Recycling old electronic components facilitates the recovery of valuable materials that can be used to produce new ones. As a result, energy is saved, pollution and greenhouse gas emissions are reduced, and many natural resources are salvaged by extracting fewer raw materials. The paper focuses on rapid sorting method developed for separating the dismembered components of a personal computers motherboard. After a simultaneous disassembly process of the board, some of the components like plastics, silicon chips, surface-mount devices and magnetic components need to be intelligently sorted, therefore economically separated. In the sorting process a conveyor belt was used to transport the components one by one. A contour vision sensor was used to acquire and process the images, consequently to recognize the components. The visual sensor binary code output is connected to a Mitsubishi FX3G programmable logic controller. Finally, electro pneumatic valves with directional air flow control are used to position the objects from the conveyor belt into separate bins.

There is nothing new about the fact that higher concentrations (up to 50 times) of valuable materials can be found in e-waste, compared to mined ores. Moreover, the constant accumulation of excessive amounts of waste equipment has a negative impact on the environment. The components found in electronic equipment may contain hazardous materials or materials that could be recycled and reintroduced into production processes, thus reducing the carbon footprint created by waste electrical and electronics equipment (WEEE). Sustainable e-waste recycling requires high-value, integrated recovery systems. By implementing a two-stage experimental sorting stand, this paper proposes an efficient and fast sorting method that can be industrially scaled up to reduce the time, energy and costs needed to sort electronic waste (e-waste). The sorting equipment is in fact an ensemble of sensors consisting of cameras, color sensors, proximity sensors, metal detectors and a hyperspectral camera. The first stage of the system sorts the components based on the materials’ spectral signature by using hyperspectral image (HSI) processing and, with the help of a robotic arm, removes the marked components from the conveyor belt. The second stage of the sorting stand uses a contour vision camera to detect specific shapes of the components to be sorted with the help of pneumatic actuators. The experimental sorting stand is able to distinguish up to five types of components with an efficiency of 89%.

The current study aimed to investigate the effect of different treatments applied to sessile oak seeds in order to improve the germination features, development and growth of seedlings. The seeds were subjected to electric field, electromagnetic field and γ rays’ treatments. For the electric field study three distinct voltages (10V, 30V and 50V) and three values of exposure time (15 min., 35 min. and 60 min.) were considered. Displaying the seeds in an electric field for 60 minutes increased the germination rate with 58.89%. The highest value for the seedling’s height was obtained when seeds were exposed for 15 minutes at an intensity of 30V. The interaction between the intensity and the timing showed that treating seeds with 30V for 35 minutes improved significantly the seedlings peculiarities. In regard to the electromagnetic field, the seeds were subjected to a single value of electromagnetic field (B=0,4mT; i=0,680 A) and three values of exposure time: 10 min., 20 min. and 30 min. The sessile oak seeds subjected to the electromagnetic field for 20 minutes induced the highest value of germination percentage (90.00%) and germination index (1.81) and those seeds produced plantlets with high diameter (2.69 mm) and with significant differences towards non-treated plants. For γ rays treatments a dosage of 2 Gy induced the highest value of germination percentage (86.67%), of germination index (2.02); of germination energy (7.7); and of germination speed (7.24). A dosage of 6 Gy decreased the values of the same parameters. Our results showed that a dosage of 2 Gy improved also the growth parameters such as plantlets diameter (2.65 mm) and height (11.25 cm). In short, the physical treatments represent an ecological substitute approach towards the chemical substances presently used to improve seeds germination. Using physical treatments for seeds germination and growth of seedlings might be a harmless alternative for environment.

This paper presents a new analysis method of a multi-phased electric system, based upon the concept of analytic signals. Analytic signals are complex values, formed by pairs of real signals connected through the Hilbert Transform. Analytic signals are associated to the voltage and current signals that characterize the multi-phased electric system. Fortescue transformations, formally similar to those applied to the phasor values, are applied to these analytic signals. These transformations lead to analytic output signal sets, which will be called here symmetrical components sequences of the analytic signals. Each sequence is formed of the same number of analytic signals, which have the same module and are shifted one to the other with the same angle. Symmetrical components of analytic signals allow the simple calculus of powers of the multi-phased system, for each sequence. The analysis presented in the paper is suited for creating practical working algorithms in discrete time.

Glaucoma is one of the most important causes of blindness in the world. To diagnose it is necessary to measure the intraocular pressure, which is the most important risk factor in glaucoma. Biomechanical corneal properties are another important factor in assessment of glaucoma. We used Reichert Ocular Response Analyzer to measure these two important parameters in a glaucoma suspect patient: Compensated Intraocular Pressure (IOPcc) and Corneal Hysteresis (CH). The last one we use to calculate specific damping capacity (phi) of the cornea. IOPcc and phi are used to create a graphical system where collected data about the patient make possible to appreciate the risk to develop glaucoma. We grade this risk in 6 stages (from stage 0 which is considered normal to stage 5 where we find pathological values for the two analyzed parameters). We name this system: Corneal Effort Staging System, and we propose it to be used in clinical daily practice to appreciate the IOP as a stress gradient for an eye in a glaucoma suspect. These classifications facilitate the clinically diagnosis and management in glaucoma.