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IoT platforms for the transportation industry are portable with limited battery life and need real-time and long-term monitoring operations. Since MQTT and HTTP are widely used as the main communication protocols in the IoT, it is imperative to analyze their power consumption to provide quantitative results that help maximize battery life in IoT transportation systems. Although is well known that MQTT consumes less power than HTTP, a comparative analysis of their power consumption with long-time tests and different conditions has not yet been conducted. In this sense, a design and validation of an electronic cost-efficient platform system for remote real-time monitoring is proposed using a NodeMCU module, in which experimentation is carried out for HTTP and MQTT with different QoS levels to make a comparison and demonstrate the differences in power consumption. Furthermore, we characterize the behavior of the batteries in the systems and compare the theoretical analysis with real long-time test results. The experimentation using the MQTT protocol with QoS 0 and 1 was successful, resulting in power savings of 6.03% and 8.33%, respectively, compared with HTTP, demonstrating many more hours in the duration of the batteries, which could be very useful in technological solutions for the transport industry.
RESUMO
Artificial neural networks (ANN) and data analysis (DA) are powerful tools for supporting decision-making. They are employed in diverse fields, and one of them is nanotechnology; for example, in predicting silver nanoparticles size. To our knowledge, we are the first to use ANN to predict liposome size (LZ). Liposomes are lipid nanoparticles used in different biomedical applications that can be produced in Dean-Forces-based microdevices such as the Periodic Disturbance Micromixer (PDM). In this work, ANN and DA techniques are used to build a LZ prediction model by using the most relevant variables in a PDM, the Flow Rate Radio (FRR), and the Total Flow Rate (TFR), and the temperature, solvents, and concentrations were kept constant. The ANN was designed in MATLAB and fed data from 60 experiments with 70% training, 15% validation, and 15% testing. For DA, a regression analysis was used. The model was evaluated; it showed a 0.98147 correlation coefficient for training and 0.97247 in total data compared with 0.882 obtained by DA.
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In this study, we carried out a heterogeneous cytoplasmic lipid content screening of Neochloris oleoabundans microalgae by dielectrophoresis (DEP), using castellated glassy carbon microelectrodes in a PDMS microchannel. For this purpose, microalgae were cultured in nitrogen-replete (N+) and nitrogen-deplete (N-) suspensions to promote low and high cytoplasmic lipid production in cells, respectively. Experiments were carried out over a wide frequency window (100 kHz-30 MHz) at a fixed amplitude of 7 VPP. The results showed a statistically significant difference between the dielectrophoretic behavior of N+ and N- cells at low frequencies (100-800 kHz), whereas a weak response was observed for mid- and high frequencies (1-30 MHz). Additionally, a finite element analysis using a 3D model was conducted to determine the dielectrophoretic trapping zones across the electrode gaps. These results suggest that low-cost glassy carbon is a reliable material for microalgae classification-between low and high cytoplasmic lipid content-through DEP, providing a fast and straightforward mechanism.
RESUMO
Dielectric characteristics are useful to determine crucial properties of liquids and to differentiate between liquid samples with similar physical characteristics. Liquid recognition has found applications in a broad variety of fields, including healthcare, food science, and quality inspection, among others. This work demonstrates the fabrication, instrumentation, and functionality of a portable wireless sensor node for the permittivity measurement of liquids that require characterization and differentiation. The node incorporates an interdigitated microelectrode array as a transducer and a microcontroller unit with radio communication electronics for data processing and transmission, which enable a wide variety of stand-alone applications. A laser-ablation-based microfabrication technique is applied to fabricate the microelectromechanical systems (MEMS) transducer on a printed circuit board (PCB) substrate. The surface of the transducer is covered with a thin layer of SU-8 polymer by spin coating, which prevents it from direct contact with the Cu electrodes and the liquid sample. This helps to enhance durability, avoid electrode corrosion and contamination of the liquid sample, and to prevent undesirable electrochemical reactions to arise. The transducer's impedance was modeled as a Randles cell, having resistive and reactive components determined analytically using a square wave as stimuli, and a resistor as a current-to-voltage converter. To characterize the node sensitivity under different conditions, three different transducer designs were fabricated and tested for four different fluids, i.e., air, isopropanol, glycerin, and distilled water-achieving a sensitivity of 1.6965 +/- 0.2028 εr/pF. The use of laser ablation allowed the reduction of the transducer footprint while maintaining its sensitivity within an adequate value for the targeted applications.
RESUMO
Circulating tumour cells (CTCs) are active participants in the metastasis process and account for â¼90% of all cancer deaths. As CTCs are admixed with a very large amount of erythrocytes, leukocytes, and platelets in blood, CTCs are very rare, making their isolation, capture, and detection a major technological challenge. Microfluidic technologies have opened-up new opportunities for the screening of blood samples and the detection of CTCs or other important cancer biomarker-proteins. In this study, the authors have reviewed the most recent developments in microfluidic devices for cells/biomarkers manipulation and detection, focusing their attention on immunomagnetic-affinity-based devices, dielectrophoresis-based devices, surface-plasmon-resonance microfluidic sensors, and quantum-dots-based sensors.