The role of self-learning in promotion of skills in small employee medium sized of Russian enterprises

This investigation seeks to study the role of self-learning educations in promotion of professional competences employee skills of small employee medium sized enterprises. For this purpose, by providing a 15-question researcher-made questionnaire about the quality employee manner of self-learning educations, the exit poll was accomplished from the small employee medium sized enterprises of Russia so that the main question of this research was answered. This research in the domain of applicable researches is from the field type employee the methodology is descriptive from survey type. The statistical population of this investigation is formed from all employees of small employee medium sized enterprises in 19 regions of Russia that were doing their duties in the educational year of 2016-2017. The sample size of employees with regard to the Krejcie-Morgan's table was considered about 450 employees. In this investigation, in order to determine the reliability of questionnaire with emphasis on internal homogeneity, Cronbach's Alpha has been used that Alpha's coefficient was gained equal to 0.933 which is relatively high employee expresses high validity of the researcher-made questionnaire employee this researcher-made questionnaire has the content validity. Also, analysis of findings through Pearson correlation test showed that there is significant relation between the self-learning education courses employee professional skills of employees with confidence of 99% (P<0.01) employee about 60% of total variance of scores of professional skills of employees is arising from variance of scores of self-learning education courses (in other words, about 60% have common variance)

Development of two-photon polymerised scaffolds for optical interrogation and neurite guidance of human iPSC-derived cortical neuronal networks

Recent progress in the field of human induced pluripotent stem cells (iPSCs) has led to the efficient production of human neuronal cell models for in vitro study. This has the potential to enable the understanding of live human cellular and network function which is otherwise not possible. However, a major challenge is the generation of reproducible neural networks together with the ability to interrogate and record at the single cell level. A promising aid is the use of biomaterial scaffolds that would enable the development and guidance of neuronal networks in physiologically relevant architectures and dimensionality. The optimal scaffold material would need to be precisely fabricated with submicron resolution, be optically transparent, and biocompatible. Two-photon polymerisation (2PP) enables precise microfabrication of three-dimensional structures. In this study, we report the identification of two biomaterials that support the growth and differentiation of human iPSC-derived neural progenitors into functional neuronal networks. Furthermore, these materials can be patterned to induce alignment of neuronal processes and enable the optical interrogation of individual cells. 2PP scaffolds with tailored topographies therefore provide an effective method of producing defined in vitro human neural networks for application in influencing neurite guidance and complex network activity

Plexiform neurofibroma of the submandibular gland in patient with von Recklinghausen's disease

Plexiform neurofibroma of the submandibular gland is an extremely rare tumor. Herein, we report a case of plexiform neurofibroma in a patient with a von Recklinghausen's disease (NF-1) who presented with a submandibular mass mimicking a submandibular gland tumor. Complete surgical excision provides the best treatment and final diagnosis. A neurofibroma should be considered in the differential diagnosis for submandibular mass

Tear fluid biomarkers in ocular and systemic disease: potential use for predictive, preventive and personalised medicine

In the field of predictive, preventive and personalised medicine, researchers are keen to identify novel and reliable ways to predict and diagnose disease, as well as to monitor patient response to therapeutic agents. In the last decade alone, the sensitivity of profiling technologies has undergone huge improvements in detection sensitivity, thus allowing quantification of minute samples, for example body fluids that were previously difficult to assay. As a consequence, there has been a huge increase in tear fluid investigation, predominantly in the field of ocular surface disease. As tears are a more accessible and less complex body fluid (than serum or plasma) and sampling is much less invasive, research is starting to focus on how disease processes affect the proteomic, lipidomic and metabolomic composition of the tear film. By determining compositional changes to tear profiles, crucial pathways in disease progression may be identified, allowing for more predictive and personalised therapy of the individual. This article will provide an overview of the various putative tear fluid biomarkers that have been identified to date, ranging from ocular surface disease and retinopathies to cancer and multiple sclerosis. Putative tear fluid biomarkers of ocular disorders, as well as the more recent field of systemic disease biomarkers, will be shown

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods

Decreased Prevalence of Lymphatic Filariasis among Diabetic Subjects Associated with a Diminished Pro-Inflammatory Cytokine Response (CURES 83)

Epidemiological studies have shown an inverse correlation between the incidence of lymphatic filariasis (LF) and the incidence of allergies and autoimmunity. However, the interrelationship between LF and type-2 diabetes is not known and hence, a cross sectional study to assess the baseline prevalence and the correlates of sero-positivity of LF among diabetic subjects was carried out (n = 1416) as part of the CURES study. There was a significant decrease in the prevalence of LF among diabetic subjects (both newly diagnosed [5.7%] and those under treatment [4.3%]) compared to pre-diabetic subjects [9.1%] (p = 0.0095) and non-diabetic subjects [10.4%] (p = 0.0463). A significant decrease in filarial antigen load (p = 0.04) was also seen among diabetic subjects. Serum cytokine levels of the pro-inflammatory cytokines—IL-6 and GM-CSF—were significantly lower in diabetic subjects who were LF positive, compared to those who were LF negative. There were, however, no significant differences in the levels of anti-inflammatory cytokines—IL-10, IL-13 and TGF-β—between the two groups. Although a direct causal link has yet to be shown, there appears to be a striking inverse relationship between the prevalence of LF and diabetes, which is reflected by a diminished pro-inflammatory cytokine response in Asian Indians with diabetes and concomitant LF