in Water.

Glyphosate, which has been widely reported to be a toxic pollutant, is often present at trace amounts in the environment. In this study, a novel copper-aluminum metal hydroxide doped graphene nanoprobe (labeled as CuAl-LDH/Gr NC) was first developed to construct a non-enzymatic electrochemical sensor for detection trace glyphosate. The characterization results showed that the synthesized CuAl-LDH had a high-crystallinity flowered structure, abundant metallic bands and an intercalated functional group.

A Dynamic Estimation of Service Level Based on Fuzzy Logic for Robustness in the Internet of Things.

The Internet of things (IoT) technology is developing rapidly, and the IoT services are penetrating broadly into every aspect of people’s lives. As the large amount of services grows dramatically, how to discover and select the best services dynamically to satisfy the actual needs of users in the IoT service set, the elements of which have the same function, is an unavoidable issue. Therefore, for the robustness of the IoT system, evaluating the quality level of the IoT service to provide a reference for the users choosing the most appropriate service has become a hot topic.