Advanced bibliometric to model the relationship between entry behaviour and networking emerging technological communities.

Organisational ecology and social network theory are used to explain entries in technological communities. Using bibliometric data on 411 organisations in the field of plant biotechnology, we test several hypotheses that entry is not only influenced by the density of the field, but also by the structure of the R&D network within the community. The empirical findings point to the usefulness of bibliometric data in mapping change and evolution in technological communities as well as to the effects of networking on entry behaviour.Model;

A Cognitively Founded Model of the Social Emergence of Lexicon

This paper suggests a model of the process through which a set of symbols, initially without any intrinsic meaning, acquires endogenously a conventional and socially shared meaning. This model has two related aspects. The first is the cognitive aspect, represented by the process through which each agent processes the information gathered during the interactions with other agents. In this paper, the agents are endowed with the cognitive skills necessary to categorize the input in a lexicographic way, a categorization process that is implemented by the means of data mining techniques. The second aspect is the social one, represented by the process of reiterate interactions among the agents who compose a population. The framework of this social process is that of evolutionary game theory, with a population of agents who are randomly matched in each period in order to play a game that, in this paper, is a kind of signaling game. The simulations show that the emergence of a socially shared meaning associated to a combination of symbols is, under the assumptions of this model, a statistically inevitable occurrence.Social Conventions, Fast and Frugal Heuristic Theory, Emergence of Lexicon, Data Mining, Signaling Games

Deep Convolutional Attention based Bidirectional Recurrent Neural Network for Measuring Correlated Colour Temperature from RGB images

Information on the connected colour temperature, which affects the image due to the surrounding illumination, is critical, particularly for natural lighting and capturing images. Several methods are introduced to detect colour temperature precisely; however, the majority of them are difficult to use or may generate internal noise. To address these issues, this research developed a hybrid deep model that properly measures temperature from RGB images while reducing noise. The proposed study includes image collection, pre-processing, feature extraction and CCT evaluation. The input RGB pictures are initially generated in the CIE 1931 colour space. After that, the raw input samples are pre-processed to improve picture quality by performing image cropping and scaling, denoising by hybrid median-wiener filtering and contrast enhancement via Rectified Gamma-based Quadrant Dynamic Clipped Histogram Equalisation (RG_QuaDy_CHE). The colour and texture features are eliminated during pre-processing to obtain the relevant CCT-based information. The Local Intensity Grouping Order Pattern (LIGOP) operator extracts the texture properties. In contrast, the colour properties are extracted using the RGB colour space’s mean, standard deviation, skewness, energy, smoothness and variance. Finally, using the collected features, the CCT values from the submitted images are estimated using a unique Deep Convolutional Attention-based Bidirectional Recurrent Neural Network (DCA_BRNNet) model. The Coati Optimisation Algorithm (COA) is used to improve the performance of a recommended classifier by modifying its parameters. In the Result section, the suggested model is compared to various current techniques, obtaining an MAE value of 529K and an RMSE value of 587K, respectively

Mathematical analysis, modelling and simulation of microbial population dynamics

The physiology of unicellular organisms results from a central metabolism which input-output balance accounts for both the cells’ state and their culture medium’s abundance. When bacteria are cultivated in a locally fed fermenter and transported in a turbulent flow, they have to deal with concentration gradients throughout their trajectory in the reactor. Simulating this physics in a multiscale modelling approach requires taking into account not only the well-known laws of hydrodynamics, but also the cells’ biochemistry which is still ill-understood to date. Moreover, the prohibitive cost of the numerics forces to reduce the models to constrain the duration of the experiments to a few weeks. In this context, special consideration has been given to the biological phase. The bacteria population dynamics is given by an integro-differential transport-rupture equation in the space of the particles’ inner coordinates. Picking the most appropriate variables is of paramount importance to best report the time evolution of the cells’ state throughout their history in the fermenter, the latter being comparable to a markovian process. The microorganisms’ length testifies to their morphology and their progress in the cell cycle, whereas the uptake rate of the surrounding resources leads to an evaluation of the material transfer between the liquid and biotic phases. The result is the estimation of the source term in the organisms’ central metabolism which outputs are the apparent rate of anabolism and, if over-uptake, activation of peripheral reactions to combust the surplus in organic compounds. Beyond their own history, the individuals’ metabolic yields can be impacted by the substrate availability at their neighbourhood, which stems from the feeding and the level of mixing in the reactor. The state variables have a compact support, what raises the question of the mathematical problem’s wellposedness, similarly as solving a PDE over a bounded set is traditionally more difficult than over $\mathbb{R}^{n}$, $n \in \mathbb{N}$. It is shown that the Malthus eigenfunction associated with the transport-rupture equation is $\mathcal{C}^{1}$ as soon as fragmentation trumps cell growth near the right-hand edge of the size-distribution’s support. All in all, the solution is continuous at each time in the state space. These results allow the implementation of numerical codes to solve (in this work, by Monte-Carlo, Finite Volume, or Quadrature of MOMents methods) the well-posed problem, the algorithms being exploited to simulate five biochemical engineering experiments which conclusions are detailed in the literature

Optimization of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm

In order to reduce the S-N curve dispersion of titanium alloy welded joints and improve the prediction accuracy of fatigue life, a novel optimization method of S-N curve fitting based on neighborhood rough set reduction with improved firefly algorithm (IFANRSR) is proposed. Firstly, we propose an improved firefly algorithm (IFA) by updating the position and step size, combining IFA algorithm and neighborhood rough set into an IFANRSR algorithm for attribute reduction. Then, according to the fatigue data of titanium alloy welded joints, the fatigue decision system of welded joints is established, and the key factors affecting the fatigue life of welded joints are determined. Next, according to the set of key influencing factors obtained based on IFANRSR algorithm, the fatigue characteristics domains are divided, and the S-N curves are fitted on each fatigue characteristics domain, to obtain a group of S-N curves. To demonstrate the effectiveness of IFA algorithm, six benchmark functions are used, then the availability of IFANRSR algorithm is evaluated in comparison with other algorithms on four UCI datasets. Finally, the results of the goodness-of-fit show that the dispersion of fatigue data is reduced, which can effectively improve the prediction accuracy of fatigue life

Dynamics of Macrosystems; Proceedings of a Workshop, September 3-7, 1984

There is an increasing awareness of the important and persuasive role that instability and random, chaotic motion play in the dynamics of macrosystems. Further research in the field should aim at providing useful tools, and therefore the motivation should come from important questions arising in specific macrosystems. Such systems include biochemical networks, genetic mechanisms, biological communities, neutral networks, cognitive processes and economic structures. This list may seem heterogeneous, but there are similarities between evolution in the different fields. It is not surprising that mathematical methods devised in one field can also be used to describe the dynamics of another. IIASA is attempting to make progress in this direction. With this aim in view this workshop was held at Laxenburg over the period 3-7 September 1984. These Proceedings cover a broad canvas, ranging from specific biological and economic problems to general aspects of dynamical systems and evolutionary theory

Migration and Settlement: 12. Bulgaria

This collection of national reports deals with the comparative analysis of internal migration and spatial population growth in the 17 National Member Organization countries of IIASA. Patterns of population change are explored by applying the new multiregional methodologies and computer programs elaborated in the Human Settlements and Services Area. All reports have the same structure and include multiregional life tables, spatial mortality, fertility, and migration expectancies, and multiregional population projections. Each Migration and Settlement report is authored by a native collaborating scholar familiar with the demographic setting of his/her country. In this report, Dimiter Philipov analyzes recent changes in Bulgaria's patterns of population redistribution and studies in detail the demographic dynamics of seven economic planning regions

Society in space and time : an attempt to provide a theoretical foundation from an historical geographic point of view

The idea of developing a theoretical conception for explaining anthropogene structures and processes in space and time, reverts to the early 1960´s, when I discovered latent rotation effects in the course of an investigation of population and traffic movement in various West German cities (D. FLIEDNER, 1962a). However, at the time I was not yet in a position to interpret these "cyclonic tendencies" as the expression of an universal phenomenon. Having subsequently been engaged in various historical geographic investigations, I felt it appropriate to take up the theme once again. I found further encouragement in numerous stimulating discussions with my brother, Dr. Siegfried Fliedner, Bremen, on basic and methodological problems concerning the complex of questions dealt with here. This treatise should be seen as an attempt by an anthropo-geographer to recognize phenomena, structures and processes in the scientific environment he is familiar with, to bring them into proper perspective and to understand their underlying basic order. During the course of this work it very soon became evident that a limitation of these considerations to anthropo-geographical facts and circumstances in the traditional sense could not lead to success. The fact that the conventional structure of science is rooted in a different conception of reality sometimes proved to be an impediment to a more comprehensive theoretical interpretation. Thus, it was necessary in several points to venture far beyond the limits of geography. Of course, the further away I moved from anthropo-geography, going on into related branches, the more difficult it became to evaluate the facts. However, this had to be the course of procedure, as it was also a matter of breaking the spell of isolation surrounding more or less every scientist in his own discipline. May my colleagues involved in related sciences or in those branches touched upon in this treatise not regard my exposition as an encroachment on their domain

Analysis and Control of Socio-Cultural Opinion Evolution in Complex Social Systems

The overarching goal of this thesis is to further our understanding about opinion evolution in networked societies. Such insights can be used in a variety of fields such as economy, marketing, transportation, egress, etc. Three main subjects build up this interdisciplinary research: Sociology, Statistical Mechanics, and Network Sciences. In this thesis, for macrolevel (or society-level) analyses, techniques from statistical mechanics have been borrowed to mathematically model the opinion dynamic on different network topologies based on different interaction models. Also, for micro-level (individual-level) analyses, Individual Decision Making Algorithms (IDMA) have been designed. To account for both macro-level and micro-level dynamics, these two regimes are combined resulting in a more accurate model for opinion propagation. Assessing the controllability of such dynamics through experiments in presence of actual humans is the part of this thesis

Identification of time-varying cable forces based on parameter optimization variational mode decomposition.

Accumulated fatigue damage is one of the main causes of damage and destruction of actual bridge structures. for cable bridges, the cable is the key force component. the cumulative fatigue damage of the cable seriously threatens the safety of the bridge structure. the traditional cable force test based on the vibration method can only identify the average cable force of a bridge cable over a period of time. however, due to vehicle load and environmental factors, the cable force of the bridge cable is time-varying. time-varying cable force is the main cause of fatigue damage, and it is also the basis for the safety assessment of cable limit state and the evaluation of cumulative fatigue damage. to this end, this paper studies the identification method of bridge cable time-varying cable force based on variational mode decomposition. the main research contents of this article include: based on the time-frequency analysis method of variational mode decomposition, a new method for identifying time-varying cable forces is proposed. the time-frequency analysis method of variational modal decomposition is a new development method in the field of current signal processing. its principle is to obtain a limited number of imf and extract the instantaneous frequency of the time-varying system by performing hilbert transform on the obtained imf. firstly, according to the time-frequency analysis method of variational modal decomposition, the time-varying modal frequency is identified from the measured cable acceleration. then, the bridge cable is simplified into an ideal tension string, and the cable force is identified based on the relationship between the cable force and frequency established by the classic string vibration theory. the frequency-doubling relationship of the vibration of the cable is used to reduce the optimization variables of the method, improve the calculation efficiency, reduce the influence of noise on the different instantaneous frequencies of the cable, and improve the accuracy of frequency identification of time-varying modal. finally, the time-varying modal frequency of the identified cable is substituted into the cable force formula to obtain the time-varying cable force of the cable. in the practical application of variational modal decomposition, the choice of penalty factors and the number of components has a great influence on the final signal decomposition results. in order to automatically determine the best parameter combination, the particle swarm optimization algorithm is used to search for these two influencing parameters in parallel . simulation signal and engineering signal processing results show that the proposed method can achieve identification of time-varying frequency. for the end effect of the instantaneous frequency curve, the signal extension method is used. reduce the error of the instantaneous frequency at the end point. design and build a tilting cable vibration test platform, and compare the test results with the calculation results to further verify the correctness of the method in this paper. the results show that the cable force error is about ± 5.0%