Kinetic Ising models are powerful tools for studying the non-equilibrium dynamics of complex systems. As their behavior is not tractable for large networks, many mean-field methods have been proposed ...for their analysis, each based on unique assumptions about the system's temporal evolution. This disparity of approaches makes it challenging to systematically advance mean-field methods beyond previous contributions. Here, we propose a unifying framework for mean-field theories of asymmetric kinetic Ising systems from an information geometry perspective. The framework is built on Plefka expansions of a system around a simplified model obtained by an orthogonal projection to a sub-manifold of tractable probability distributions. This view not only unifies previous methods but also allows us to develop novel methods that, in contrast with traditional approaches, preserve the system's correlations. We show that these new methods can outperform previous ones in predicting and assessing network properties near maximally fluctuating regimes.
In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications ...like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. However, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. We develop a pore recognition approach to quantify similarity of pore structures and classify them using topological data analysis. This allows us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures. Using methane storage as a case study, we also show that materials can be divided into topologically distinct classes requiring different optimization strategies.
Using the first law of thermodynamics, we propose a relation between the system entropy (
S
) and its IR (
L
) and UV (
Λ
) cutoffs. In addition, applying this relation to the apparent horizon of ...flat FRW universe, whose entropy meets the Rényi entropy, a new holographic dark energy model is addressed. Thereinafter, the evolution of the flat FRW universe, filled by a pressureless source and the obtained dark energy candidate, is studied. In our model, there is no mutual interaction between the cosmos sectors. We find out that the obtained model is theoretically powerful to
explain
the current accelerated phase of the universe. This result emphasizes that the generalized entropy formalism is suitable for describing systems including the long-range interactions such as gravity.
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Focal epileptic seizures can remain localized or, alternatively, spread across brain areas, often resulting in impairment of cognitive function and loss of consciousness. Understanding the factors ...that promote spread is important for developing better therapeutic approaches. Here, we show that: (1) seizure spread undergoes "critical" phase transitions in models (epileptor-networks) that capture the neural dynamics of spontaneous seizures while incorporating patient-specific brain network connectivity, axonal delays and identified epileptogenic zones (EZs). We define a collective variable for the spreading dynamics as the spread size, i.e. the number of areas or nodes in the network to which a seizure has spread. Global connectivity strength and excitability in the surrounding non-epileptic areas work as phase-transition control parameters for this collective variable. (2) Phase diagrams are predicted by stability analysis of the network dynamics. (3) In addition, the components of the Jacobian's leading eigenvector, which tend to reflect the connectivity strength and path lengths from the EZ to surrounding areas, predict the temporal order of network-node recruitment into seizure. (4) However, stochastic fluctuations in spread size in a near-criticality region make predictability more challenging. Overall, our findings support the view that within-patient seizure-spread variability can be characterized by phase-transition dynamics under transient variations in network connectivity strength and excitability across brain areas. Furthermore, they point to the potential use and limitations of model-based prediction of seizure spread in closed-loop interventions for seizure control.
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The spread of seizures across brain networks is the main impairing factor, often leading to loss-of-consciousness, in people with epilepsy. Despite advances in recording and modeling brain activity, ...uncovering the nature of seizure spreading dynamics remains an important challenge to understanding and treating pharmacologically resistant epilepsy. To address this challenge, we introduce a new probabilistic model that captures the spreading dynamics in patient-specific complex networks. Network connectivity and interaction time delays between brain areas were estimated from white-matter tractography. The model's computational tractability allows it to play an important complementary role to more detailed models of seizure dynamics. We illustrate model fitting and predictive performance in the context of patient-specific Epileptor networks. We derive the phase diagram of spread size (order parameter) as a function of brain excitability and global connectivity strength, for different patient-specific networks. Phase diagrams allow the prediction of whether a seizure will spread depending on excitability and connectivity strength. In addition, model simulations predict the temporal order of seizure spread across network nodes. Furthermore, we show that the order parameter can exhibit both discontinuous and continuous (critical) phase transitions as neural excitability and connectivity strength are varied. Existence of a critical point, where response functions and fluctuations in spread size show power-law divergence with respect to control parameters, is supported by mean-field approximations and finite-size scaling analyses. Notably, the critical point separates two distinct regimes of spreading dynamics characterized by unimodal and bimodal spread-size distributions. Our study sheds new light on the nature of phase transitions and fluctuations in seizure spreading dynamics. We expect it to play an important role in the development of closed-loop stimulation approaches for preventing seizure spread in pharmacologically resistant epilepsy. Our findings may also be of interest to related models of spreading dynamics in epidemiology, biology, finance, and statistical physics.
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Dyspnoea is a debilitating and distressing symptom that is reflected in different verbal descriptors. Evidence suggests that dyspnoea, like pain perception, consists of sensory quality and affective ...components. The objective of this study was to develop an instrument that measures overall dyspnoea severity using descriptors that reflect its different aspects.
81 dyspnoea descriptors were administered to 123 patients with chronic obstructive pulmonary disease (COPD), 129 with interstitial lung disease and 106 with chronic heart failure. These were reduced to 34 items using hierarchical methods. Rasch analysis informed decisions regarding further item removal and fit to the unidimensional model. Principal component analysis (PCA) explored the underlying structure of the final item set. Validity and reliability of the new instrument were further assessed in a separate group of 53 patients with COPD.
After removal of items with hierarchical methods (n = 47) and items that failed to fit the Rasch model (n = 22), 12 were retained. The "Dyspnoea-12" had good internal reliability (Cronbach's alpha = 0.9) and fit to the Rasch model (chi(2) p = 0.08). Items patterned into two groups called "physical"(n = 7) and "affective"(n = 5). In the separate validation study, Dyspnoea-12 correlated with the Hospital Anxiety and Depression Scale (anxiety r = 0.51; depression r = 0.44, p<0.001, respectively), 6-minute walk distance (r = -0.38, p<0.01) and MRC (Medical Research Council) grade (r = 0.48, p<0.01), and had good stability over time (intraclass correlation coefficient = 0.9, p<0.001).
Dyspnoea-12 fulfills modern psychometric requirements for measurement. It provides a global score of breathlessness severity that incorporates both "physical" and "affective" aspects, and can measure dyspnoea in a variety of diseases.
Induction machines (IMs) are widely used in different applications. Unpredicted breakdown of these machines usually leads to costly downtimes and repairs. These expenses can be minimized using proper ...condition monitoring techniques. Eccentricity fault is one of the widespread faults causing machine malfunction; its detection could be useful for prevention of harmful consequences. In this paper, different works on eccentricity diagnosis of IMs with different types of supply have been reviewed. It commences from the simplest open-loop machine, and by considering torque variation and inverter switching gradually turns to complicated closed-loop machine with different control strategies. While in most cases, current is used for condition monitoring, in some instances power and voltage are employed for fault diagnosis. Due to extensive use of IMs in wind turbines as doubly-fed induction generator (DFIG), in addition to declaration of importance of eccentricity fault diagnosis in DFIG, detection of eccentricity fault in DFIG is also reviewed.
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New achievements in the realm of nanoscience and innovative techniques of nanomedicine have moved micro/nanoparticles (MNPs) to the point of becoming actually useful for practical applications in the ...near future. Various differences between the extracellular and intracellular environments of cancerous and normal cells and the particular characteristics of tumors such as physicochemical properties, neovasculature, elasticity, surface electrical charge, and pH have motivated the design and fabrication of inventive "smart" MNPs for stimulus-responsive controlled drug release. These novel MNPs can be tailored to be responsive to pH variations, redox potential, enzymatic activation, thermal gradients, magnetic fields, light, and ultrasound (US), or can even be responsive to dual or multi-combinations of different stimuli. This unparalleled capability has increased their importance as site-specific controlled drug delivery systems (DDSs) and has encouraged their rapid development in recent years. An in-depth understanding of the underlying mechanisms of these DDS approaches is expected to further contribute to this groundbreaking field of nanomedicine. Smart nanocarriers in the form of MNPs that can be triggered by internal or external stimulus are summarized and discussed in the present review, including pH-sensitive peptides and polymers, redox-responsive micelles and nanogels, thermo- or magnetic-responsive nanoparticles (NPs), mechanical- or electrical-responsive MNPs, light or ultrasound-sensitive particles, and multi-responsive MNPs including dual stimuli-sensitive nanosheets of graphene. This review highlights the recent advances of smart MNPs categorized according to their activation stimulus (physical, chemical, or biological) and looks forward to future pharmaceutical applications.
New achievements in the realm of nanoscience and innovative techniques of nanomedicine have moved micro/nanoparticles (MNPs) to the point of becoming actually useful for practical applications in the near future.
Networks of excitable nodes have recently attracted much attention particularly in regards to neuronal dynamics, where criticality has been argued to be a fundamental property. Refractory behavior, ...which limits the excitability of neurons is thought to be an important dynamical property. We therefore consider a simple model of excitable nodes which is known to exhibit a transition to instability at a critical point (λ = 1), and introduce refractory period into its dynamics. We use mean-field analytical calculations as well as numerical simulations to calculate the activity dependent branching ratio that is useful to characterize the behavior of critical systems. We also define avalanches and calculate probability distribution of their size and duration. We find that in the presence of refractory period the dynamics stabilizes while various parameter regimes become accessible. A sub-critical regime with λ < 1.0, a standard critical behavior with exponents close to critical branching process for λ = 1, a regime with 1 < λ < 2 that exhibits an interesting scaling behavior, and an oscillating regime with λ > 2.0. We have therefore shown that refractory behavior leads to a wide range of scaling as well as periodic behavior which are relevant to real neuronal dynamics.
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Artificial intelligence (AI) techniques have been ascertained useful in the analysis and description of infectious areas in radiological images promptly. Our aim in this study was to design a ...web-based application for detecting and labeling infected tissues on CT (computed tomography) lung images of patients based on the deep learning (DL) method as a type of AI.
The U-Net architecture, one of the DL networks, is used as a hybrid model with pre-trained densely connected convolutional network 121 (DenseNet121) architecture for the segmentation process. The proposed model was constructed on 1031 persons' CT-scan images from Ibn Sina Hospital of Iran in 2021 and some publicly available datasets. The network was trained using 6000 slices, validated on 1000 slices images, and tested against the 150 slices. Accuracy, sensitivity, specificity, and area under the receiver operating characteristics (ROC) curve (AUC) were calculated to evaluate model performance.
The results indicate the acceptable ability of the U-Net-DenseNet121 model in detecting COVID-19 abnormality (accuracy = 0.88 and AUC = 0.96 for thresholds of 0.13 and accuracy = 0.88 and AUC = 0.90 for thresholds of 0.2). Based on this model, we developed the "Imaging-Tech" web-based application for use at hospitals and clinics to make our project's output more practical and attractive in the market.
We designed a DL-based model for the segmentation of COVID-19 CT scan images and, based on this model, constructed a web-based application that, according to the results, is a reliable detector for infected tissue in lung CT-scans. The availability of such tools would aid in automating, prioritizing, fastening, and broadening the treatment of COVID-19 patients globally.
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