Load is the main external disturbance of a parallel robot manipulator. This disturbance will cause dynamic coupling among different degrees of freedom and make heaps of model-based control methods difficult to apply. In order to compensate this disturbance, it is crucial to obtain an accurate dynamic model of load. However, in practice, the load is always uncertain and its dynamic parameters are arduous to know a priori. To cope with this problem, this paper proposes a novel and simple approach to (...) identify the dynamic parameters of load. Firstly, the dynamic model of the parallel robot manipulator with uncertain load is established and the dynamic coupling caused by load is also analyzed. Then, according to the dynamic model, the excitation signal is designed and a weak nonlinear dynamic model is derived. Furthermore, the identification model is presented and the identification algorithm based on the extended Kalman filter is designed. Lastly, numerical simulation results, obtained using a six-degree-of-freedom Gough–Stewart parallel manipulator, demonstrate the good estimation performance of the proposed method. (shrink)

With the rapid development of sensor technology for automated driving applications, the fusion, analysis, and application of multimodal data have become the main focus of different scenarios, especially in the development of mobile edge computing technology that provides more efficient algorithms for realizing the various application scenarios. In the present paper, the vehicle status and operation data were acquired by vehicle-borne and roadside units of electronic registration identification of motor vehicles. In addition, a motion model and an identification (...) system for the single-vehicle lane-change process were established by mobile edge computing and self-organizing feature mapping. Two scenarios were modeled and tested: lane change with no vehicles in the target lane and lane change with vehicles in the target lane. It was found that the proposed method successfully identified the stochastic parameters in the process of driving trajectory simulation, and the standard deviation between simulation and the measured results obeyed a normal distribution. The proposed methods can provide significant practical information for improving the data processing efficiency in automated driving applications, for solving single-vehicle lane-change applications, and for promoting the formation of a closed loop from sensing to service. (shrink)

This communique presents the Q-S synchronization of two nonidentical complex nonlinear hyperchaotic systems with unknown parameters. An adaptive controller based on adaptive integral sliding mode control and parameter update laws are designed to realize the synchronization and parameter identification to a given map vector. The aforementioned strategy’s employment demands the transformation of a system into a specific structure containing a nominal part and some unknown terms. An integral sliding mode controller is used to stabilize the error system (...) by designing nominal control accompanied by compensator control. For chattering suppression, a continuous compensator of smooth nature is used instead of conventional control. The stability of the proposed algorithm is established in an impressive way, using Lyapunov criteria. A numerical simulation is performed to illustrate the validity of the proposed synchronization scheme. (shrink)

The whale optimization algorithm is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the (...) local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model, double diode model, and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules. (shrink)

In order to obtain continuous stratum information during TBM tunneling, using TBM tunneling parameters, stratum recognition is carried out through the K-nearest neighbor model, and the model is improved by the entropy weight method to improve the stratum recognition rate. By analyzing the correlation between TBM tunneling characteristic parameters and stratum, the tunneling characteristic parameter vector which is most sensitive to the stratum is obtained by sensitivity analysis, and the stratum recognition model based on the K-nearest neighbor algorithm is (...) established. Aiming at the problem that the model has a large error in complex formation recognition, a formation recognition model based on the entropy weight K-nearest neighbor algorithm is established, and the wrong data of the K-nearest neighbor model is recalculated. The recognition rate of the stratum in the new model is increased from 90.95% to 98.55%. The results show that the K-nearest neighbor model has a better recognition effect for the interval with single stratum distribution, and the recognition rate of entropy weight K-nearest neighbor model for complex stratum is significantly improved, which provides an effective method to obtain stratum information by using tunneling characteristic parameters. (shrink)

This paper proposes two parameter identification methods for a nonlinear membership function. An equation converted method is introduced to turn the nonlinear function into a concise model. Then a stochastic gradient algorithm and a gradient-based iterative algorithm are provided to estimate the unknown parameters of the nonlinear function. The numerical example shows that the proposed algorithms are effective.

Expiratory flow limitation (EFL) can occur in mechanically ventilated patients with chronic obstructive pulmonary disease and other disorders. It leads to dynamic hyperinflation with ensuing deleterious consequences. Detecting EFL is thus clinically relevant. Easily applicable methods however lack this detection being routinely made in intensive care. Using a simple mathematical model, we propose a new method to detect EFL that does not require any intervention or modification of the ongoing therapeutic. The model consists in a monoalveolar representation of the respiratory (...) system, including a collapsible airway that is submitted to periodic changes in pressure at the airway opening: EFL provokes a sharp expiratory increase in the resistance Rc of the collapsible airway. The model parameters were identified via the Levenberg-Marquardt method by fitting simulated data on the airway pressure and the flow signals recorded in 10 mechanically ventilated patients. A sensitivity study demonstrated that only 8/11 parameters needed to be identified, the remaining three being given reasonable physiological values. Flow-volume curves built at different levels of positive expiratory pressure, PEEP, during PEEP trials (stepwise increases in positive end-expiratory pressure to optimize ventilator settings) have shown evidence of EFL in three cases. This was concordant with parameter identification (high Rc during expiration for EFL patients). We conclude from these preliminary results that our model is a potential tool for the non-invasive detection of EFL in mechanically ventilated patients. (shrink)

It is widely agreed that the references of indexical expressions are fixed partly by their relations to contextual parameters such as the author, time, and place of the utterance. Because of this, indexicals are sometimes described as token-reflexive or utterance-reflexive in their semantics. But when we inquire into how indexicals help us to identify items within experience, we find that while utterance-reflexivity is essential to an interpretation of indexical tokens, it is not a factor in a speaker's identificatory use of (...) indexicals. Tokens cannot be interpreted unless they are first produced, and obviously the speaker who produces them does not depend upon utterance parameters in order to identify their referents in the way that hearers do. Consequently, the standard reflexive accounts of indexicals are of little use in explaining the speaker's identificatory use of indexicals, and must be either replaced or complemented by a further theory of the role of indexicals in thought. This paper provides an account of indexical identification that is attentive to a speaker's as well as a hearer's identification and reveals how indexicals are inextricably perspectival. (shrink)

Lentinus edodes sticks are susceptible to mold infection during the culture process, and manual identification of infected sticks is heavy, untimely, and inaccurate. Aiming to solve this problem, this paper proposes a method for identifying infected Lentinus edodes sticks based on improved ResNeXt-50 deep transfer learning. First, a dataset of Lentinus edodes stick diseases was constructed. Second, based on the ResNeXt-50 model and the pretraining weight of the ImageNet dataset, the influence of pretraining weight parameters on recognition accuracy was (...) studied. Finally, six fine-tuning strategies of the fully connected layer were designed to modify the fully connected layer of ResNeXt-50. The experimental results show that the recognition accuracy of the method proposed in this paper can reach 94.27%, which is higher than the Vgg16, GoogLeNet, ResNet50, and MobileNet v2 models by 8.47%, 6.49%, 4.68%, and 9.38%, respectively, and the F1-score can reach 0.9422. The improved method proposed in this paper can reduce the calculation pressure and overfitting problem of the model, improve the accuracy of the model in the identification of Lentinus edodes stick mold diseases, and provide an effective solution for the selection of diseased sticks. (shrink)

This study focused on the identification problems of two-input single-output system with moving average noises based on unsupervised learning methods applied to the input signals. The input signal to the autoregressive moving average model is proposed to be arriving from a source with continuous technical and environmental changes as two separate featured input signals. These two input signals were grouped in a number of clusters using the K-means clustering algorithm. The clustered input signals were supplied to the model in (...) an orderly fashion from cluster-1 up to cluster-K. To ensure that the output signal can be best predicted from the input signal which in turn leads to selecting good enough model for its intended use, the magnitude-squared coherence measure is applied to the input/output signals in the cases of clustered and nonclustered inputs, which indicates best correlation coefficient when measured with clustered inputs. From collected input-output signals, we deduce a K-means clustering based recursive least squares method for estimating the parameter of autoregressive moving average system. The simulation results indicate that the suggested method is effective. (shrink)

Complex adaptation systems are the main development direction of China’s current green innovation research. New material industry is one of the key entry points to accelerate the construction of modern industrial system and promote innovation, green, and efficient development. Under the requirements of China’s current low-carbon development, China’s green innovation system is developing rapidly. Green innovation complicates traditional innovation models and their functions and improves economic development. The purpose of this paper is to study the theoretical analysis framework of applying (...) complex adaptive systems and to analyze and calculate the characteristics of the current status of green innovation systems in multiple industries in China’s manufacturing industry. Analysis is as follows. The experimental group and the control group are set for comparison, and the data are organized by applying complex adaptive system theory and data analysis methods. Experimental data show that the coordinated development of the green innovation systems in various industries in China is not balanced, with a difference of more than three times; the parameters of the various green innovation systems within the industry are unbalanced, and the difference is more than five times. The experimental results show that the green innovation subject can strategically interact by adjusting the ratio of resource allocation through the constructed green innovation system model. The green innovation system with complex adaptive system theory is conducive to the development of the green system. It can save about 23% of green innovation time and improve efficiency by about 15%, to promote the development of green innovation in China. (shrink)

Shape memory alloy- based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is (...) a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm. (shrink)

This paper proposes underwater target identification with local features and a feature tracking algorithm for acoustic image sequences. Feature detectors and descriptors are key to feature tracking. Their performance in underwater scene is evaluated by the change of multitarget parameters. A comprehensive quantitative investigation into the performance of feature tracking is thereby presented. Experimental results confirm that the proposed algorithm can accurately track potential targets and determine whether the potential targets are static targets, dynamic targets, or false alarms according (...) to the tracking trajectories and statistical data. (shrink)

With the rapid development of Internet of Things technology, RFID technology has been widely used in various fields. In order to optimize the RFID system hardware deployment strategy and improve the deployment efficiency, the prediction of the RFID system identification rate has become a new challenge. In this paper, a neighborhood rough set and random forest combination model is proposed to predict the identification rate of an RFID system. Firstly, the initial influencing factors of the RFID system (...) class='Hi'>identification rate are reduced using neighborhood rough set theory combined with the principle of heuristic attribute reduction of neighborhood weighted dependency, thus obtaining a kernel factor subset. Secondly, a random forest prediction model is established based on the kernel factor subset, and a confusion matrix is established using out-of-bag data to evaluate the prediction results. The test is conducted under the constructed RFID experimental environment, whose results showed that the model can predict the identification rate of the RFID system in a fast and efficient way, and the classification accuracy can reach 90.5%. It can effectively guide the hardware deployment and communication parameter protocol setting of the system and improve the system performance. Compared with BP neural network and other prediction models, NRS-RF has shorter prediction time and faster calculation speed. Finally, the validity of the proposed model was verified by the RFID intelligent archives management platform. (shrink)

Observability is a modelling property that describes the possibility of inferring the internal state of a system from observations of its output. A related property, structural identifiability, refers to the theoretical possibility of determining the parameter values from the output. In fact, structural identifiability becomes a particular case of observability if the parameters are considered as constant state variables. It is possible to simultaneously analyse the observability and structural identifiability of a model using the conceptual tools of differential geometry. (...) Many complex biological processes can be described by systems of nonlinear ordinary differential equations and can therefore be analysed with this approach. The purpose of this review article is threefold: to serve as a tutorial on observability and structural identifiability of nonlinear systems, using the differential geometry approach for their analysis; to review recent advances in the field; and to identify open problems and suggest new avenues for research in this area. (shrink)

Faces provide information about a person's identity, as well as their sex, age, and ethnicity. People also infer social and personality traits from the face — judgments that can have important societal and personal consequences. In recent years, deep convolutional neural networks (DCNNs) have proven adept at representing the identity of a face from images that vary widely in viewpoint, illumination, expression, and appearance. These algorithms are modeled on the primate visual cortex and consist of multiple processing layers of simulated (...) neurons. Here, we examined whether a DCNN trained for face identification also retains a representation of the information in faces that supports social‐trait inferences. Participants rated male and female faces on a diverse set of 18 personality traits. Linear classifiers were trained with cross validation to predict human‐assigned trait ratings from the 512 dimensional representations of faces that emerged at the top‐layer of a DCNN trained for face identification. The network was trained with 494,414 images of 10,575 identities and consisted of seven layers and 19.8 million parameters. The top‐level DCNN features produced by the network predicted the human‐assigned social trait profiles with good accuracy. Human‐assigned ratings for the individual traits were also predicted accurately. We conclude that the face representations that emerge from DCNNs retain facial information that goes beyond the strict limits of their training. (shrink)

Computational modeling plays an important role in prediction and optimization of real systems and processes. Models usually have some parameters which should be set up to the proper value. Therefore, parameter estimation is known as an important part of the modeling and system identification. It usually refers to the process of using sampled data to estimate the optimum values of parameters. The accuracy of model can be increased by adjusting its parameters to the optimum value which need a (...) richer dataset. One simple solution for having a richer dataset is increasing the amount of data, but that can be costly and time consuming. When using data from animals or people, it is especially important to have a proper plan. There are several available methods for parameter estimation in dynamical systems; however there are some basic differences in chaotic systems due to their sensitivity to initial condition. Accordingly, in this paper, a new cost function which is proper for chaotic systems is applied to the chaotic one-dimensional map. Then the efficiency of a newly introduced intelligent method experimental design in extracting proper data is investigated. The results show the success of the proposed method. (shrink)

To improve the positioning accuracy of industrial robots and avoid using the coordinates of the end effector, a novel kinematic calibration method based on the distance information is proposed. The kinematic model of an industrial robot is established. The relationship between the moving distance of the end effector and the kinematic parameters is analyzed. Based on the results of the analysis and the kinematic model of the robot, the error model with displacements as the reference is built, which is linearized (...) for the convenience of the following identification. The singular value decomposition is used to eliminate the redundant parameters of the error model. To solve the problem that traditional optimization algorithms are easily affected by data noise in high dimension identification, a novel extended Kalman filter and regularized particle filter hybrid identification method is presented. EKF is used in the preidentification of the linearized error model. With the preidentification results as the initial parameters, RPF is used to identify the kinematic parameters of the linearized error model. Simulations are carried out to validate the effectiveness of the proposed method, which shows that the method can identify the error of the parameters and after compensation the accuracy of the robot is improved. (shrink)

Through the recognition of ocean sediment sonar images, the texture in the image can be classified, which provides an important basis for the classification of ocean sediment. Aiming at the problems of low efficiency, waste of human resources, and low accuracy in the traditional manual side-scan sonar image discrimination, this paper studies the application of image recognition technology in sonar image substrate texture discrimination, which is popular in many fields. At the same time, considering the scale complexity, diversity, multisources, and (...) small sample characteristics of the marine sediment sonar image texture, the transfer learning is introduced into the image recognition, and the K-means clustering algorithm is used to reset the prior frame parameters to improve the speed and accuracy of image recognition. Through the experimental comparison between the original model and the new model based on transfer learning, the AP value of the yolov3 model based on transfer learning can reach 84.39%, which is 0.97% higher than that of the original model, with considerable accuracy and room for improvement; it takes less than 0.2 seconds. This shows the applicability and development of image recognition technology in texture discrimination of bottom sonar images. (shrink)

In this paper, a mathematical model of the respiratory mechanics is used to reproduce experimental signal waveforms acquired from three newborn lambs. As the main challenge is to determine specific lamb parameters, a sensitivity analysis has been realized to find the most influent parameters, which are identified using an evolutionary algorithm. Results show a close match between experimental and simulated pressure and flow waveforms obtained during spontaneous ventilation and pleural pressure variations acquired during the application of positive pressure, since root (...) mean square errors equal to 0.0119, 0.0052 and 0.0094. The identified parameters were discussed in light of previous knowledge of respiratory mechanics in the newborn. (shrink)

The renin-angiotensin system (RAS) is critical in sodium and blood pressure (BP) regulation, and in cardiovascular-renal (CVR) diseases and therapeutics. As a contribution to SAPHIR project, we present a realistic computer model of renin production and circulating RAS, integrated into Guyton’s circulatory model ( GCM ). Juxtaglomerular apparatus, JGA , and Plasma modules were implemented in C ++/M2SL (Multi-formalism Multi-resolution Simulation Library) for fusion with GCM . Matlab © optimization toolboxes were used for parameter identification. In JGA , (...) renin production and granular cells recruitment (GCR) are controlled by perfusion pressure (PP), macula densa (MD), angiotensin II (Ang II), and renal sympathetic activity (RSNA). In Plasma , renin and ACE (angiotensin-converting enzyme) activities are integrated to yield Ang I and II. Model vs. data deviation is given as normalized root mean squared error (nRMSE; n points). Identification: JGA and Plasma parameters were identified against selected experimental data. After fusion with GCM : (1) GCR parameters were identified against Laragh’s PRA-natriuresis nomogram; (2) Renin production parameters were identified against two sets of data ([renin] transients vs. ACE or renin inhibition). Finally, GCR parameters were re-identified vs. Laragh’s nomogram (nRMSE 8%, n = 9). Validation: (1) model BP, PRA and [Ang II] are within reported ranges, and respond physiologically to sodium intake; (2) short-term Ang II infusion induces reported rise in BP and PRA. The modeled circulating RAS, in interaction with an integrated CVR, exhibits a realistic response to BP control maneuvers. This construction will allow for modelling hypertensive and CVR patients, including salt-sensitivity, polymorphisms, and pharmacotherapeutics. (shrink)

With the technological evolutionary advent, a vision-based approach presents the remote measuring approach for the analysis of vibration. The structure vibration test and model parameter identification in the detection of the structure of the bridge evaluation occupies the important position. The bridge structure to operate safely and reliably is ensured, according to the geological data of qixiashan lead-zinc mine and engineering actual situation, with the aid of international mining software Surpac. To build the 3D visualization model of the (...) application of visualization in mine production are discussed. The results show that the final solid model of -425 stope can accurately display the spatial form of each layer of stope through rotation, amplification and movement. The proposed system is effectually able to perform cutting, volume calculation and roaming in any direction, which has certain guiding significance for mine production management. An accuracy value of 98.75%, the sensitivity of 99%, specificity of 99.64% and PPV of 99.89% are achieved using the proposed 3D modelling and visualization algorithm for vibration signal processing and management. (shrink)

The later Stoics, especially – and most notably – Posidonius of Apamea, allegedly the greatest polymath of his age and the last in a celebrated line of great philosophers of the ancient world, gradually developed the belief that all parts of the universe, either ensouled or not, were actually interconnected due to the omnipresent, corporeal, primordial kosmikon pyr which, according to Stoicism, pervades each being as the honey pervades the honeycomb. As for reasonable beings, in particular, kosmikon pyr takes the (...) form of logos. Due to that power, Posidonius believes, the phases of moon, for instance, can affect the succession of tides and ebb–tides; on the same grounds, the signs can have major effect on the course of events and, therefore, allow for divination. That kind of interconnectedness, apart from justifying the interdependentness of all beings to each other, testifies for an utter kinship between them, for sympatheia. In this short paper I intent to show that Posidonius’s approach has been promptly adopted and utilized by modern holistic theories concerning Environmental Ethics, especially by Arne Naess’s Deep Ecology and Ecosophy T, in the context of which notions such as kinship, interconnectedness, interdependentness and identification of all beings are key parameters for the articulation of their argumentation. I also intent to outline some essential – in my opinion – differences in the way Posidonius and Naess make use of the notion of interconnectedness, together with some inconsistencies which can be potentially fatal, at least as far as a system of ethics is concerned. This is because notions such as cosmic sympathy may possibly establish an interesting, even charming and influential cosmology or metaphysics, but when it comes to ethics, in my opinion, they can only provide shaky grounds for establishing a firm one, since they allow for deterministic views, which more or less leave no room for personal responsibility, to wit for personal praise or blame. (shrink)

Achieving space domain awareness requires the identification, characterization, and tracking of space objects. Storing and leveraging associated space object data for purposes such as hostile threat assessment, object identification, and collision prediction and avoidance present further challenges. Space objects are characterized according to a variety of parameters including their identifiers, design specifications, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, processes, operational statuses, and associated persons, organizations, or nations. The Space Object Ontology provides a consensus-based (...) realist framework for formulating such characterizations in a computable fashion. Space object data are aligned with classes and relations in the Space Object Ontology and stored in a dynamically updated Resource Description Framework triple store, which can be queried to support space domain awareness and the needs of spacecraft operators. This paper presents the core of the Space Object Ontology, discusses its advantages over other approaches to space object classification, and demonstrates its ability to combine diverse sets of data from multiple sources within an expandable framework. Finally, we show how the ontology provides benefits for enhancing and maintaining longterm space domain awareness. (shrink)

Cost-based abduction, which is a technique for identifying the best explanation for a given observation based on the assumption of a set of hypothesis, is a useful knowledge processing framework for practical problems such as diagnosis, design and planning. However, the speed of reasoning of this approach is often slow. To overcome this problem, Kato et al. previously presented a more efficient cost-based abduction system, that utilized the A * search technique, however, the time and space complexities in this technique (...) are exponential, so the identification of the optimal solution is difficult in practical applications. In this paper, we present three new systems in which the user can define the computational complexity, for identification of a near-optimal solution. First, we introduce two search control techniques; a real-time A * search approach in which the user can define the look-ahead depth or space, and the multi-agent real-time A * approach in which the user can define the number of real-time A * agents used in the search. We describe the implementation of three cost-based abduction reasoning systems for predicate logic knowledge bases and test the proposed systems using a diagnostic logic circuit problem. The results show that proposed systems can identify a near-optimal solution according to the predefined polynomial order of complexity, including the selection of either linear or exponential computational complexity. It is also shown that inference time and success rate are dependent on the user-defined parameters, that the three proposed systems exhibit similar performance characteristics, and that they all offer significant speed advantages over the previously described technique. (shrink)

We contrast reactive and endogenously active perspectives on brain activity. Both have been pursued continuously in neurophysiology laboratories since the early 20thcentury, but the endogenous perspective has received relatively little attention until recently. One of the many successes of the reactive perspective was the identification, in the second half of the 20th century, of the distinctive contributions of different brain regions involved in visual processing. The recent prominence of the endogenous perspective is due to new findings of ongoing oscillatory (...) activity in the brain at a wide range of time scales, exploiting such techniques as single-cell recording, EEG, and fMRI. We recount some of the evidence pointing to ways in which this endogenous activity is relevant to cognition and behavior. Our major objective is to consider certain implications of the contrast between the reactive and endogenous perspectives. In particular, we relate these perspectives to two different characterizations of explanation in the new mechanistic philosophy of science. In a basic mechanistic explanation, the operations of a mechanism are characterized qualitatively and as functioning sequentially until a terminating condition is realized. In contrast, a dynamic mechanistic explanation allows for non-sequential organization and emphasizes quantitative modeling of the mechanisms's behavior. For example, with appropriate parameter values a set of differential equations can be used to demonstrate ongoing oscillations in a system organized with feedback loops. We conclude that the basic conception of mechanistic explanation is adequate for reactive accounts of brain activity, but dynamical accounts are required to explain sustained, endogenous activity. (shrink)

Mesoscale modeling is often considered merely as a practical strategy used when information on lower-scale details is lacking, or when there is a need to make models cognitively or computationally tractable. Without dismissing the importance of practical constraints for modeling choices, we argue that mesoscale models should not just be considered as abbreviations or placeholders for more “complete” models. Because many systems exhibit different behaviors at various spatial and temporal scales, bottom-up approaches are almost always doomed to fail. Mesoscale models (...) capture aspects of multi-scale systems that cannot be parameterized by simple averaging of lower-scale details. To understand the behavior of multi-scale systems, it is essential to identify mesoscale parameters that “code for” lower-scale details in a way that relate phenomena intermediate between microscopic and macroscopic features. We illustrate this point using examples of modeling of multi-scale systems in materials science and biology, where identification of material parameters such as stiffness or strain is a central step. The examples illustrate important aspects of a so-called “middle-out” modeling strategy. Rather than attempting to model the system bottom-up, one starts at intermediate scales where systems exhibit behaviors distinct from those at the atomic and continuum scales. One then seeks to upscale and downscale to gain a more complete understanding of the multi-scale system. The cases highlight how parameterization of lower-scale details not only enables tractable modeling but is also central to understanding functional and organizational features of multi-scale systems. (shrink)

An innovative, multifaceted approach to scientific experiments as designed by and shaped through interaction with the modeling process The role of scientific modeling in mediation between theories and phenomena is a critical topic within the philosophy of science, touching on issues from climate modeling to synthetic models in biology, high energy particle physics, and cognitive sciences. Offering a radically new conception of the role of data in the scientific modeling process as well as a new awareness of the problematic aspects (...) of data, this cutting-edge volume offers a multifaceted view on experiments as designed and shaped in interaction with the modeling process. Contributors address such issues as the construction of models in conjunction with scientific experimentation; the status of measurement and the function of experiment in the identification of relevant parameters; how the phenomena under study are reconceived when accounted for by a model; and the interplay between experimenting, modeling, and simulation when results do not mesh. Highlighting the mediating role of models and the model-dependence of data measurement, this volume proposes a normative and conceptual innovation in scientific modeling--that the phenomena to be investigated and modeled must not be precisely identified at the start but specified during the course of the interactions arising between experimental and modeling activities. Contributors: Nancy D. Cartwright, U of California, San Diego; Anthony Chemero, U of Cincinnati; Ronald N. Giere, U of Minnesota; Jenann Ismael, U of Arizona; Tarja Knuuttila, U of South Carolina; Andrea Loettgers, U of Bern, Switzerland; Deborah Mayo, Virginia Tech; Joseph Rouse, Wesleyan U; Paul Teller, U of California, Davis; Michael Weisberg, U of Pennsylvania; Eric Winsberg, U of South Florida. (shrink)

Bogen and Woodward’s distinction between data and phenomena raises the need to understand the structure of the data-to-phenomena and theory-to-phenomena inferences. I suggest that one way to study the structure of these inferences is to analyze the role of the assumptions involved in the inferences: What kind of assumptions are they? How do these assumptions contribute to the practice of identifying phenomena? In this paper, using examples from atmospheric dynamics, I develop an account of the practice of identifying the target (...) in the data-to-phenomena and theory-to-phenomena inferences in which assumptions about spatiotemporal scales play a central role in the identification of parameters that describe the target system. I also argue that these assumptions are not only empirical but they are also idealizing and abstracting. I conclude the paper with a reflection on the role of idealizations in modeling. (shrink)

Using the latest educational technologies and active methods of social activity development in future specialists in the educational space under today’s conditions is significantly complicated by existing levels of the object and subject studies of various patterns. The authors studied a wide range of foreign and domestic interdisciplinary works and models which describe the implementation of research methodology of the individual socialization process to study the development of specialists’ social activity. Also, the authors defined the relevant patterns of students’ involving (...) in socially significant activities due to the changes in value-motivational aspects at different stages of social activity development at the higher education institution. The structural and functional components are determined, and the research parameters specifying the changes of value-motivational factors of social activity development in future socionomy specialists are substantiated. The article provides empirical diagnostics of values among the students of socionomic professions within the sociological and pedagogical research of social activity with the participation of 610 students from four Ukrainian higher education institutions. The diagnostics was carried out by means of the shortened version of the “Portrait Questionnaire of Values - PVQ-Revised”. It has been found out that the development of social activity in future socionomy specialists is mainly related to changes in motivational aspects at different levels of generality and complexity; it includes such constructs as values, goals, behavior plans which require a qualitative analysis of social factors and identification of relevant patterns of students’ involvement in socially significant activities. (shrink)

Systems with human-centered artificial intelligence are always as good as their ability to consider their users’ context when making decisions. Research on identifying people’s everyday activities has evolved rapidly, but little attention has been paid to recognizing both the activities themselves and the motions they make during those tasks. Automated monitoring, human-to-computer interaction, and sports analysis all benefit from Web 4.0. Every sport has gotten its move, and every move is not known to everyone. In ice hockey, every move cannot (...) be monitored by the referee. Here, Convolution Neural Network-based Real-Time Image Processing Framework (CNN-RTIPF) is introduced to classify every move in Ice Hockey. CNN-RTIPF can reduce the challenges in monitoring the player’s move individually. The image of every move is captured and compared with the trained data in CNN. These real-time captured images are processed using a human-centered artificial intelligence system. They compared images predicted by probability calculation of the trained set of images for effective classification. Simulation analysis shows that the proposed CNN-RTIPF can classify real-time images with improved classification ratio, sensitivity, and error rate. The proposed CNN-RTIPF has been validated based on the optimization parameter for reliability. To improve the algorithm for movement identification and train the system for many other everyday activities, human-centered artificial intelligence-based Web 4.0 will continue to develop. (shrink)

Descartes' account of the material world relies heavily on time. Most importantly, time is a component of speed, which figures in his fundamental conservation principle and laws. However, in his most systematic discussion of the concept, time is treated as some-how reducible both to thought and to motion. Such reductionistic views, while common among Descartes' late scholastic contemporaries, are very ill-suited to Cartesian physics. I show that, in spite of the apparent identifications with thought and motion, Cartesian time retains—in the (...) form of what I will call 'successive duration'—precisely the intrinsic structure necessary to serve as an independent parameter of quantitative physics. As is often the case with Descartes, he gives the impression of embracing traditional doctrines while in fact radically transforming the underlying concepts to serve his scientific agenda. His theory of time, though formulated in Aristotelian terms, anticipates Newton in important respects. (shrink)

The Mirror Game (MG) is a common exercise in dance/movement therapy and drama therapy. It is used to promote participants’ ability to enter and remain in a state of togetherness. In spite of the wide use of the MG by practitioners, it is only recently that scientists begun to use the MG in research, examining its correlates, validity and reliability. This study joins this effort by reporting on the identification of scale items to describe the nonverbal behaviour expressed during (...) the MG and its correlation to measures of attachment. Thus, we explored the application of the MG as a tool for assessing the embodiment of attachment in adulthood. Forty-eight participants (22 females, mean age = 33.2) played the MG with the same gender-matched expert players. All MG were videotaped. In addition, participants were evaluated on two central measurements of attachment in adulthood: The Adult Attachment Interview (AAI) and the Experience in Close Relationship questionnaire (ECR). To analyse the data, we developed the “mirror game scale” that coded the nonverbal behavior during the movement interaction, using 19 parameters. The sub-scales were reduced using factor analysis into two dimensions referred to as “together” and “free”. The free factor was significantly correlated to both measurements of attachment: Participants classified as having secure attachment on the AAI, received higher scores on the MG free factor than participants classified as insecure (t (46) = 7.858, p =.000). Participants who were high on the avoidance dimension on the ECR, were low on the MG free factor (r (48) =-.-285, p =.007). This is the first study to examine the MG as it is used by practitioners and its correlation to highly standardized measures. This exploratory study may be considered as part of the first steps of exploring the MG as a standardized assessment tool. The advantages of the MG as a simple, non-verbal movement interaction demonstrate some of the strengths of dance/movement and drama therapy practice. (shrink)

This work presents a mathematical model describing the interactions between the cross-shore structure of small pelagic fish population an their exploitation by coastal and offshore fisheries. The complete model is a system of seven ODE’s governing three stocks of small pelagic fish population moving and growing between three zones. Two types of fishing fleets are inter-acting with the fish population, industrial boats, constrained to offshore area, and artisanal boats, operating from the shore. Two time scales were considered and we use (...) aggregation methods that allow us to reduce the dimension of the model and to obtain an aggregated model, which is a four dimension one. The analysis of the aggregated model is performed. We discuss the possible equilibriums and their meaning in terms of fishery management. An interesting equilibrium state can be obtained for which we can expect coexistence and a stable equilibrium state between fish stocks and fishing efforts. Some identification parameters are also given in the discussion part of the model. (shrink)

This essay argues that relativist semantics provide fruitful frameworks for the study of the relationships between meaning and truth-conditions, and consequently for the analysis of the logical properties of expressions. After a discussion of the role of intensionality and indexicality within classic double-indexed semantics, I explain that the non-relativistic identification of the parameters needed for the definition of truth and for the interpretation of indexicals is grounded on considerations that are irrelevant for the assessment of the relationships between meaning (...) and truth. (shrink)

Frank has recently argued for an information-theoretic interpretation of natural selection. This interpretation is based on the identification of a measure related to the Malthusian parameter (for population change) with the Jeffreys divergence between the present allelic distribution of the population and that distribution in the next generation. It is pointed out in this analysis that this identification only holds if the mean fitness of the population is a constant, that is, there is no selection. This problem (...) is used to argue for the superiority of the standard dynamical interpretation of natural selection over its information-theoretic counterpart. (shrink)

Due to the ongoing advancements in technology, socio-technical collaboration has become increasingly prevalent. This poses challenges in terms of governance and accountability, as well as issues in various other fields. Therefore, it is crucial to familiarize decision-makers and researchers with the core of human–machine collaboration. This study introduces a taxonomy that enables identification of the very nature of human–machine interaction. A literature review has revealed that automation and technical autonomy are main parameters for describing and understanding such interaction. Both (...) aspects must be carefully evaluated, as their increase has potentially far-reaching consequences. Hence, these two concepts comprise the taxonomy’s axes. Five levels of automation and five levels of technical autonomy are introduced below, based on the assumption that both automation and autonomy are gradual. The levels of automation were developed from existing approaches; those of autonomy were carefully derived from a review of the literature. The taxonomy’s use is also explained, as are its limitations and avenues for further research. (shrink)

False information is always produced after the outbreak of major emergencies. Taking this into consideration, this paper discusses the behavior of multiple parties in relation to false information dissemination after major emergencies. First, a game model is constructed, using relevant knowledge of evolutionary game theory, between three parties: regulatory institutions, opinion leaders, and ordinary Internet users. Second, the model equations are solved, and the evolutionary stability strategies of each game party under different circumstances are analyzed. Third, a numerical simulation is (...) applied to the evolutionary trends under different strategy combinations with varying parameters. The results show that the probability of each game party making ideal decisions is positively correlated with the degree of punishment imposed by regulatory institutions on opinion leaders who release false information, the reward provided by regulatory institutions on opinion leaders who release positive information, the degree of participation and satisfaction gained by Internet users in adopting positive information, the richness of authentic content released by opinion leaders, and the psychological identification of Internet users with opinion leaders. Meanwhile, the probability of each game party making ideal decisions is negatively correlated with investigation and evidence collection costs borne by opinion leaders who release positive information, the additional income for opinion leaders who have false information adopted by Internet users, the costs of Internet users’ time and energy when they adopt information released by opinion leaders, and the costs of independently judging the accuracy of information by Internet users. (shrink)

Scientometrics, as a rule, is considered in details – the more accurate and ex­pressive the detailing, the more effective is the accounting of scientific research: the measurement of quantitative parameters of the results of scientific activity is aimed at improving the quality of scientific communication and, ultimately, the progress of science. This led to the transition from usual bibliometrics to sciento­metrics, and later to other more sophisticated forms of accounting for scientific activity, which can be divided into infometrics and altmetrics. (...) Infometrics and its modifications take into account everything that could be formalized, while altmetrics seeks to include qualitative parameters in the realm of quantitative measurements. In this article, attention is focused precisely on the analysis of the relationship between quantitative methods and quantitative indicators of scien­tific activity with the progress of science thanks to a comparative assessment of bibliometrics, scientific metrics and infometrics from philosophical positions. A way out of the problematic situations in which attempts to provide a quantita­tive description of the quality of scientific activity inevitably fall, should be sought beyond the quantitative methods themselves – in the identification of their ulti­mate goal. Such a goal is the development of humanity through the progress of science. The UN program “Transforming Our World: Agenda 2030 for Sustainable Development” offers a systematic vision of ways to accomplish this task, which, in particular, sets the goal of achieving equal development opportunities for every person. Bibliometrics, scientometrics and infometrics contribute to overcoming many types of social inequality – provided that people are actively involved in sci­entific activity: personal scientific achievements level out property, gender, reli­gious, ethnic and many other types of social inequality and give every participant of scientific research access to a public career and personal development. The development of each person on a rational basis is the ultimate goal of science, which is the basis of the philosophy of the strategy of global sustainable develop­ment of Mankind. (shrink)

Our studies of naturally occurring human dialogue have led to the recognition of a class of regularities which characterize impoltant aspects of communication. People appear to interact according to established patterns which span several turns in a dialogue and which recur frequently. These patterns appear to be organized around the goals which the dialogue serves for each participant. Many things which are said later in a dialogue can only be interpreted as pursuit of these goals, established by earlier dialogue.These patterns (...) have been represented b) a set of knowledge structures called Dialogue‐Games, capturing shared, conventional knowledge that people have about communication and how it can be used to achieve goals. A Dialogue‐Game has Parameters, which represent those elements that vary across instances of a particular pattern‐the particular dialogue participants and the content topic. The states of the world which must be in effect for a particular Dialogue‐Game to be employed successfully are represented by Specifications of these Parameters. Finally, the expected sequence of intermediate states that occur during instances of a particular conventional pattern are represented by the Components of the corresponding Dialogue‐Game.Representations for several Dialogue‐Games are presented here, based on our analyses of different kinds of naturally occurring dialogue. A process model is discussed, showing Dialogue‐Game identification, pursuit, and termination as part of the comprehension of dialogue utterances. This Dialogue‐Game model captures some of the important functional aspects of language, especially indirect uses to achieve implicit communication. (shrink)

The largest group of migrants in Germany is the Turkish people, many of whom have low skills levels, are Muslim, and are slow to integrate themselves into their host communities. German immigration policy has been significantly revised since the early 1990s, and a new Immigration Act came into force in 2005, containing more inclusive stances on citizenship and integration of migrants. There is a strong rhetoric of acceptance and open doors, within certain parameters, but the gap between the rhetoric and (...) practice is still wide enough to allow many migrants, particularly women, to fall through it. Turkish-Muslim women bear the brunt of the difficulties faced once they have arrived in Germany, and many of them are subject to domestic abuse, joblessness and poverty because of their invisibility to the German state, which is the case largely because German immigration policy does not fully realise a role and place for women migrants. The policy also does not sufficiently account for ethnic and cultural identification, or limitations faced by migrants in that while it speaks to integration, it does not fully enable this process to take place effectively. Even though it has made many advances in recent years towards a more open and inclusive immigration policy, Germany is still a 'reluctant' country of immigration, and this reluctance stops it from making any real strides towards integrating migrants fully into German society at large. The German government needs to take a much firmer stance on the roles of migrant women in its society, and the nature of the ethnic and religious identities of Muslim immigrants, in order to both create and implement immigration policy that truly allows immigrants to become full and contributing members to German social and economic life, and to bring it in line with the European Union's common directives on immigration. (shrink)