The presented research deals with the development of the numerical model for resins used for stereolithography (SLA) rapid prototyping. SLA is an additive method of production of models, prototypes, elements or parts of constructions with the use of 3D printing that covers photochemical processes by which light causes chemical monomers to link together to form polymers. Such method is very useful in design visualization, but also can be applied in numerical modelling for the purpose of validation and verification. In this application the resin strength parameters must be described and on the base of them the numerical material model is developed and validated. Such a study for SLA resins was presented in the paper.

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To a large extent, an audio fade is distinctly acknowledged as a strict increase (fade-up) or decrease (fade-down) of the volume of an audio content. In this broad context, the widely used fade-in and fade-out sound effects, applied to receive smooth transitions from and down to silence, respectively, appear to be restrictive. Taking into account the increasing demand for multimedia techniques adapted for real-time computing, the present investigation advances straightforward procedures intended for customizing the audio fade-up and fade-down profiles, having at hand well-proven techniques of shaping the fade-in and fade-out audio effects, suitable for fast computing.​

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In this paper, development trends of information systems, information systems technology and enterprise information management were analyzed in the context of Industry 4.0 tools. In the first part (par. 1-2), fundamental definitions referred to the subject were presented as well as historic background of Integrated Management Information Systems. In the second part (par. 3), evolution and trends in ERP class systems, electronic economy tools and Product Lifecycle Management software were described. In the third part (par. 4-5), observed trends in information systems technology, in relation to Industry 4.0 tools, were discussed including manufacturing resources, production objects and novel management strategies approach. Many conclusions were related with actual manufacturing practices observed by the authors.​​

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The aim of the study was to evaluate the influence of type of the solvent (water, aqueous ethanol and ethanol) on the antioxidant properties of four various herbs: couch grass (A. repens), milk thistle (S. marianum), dandelion (T. officinale) and fireweed (E. angustifolium) measurement by three common UV-VIS methods (TPC, ABTS+, DPPH). The results were collected through the Vision-Pro™ UV-VIS spectrophotometer software. Aqueous ethanol was the most effective solvent for extraction for all type of herbs. Fireweed contains the highest amount of polyphenol compounds (0.625 µg GA/ml). The lowest antioxidant capacity was presented by extracts from couch grass (0.019 µg GA/ml).

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The amount of data processed and stored in the cloud is growing dramatically. The traditional storage devices at both hardware and software levels cannot meet the requirement of the cloud. This fact motivates the need for a plat¬form which can handle this problem. Hadoop is a deployed platform proposed to overcome this big data problem which often uses MapReduce architecture to process vast amounts of data of the cloud system. Hadoop has no strategy to assure the safety and confidentiality of the files saved inside the Hadoop distributed File system (HDFS). In the cloud, the protection of sensitive data is a critical issue in which data encryption schemes plays avital rule. This research proposes a hybrid system between two well-known asymmetric key cryptosystems (RSA, and Paillier) to encrypt the files stored in HDFS. Thus before saving data in HDFS, the proposed cryptosystem is utilized for encrypting the data. Each user of the cloud might upload files in two ways, non-safe or secure. The hybrid system shows higher computational complexity and less latency in comparison to the RSA cryptosystem alone. 

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This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems.
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This paper presents a method of identifying the width of backlash zone in an electromechanical system generating noises. The system load is a series of rectangular pulses of constant amplitude, generated at equal intervals. The need for identification of the backlash zone is associated with a gradual increase of its width during the drive operation. The study uses wavelet analysis of signals and analysis of neural network weights obtained from the processing without supervised learning. The time-frequency signal representations of accelerations of the mechanical load components were investigated. 

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