A spongy, porous nano calcium oxide (NC) with point zero charge of 11.80 was synthesized using the shell of a Gastropod (Achatina achatina) via the sol–gel technique. The ability of the NC to adsorb Cr (VI) from aqueous solution was assessed systematically in a batch process via isothermal, kinetic, and variable process optimization procedure. The sorption data had the best fitting for the Langmuir isotherm model and the monolayer sorption capacity (q m, mg/g) value of 125 mg/g was obtained. The initial solution pH had no palpable effect on the monolayer sorption capacity. The amount of sorbed Cr (VI) increased with contact time and initial Cr (VI) concentration and attained equilibrium within 120 min. The fitting of the different kinetic models to the sorption data, by error analysis, using the linear coefficient determinations (r 2) and the Chi-square statistical analysis (χ2), showed that the mechanism of the sorption process is better described by the pseudo second order kinetic model. Thermodynamic evaluation showed that the sorption process was spontaneous (ΔG o: −15.27; −14.87 and −14.45 kJ/mol at 303, 313, and 333 °C, respectively), exothermic (ΔH 0 = −22.568 kJ/mol) and increased disorder (ΔS 0 = 0.0244) appeared on the NC–solution interface during the adsorption process.

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License plate detection and recognition are critical components of the development of a connected Intelligent transportation system, but are underused in developing countries because to the associated costs. Existing license plate detection and recognition systems with high accuracy require the usage of Graphical Processing Units (GPU), which may be difficult to come by in developing nations. Single stage detectors and commercial optical character recognition engines, on the other hand, are less computationally expensive and can achieve acceptable detection and recognition accuracy without the use of a GPU. In this work, a pretrained SSD model and a tesseract tessdata-fast traineddata were fine-tuned on a dataset of more than 2,000 images of vehicles with license plate. These models were combined with a unique image preprocessing algorithm for character segmentation and tested using a general-purpose personal computer on a new collection of 200 automobiles with license plate photos. On this testing set, the plate detection system achieved a detection accuracy of 99.5 % at an IOU threshold of 0.45 while the OCR engine successfully recognized all characters on 150 license plates, one character incorrectly on 24 license plates, and two or more incorrect characters on 26 license plates. The detection procedure took an average of 80 milliseconds, while the character segmentation and identification stages took an average of 95 milliseconds, resulting in an average processing time of 175 milliseconds per image, or 6 photos per second. The obtained results are suitable for real-time traffic applications.

Internet of things (IoT) connected devices operate at extremely low voltages that are susceptible to common-mode noise and electromagnetic interference. As a result of this, integrating IoT devices with low or high-voltage direct current power sources requires galvanic isolation which is often expensive to attain. In this work, the use of a low-cost conventional optocoupler (4N35) in the galvanic isolation of an IoT voltmeter required to measure the potential difference of a low voltage direct current source with a maximum relative error of 1% was investigated and experimentally verified. The proposed isolator circuit was first simulated using NI Multism and then fabricated on a printed circuit board for experimental verification after satisfactory simulation results. Measurement results from the experimental verification process were used to fit quadratic and cubic regression equations that approximate the input signal voltage from the isolator’s output voltage measured by the IoT voltmeter. Lastly, the isolator and IoT voltmeter were connected to a variable 100-1000 VDC source via a potential divider network for performance verification at a voltage step of 100 VDC. Here, the isolator successfully achieved its primary goal of providing galvanic isolation between the voltage source and the IoT voltmeter while maintaining a maximum relative error of 1%.