Factor analysis of agricultural mechanization challenges in Iran

A descriptive survey research was undertaken in order to assess challenges facing agricultural mechanization development in Iran.  The research population included agricultural mechanization experts, managers and specialists in private and governmental sections.  Using proportional stratified sampling, a sample of 119 was constituted out of a total population of 809 based on the Cochran formula.  Data were collected using questionnaire on which the statements were collected after literature review of research and interviews with mechanization specialists.  The questionnaire was validated by a panel of experts and its reliability index was established by a Cronbach’s coefficient.  A pilot study was conducted with 30 questionnaires (not included in the sample population) to determine the reliability of the questionnaire.  Computed Cronbach’s alpha score was 75%, which indicated that the questionnaire was highly reliable.  All survey data were analyzed using the Statistical Package for Social Sciences (SPSS 16.0).  The results of factor analysis indicated that 69% of the variances of the challenges could be classified in seven groups, namely: programming, technical, infrastructural, managerial, economical, research and extension, and content area.  From each group the most important challenges facing agricultural mechanization development in Iran include: inefficiency of subside payment methods for buying agricultural machinery, large number of time-worn agricultural machinery, incomplete collection of agricultural equipments for power generator machinery (tractor), slow trend of beneficiaries in accepting new technologies, financial weakness of agricultural beneficiaries, inefficiency of agricultural extension and education methods, and weakness of agricultural machinery producers and operators in protecting their guild benefits.   Keywords: agricultural mechanization, challenge, extension, factor analysis, Ira

Effect of Mechanical Stimulation on Differentiation of Human Mesenchymal Stem Cells to Different Cell Lines: A Systematic Review

Background and Aim: Stem cells due to their great potential can help in establishing tissue engineering as a new treatment modality . Numerous studies have evaluated the effect of various chemical and mechanical stimuli on these cells. In this respect, the role of mechanical loads is undeniable. This systematic review evaluated studies on the effects of mechanical loads on differentiation of mesenchymal stem cells to different cell lineages published in the past 12 years .   Materials and Methods: In this systematic review, PUBMED database was used to search key words namely “human mesenchymal stem cell”, “strain,”, “mechanical loading,” and “differentiation”, in the literature published from 2000 to July 2012. The inclusion criteria were the publication year, language of articles, type of cells and study objectives .   Results: In total, 46 articles were evaluated qualitatively . In most studies, applied mechanical loads led to the anticipated differentiation. Studies showed that the combination of two forces increased differentiation. The a mount of applied strain also influenced the type of differentiation .   Conclusion: This review indicated that advances made on the effects of mechanical loads on stem cells can be used for improving tissue engineering treatments

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In recent years, robotic engineering has been enriched with Artificial Intelligence (AI) technology, preparing the industries to enter the Industry 4.0 era. The powerful neoteric paradigm of AI can serve automotive industries (as one of the largest sectors in the world), to inevitably change their outdated manufacturing strategies. These industrial sectors are increasingly encountering mega data that inevitably carry uncertainty, for which the available methodologies are not capable to deal with that efficiently. To theoretically resolve this gap, a generalized intuitionistic fuzzy set (IFS) theory is proposed here as an efficient, fast, and flexible method. Based on the membership and non-membership degrees, multi-aspect Γ -systems is developed to model the complex real systems. Inspired by multi-attribute Γ -systems and IFS approach, a novel mathematical concept namely intuitionistic fuzzy Γ -subring (IFΓ R) method, is developed to establish an AI platform for robotic automotive manufacturing. Significant characteristics of IFΓ R are developed, including the overlapping of elements with IFΓ R property is IFΓ R, also image and inverse image of elements with IFΓ R property are IFΓ R under Γ -ring homomorphism. Additionally, the connection between upper and lower bound level cuts and image/inverse image property are parametrically discussed. With the effect of surjective homomorphism on upper and lower level cuts, there would be equivalent upper and lower level cuts of image/inverse image in IFΓ R environment. The developed notion of IFΓ I is obtained as the generalization of Γ -ideal under Γ -ring R along with the resultant fundamental properties of IFΓ I, where the overlapping/intersection family of IFΓ Is is proved to be IFΓ I. Also, the upper and lower bound level cuts of elements with IFΓ I property are Γ -ideals. Finally, the proposed IFΓ R method is utilized for automotive AI systems (AAIS) by means of mathematical algebraic notions of Γ -ring, IFS, Γ -ring isomorphism, and upper and lower bound levels. The developed methodology is validated using real dataset of industrial robots in supply chain and then, the elements are characterized in terms of metric overall factory effectiveness. With a systematic pattern of Γ -ring structure, the IFΓ R model is accomplished on elements, and the intercomponent correspondence of AAIS is established with the Γ -ring isomorphism. Based on QC (quality criteria) and non-QC indexes, as the derivation of upper and lower bound level cuts, the analysis of parameters (robots) is simplified for the identification of effective and compatible components in AAIS. The generalized IFS-based method for complex systems has a potential to be used in different AI platforms