Economic productivity is one of the most important concepts in economics. Increasing economic productivity will boost a society. This study intends to compare the impact of different criteria on the economic productivity of Iranian factories with differential methods and neural networks. The model estimation results using the difference-in-difference method show that exports have a significant positive effect on productivity growth. The model estimation results using the neural network method show that export has a significant positive effect on productivity growth. The presentation of export development variables in two methods of difference in difference and neural networks showed that the level of productivity could be increased well according to the predictability of the neural network.

In this paper, Schwarz lemma at the boundary is considered for analysis of transfer functions used in control systems. Two theorems are presented with their proofs by performing boundary analysis of the derivative of positive real functions evaluated at the origin. Considering that the transfer function,H(s) , is an analytic function defined on the right half of the s- plane, inequalities for |H(0)| are obtained by assuming that is also analytic at the boundary point s=0 on the imaginary axis with H(0)=0. Finally, the sharpness of these inequalities is proved. As result of the sharpness analysis, different extremal functions corresponding to different transfer functions are obtained. The related block diagrams and root-locus curves are also presented for considered transfer functions. According to the root-locus diagrams, marginally stable transfer functions are obtained as the natural results of the theorems proposed in the study.

Businesses have undergone many changes and transformations in their production systems from past to present in order to survive in a competitive environment. The common purpose of all of these transformations is to deliver the products and services the customer wants at the desired quality, at any time, and at a low cost. Especially with Industry 4.0, which is called the Fourth Industrial Revolution, companies have started to analyze large amounts of data obtained from their systems by using advanced technologies. Thus, they turned their production systems into smart production with very little human intervention. Artificial intelligence techniques play a key role in making smart production. Production efficiency and product quality are improved by analyzing large production data, which is difficult to obtain with traditional methods, in real-time by techniques such as deep learning and machine learning used in artificial intelligence. Due to these advantages, artificial intelligence has been widely used in almost every sector today. In this study, it is aimed to examine the use of artificial intelligence, which has become more important with industry 4.0, in smart production. In the study, firstly, the relationship between industry 4.0 and smart production was explained, then the use of artificial intelligence in smart production and the benefits obtained were emphasized. Finally, some suggestions were presented to companies for the use of artificial intelligence in production systems.

This paper focused on to prepare Bi2O3-multiwalled carbon nanotube (MWCNT) nanocomposites electrodes for supercapacitor applications with high mass loading. Nature inspired dispersing agent Celestine blue (CB), different adsorption mechanism in different materials, dispersed MWCNT and Bi2O3 and allowed to prepare homogenous electrode with high mass loading on a porous NiFoam current collector. The areal capacitance of 555 mF cm-2 was obtained from the composite electrode with 25 mg cm-2 active mass loading in 1M Na2SO4 electrolyte. Bi2O3/MWCNT/CB composited electrode testing results showed promising results for supercapacitor applications.

Photovoltaic systems are one of the important methods used to obtain electricity from solar energy. Photovoltaic module temperature and the angle of incidence of solar radiation are the most important factors affecting the electrical energy obtained from the photovoltaic module. There are many experimental and numerical studies in the literature on increasing electricity production by cooling photovoltaic modules. Cooling with air, liquid, phase change materials, thermoelectric effect, and immersion in water are the commonly used cooling methods. In this study, the cooling of the photovoltaic module by the water immersion method was experimentally investigated. In addition to the results of the related studies in the literature, analyzes were made for different immersion depths and the most appropriate immersion depth was tried to be determined. Three scenarios, 5 cm, 20 cm, and 30 cm immersion depth were investigated in the immersion of the PV module in liquid. As a result of immersion of the PV module in water, the PV module temperature decreased, and the voltage output increased for all parameter values. The highest voltage increase was obtained for 5 cm immersion depth (7.85%). Although PV module temperatures decreased with increasing immersion depth, the voltage output decreased after 5 cm immersion depth.

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