Partitioning Protocol Research Articles

Generalised hydrodynamics of TT¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ extrm{T}\\overline{\ extrm{T}} $$\\end{document}-deformed integrable quantum field theories

In this paper we evaluate the averages of conserved densities and currents associated to charges of generic spin in (1+1)-dimensional massive integrable Quantum Field Theories perturbed by the irrelevant TT¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ extrm{T}\\overline{\ extrm{T}} $$\\end{document} operator. By making use of the Thermodynamic Bethe Ansatz approach and of the theory of Generalised Hydrodynamics, we study the non-equilibrium steady state averages of conserved densities and currents in a partitioning protocol. We show that, in particular limits, averages can be evaluated exactly in terms of quantities known from the unperturbed theory. In the massless limit we recover known results for the energy and momentum currents and generalise those to any higher spin conserved quantities. We extend some of our results to perturbations of the generalised TT¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ extrm{T}\\overline{\ extrm{T}} $$\\end{document} type. For the massive free fermion theory, we find an analytic expression for the effective inverse temperature after at TT¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \ extrm{T}\\overline{\ extrm{T}} $$\\end{document} perturbation in terms of the bare inverse temperature by making use of Lambert’s W function.

Methylmercury in northern pike (Esox lucius) liver and hepatic mitochondria is linked to lipid peroxidation

Methylmercury (MeHg) readily bioaccumulates and biomagnifies in aquatic food webs leading to elevated concentrations in fish and may thus induce toxicity. Oxidative stress is a suggested effect of MeHg bioaccumulation in fish. However, studies on how MeHg triggers oxidative stress in wild fish are scarce. The purpose of this study was to link the subcellular distribution of MeHg in the liver of northern pike from the St. Maurice River (Québec, Canada), affected by two run-of-river (RoR) dams, artificial wetlands, forest fires, and logging activity, to lipid peroxidation as an indicator of oxidative stress. We also evaluated the protective effects of the glutathione (GSH) system and selenium (Se), as they are known to alleviate MeHg toxicity. A customized subcellular partitioning protocol was used to separate the liver into metal-sensitive (mitochondria, microsome/lysosome and HDP – heat-denatured proteins) and metal-detoxified fractions (metal-rich granules and HSP – heat-stable proteins). We examined the relation among THg, MeHg, and Se concentration in livers and subcellular fractions, and the hepatic ratio of total GSH (GSHt) to oxidized glutathione (GSSG) on lipid peroxidation levels, using the concentrations of malondialdehyde (MDA), a product of lipid peroxidation. Results showed that hepatic MDA concentration was positively correlated with the combined MeHg and Se concentrations in northern pike liver (r2 = 0.88, p < 0.001) and that MDA concentrations were best predicted by MeHg associated with the mitochondria (r2 = 0.71, p < 0.001). This highlights the need for additional research on the MeHg influence on fish health and the interactions between Hg and Se in northern pike.

Perturbed Progressive Learning for Semisupervised Defect Segmentation.

Recently, with the development of intelligent manufacturing, the demand for surface defect inspection has been increasing. Deep learning has achieved promising results in defect inspection. However, due to the rareness of defect data and the difficulties of pixelwise annotation, the existing supervised defect inspection methods are too inferior to be implemented in practice. To solve the problem of defect segmentation with few labeled data, we propose a simple and efficient method for semisupervised defect segmentation (SSDS), named perturbed progressive learning (PPL). On the one hand, PPL decouples the predictions of student and teacher networks as well as alleviates overfitting on noisy pseudo-labels. On the other hand, PPL encourages consistency across various perturbations in a broader stagewise scope, alleviating drift caused by the noisy pseudo-labels. Specifically, PPL contains two training stages. In the first stage, the teacher network gives the unlabeled data with pseudo-labels that are divided into the easy and hard groups. The labeled data and the unlabeled data in the easy group with their perturbation are both used to train for a better-performing student network. In the second stage, the unlabeled data in the hard group are predicted by the obtained student network, so the refined pseudo-labeled data are enlarged. All the pseudo-labeling data and labeled data with their perturbation are used to retrain the student network, progressively improving the defect feature representation. We build a mobile screen defect dataset (MSDD-3) with three classes of defects. PPL is implemented on MSDD-3 as well as other public datasets. Extensive experimental results demonstrate that PPL significantly surpasses the state-of-the-art methods across all evaluation partition protocols.

A Multi-View Integrated Ensemble for the Background Discrimination of Semi-Supervised Semantic Segmentation

The key to semi-supervised semantic segmentation is to assign the appropriate pseudo-label to the pixels of unlabeled images. Recently, various approaches to consistency-based training and the filtering of reliable pseudo-labels have shown remarkable results. Nonetheless, there are still issues to be addressed. We find that recent approaches have specific problems in common. In pseudo-labels for training unlabeled images, we confirm that false foreground class pseudo-labels are mostly caused by background class confusion, not confusion between different foreground classes. To solve this problem, we propose a foreground and background discrimination model for semi-supervised semantic segmentation. Our proposed model is trained using a novel approach called multi-view integrated ensemble (MVIE) via output perturbation. Experimental results in various partition protocols show that our approach outperforms the existing state of the art (SOTA) in binary prediction on unlabeled data, and the segmentation model trained with the help of our model outperforms existing models.

CPS: Cross-interface network Partitioning and Scheduling towards QoS-aware data flow delivery in multimedia IoT

In the Internet of Things (IoT), multimedia traffic for audio, image, and video accounts for the largest proportion (over 78.7%) of the total traffic, bringing forward the vision of multimedia IoT (M-IoT). As part of the realization of loT, M-IoT is a general network paradigm that constitutes many smart objects equipped with the capability to collect multimedia data from the physical environment and deliver the data to other things. To satisfy a certain level of user experience, Quality of Service (QoS) is required to be regulated to ensure acceptable delivery of the multimedia content. As the most widely-used wireless technology, WiFi has been recommended for IoT communications for its high data rate, native IP compatibility, and good reusability of the existing infrastructures. However, WiFi suffers from channel contention, especially during multi-hop communications, which degrades the QoS performance and hinders its use for many M-IoT services. Although numerous protocols have been proposed to mitigate WiFi contention, they often consume much WiFi bandwidth for network control, lowering the level of achievable QoS performance. To address this issue, we propose a distributed Cross-interface network Partitioning and Scheduling (CPS) protocol, which leverages the co-existing ZigBee communications to divide the network into partitions and allows only one node in each partition to use its WiFi interface to transmit data at any time, for bandwidth-efficient and delay-constrained data flow delivery in M-IoT. A prototype node is implemented by integrating COTS ZigBee and WiFi interfaces into a BeagleBone Green wireless platform for IoT. Extensive field experiments are conducted in a multi-hop network of 24 prototype nodes that deliver real multimedia data (images and videos). The experiment results show that CPS outperforms the standard WiFi and a state-of-the-art contention control scheme (by 62.6% and 26.4% under high data traffic, respectively) in terms of a QoS metric capturing two basic performance metrics (i.e., bandwidth efficiency and end-to-end delay) of multi-hop communications, while retaining fair QoS performance and high energy efficiency.

Labour-saving detection of hybrid rice rows at the pollination stage based on a multi-perturbed semi-supervised model

Autonomous navigation of the hybrid rice pollination process is essential to increasing seed production. Detecting crop rows in real-time with flexible and cost-effective machine vision equipment is crucial to achieving autonomous navigation. The use of deep learning-based models has proven effective in crop row detection. However, training such models is highly dependent on manually finely annotated image data, which becomes a bottleneck that hinders their practical application and improvement of crop row detection accuracy. This study proposed a multi-perturbed semi-supervised learning-based model to enhance the semantic segmentation performance of hybrid rice regions by mining valuable information from easily accessible unlabelled images. The input data perturbation and network perturbation developed effectively alleviated the overfitting and teacher-student weight coupling issues when applying semi-supervised models to the hybrid rice image dataset with high content similarity. Navigation centrelines were obtained by post-processing the segmentation masks of the crop regions. Under a 1/2 partition protocol of labelled and unlabelled images, the semantic segmentation performance of the proposed approach achieved a mean intersection over union (mIoU) of 0.887, outperformed its supervised baseline (DeepLabv3+ ) and original model (mean teacher) by 1.8% and 4.1%, respectively. 82.38% of the crop row were accurately detected using the proposed model, demonstrating an improvement of 3.04% and 14.06% over its baseline and the original model, respectively.

Apoptotic, Anti-Inflammatory Activities and Interference with the Glucocorticoid Receptor Signaling of Fractions from Pistacia lentiscus L. var. chia Leaves.

In this study acetonic extracts of leaves of Pistacia lentiscus L. var. chia (mastiha tree) grown in the south as well as in the north Chios Greek island were isolated and further fractionated to give three different polarity fractions: apolar, medium-polar, and polar. The isolated fractions were assessed as regards their main composition, cytotoxic, anti-inflammatory activities, and interference with the glucocorticoid receptor (GR) signaling, applying cytotoxic assay, luciferase assays, and Western blot analysis of apoptosis-, energy-, and inflammation-associated molecules. Differences in cell viability have been detected among different polarity leaf fractions as well as among fractions of different plant origin with polar fractions showing the highest cytotoxicity. Fractions-induced anti-inflammatory activities and suppressive effects on the dexamethasone (DEX)-induced GR transcriptional activation were unveiled. The partition protocol of leaves fractions applied uncovers the enhanced glucocorticoid-associated biological activities of the medium-polar fractions, which may be associated with their enrichment in the triterpenoids that showed structural similarity with the glucocorticoids. A reduction in GR protein levels is observed by the fraction which is shown to be associated with the medium polar-induced proteolytic degradation of the receptor. In addition, the enhanced cytotoxic, anti-inflammatory, and potential anti-glycemic activities of the fractions from the Southern P. lentiscus L. that exclusively produce the mastiha resin, is revealed, indicating that leaves fractions from mastiha tree, similarly to mastiha tree resin, may have the potential to be further analyzed for their potent applications in the pharmaceutical cosmetic and nutraceutical fields.

The staircase model: massless flows and hydrodynamics

The staircase model (SM) is a simple generalization of the sinh-Gordon model, obtained by complexifying the coupling constant. This produces a new theory with many interesting features. Chief among them is the fact that scaling functions such as Zamolodchikov’s c-function display ‘roaming’ behaviour, that is, they visit the vicinity of an infinite number of conformal fixed points, the unitary minimal models of conformal field theory. This rich structure also makes the model an interesting candidate for study using the generalized hydrodynamic approach to integrable quantum field theory (IQFT). By studying hydrodynamic quantities such as the effective velocities of quasiparticles we can develop a more physical picture of interaction in the theory, both at and away from equilibrium. Indeed, we find that in the SM the effective velocity displays monotonicity features not found in any other IQFT. These admit a natural interpretation when investigated in the context of the massless theories known as models, which provide an effective description of massless flows between consecutive unitary minimal models. Consequently, we find that the effective velocity in the SM can be reconstructed by a ‘cut and paste’ procedure involving the equivalent functions associated to the massless excitations of suitable models. We also investigate the average currents and densities of higher spin conserved quantities in the partitioning protocol.

Projector-Free Capped-Fragment Scheme within Density Functional Embedding Theory for Covalent and Ionic Compounds.

Quantum-mechanics-(QM)-based simulations now routinely aid in understanding and even discovering new chemistries involving molecules and materials exhibiting desired functionalities. Ab initio correlated wavefunction (CW) theories systematically improve QM methods, with many exhibiting high accuracy. However, execution of CW methods requires expensive computations that typically scale poorly with system size. Divide-and-conquer approaches partition large systems into smaller fragments; a lower level of theory treats fragment interactions while a preferred higher level of theory describes the important fragment. These methods offer ways to incorporate CWs into chemical simulations of large systems, e.g., biomolecules, surfaces, large inorganic clusters, bulk crystals, etc. Here we propose a partitioning protocol that utilizes capping atoms to saturate severed covalent bonds at fragment interfaces and density functional embedding theory (DFET) to describe fragment interactions. The capping groups in each fragment provide an ad-hoc potential that approximates the effects of the environment. An embedding potential optimized via DFET then serves as an augmentation of the capping group to simulate the effects of the environment. We concurrently use an auxiliary fragment (a separate system comprised of only the combined capping groups) to account for, and thereby correct, the electron density contributions of all the capping groups added to all of the fragments. This method depends only on the capped-subsystem and auxiliary-fragment electron densities, forgoing, as with the original DFET developed for metallic systems, orbital-based projector approaches that determine a nonlocal action of the embedding potential onto the fragment electrons. By using an auxiliary fragment, the method maintains a purely electron-density-dependent embedding potential, substantially lessening the cost and leading to simpler implementation. Here, we demonstrate the utility of our capped-DFET and ensuing capped embedded CW method in two contrasting systems, namely, an organic molecule and an ionic metal oxide cluster.

Euler-scale dynamical fluctuations in non-equilibrium interacting integrable systems

We derive an exact formula for the scaled cumulant generating function of the time-integrated current associated to an arbitrary ballistically transported conserved charge. Our results rely on the Euler-scale description of interacting, many-body, integrable models out of equilibrium given by the generalized hydrodynamics, and on the large deviation theory. Crucially, our findings extend previous studies by accounting for inhomogeneous and dynamical initial states in interacting systems. We present exact expressions for the first three cumulants of the time-integrated current. Considering the non-interacting limit of our general expression for the scaled cumulant generating function, we further show that for the partitioning protocol initial state our result coincides with previous results of the literature. Given the universality of the generalized hydrodynamics, the expression obtained for the scaled cumulant generating function is applicable to any interacting integrable model obeying the hydrodynamic equations, both classical and quantum.

Large deviations of energy transfers in nonequilibrium CFT and asymptotics of non-local Riemann–Hilbert problems

A wide class of $$1+1$$ dimensional unitary conformal field theories allows for an explicit construction of nonequilibrium “profile states” interpolating smoothly between different equilibria on the left and on the right. It has been recently established that the generating function for the full counting statistics of energy transfers in such states may be expressed in terms of the solution to a non-local Riemann–Hilbert problem. Following earlier works on the statistics of energy transfers, in particular the ones of Bernard–Doyon on the “partitioning protocol” in conformal field theory, the full counting statistics of energy transfers in the profile states was conjectured to satisfy a large deviation principle in the limit of long transfer-times. The present paper establishes rigorously this conjecture by carrying out the long-time asymptotic analysis of the underlying non-local Riemann–Hilbert problem.

Generalized hydrodynamics study of the one-dimensional Hubbard model: Stationary clogging and proportionality of spin, charge, and energy currents

In our previous work [Nozawa and Tsunetsugu, Phys. Rev. B 101, 035121 (2020)], we studied the quench dynamics in the one-dimensional Hubbard model based on the generalized hydrodynamics theory for a partitioning protocol and showed the presence of a clogging phenomenon. Clogging is a phenomenon where vanishing charge current coexists with nonzero energy current, and we found it for the initial conditions that the left half of the system is prepared to be half filling at high temperatures with the right half being empty. Clogging occurs at all the sites in the left half and lasts for a time proportional to its distance from the connection point. In this paper, we use various different initial conditions and discuss two issues. The first issue is the possibility of clogging in a stationary state. When the electron density in the right half is initially set nonzero, we found that the left half-filled part expands for various sets of parameters in the initial condition. This means that the clogging phenomenon occurs at all the sites in the long-time stationary state, and we also discuss its origin. In addition, stationary clogging is accompanied by a back current, namely, particle density current flows towards the high-density region. We also found that spin clogging occurs for some initial conditions, i.e., the vanishing spin current coexists with nonzero energy current. The second issue is the proportionality of spin and charge currents. We found two spatiotemporal regions where the current ratio is fixed to a nonzero constant. We numerically studied how the current ratio depends on various initial conditions. We also studied the ratio of charge and energy currents.

On the hydrodynamics of unstable excitations

The generalized hydrodynamic (GHD) approach has been extremely successful in describing the out-of-equilibrium properties of a great variety of integrable many-body quantum systems. It naturally extracts the large-scale dynamical degrees of freedom of the system, and is thus a particularly good probe for emergent phenomena. One such phenomenon is the presence of unstable particles, traditionally seen via special analytic structures of the scattering matrix. Because of their finite lifetime and energy threshold, these are especially hard to study. In this paper we apply the GHD approach to a model possessing both unstable excitations and quantum integrability. The largest family of relativistic integrable quantum field theories known to have these features are the homogeneous sine-Gordon models. We consider the simplest non-trivial example of such theories and investigate the effect of an unstable excitation on various physical quantities, both at equilibrium and in the non-equilibrium state arising from the partitioning protocol. The hydrodynamic approach sheds new light onto the physics of the unstable particle, going much beyond its definition via the analytic structure of the scattering matrix, and clarifies its effects both on the equilibrium and out-of-equilibrium properties of the theory. Crucially, within this dynamical perspective, we identify unstable particles as finitely-lived bound states of co-propagating stable particles of different types, and observe how stable populations of unstable particles emerge in large-temperature thermal baths.

Adaptive methodology to determine hydrophobicity of nanomaterials in situ.

The hydrophobicity of nanoparticles (NPs) is a key property determining environmental fate, biological partitioning and toxicity. However, methods to characterize surface hydrophobicity are not uniformly applied to NPs and cannot quantify surface changes in complex environments. Existing methods designed to evaluate the hydrophobicity of bulk solids, chemicals, and proteins have significant limitations when applied to NPs. In this study, we modified and evaluated two methods to determine the hydrophobicity of NPs, hydrophobic interaction chromatography (HIC) and dye adsorption, and compared them to the standard octanol-water partitioning protocol for chemicals. Gold, copper oxide, silica, and amine-functionalized silica NPs were used to evaluate methods based on their applicability to NPs that agglomerate and have surface coatings. The octanol water partitioning and HIC methods both measured Au NPs as hydrophilic, but despite having a small size and stable suspension, NPs could not be fully recovered from the HIC column. For the dye adsorption method, hydrophobic (Rose Bengal) and hydrophilic (Nile Blue) dyes were adsorbed to the NP surface, and linear isotherm parameters were used as a metric for hydrophobicity. CuO was determined to be slightly hydrophilic, while SiO2 was hydrophilic and Ami-SiO2 was hydrophobic. The advantages and limitations of each method are discussed, and the dye adsorption method is recommended as the most suitable for application across broad classes of nanomaterials. The dye assay method was further used to measure changes in the surface hydrophobicity of TiO2 NPs after being suspended in natural water collected from the Alsea Rivers watershed in Oregon. TiO2 NPs adsorbed Rose Bengal when suspended in ultrapure water, but adsorbed Nile Blue after being incubated in natural water samples, demonstrating a shift from hydrophobic to hydrophilic properties on the outer surface. The dye adsorption method can be applied to characterize surface hydrophobicity of NPs and quantify environmental transformations, potentially improving environmental fate models.

Introducing iFluid: a numerical framework for solving hydrodynamical equations in integrable models

We present an open-source Matlab framework, titled iFluid, for simulating the dynamics of integrable models using the theory of generalized hydrodynamics (GHD). The framework provides an intuitive interface, enabling users to define and solve problems in a few lines of code. Moreover, iFluid can be extended to encompass any integrable model, and the algorithms for solving the GHD equations can be fully customized. We demonstrate how to use iFluid by solving the dynamics of three distinct systems: (i) The quantum Newton's cradle of the Lieb-Liniger model, (ii) a gradual field release in the XXZ-chain, and (iii) a partitioning protocol in the relativistic sinh-Gordon model.

Transport fluctuations in integrable models out of equilibrium

We propose exact results for the full counting statistics, or the scaled cumulant generating function, pertaining to the transfer of arbitrary conserved quantities across an interface in homogeneous integrable models out of equilibrium. We do this by combining insights from generalised hydrodynamics with a theory of large deviations in ballistic transport. The results are applicable to a wide variety of physical systems, including the Lieb-Liniger gas and the Heisenberg chain. We confirm the predictions in non-equilibrium steady states obtained by the partitioning protocol, by comparing with Monte Carlo simulations of this protocol in the classical hard rod gas. We verify numerically that the exact results obey the correct non-equilibrium fluctuation relations with the appropriate initial conditions.

Generalized hydrodynamic approach to charge and energy currents in the one-dimensional Hubbard model

We have studied nonequilibrium dynamics of the one-dimensional Hubbard model using the generalized hydrodynamic theory. We mainly investigated the spatiotemporal profile of particle density (equivalent to charge density), energy density, and their currents using the partitioning protocol; the initial state consists of two semi-infinite different thermal equilibrium states joined at the origin. In this protocol, a transient region where currents flow appears around the origin, and this region expands its size linearly with time. We examined how density and current profiles depend on initial conditions. Inside the transient region, we have found a clogged region where charge current is zero but nonvanishing energy current flows. This phenomenon is similar to spin-charge separation in Tomonaga-Luttinger liquids, in which spin and charge excitations propagate with different velocities. This region appears when one of the initial states has half-filled electron density, and it is located adjacent to this initial state. We have proved analytically the existence of the clogged region in the infinite temperature case of the half-filled initial state. The existence is confirmed also for finite temperatures by numerical calculations of generalized hydrodynamics. A similar analytical proof is also given for a clogged region of spin current when magnetic field is applied to one and only one of the two initial states. A universal proportionality of charge and spin currents is also proved for a special region, for general initial conditions of electron density and magnetic field. To examine the clogged region of charge current, we have calculated the current contributions of different types of quasiparticles in the Hubbard model. It is found that the charge current component carried by scattering states (i.e., Fermi liquid type) is canceled completely in the clogged region by the counter flows carried by bound-state quasiparticles (called $k\text{\ensuremath{-}}\mathrm{\ensuremath{\Lambda}}$ strings). Their contributions are not canceled for energy current, which is also proved analytically for special cases, and these different behaviors are the origin of the clogged region. Except for the clogged region, charge and energy densities are in good proportion to each other, and their ratio depends on the initial conditions. This proportionality, in nonequilibrium dynamics, is reminiscent of Wiedemann-Franz law in thermal equilibrium, which states current proportionality determined by temperature. The long-time stationary values of charge and energy currents were also studied with varying initial conditions. We have compared the results with the values of noninteracting systems and discussed the effects of electron correlations. The ratio of these stationary currents is also analyzed. We have found that the temperature dependence of the ratio is strongly suppressed by electron correlations and even reversed.

Classification and prediction of diabetes disease using machine learning paradigm.

Diabetes is a chronic disease characterized by high blood sugar. It may cause many complicated disease like stroke, kidney failure, heart attack, etc. About 422 million people were affected by diabetes disease in worldwide in 2014. The figure will be reached 642 million in 2040. The main objective of this study is to develop a machine learning (ML)-based system for predicting diabetic patients. Logistic regression (LR) is used to identify the risk factors for diabetes disease based on p value and odds ratio (OR). We have adopted four classifiers like naïve Bayes (NB), decision tree (DT), Adaboost (AB), and random forest (RF) to predict the diabetic patients. Three types of partition protocols (K2, K5, and K10) have also adopted and repeated these protocols into 20 trails. Performances of these classifiers are evaluated using accuracy (ACC) and area under the curve (AUC). We have used diabetes dataset, conducted in 2009-2012, derived from the National Health and Nutrition Examination Survey. The dataset consists of 6561 respondents with 657 diabetic and 5904 controls. LR model demonstrates that 7 factors out of 14 as age, education, BMI, systolic BP, diastolic BP, direct cholesterol, and total cholesterol are the risk factors for diabetes. The overall ACC of ML-based system is 90.62%. The combination of LR-based feature selection and RF-based classifier gives 94.25% ACC and 0.95 AUC for K10 protocol. The combination of LR and RF-based classifier performs better. This combination will be very helpful for predicting diabetic patients.

Hydrodynamics of massless integrable RG flows and a non-equilibrium c-theorem

We study Euler scale hydrodynamics of massless integrable quantum field theories interpolating between two non-trivial renormalisation group fixed points after inhomogeneous quantum quenches. Using a partitioning protocol with left and right initial thermal states and the recently developed framework of generalised hydrodynamics, we focus on current and density profiles for the energy and momentum as a function of ξ = x/t, where both x and t are sent to infinity. Studying the first few members of the An and Dn massless flows we carry out a systematic treatment of these series and generalise our results to other unitary massless models.In our analysis we find that the profiles exhibit extended plateaux and that non-trivial bounds exist for the energy and momentum densities and currents in the non-equilibrium stationary state, i.e. when ξ = 0. To quantify the magnitude of currents and densities, dynamical central charges are defined and it is shown that the dynamical central charge for the energy current satisfies a certain monotonicity property. We discuss the connection of the Landauer-Büttiker formalism of transport with our results and show that this picture can account for some of the bounds for the currents and for the monotonicity of the dynamical central charge. These properties are shown to be present not only in massless flows but also in the massive sinh-Gordon model suggesting their general validity and the correctness of the Landauer-Büttiker interpretation of transport in integrable field theories. Our results thus imply the existence of a non-equilibrium c-theorem as well, at least in integrable models. Finally we also study the interesting low energy behaviour of the A2 model that corresponds to the massless flow from the tricritical to the critical Ising field theory.

IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment.

Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and plantar warts. There are various treatment methods for this disease, including the popular immunotherapy and cryotherapy methods. Manual evaluation of the WD treatment response is challenging. Furthermore, traditional machine learning methods are not robust enough in WD classification as they cannot deal effectively with small number of attributes. This study proposes a new evolutionary-based computer-aided diagnosis (CAD) system using machine learning to classify the WD treatment response. The main architecture of our CAD system is based on the combination of improved adaptive particle swarm optimization (IAPSO) algorithm and artificial immune recognition system (AIRS). The cross-validation protocol was applied to test our machine learning-based classification system, including five different partition protocols (K2, K3, K4, K5 and K10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using the K10 protocol that provided the precision, recall, F-measure and accuracy values of 0.8908, 0.8943, 0.8916 and 90%, respectively. Our IAPSO system showed the reliability of 98.68%. It was implemented in Java, while integrated development environment (IDE) was implemented using NetBeans. Our encouraging results suggest that the proposed IAPSO-AIRS system can be employed for the WD management in clinical environment.