Nowadays, governments and companies are looking for solutions to increase the collection level of various waste types by using new technologies and devices such as smart sensors, Internet of Things (IoT), cloud platforms etc. In order to fulfil this need, this paper presents solutions provided by a research project involving the design, development and implementation of fully automated waste collection systems with an increased usage degree, productivity and storage capacity. The paper will focus on the main results of this research project in turning the automated waste collection system into a smart system so that it can be easily integrated in any smart city infrastructure. For this purpose, the Internet of Things platform for the automated waste collection system provided by the project will allow real time monitoring and communication with central systems. Details about each module are sent to the central systems: various modules' statuses (working, blocked, needs repairs or maintenance etc.); equipment status; storage systems status (allowing full reports for all waste types); the amount of waste for each module, allowing optimal discharging; route optimization for waste discharging etc. To do that, we describe here an IoT cloud solution integrating device connection, data processing, analytics and management.

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The studies presented in this article are based on the variation of some constructive parameters on various types of materials for a prehension system jaws. The pneumatic actuation prehension system is part of the structure of a manipulator integrated within a teaching platform for installation. In the tests, the varied parameters were the following: the type of plastic material, the way of printing on the 3D printer bed, the degree of fill (the density) and the geometric shape. The experimental tests have resulted in an optimal version of the prehension system jaws.

A topical issue globally is the development and implementation of renewable energy sources for sustainable development. To meet current requirements, the research in this paper is directed towards finding solutions to increase the performance and efficiency of wind power plants by implementing innovative solutions for hollow roller bearings developed through the use of sustainable growth programs in the field of green energy. Another solution that has the effect of increasing wind power performance consists of the implementation of a new large-size lubrication system for large-size bearings in wind energy units, which will increase their durability by developing maintenance capabilities. In this research, we will explore the possibility of introducing an innovative automated lubrication system in hollow roller bearings. The main results of the research, the innovative constructive solutions, will lead to important savings by lowering wind farm maintenance costs, increasing the durability of large bearings, and increasing the energy efficiency and yield of the whole system. The expected impact of implementing the solutions found will mainly be in the field of sustainable growth and environmental development.

The paper shows the importance of designing an ABS (Acrylonitrile-Butadiene-Styrene) plastic part which will be produced using FDM (Fused Deposition Modeling) technology; it is obtained a product with the same characteristics provided by the operating guide book. Thus, this solution combines both the capacity of the designer as well as the applied technology and can produce similar or improved plastic components, at the same time maintaining the functional characteristics of the work piece. This paper is a plea for the application of 3D printing using FDM technology for manufacturing components (spare parts) out of production, because the technological systems users no longer have other solutions available for replacing outworn plastic parts. 3D printing using FDM technology is a fast option for replacing outworn components, the modeling, simulation and printing time being shorter than the purchase time of a new subassembly or assembly that has been remanufactured and modernized.

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The research started from the fact that the coacervation process represents the process of formation of macromolecular aggregates after separation from the phase that takes place in a homogeneous polymer solution as a result of the addition of a non-solvent. This process is very complex, and takes place in several stages of emulsification technology. The first step of the research created a sample through an encapsulation process of complex coacervation, followed by the creation of three different samples with specific emulsification technologies. Each resulting sample and step of emulsification went through rheological analysis, including the development of evolutions of the complex viscosity, loss module and respective storage module. When we analyzed the rheological properties of each sample at different emulsification stages, we reached the conclusion that, at the moment when the polymerization reaction develops the methyl methacrylate (MMA), the loss modules of the samples were stronger than the storage modules. In this context, the emulsification technology strongly influenced the process of forming the polymethyl methacrylate (PMMA) layer over the butyl stearate particles. In addition, in order to obtain the corresponding microcapsules, it was preferable for the butyl stearate particles covered with MMA to be vigorously stirred in a short period of time, under 250 s, because after that the polymerization process of the MMA on the surface of the particles begins. When producing microcapsules, it is very important that the whole process of emulsification be accompanied by rigorous stirring.

Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used.