Mould design optimisation by FEM

Production can be briefly defined as creating something new as a result of effort. There are many different production methods. With the industrial revolutions, the number of these methods has increased, and these methods have developed. Over time, heavy and laborious work that people had to do began to be done by machines. The casting method, which is one of the most common production methods in the world, is to pour the molten metal or its alloy into a mould cavity suitable for the shape of the desired product and remove it from the mould after it solidifies. In this study, firstly, the manufacturing method by casting was mentioned, and the difficulties of mould design in the casting method were explained. Secondly, the benefits of computer-aided simulation programs for casting are explained. As an example, a model was designed and different runners were added to this model. These models, which were prepared afterwards, were cast in a virtual environment with the FLOW-3D CAST program, which is a simulation program. Casting results and casting defects after these castings were compared and interpreted. The results show that it is important for the casting quality to keep the runner diameters as small as possible in runner designs. Two or three times more air voids are formed in the sand mould casting method compared to the permanent mould casting. Additionally, it was observed that the casting material had less shrinkage in the sand mould casting method. It is concluded that sand mould casting is disadvantageous in terms of the parameter of time.

Automatic Detection of Microsleep Episodes With Deep Learning

Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30 s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e., with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We performed a visualization of the internal representation of the data by the artificial neuronal network performing best using t-distributed stochastic neighbor embedding (t-SNE). Visualization revealed that MSEs and wakefulness were mostly separable, though not entirely, and MSEc and ED largely intersected with the two main classes. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. The code of the algorithms (https://github.com/alexander-malafeev/microsleep-detection) and data (https://zenodo.org/record/3251716) are available.

A Review of 3D Printing in Construction and its Impact on the Labor Market

Construction industry is very labor-intensive and one of the major sources of employment in the world. The industry is experiencing low productivity with minimum technological innovations for decades. In recent times, various automation technologies including 3D printing have received increasing interests in construction. 3D printing in construction is found to be very promising to automate the construction processes and have the potential of saving laborious work, material waste, construction time, risky operation for humans, etc. There has been a comprehensive body of research conducted to understand the recent advances, future prospects and challenges of large-scale adoption of 3D printing in construction projects. Being one the labor-intensive industries, this study also investigates the possible impact on the labor market with increasing adoption of 3D printing in construction. It is found that 3D printing can reduce significant number of labors which can solve the labor shortage problem, especially for the countries where construction is heavily dependent on immigrant workers. In contrast, 3D printing might not be favorable for the countries where construction is one of the main workforces and labor is less expensive. Moreover, 3D construction printing will also require people with special skills related to this new technology.

SUPRAMOLECULAR ANTIHELMINTHIC COMPLEX ALTRIK-EXTRA FOR THE TREATMENT AND PREVENTION OF MAJOR ANIMAL HELMINTHOSES

Animal helminthiasis is widespread in Russia and other countries, which cause great economic damage to animal husbandry due to a decrease in all types of productivity, lagging in the growth and development of young animals and the death of animals, especially young animals. As a result of joint work of employees of the Institute of Organoelement compounds N.A. Nesmeyanova (INEOS RAS, Moscow) and VNIIP K.I. Scriabina, using mechanochemical nanotechnology, developed an innovative drug in the form of a supramolecular complex based on the substances albendazole-extra and triclabendazole and water-soluble low-molecular polyvinylpyrrallidone (PVP-17) in the ratio ABZ-extra: TKB:PVP-17=1:1:8, where 10.0 mg of the drug contains 1.0 mg of ABZ and TKB, which is a white powder with a particle size of 0.1–10 microns. The essence of this work was the development of a complex drug that could effectively and without negative consequences affect the listed 3 classes of helminths that parasitize in mixed form in animals, in order to reduce the laborious work on prevention and treatment. The supramolecular complex of albendazole-extra and triclabendazole, named by us "Altrik-Extra", is intended for the treatment of animals with acute and chronic form of fasciosis, nematodoses of the digestive and pulmonary tracts, and it also has cestodocidal activity, which is important for deworming animals in the summer, when animals are parasitized by helminths of the listed classes in mixed form.

Genealogical analysis of large-fruited sweet cherry varieties in accordance with the S-locus of parental forms, and the pattern of inheritance of large-fruited in the presence of S5 and S9 alleles in the genome

The use of various molecular techniques greatly assists breeders in the selection of parental pairs in directional crossing. The selection of forms with the desired properties at the stage of hybrid progeny is a very laborious work, especially in the selection of fruit crops. This is due to the long life cycle and the impossibility of identifying valuable traits in the first years of the life of a hybrid plant. The use of the definition of S-loci in the genotype of a hybrid of sweet cherry allows at the early stage, before entering fruiting, to isolate genotypes with valuable traits.

A simple and reliable method for creating PCR-detectable mutants in Arabidopsis with the polycistronic tRNA–gRNA CRISPR/Cas9 system

To develop an easy and robust method for creating genetically stable and easily detectable Arabidopsis mutants, we adopted the polycistronic tRNA–gRNA CRISPR/Cas9 (PTG/Cas9) system, a multiplex gene-editing tool in rice, with PTOX as the reporter gene. The PTG/Cas9 system has a great potential in generating large deletions detectable by PCR, which greatly simplifies the laborious work of mutant screening. We constructed a PTOX–PTG/Cas9 system with five gRNAs and introduced it into Arabidopsis. At T1 generation, 24.4% of transgenic plants were chimeric with PCR-detectable deletions in PTOX locus, but no homozygous mutant was found, indicating that gene editing occurred predominantly in somatic cells. After a self-cross propagation, 60% of T1 chimeric plants were able to produce homozygous, heterozygous, or bi-allelic ptox offsprings. Inheritable homozygous ptox mutants without Cas9 gene can be obtained earliest at T2 generation. We further targeted five other genes using the same procedure and achieved homozygous Cas9-free mutants with large deletions for all genes within three generations. We established a standard and reliable protocol to generate stable inherited deletion mutants in 2–3 generations along with simple PCR screening methods. We conclude that the rice PTG/Cas9 system is an efficient, easy, and rapid tool to edit genes in Arabidopsis. We propose that it could be applied to other genes in Arabidopsis, and it might have the potential to edit genes in other plant species as well.

DEVELOPMENT OF UNIVERSITY TEACHER’S INNOVATIVE CULTURE

Purpose: The aim of research is to analyze the development of university teacher’s innovative culture. Methodology: This is an analytical-descriptive research that obtained data from questionnaires and interviews. Content analysis and model structure have been used to analyze the data. Main Findings: Results showed that the formed teacher’s innovative space brings a future specialist to the understanding that the quality of life depends on the laborious work on one’s own spirituality, morality and worldview. The future specialist gets experience of professional activity on the basis of universal humane values, which is the main result of the modeled innovative culture in the pedagogical space. Applications: This research can be used by teachers, the education system and graduate students. Novelty/Originality: For the first time, modelling the innovation process of teachers has been studied.

Gender and Age in the Professions: Intersectionality, Meta-work, and Social Change

Sociologists have paid little attention to the shifting significance of gender to professional work. Nevertheless, there is evidence that the meanings attached to gender, and the gendering of work, have shifted over time, such that the experiences of newer cohorts of professionals differ from those of professionals in previous generations. In this paper, we show how combining intersectionality theory and life course approaches facilitates the exploration of inequalities by gender, class, and race/ethnicity across generations and age cohorts. We present empirical research findings to demonstrate how this approach illuminates the convergence of gender and age in the professions to confer privilege and produce disadvantage in professional workplaces. Subsequently, we introduce the concept of meta-work—hidden, invisible and laborious work performed by non-traditional and disadvantaged professionals—through which they endeavor to cope with structural inequalities embedded in the professions. As professions and professional workplaces are still designed primarily for middle-class, dominant-ethnicity men, professionals who do not fit these categories need to invest extra time and energy to develop individual strategies and tactics to cope with professional pressures in and around their work. Meta-work is intrinsically linked to the traditional and normative ideals surrounding professional roles and identities, and therefore is intimately connected with professionals’ sense of self and their feeling of belonging to professional communities. Meta-work, and the tactics and strategies that result from it, are important coping mechanisms for some professionals, enabling them to deal with rapidly changing work realities and a lack of collegial support. Finally, we highlight several areas for future research on the intersections of gender and age in the professions.

IMPROVEMENT OF REINFORCEMENT METHOD OF R/C CYLINDRICAL SHELL WITH OPENING

Reinforced concrete (R/C) chimney with a hole is prone to decrease the strength. Therefore, the particular reinforcements have been placed around the hole. Both meridional and hoop reinforcements have been placed additionally equivalent to the loss amount of reinforcements at the hole and the diagonal reinforcements have been placed to prevent the corner failures as well. Diagonal reinforcements in the shell were curved shape. It is the laborious work to place them. In this analysis, the improved reinforcement method was proposed instead of placing the conventional diagonal reinforcements. In the proposed methods, only the hoop and the meridional reinforcements were arranged around the corners instead of diagonal reinforcements. FEM analyses were done to evaluate the proposed reinforcing method. Numerical model was the cylindrical panel with a rectangular opening. From the numerical analyses, it was concluded that the R/C panel with proposed reinforcement method showed the same behavior as that with conventional reinforcement method.

Exploring Weighted Dual Graph Regularized Non-Negative Matrix Tri-Factorization Based Collaborative Filtering Framework for Multi-Label Annotation of Remote Sensing Images

Manually annotating remote sensing images is laborious work, especially on large-scale datasets. To improve the efficiency of this work, we propose an automatic annotation method for remote sensing images. The proposed method formulates the multi-label annotation task as a recommended problem, based on non-negative matrix tri-factorization (NMTF). The labels of remote sensing images can be recommended directly by recovering the image–label matrix. To learn more efficient latent feature matrices, two graph regularization terms are added to NMTF that explore the affiliated relationships on the image graph and label graph simultaneously. In order to reduce the gap between semantic concepts and visual content, both low-level visual features and high-level semantic features are exploited to construct the image graph. Meanwhile, label co-occurrence information is used to build the label graph, which discovers the semantic meaning to enhance the label prediction for unlabeled images. By employing the information from images and labels, the proposed method can efficiently deal with the sparsity and cold-start problem brought by limited image–label pairs. Experimental results on the UCMerced and Corel5k datasets show that our model outperforms most baseline algorithms for multi-label annotation of remote sensing images and performs efficiently on large-scale unlabeled datasets.