A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or “conformational cycle,” required for function is proposed. It is suggested that the free‐energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free‐energy landscape. Some network connections are consistent in (...) every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states. (shrink)

Ribonucleotide reductase (RNR) catalyzes the rate limiting step in the production of deoxyribonucleotides needed for DNA synthesis. In addition to the well documented allosteric regulation, the synthesis of the enzyme is also tightly regulated at the level of transcription. mRNAs for both subunits are cell cycle regulated and inducible by DNA damage in all organisms examined, including E. coli, S. cerevisiae and H. sapiens. This DNA damage regulation is thought to provide a metabolic state that facilitates DNA replicational repair (...) processes. S. cerevisiae also encodes a second large subunit gene, RNR3, that is expressed only in the presence of DNA damage. Genetic analysis of the DNA damage response in S. cerevisiae has shown that RNR expression is under both positive and negative control. Among mutants constitutive for RNR expression are the general transcriptional repression genes, SSN6 and TUP1. Mutations in POL1 and POL3 also activate RNR expression, indicating that the DNA damage sensory network may respond directly to blocks in DNA synthesis. A protein kinase, Dun1, has been identified that controls inducibility of RNR1, RNR2 and RNR3 in response to DNA damage and replication blocks. This result suggests that the RNR genes in S. cerevisiae form a regulon that is coordinately regulated by protein phosphorylation in response to DNA damage. (shrink)

The interconversion of key regulatory proteins between phosphorylated and dephosphorylated forms is an extremely versatile mechanism for reversible altering their activities, and in mammalian cells may be almost as common as allosteric regulation. It is now evident that protein phosphorylation is the basis of a complex network of interlocking systems which allow a variety of hormones and other extracellular signals, acting through just a few second messengers to coordinate biochemical functions.

Network analysis is increasingly appreciated as a methodology in the social sciences. In recent years, it is also receiving attention among historians of science. History of economics is no exception in that researchers have begun to use network analysis to study a variety of topics, including collaborations and interactions in scientific communities, the spread of economic theories within and across fields, or the formation of new specialties in the discipline of economics. Against this backdrop, a debate is emerging (...) about how network analysis can help address questions that are pertinent to the history of economics. With this paper, we want to push this debate one step forward by offering and discussing five reasons why network analysis should have a future in the history of economics. (shrink)

The new business opportunities and prospects emerging in Europe within the Common Market and other Western and European countries also raise important ethical challenges. This work comprises a collection of ethical insights to enhance the conduct of business in an evolving Europe.

Medical explanations have often been thought on the model of biological ones and are frequently defined as mechanistic explanations of a biological dysfunction. In this paper, I argue that topological explanations, which have been described in ecology or in cognitive sciences, can also be found in medicine and I discuss the relationships between mechanistic and topological explanations in medicine, through the example of network medicine and medical genetics. Network medicine is a recent discipline that relies on the analysis (...) of various disease networks in order to find organizing principles in disease explanation. My aim is to show how topological explanations in network medicine can help solving the conceptual issues that pure mechanistic explanations of the genetics of disease are currently facing, namely the crisis of the concept of genetic disease, the progressive geneticization of diseases and the dissolution of the distinction between monogenic and polygenic diseases. However, I will also argue that topological explanations should not be considered as independent and radically different from mechanistic explanations for at least two reasons. First, in network medicine, topological explanations depend on and use mechanistic information. Second, they leave out some missing gaps in disease explanation that require, in turn, the development of new mechanistic explanations. Finally, I will insist on the specific contribution of topological explanations in medicine: they push us to develop an explanation of disease in general, instead of focusing on single explanations of individual diseases. This last point may have major consequences for biomedical research. (shrink)

People differ in the size of their social network, and thus in the properties of the linguistic input they receive. This article examines whether differences in social network size influence individuals’ linguistic skills in their native language, focusing on global comprehension of evaluative language. Study 1 exploits the natural variation in social network size and shows that individuals with larger social networks are better at understanding the valence of restaurant reviews. Study 2 manipulated social network size (...) by randomly assigning participants to learn novel evaluative words as used by two versus eight speakers. It replicated the finding from Study 1, showing that those exposed to a larger social network were better at comprehending the valence of product reviews containing the novel words that were written by novel speakers. Together, these studies show that the size of one's social network can influence success at language comprehension. They thus open the door to research on how individuals’ lifestyle and the nature of their social interactions can influence linguistic skills. (shrink)

Measuring event-related potentials (ERPs) has been fundamental to our understanding of how language is encoded in the brain. One particular ERP response, the N400 response, has been especially influential as an index of lexical and semantic processing. However, there remains a lack of consensus on the interpretation of this component. Resolving this issue has important consequences for neural models of language comprehension. Here we show that evidence bearing on where the N400 response is generated provides key insights into what it (...) reflects. A neuroanatomical model of semantic processing is used as a guide to interpret the pattern of activated regions in functional MRI, magnetoencephalography and intracranial recordings that are associated with contextual semantic manipulations that lead to N400 effects. (shrink)

Accurate and rapid defect identification based on pulsed eddy current testing plays an important role in the structural integrity and health monitoring of in-service equipment in the renewable energy system. However, in conventional data-driven defect identification methods, the signal feature extraction is time consuming and requires expert experience. To avoid the difficulty of manual feature extraction and overcome the shortcomings of the classic deep convolutional network, such as large memory and high computational cost, an intelligent defect recognition pipeline based (...) on the general Warblet transform method and optimized two-dimensional DCNN is proposed. The GWT method is used to convert the one-dimensional PECT signal to a 2D grayscale image used as the input of 2D DCNN. A compound method is proposed to optimize the baseline VGG16, a well-known DCNN, from four aspects including reducing the input size, adding batch normalization layer after every convolutional layer and fully connection layer, simplifying the FCs, and removing unimportant filters in Convs so as to reduce memory and computational costs while improving accuracy. Through a pulsed eddy current testing experiment considering interference factors including liftoff and noise, the following conclusion can be obtained. The time-frequency representation obtained by the GWT method not only has excellent ability in terms of the transient component analysis but also is less affected by the reduction of image size; the proposed optimized DCNN can accurately identify defect types without manual feature extraction. And compared to the baseline VGG16, the accuracy obtained by the optimized DCNN is improved by 7%, to about 99.58%, and the memory and computational cost are reduced by 98%. Moreover, compared with other well-known DCNNs, such as GoogLeNet, Inception V3, ResNet50, and AlexNet, the optimized network has significant advantages in terms of accuracy and computational cost, too. (shrink)

By investigating the status quo of the swimming training market in a certain area, we can obtain information on the current development of the swimming training market in a certain area and study the laws of the development of the market so as to provide a theoretical basis for the development of the market. This paper designs an evaluation algorithm suitable for swimming training based on the improved AlexNet network. The algorithm model uses a 3 × 3 size convolution (...) kernel to extract features, and the pooling layer uses a nonoverlapping pooling strategy. In order to accelerate the network convergence, the model introduces batch normalization technology. The algorithm uses data augmentation technology to expand the data set, including rotation and random erasure, to a certain extent alleviating the problem of overfitting. The results of the study showed that there were no significant differences in fat, minerals, protein, body mass index, basal metabolic rate, and total energy expenditure in the body composition ratios of children in the convolutional neural network assessment group and the control group, while muscle and total body water were not significantly different. However, there are significant differences in fat-free body weight and muscle strength of various segments of the body, among which there are very significant differences in muscle strength of lower limbs in each segment of the body. There were no significant differences in minerals, body mass index, basal metabolic rate, total energy expenditure, and lower limb muscle strength in the body composition ratios of men and women in the convolutional neural network assessment group. There are significant differences in body weight, upper limb muscle strength, and trunk muscle strength. There were no significant differences in the proportions of body composition between men and women in the control group, except for fat and protein. (shrink)

This is an excerpt of a report that highlights and explores five questions that arose from the Network for Sensory Research workshop on perceptual learning and perceptual recognition at the University of York in March, 2012. This portion of the report explores the question: What is perceptual learning?

There is wide agreement that community engagement is important for many research types and settings, often including interaction with ‘representatives’ of communities. There is relatively little published experience of community engagement in international research settings, with available information focusing on Community Advisory Boards or Groups (CAB/CAGs), or variants of these, where CAB/G members often advise researchers on behalf of the communities they represent. In this paper we describe a network of community members (‘KEMRI Community Representatives’, or ‘KCRs’) linked to (...) a large multi-disciplinary research programme on the Kenyan Coast. Unlike many CAB/Gs, the intention with the KCR network has evolved to be for members to represent the geographical areas in which a diverse range of health studies are conducted through being typical of those communities. We draw on routine reports, self-administered questionnaires and interviews to: 1) document how typical KCR members are of the local communities in terms of basic characteristics, and 2) explore KCR's perceptions of their roles, and of the benefits and challenges of undertaking these roles. We conclude that this evolving network is a potentially valuable way of strengthening interactions between a research institution and a local geographic community, through contributing to meeting intrinsic ethical values such as showing respect, and instrumental values such as improving consent processes. However, there are numerous challenges involved. Other ways of interacting with members of local communities, including community leaders, and the most vulnerable groups least likely to be vocal in representative groups, have always been, and remain, essential. (shrink)

The purpose of this paper is to present a formal model of social net- works suitable for studying questions in social epistemology. We show how to use this model, in conjunction with a computer program for simulating groups of inquirers, to draw conclusions about the epistemological prop- erties of different social practices. This furnishes us with the beginnings of a systematic research program in social epistemology, from which to approach problems pertaining to epistemic value, optimal organization, and the dynamics of (...) multi-agent inquiry. (shrink)

Between-subject variability in cognitive performance has been related to inter-individual differences in functional brain networks. Targeting the dorsal attention network we questioned whether resting-state functional connectivity within the DAN can predict individual performance in spatial attention tasks and whether there is short-term adaptation of DAN-FC in response to task engagement. Twenty-seven participants first underwent resting-state fMRI, they subsequently performed different tasks of spatial attention [including visual search ] and immediately afterwards received another rs-fMRI. Intra- and inter-hemispheric FC between core (...) hubs of the DAN, bilateral intraparietal sulcus and frontal eye field, was analyzed and compared between PRE and POST. Furthermore, we investigated rs-fMRI-behavior correlations between the DAN-FC in PRE/POST and task performance parameters. The absolute DAN-FC did not change from PRE to POST. However, different significant rs-fMRI-behavior correlations were revealed for intra-/inter-hemispheric connections in the PRE and POST run. The stronger the FC between left FEF and IPS before task engagement, the better was the learning effect in VS. And the faster the VS, the stronger was the FC between right FEF and IPS after task engagement. To conclude, DAN-FC relates to the individual performance in spatial attention tasks supporting the view of functional brain networks as priors for cognitive ability. Despite a high inter- and intra-individual stability of DAN-FC, the change of FC-behavior correlations after task performance possibly indicates task-related adaptation of the DAN, underlining that behavioral experiences may shape intrinsic brain activity. However, spontaneous state fluctuations of the DAN-FC over time cannot be fully ruled out as an alternative explanation. (shrink)

Studying drug relationships can provide deeper information for the construction and maintenance of biomedical databases and provide more important references for disease treatment and drug development. The research model has expanded from the previous focus on a certain drug to the systematic analysis of the pharmaceutical network formed between drugs. Network model is suitable for the study of the nonlinear relationship of the pharmaceutical relationship by modeling the data learning. Association rule mining is used to find the potential (...) correlations between the various sets of massive data. Therefore, based on the network model, this research proposed an algorithm for drug interaction under improved association rules, which achieved accurate analysis and decision-making of drug relationship. Meanwhile, this research applied the established association rule algorithm to discuss the relationship between Chinese medicine and mental illness medicine and conducted the algorithm research and simulation analysis of the association relationship. The results showed the association rule algorithm based on the network model constructed was better than other association algorithms. It had reliability and superiority in decision-making in improving the drug-drug relationship. It also promoted the rational use of medicines and played a guiding role in pharmaceutical research. This provides scientific research personnel with research basis and research ideas for disease-related diagnosis. (shrink)

As value chains become longer with increases in outsourcing and subcontracting, the challenges of fixing responsibility become more difficult. Using concepts from the literature on social networks, this paper considers issues of diffusion of responsibility and plausible deniability in such relationships. Specifically, this paper isolates three sources of denial of – or defense against – attributions of responsibility: connection, control and knowledge. It goes on to consider the effects on network density and actor centrality as third parties (tertius illuminans) (...) alter the structure of these networks. Finally, preliminary conclusions are considered including suggestions for addressing these new challenges as well as the potential for conceptual cross-fertilization between network analysis and organizationalethics. (shrink)

"_The Moment of Complexity_ is a profoundly original work. In remarkable and insightful ways, Mark Taylor traces an entirely new way to view the evolution of our culture, detailing how information theory and the scientific concept of complexity can be used to understand recent developments in the arts and humanities. This book will ultimately be seen as a classic."-John L. Casti, Santa Fe Institute, author of _Gödel: A Life of Logic, the Mind, and Mathematics_ The science of complexity accounts for (...) that inscrutable mix of chaos and order that governs our natural world. Complexity explains how networks emerge and function, how species organize into ecosystems, how stars form into galaxies, and how just a few sequences of DNA can account for so many different life forms. Recently, the idea of complexity has taken the worlds of business and politics by storm. The concept is used to account for phenomena as varied as the behavior of the stock market, the response of voting populations, and the effects of risk management. Even Disney has used complexity theory to manage crowd control at its theme parks. Given the startling development of new information technologies, we now live in a moment of unprecedented complexity, an era in which change occurs faster than our ability to comprehend it. With _The Moment of Complexity_, Mark C. Taylor offers a timely map for this unfamiliar terrain opening in our midst, unfolding an original philosophy through a remarkable synthesis of science and culture. According to Taylor, complexity is not just a breakthrough scientific concept, but the defining quality of the post-Cold War era. The flux of digital currents swirling around us, he argues, has created a new network culture with its own distinctive logic and dynamic. Drawing on resources from information theory and evolutionary biology, Taylor explains the operation of complex adaptive systems in social and cultural processes and captures a whole new zeitgeist in the making. To appreciate the significance of our emerging network culture, he claims, we need not only to understand contemporary scientific and technological transformations, but also to explore the subtle influences of art, architecture, philosophy, religion, and higher education. _The Moment of Complexity_, then, is a remarkable work of cultural analysis on a scale rarely seen today. To follow its trajectory is to learn how we arrived at this critical moment in our culture, and to know where we might head in the twenty-first century. (shrink)

Lindquist et al. provide a convincing case against what they call the locationist account of emotion. Their quantitative approach elegantly illustrates the shortcomings of this still-entrenched viewpoint. Here, I discuss how a network perspective will advance our understanding of structure-function mappings in general, and the relationship between emotion and cognition in the brain.

Multiple cortical areas of the human brain motor system interact coherently in the low frequency range (< 0.1 Hz), even in the absence of explicit tasks. Following stroke, cortical interactions are functionally disturbed. How these interactions are affected and how the functional organization is regained from rehabilitative treatments as people begin to recover motor behaviors has not been systematically studied. We recorded the intrinsic functional magnetic resonance imaging (fMRI) signals from 30 participants: 17 young healthy controls and 13 aged stroke (...) survivors. Stroke participants underwent mental practice (MP) or both mental practice and physical therapy (MP + PT) within 14-51 days following stroke. We investigated the network activity of five core areas in the motor-execution network, consisting of the left primary motor area (LM1), the right primary motor area (RM1), the left pre-motor cortex (LPMC), the right pre-motor cortex (RPMC) and the supplementary motor area (SMA). We discovered that (i) the network activity dominated in the frequency range 0.06 Hz – 0.08 Hz for all the regions, and for both able-bodied and stroke participants (ii) the causal information flow between the regions: LM1 and SMA, RPMC and SMA, RPMC and LM1, SMA and RM1, SMA and LPMC, was reduced significantly for stroke survivors (iii) the flow did not increase significantly after MP alone and (iv) the flow among the regions during MP+PT increased significantly. We also found that sensation and motor scores were significantly higher and correlated with directed functional connectivity measures when the stroke-survivors underwent MP+PT but not MP alone. The findings provide evidence that a combination of mental practice and physical therapy can be an effective means of treatment for stroke survivors to recover or regain the strength of motor behaviors, and that the spectra of causal information flow can be used as a reliable biomarker for evaluating rehabilitation in stroke survivors. (shrink)

Investigating instances where lexical selection fails can lead to deeper insights into the cognitive machinery and architecture supporting successful word retrieval and speech production. In this paper, we used a multiplex lexical network approach that combines semantic and phonological similarities among words to model the structure of the mental lexicon. Network measures at different levels of analysis (degree, network distance, and closeness centrality) were used to investigate the influence of network structure on picture naming accuracy and (...) errors by people with Anomic, Broca's, Conduction, and Wernicke's aphasia. Our results reveal that word retrieval is influenced by the multiplex lexical network structure in at least two ways—(a) the accuracy of production and error type on incorrect productions were influenced by the degree and closeness centrality of the target word, and (b) error type also varied in terms of network distance between the target word and produced error word. Taken together, the analyses demonstrate that network science techniques, particularly the use of the multiplex lexical network to simultaneously represent semantic and phonological relationships among words, reveal how the structure of the mental lexicon influences language processes beyond traditionally examined psycholinguistic variables. We propose a framework for how the multiplex lexical network approach allows for understanding the influence of mental lexicon structure on word retrieval processes, with an eye toward a better understanding of the nature of clinical impairments, like aphasia. (shrink)

The appeal to expert opinion is an argument form that uses the verdict of an expert to support a position or hypothesis. A previous scheme-based treatment of the argument form is formalized within a Bayesian network that is able to capture the critical aspects of the argument form, including the central considerations of the expert's expertise and trustworthiness. We propose this as an appropriate normative framework for the argument form, enabling the development and testing of quantitative predictions as to (...) how people evaluate this argument, suggesting that such an approach might be beneficial to argumentation research generally. We subsequently present two experiments as an example of the potential for future research in this vein, demonstrating that participants' quantitative ratings of the convincingness of a proposition that has been supported with an appeal to expert opinion were broadly consistent with the predictions of the Bayesian model. (shrink)

As value chains become longer with increases in outsourcing and subcontracting, the challenges of fixing responsibility become more difficult. Using concepts from the literature on social networks, this paper considers issues of diffusion of responsibility and plausible deniability in such relationships. Specifically, this paper isolates three sources of denial of – or defense against – attributions of responsibility: connection, control and knowledge. It goes on to consider the effects on network density and actor centrality as third parties (tertius illuminans) (...) alter the structure of these networks. Finally, preliminary conclusions are considered including suggestions for addressing these new challenges as well as the potential for conceptual cross-fertilization between network analysis and organizationalethics. (shrink)

Research programs regularly compete to achieve the same goal, such as the discovery of the structure of DNA or the construction of a TEA laser. The more the competing programs share information, the faster the goal is likely to be reached, to society’s benefit. But the “priority rule”-the scientific norm according to which the first program to reach the goal in question must receive all the credit for the achievement-provides a powerful disincentive for programs to share information. How, then, is (...) the clash between social and individual interest resolved in scientific practice? This chapter investigates what Robert Merton called science’s “communist” norm, which mandates universal sharing of knowledge, and uses mathematical models of discovery to argue that a communist regime may be on the whole advantageous and fair to all parties, and so might be implemented by a social contract that all scientists would be willing to sign. (shrink)

An adaptive fractional-order nonsingular terminal sliding mode controller for a microgyroscope is presented with uncertainties and external disturbances using a fuzzy neural network compensator based on a backstepping technique. First, the dynamic of the microgyroscope is transformed into an analogical cascade system to guarantee the application of a backstepping design. Then, a fractional-order nonsingular terminal sliding mode surface is designed which provides an additional degree of freedom, higher precision, and finite convergence without a singularity problem. The proposed control scheme (...) requires no prior knowledge of the unknown dynamics of the microgyroscope system since the fuzzy neural network is utilized to approximate the upper bound of the lumped uncertainties and adaptive algorithms are derived to allow online adjustment of the unknown system parameters. The chattering phenomenon can be reduced simultaneously by the fuzzy neural network compensator. The stability and finite time convergence of the system can be established by the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the proposed controller and the comparison of root mean square error between different fractional orders and integer order is given to signify the high precision tracking performance of the proposed control scheme. (shrink)

This special issue on network ethics offers 15 scholarly articles from a variety of disciplines and fields of study, all aimed at exploring some important aspect of how networks develop, enact, and enforce ethical norms. The articles are ordered according to the levels of analysis each deals with, ranging from the cognitive/intra-personal to the systemic/societal. Taken together, these articles provide a fresh look at how networks are changing the way business is done and the way we think about ethics.

This paper proposes several recurrent neural network-based models for recognizing requisite and effectuation parts in Legal Texts. Firstly, we propose a modification of BiLSTM-CRF model that allows the use of external features to improve the performance of deep learning models in case large annotated corpora are not available. However, this model can only recognize RE parts which are not overlapped. Secondly, we propose two approaches for recognizing overlapping RE parts including the cascading approach which uses the sequence of BiLSTM-CRF (...) models and the unified model approach with the multilayer BiLSTM-CRF model and the multilayer BiLSTM-MLP-CRF model. Experimental results on two Japan law RRE datasets demonstrated advantages of our proposed models. For the Japanese National Pension Law dataset, our approaches obtained an \ score of 93.27% and exhibited a significant improvement compared to previous approaches. For the Japan Civil Code RRE dataset which is written in English, our approaches produced an \ score of 78.24% in recognizing RE parts that exhibited a significant improvement over strong baselines. In addition, using external features and in-domain pre-trained word embeddings also improved the performance of RRE systems. (shrink)

Difficulty retrieving information (e.g., words) from memory is prevalent in neurogenic communication disorders (e.g., aphasia and dementia). Theoretical modeling of retrieval failures often relies on clinical data, despite methodological limitations (e.g., locus of retrieval failure, heterogeneity of individuals, and progression of disorder/disease). Techniques from network science are naturally capable of handling these limitations. This paper reviews recent work using a multiplex lexical network to account for word retrieval failures and highlights how network science can address the limitations (...) of clinical data. Critically, any model we employ could impact clinical practice and patient lives, harkening the need for theoretically well-informed network models. (shrink)

Recreational Drugs European Network (ReDNet) project aims to use the Psychonaut Web Mapping Project database (Psychonaut Web Mapping Group, 2009) containing novel psychoactive compounds usually not mentioned in the scientific literature and thus unknown to clinicians as a unique source of information. The database will be used to develop an integrated ICT prevention approach targeted at vulnerable individuals and focused on novel synthetic and herbal compounds and combinations. Particular care will be taken in keeping the health professionals working directly (...) with young people showing problematic behaviors regularly updated in terms of novel compounds and combinations as well. A user-friendly project website will be developed aimed primarily at delivering the information/prevention approaches, but will also be a way of communicating with project partners and relevant stakeholders (e.g. thematic forum facilities, instant messages, blogs, video chat, wikiblog, newsletters distributed via mailing list). The website will support various ICT prevention tools, including an SMS alert service. Different areas and sections will be aimed specifically at the different target groups. -/- . (shrink)

Morphological EvoDevo is a field of biological inquiry in which explicit relations between evolutionary patterns and growth or morphogenetic processes are made. Historically, morphological EvoDevo results from the coming together of several traditions, notably Naturphilosophie, embryology, the study of heterochrony, and developmental constraints. A special feature binding different approaches to morphological EvoDevo is the use of formalisms and mathematical models. Here we will introduce anatomical network analysis, a new approach centered on connectivity patterns formed by anatomical parts, with its (...) own concepts and tools specifically designed for the study of morphological EvoDevo questions. Riedl’s concept of burden is tightly related to the use of anatomical networks, providing a nexus between the evolutionary patterns and the structural constraints that shape them. (shrink)

Based on the finding in novices that four months of meditation training significantly increases frontal default mode network (DMN) module/subnet synchrony while decreasing left and right posterior DMN modules synchrony, the current study tested the prediction whether experienced meditators (those who are practising meditation intensively for several years) had a change in the DMN “trinity” of modules as a baseline trait characteristic and whether this change is in a similar direction as in the novice trainees who practised meditation for (...) only four months. Comparison of functional connectivity within DMN subnets (measured by electroencephalogram operational synchrony in the three separate DMN modules) between five experienced meditators and 10 naïve participants (who were about to start the meditation training) fully support the prediction. Interpretation that links such DMN subnets changes to the three-dimensional components of the experiential selfhood was proposed. (shrink)

The energy sector contributes approximately two-thirds of global greenhouse gas emissions. In this context, the sector must adapt to new supply and demand networks for all future energy sources. The ongoing transformation in the European energy field is driven by the ambition of the European Union to reach the climate objectives set for 2030. The main actions are increasing renewable energy production, adapting transition fuels like natural gas to reduce emissions, improving energy efficiency across all economic sectors, prioritizing building, transportation, (...) and industry, developing Carbon Capture and Storage technologies, and ensuring universal access to clean and affordable energy. The significant changes envisaged in the energy sector to increase renewable energy production and consumption require improved integration and more use of predictive tools to support stakeholders' decision-making processes. This article presents a case study to assess the performance of predictive models based on a Feed-Forward Neural Network architecture that employ Root Mean Squared Propagation as their optimization function in terms of choosing the most appropriate activation function for this kind of data input and output. The training and testing phases of the models use data about building energy consumption. The lowest training time and Root Mean Square Error values were similar for Rectified Linear Unit and Tanh activation functions. The model with the Rectified linear Unit activation function performed best. (shrink)

Paul Churchland's philosophical work enjoys an increasing popularity. His imaginative papers on cognitive science and the philosophy of psychology are widely discussed. Scientific Realism and the Plasticity of Mind (1979), his major book, is an important contribution to the debate on realism. Churchland provides us with the intellectual tools for constructing a unified scientific Weltanschauung. His network theory of language implies a provocative view of the relation between science and common sense. This paper contains a critical examination of Churchland's (...) network theory of language, which is the foundation of his philosophy. It is argued that the network theory should be seen as deriving its point from traditional empiricism. The network theory enables the empiricist to resist the phenomenalistic temptations inherent in his position, and to build a realist philosophy on the basis of the representative theory of perception. This interpretation is confirmed by the fact that the representative theory is presupposed by Churchland's main argument in favour of the network view. Churchland tends to conceive of himself as a naturalistic epistemologist. But the philosophical faction to which Churchland belongs is rather that of modern neo?Kantianism. (shrink)