La sociologia a confronto con l’immaginario

Challenges in process control for continuous processing for production of monoclonal antibody products

Dynamic temperature modeling of continuous annealing furnace using GGAP-RBF neural network

Evaluation of the Process Performance in Wire EDM Based on an Online Process Monitoring System

A hot continuous rolling process control system, a complex system, has a unique mathematical model system, which is discussed. The system has many complicated methods to deal with large quantities of information from the real process. The models, defined to be in three layers, underlie the whole model system of the control process of hot continuous rolling. The accuracy of the models has close connection with the technological goals-the core of the process control by which the models are combined with each other. Upgrading the accuracy of the models, especially the online models, is probably the short way to improve the control accuracy. An example is also presented.

The generation of continuous random processes with jointly specified probability density and covariation functions is considered. The proposed approach is based on the interpretation of the simulated process as a stationary output of a nonlinear dynamic system, excited by white Gaussian noise and described by a system of a first-order stochastic differential equations (SDE). The authors explore how the statistical characteristics of the equation's solution depends on the form of its operator and on the intensity of the input noise. Some aspects of the approximate synthesis of stochastic differential equations and examples of their application to the generation of non-Gaussian continuous processes are considered. The approach should be useful in signal processing when it is necessary to translate the available a priori information on the real random process into the language of its Markov model as well as in simulation of continuous correlated processes with the known probability density function. >

New Atheism declares God to be “a scientific hypothesis” that can be tested like any other. An examination of their claim, and the approaches they employ to substantiate it, reveals a particular understanding of God and causality, where God’s action would entail an observable rupture of causal closure. The modern Western intelligent design (ID) movement posits very specific ID arguments, all of which, under examination, share assumptions about an external designer and the nature of the designing act. The argument from irreducible complexity entails more than an object’s irreducibility, but particularly the idea that natural causal mechanisms are inadequate to account for the complex object. Specified complexity argues that certain patterns of information cannot come about by natural processes alone. The fine-tuning argument also presupposes a lack of plausible naturalistic explanations for the universal constants identified by science. It becomes clear that they share an underlying assumption of the New Atheists’ “God hypothesis,” that a complete, natural causal account would render the resulting phenomena unconvincing as evidence for an external designer’s existence. These assumptions are compared with the concept of God’s action in the divine action models (DAMs) of the three Sunnī theological schools: Ashʿarī, Māturīdī, and Salafī. Upon examination, all three models assume a maximally active God and allow natural phenomena to be accounted for by continuous and unbroken natural causal processes. The paper concludes that Sunnī concepts of God’s divine action are immune to New Atheist critiques, particularly their manner of weaponizing science against religion, because the underlying assumptions about God are different. Moreover, the responses to New Atheism made by the Western ID movement are equally inapplicable from the standpoint of Sunnī DAMs, since those responses share with New Atheism metaphysical assumptions the DAMs dismiss.

This paper considers how 5- to 11-year-olds’ verbal reasoning about the causality underlying extended, dynamic natural processes links to various facets of their statistical thinking. Such continuous processes typically do not provide perceptually distinct causes and effect, and previous work suggests that spatial–temporal analysis, the ability to analyze spatial configurations that change over time, is a crucial predictor of reasoning about causal mechanism in such situations. Work in the Humean tradition to causality has long emphasized on the importance of statistical thinking for inferring causal links between distinct cause and effect events, but here we assess whether this is also viable for causal thinking about continuous processes. Controlling for verbal and non-verbal ability, two studies (N = 107; N = 124) administered a battery of covariation, probability, spatial–temporal, and causal measures. Results indicated that spatial–temporal analysis was the best predictor of causal thinking across both studies, but statistical thinking supported and informed spatial–temporal analysis: covariation assessment potentially assists with the identification of variables, while simple probability judgment potentially assists with thinking about unseen mechanisms. We conclude that the ability to find out patterns in data is even more widely important for causal analysis than commonly assumed, from childhood, having a role to play not just when causally linking already distinct events but also when analyzing the causal process underlying extended dynamic events without perceptually distinct components.

Magnesium hydroxide was used as a coagulant for treating reactive orange wastewater in a real continuous process. Effects of kaolin on coagulation performance and floc properties were investigated with controlled experiments through floc size distribution, zeta potential, scanning electron microscopy and Fourier transform infrared spectroscopy. Kaolin had significant influence on magnesium hydroxide-reactive orange floc formation and growth. The results showed that average floc size reached 16.31, 12.88 and 20.50 μm, respectively, in the rapid mixer, flocculation basin and sedimentation tank when kaolin concentration was 10 mg/L and reactive orange initial concentration was 0.25 g/L. The floc size tended to increase with the increase of kaolin suspension to 10 mg/L. All of the flocs under investigation showed that floc breakage led to decreased average floc size and remained stable in the flocculation basin. Reactive orange and kaolin could be removed effectively in the continuous coagulation process. Reactive orange was adsorbed in the surface of magnesium hydroxide through charge neutralization and adsorption.

Dynamic modeling for the quality of large-scale process is studied in this paper combined with continuous annealing process. This kind of process is constituted with several sub-processes. There is complex nonlinear mapping between the sub-process set points and the final quality. The quality model should be constructed and updated based on the new data from the real process. To meet this demand, a novel generalized growing and pruning RBF (GGAP-RBF) network is used to establish the quality model. GGAP-RBF is a sequential learning algorithm so that we can establish the model dynamically. Last, we do some on-line application study on the continuous annealing furnace in a steel factory. The quality model between the furnace temperature of each zone in the furnace and the exit strip temperature is constructed.KeywordsRadial Basis FunctionHide NeuronRadial Basis Function Neural NetworkRadial Basis Function NetworkHeating FurnaceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We have witnessed a skyrocketing development of tight gas in China. However, all developed gas field in China face the obstacle of the decrease of production. The Xujiahe Formation in Sichuan Basin shows a promising future for the next big gas field. In this work, we will shed light on the densification mechanism and natural gas accumulation process of Triassic Xujiahe Formation, Hechuan Area, Sichuan Basin through physical experiments and theory analysis. We describe the reservoir characteristics. The diagenesis types and characteristics of sandstone reservoirs in the promising future of the next big gas field. In this work, we will shed light on the densification mechanism and natural gas accumulation process of Triassic Xujiahe Formation, Hechuan Area, Sichuan Basin through physical experiments and theory analysis. We describe the reservoir characteristics. The diagenesis types and characteristics of sandstone reservoirs in the second Member of Xujiahe Formation in Hechuan area are analyzed by means of rock thin section, cast thin section, and mercury injection. By observing the microscopic characteristics, types, and homogenization temperature distribution of fluid inclusions, the accumulation period of natural gas in Hechuan gas field is given, and the accumulation process of natural gas is revealed. The results show that (1) the natural gas charging the tight sandstone reservoirs of the Upper Triassic Xujiahe Formation in Hechuan area is a continuous process. The gas accumulation period can be roughly divided into two stages; (2) combining with the analysis of the hydrocarbon accumulation period of time, we put forward the natural gas accumulation model of the Xujiahe Formation, Hechuan area: the natural gas accumulation is accompanied by formation densification.

We show that a large class of continuous time processes admits a Banach autoregressive representation. This fact allows us to obtain various limit theorems for continuous time processes. In particular we prove the law of iterated logarithm for processes which satisfy a stochastic dierential equation.

Many researches on mold wear are based on numerical simulation of single stamping, which is difficult to reflect the cumulative wear caused in the real continuous stamping process. By designing the stamping trajectory, a numerical simulation method based on continuous stamping is proposed. The modeling of the mold is analyzed and the formula of the wear amount is deduced. And a series of tests are designed to analyze the surface morphology which provided the theoretical and factual basis for numerical simulation and verifies the correctness of the numerical simulation method. Then the variation of heat, wear and stress field distribution during continuous stamping process are investigated by numerical simulation. The relationship between the stress and wear increment of the mold is studied. Finally, the variation rule of the wear amount after continuous stamping is summarized and the criterion of entering the steady wear state is given. The maximum wear depth is fitted linearly and the service life is predicted. This provides a theoretical basis for studying the wear rules of the dies in the continuous stamping process.

Frequent and wide changes in operation conditions are quite common in real process industry, resulting in typical wide-range nonstationary and transient characteristics along time direction. The considerable challenge is, thus, how to solve the conflict between the learning model accuracy and change complexity for analysis and monitoring of nonstationary and transient continuous processes. In this work, a novel condition-driven data analytics method is developed to handle this problem. A condition-driven data reorganization strategy is designed which can neatly restore the time-wise nonstationary and transient process into different condition slices, revealing similar process characteristics within the same condition slice. Process analytics can then be conducted for the new analysis unit. On the one hand, coarse-grained automatic condition-mode division is implemented with slow feature analysis to track the changing operation characteristics along condition dimension. On the other hand, fine-grained distribution evaluation is performed for each condition mode with Gaussian mixture model. Bayesian inference-based distance (BID) monitoring indices are defined which can clearly indicate the fault effects and distinguish different operation scenarios with meaningful physical interpretation. A case study on a real industrial process shows the feasibility of the proposed method which, thus, can be generalized to other continuous processes with typical wide-range nonstationary and transient characteristics along time direction. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial processes in general have nonstationary characteristics which are ubiquitous in real world data, often reflected by a time-variant mean, a time-variant autocovariance, or both resulting from various factors. The focus of this study is to develop a universal analytics and monitoring method for wide-range nonstationary and transient continuous processes. Condition-driven concept takes the place of time-driven thought. For the first time, it is recognized that there are similar process characteristics within the same condition slice and changes in the process correlations may relate to its condition modes. Besides, the proposed method can provide enhanced physical interpretation for the monitoring results with concurrent analysis of the static and dynamic information which carry different information, analogous to the concepts of “position” and “velocity” in physics, respectively. The static information can tell the current operation condition, while the dynamic information can clarify whether the process status is switching between different steady states. It is noted that the condition-driven concept is universal and can be extended to other applications for industrial manufacturing applications.

We construct and study a continuous real-valued random process, which is of a new type: It is self-interacting (self-repelling) but only in a local sense: it only feels the self-repellance due to its occupation-time measure density in the `immediate neighbourhood' of the point it is just visiting. We focus on the most natural process with these properties that we call `true self-repelling motion'. This is the continuous counterpart to the integer-valued `true' self-avoiding walk, which had been studied among others by the first author. One of the striking properties of true self-repelling motion is that, although the couple (X t , occupation-time measure of X at time t) is a continuous Markov process, X is not driven by a stochastic differential equation and is not a semi-martingale. It turns out, for instance, that it has a finite variation of order 3/2, which contrasts with the finite quadratic variation of semi-martingales. One of the key-tools in the construction of X is a continuous system of coalescing Brownian motions similar to those that have been constructed by Arratia [A1, A2]. We derive various properties of X (existence and properties of the occupation time densities L t (x), local variation, etc.) and an identity that shows that the dynamics of X can be very loosely speaking described as follows: −dX t is equal to the gradient (in space) of L t (x), in a generalized sense, even though x↦L t (x) is not differentiable.

Summary 1 Classical evolutionary theory states that senescence should arise as a consequence of the declining force of selection late in life. Although the quantitative genetic predictions of hypotheses derived from this theory have been extensively tested in laboratory studies of invertebrate systems, relatively little is known about the genetics of ageing in the wild. 2 Data from long-term ecological studies is increasingly allowing quantitative genetic approaches to be used in studies of senescence in free-living populations of vertebrates. We review work to date and argue that the patterns are broadly consistent with theoretical predictions, although there is also a clear need for more empirical work. 3 We argue that further advances in this field of research might be facilitated by increased use of reaction norm models, and a decreased emphasis on attempting to discriminate between mutation accumulation and antagonistic pleiotropy models of senescence. We also suggest a framework for the better integration of environmental and genetic effects on ageing. 4 Finally, we discuss some of the difficulties in applying quantitative genetic models to studies of senescence outside the laboratory. In particular we highlight the problems that viability selection can cause for an accurate estimation of parameters used in the prediction of age-trajectory evolution.