Effective Factors on Knowledge Commercialization in Payam-e-Noor University

Commercialization of research results refers to a set of efforts aimed at raising capital and increasing the relationship between the academic and research sector and economic and social sector. The present study intends to identify and rank the factors affecting the commercialization process of research results at Payam-e-Noor University in order to determine the extent of influence of the identified factors. Therefore, this is an applied research in terms of purpose, which studies the factors affecting the process of knowledge commercialization in previous studies, using the confirmatory factor analysis approach. Using fuzzy hierarchical analysis, it was determined that legal, economic, manpower, cultural, structural and political, and communicational and information-related barriers are the first to sixth barriers to the knowledge commercialization in Payam-e-Noor University. Using fuzzy DEMATEL technique, the effectiveness and affectability of factors involved in the process of knowledge commercialization were identified. Accordingly, weak legal framework for supporting idea people at the university, inefficiency and ineffectiveness of the rules and regulations for commercialization, lack of regulation for the apportionment of financial gain from commercialization among scholars, lack of skilled and expert human resources in the universities, lack of facilities and financial resources for research commercialization, and lack of mutual recognition between university and industry had the most affectability. In other words, these are the dependent/outcome variables of the model. On the other hand, inadequate knowledge of the faculty members, poor fund management in the university, weakness of universities in wealth creation, absence of university entrepreneurial missions, the absence of up-to-date and effective idea banks and databases in the universities, and lack of effective communication between students and industry sector’s activists had the most effectiveness. In other words, these variables are the independent/causal variables of the model

Gaussian Likelihoods in Bayesian Neural Networks

Bayesian neural networks (BNNs) offer a promising probabilistic take on neural networks, allowing uncertainty quantification in both model predictions and parameters. Being a relatively new and evolving field of research, many aspects of Bayesian neural networks still need to be better understood. In this thesis, we explore the Gaussian likelihood function commonly used when modeling regression problems with Bayesian neural networks. Using variational inference, we train several Bayesian neural networks on synthetic datasets and investigate the Gaussian variance parameter (sigma). We explore how it impacts the training process and shapes the resulting posterior distribution. We also explore an alternate approach where a prior distribution is placed on the variance parameter, and its value is inferred from the data. While the data presented in this thesis is too limited to draw any definitive conclusions, we provide some interesting insights. We demonstrate that extreme values for sigma can lead to tendencies of overfitting or underfitting BNNs. Additionally, inferring the variance parameter from the data can yield results on par with an "optimal" fixed parameterization of the likelihood function. We also showcase that misspecified Bayesian neural networks can produce overconfident uncertainty estimates and that inferring the variance parameter can help compensate for this limitation.Masteroppgave i informatikkINF399MAMN-INFMAMN-PRO

Numerical study on the nonlinear thermomechanical behaviour of refractory masonry with dry joints

Refractory masonry with dry joints is widely employed as a protective lining in industrial applications requiring high-temperature treatments. The thermal and mechanical behaviour of alumina spinel refractory masonry is investigated for a wide range of mechanical loading conditions at ambient and high temperature up to 1500 °C within the framework of the ATHOR project. This paper discusses the different numerical analysis approaches for the simulation of the experimental results. Micro and macro modelling approaches show good agreement with the large scale uniaxial and biaxial compression tests for loading and unloading at the ambient temperature. Simulations carried out for large scale uniaxial and biaxial creep tests as well as biaxial relaxation tests at 1500 °C show good agreement. The numerical results indicate the ability of these modelling approaches to represent the complex thermomechanical behaviour of the refractory masonry. Both methods demonstrate an orthotropic and highly nonlinear behaviour of the refractory masonry as observed in the experimental campaign. The numerical outcome, validated with experimental results demonstrate compatibility between micro and macro modelling approach that can be employed to evaluate local and global behaviour of large industrial installations.This work was supported by the funding scheme of the European Commission, Marie Skłodowska-Curie Actions Innovative Training Networks in the frame of the project ATHOR - Advanced THermomechanical multiscale mOdelling Refractory linings 764987 Grant. The first, sixth and seventh author also acknowledge the financial support by FCT / MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB / 04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE underreference LA/P/0112/2020. This work is financed by national funds through FCT - Foundation for Science and Technology, under grant agreement 2021.05961.BD attributed to the first author

Defining the Plasticity of Transcription Factor Binding Sites by Deconstructing DNA Consensus Sequences: The PhoP-Binding Sites among Gamma/Enterobacteria

Transcriptional regulators recognize specific DNA sequences. Because these sequences are embedded in the background of genomic DNA, it is hard to identify the key cis-regulatory elements that determine disparate patterns of gene expression. The detection of the intra- and inter-species differences among these sequences is crucial for understanding the molecular basis of both differential gene expression and evolution. Here, we address this problem by investigating the target promoters controlled by the DNA-binding PhoP protein, which governs virulence and Mg2+ homeostasis in several bacterial species. PhoP is particularly interesting; it is highly conserved in different gamma/enterobacteria, regulating not only ancestral genes but also governing the expression of dozens of horizontally acquired genes that differ from species to species. Our approach consists of decomposing the DNA binding site sequences for a given regulator into families of motifs (i.e., termed submotifs) using a machine learning method inspired by the “Divide & Conquer” strategy. By partitioning a motif into sub-patterns, computational advantages for classification were produced, resulting in the discovery of new members of a regulon, and alleviating the problem of distinguishing functional sites in chromatin immunoprecipitation and DNA microarray genome-wide analysis. Moreover, we found that certain partitions were useful in revealing biological properties of binding site sequences, including modular gains and losses of PhoP binding sites through evolutionary turnover events, as well as conservation in distant species. The high conservation of PhoP submotifs within gamma/enterobacteria, as well as the regulatory protein that recognizes them, suggests that the major cause of divergence between related species is not due to the binding sites, as was previously suggested for other regulators. Instead, the divergence may be attributed to the fast evolution of orthologous target genes and/or the promoter architectures resulting from the interaction of those binding sites with the RNA polymerase

Northern Conifer Forest Management: Silvicultural, Economic, and Ecological Outcomes From 65 Years of Study

While forest managers once sought primarily to produce sustainable revenue from harvests, there is now growing value placed on non-timber outcomes like wildlife habitat, climate adaptability, and carbon storage. When deciding how to manage land for the future, foresters and landowners must assess the many outcomes of forestry activities and be aware of the tradeoffs inherent to achieving different goals. Given the slow growth of trees relative to other commercial crops, it is rare to have the continuity of land ownership, researchers, and funding needed to follow a stand for a full rotation or to observe a tree from recruitment to maturity. Because a given forester will rarely see results of their management decisions decades in the future, long-term studies can help forest managers anticipate the results of treatments they apply. We examined effects of over 65 years of even-aged (uniform shelterwood) and uneven-aged (single-tree selection) silviculture and exploitive harvesting practices (diameter-limit cutting and commercial clearcutting) on a variety of silvicultural, economic, and ecological outcomes, using a long-term U.S. Forest Service study at the Penobscot Experimental Forest in central Maine, U.S. We found that while some treatments achieved their original objectives, changes in markets and growing awareness of ecological values (e.g. habitat provision and carbon storage) influenced our assessment of these outcomes today. For example, the shelterwood treatments successfully controlled species composition and structure, but those stands may not be resilient to environmental or market changes. Selection treatments created stands of high-quality, large trees and diverse habitat structures, but did not encourage species adaptable to future climate conditions. Exploitive harvesting encouraged climate change-resilient species like red maple, but led to poor tree quality, growth rates, and economic value. These findings underscore that we must consider outcomes beyond short-term wood production, and time may change how we interpret structural and compositional results as new objectives and socio-ecological contexts arise

Development of a simple unified volatility-based scheme (SUVS) for secondary organic aerosol formation using genetic algorithms

A new method is proposed to simplify complex atmospheric chemistry reaction schemes, while preserving SOA formation properties, using genetic algorithms. The method is first applied in this study to the gas-phase α-pinene oxidation scheme. The simple unified volatility-based scheme (SUVS) reflects the multi-generation evolution of chemical species from a near-explicit master chemical mechanism (MCM) and, at the same time, uses the volatility-basis set speciation for condensable products. The SUVS also unifies reactions between SOA precursors with different oxidants under different atmospheric conditions. A total of 412 unknown parameters (product yields of parameterized products, reaction rates, etc.) from the SUVS are estimated by using genetic algorithms operating on the detailed mechanism. The number of organic species was reduced from 310 in the detailed mechanism to 31 in the SUVS. Output species profiles, obtained from the original subset of the MCM reaction scheme for α-pinene oxidation, are reproduced with maximum fractional error at 0.10 for scenarios under a wide range of ambient HC/NO<sub>x</sub> conditions. Ultimately, the same SUVS with updated parameters could be used to describe the SOA formation from different precursors

JPL bibliography 39-12 - Prerelease for December 1970

Bibliography of technical reports on scientific and engineering studies, December 197