Global solar irradiation prediction using a multi-gene genetic programming approach

This is the author accepted manuscript. The final version is available from AIP Publishing via the DOI in this record.In this paper, a nonlinear symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for a data-driven modelling between the dependent and the independent variables. The technique is applied for modelling the measured global solar irradiation and validated through numerical simulations. The proposed modelling technique shows improved results over the fuzzy logic and artificial neural network (ANN) based approaches as attempted by contemporary researchers. The method proposed here results in nonlinear analytical expressions, unlike those with neural networks which is essentially a black box modelling approach. This additional flexibility is an advantage from the modelling perspective and helps to discern the important variables which affect the prediction. Due to the evolutionary nature of the algorithm, it is able to get out of local minima and converge to a global optimum unlike the back-propagation (BP) algorithm used for training neural networks. This results in a better percentage fit than the ones obtained using neural networks by contemporary researchers. Also a hold-out cross validation is done on the obtained genetic programming (GP) results which show that the results generalize well to new data and do not over-fit the training samples. The multi-gene GP results are compared with those, obtained using its single-gene version and also the same with four classical regression models in order to show the effectiveness of the adopted approach

Classification of Chest Diseases using Wavelet Transforms and Transfer Learning

Chest X-ray scan is a most often used modality by radiologists to diagnose many chest related diseases in their initial stages. The proposed system aids the radiologists in making decision about the diseases found in the scans more efficiently. Our system combines the techniques of image processing for feature enhancement and deep learning for classification among diseases. We have used the ChestX-ray14 database in order to train our deep learning model on the 14 different labeled diseases found in it. The proposed research shows the significant improvement in the results by using wavelet transforms as pre-processing technique.Comment: 8 pages, 4 figures, Presented in International Conference On Medical Imaging And Computer-Aided Diagnosis (MICAD 2020), proceeding will be published with Springer in their "Lecture Notes in Electrical Engineering (LNEE)" (ISSN: 1876-1100

Multiple dynamical time-scales in networks with hierarchically nested modular organization

Many natural and engineered complex networks have intricate mesoscopic organization, e.g., the clustering of the constituent nodes into several communities or modules. Often, such modularity is manifested at several different hierarchical levels, where the clusters defined at one level appear as elementary entities at the next higher level. Using a simple model of a hierarchical modular network, we show that such a topological structure gives rise to characteristic time-scale separation between dynamics occurring at different levels of the hierarchy. This generalizes our earlier result for simple modular networks, where fast intra-modular and slow inter-modular processes were clearly distinguished. Investigating the process of synchronization of oscillators in a hierarchical modular network, we show the existence of as many distinct time-scales as there are hierarchical levels in the system. This suggests a possible functional role of such mesoscopic organization principle in natural systems, viz., in the dynamical separation of events occurring at different spatial scales.Comment: 10 pages, 4 figure

A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data

With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets. Simply including all the data does not only incur high processing costs but can even harm the prediction. We formulate the smart and efficient selection of a training dataset from big medical image data as a multi-armed bandit problem, solved by Thompson sampling. Our method assumes that image features are not available at the time of the selection of the samples, and therefore relies only on meta information associated with the images. Our strategy simultaneously exploits data sources with high chances of yielding useful samples and explores new data regions. For our evaluation, we focus on the application of estimating the age from a brain MRI. Our results on 7,250 subjects from 10 datasets show that our approach leads to higher accuracy while only requiring a fraction of the training data.Comment: MICCAI 2017 Proceeding

Gauss-Bonnet Black Holes and Heavy Fermion Metals

We consider charged black holes in Einstein-Gauss-Bonnet Gravity with Lifshitz boundary conditions. We find that this class of models can reproduce the anomalous specific heat of condensed matter systems exhibiting non-Fermi-liquid behaviour at low temperatures. We find that the temperature dependence of the Sommerfeld ratio is sensitive to the choice of Gauss-Bonnet coupling parameter for a given value of the Lifshitz scaling parameter. We propose that this class of models is dual to a class of models of non-Fermi-liquid systems proposed by Castro-Neto et.al.Comment: 17 pages, 6 figures, pdfLatex; small corrections to figure 10 in this versio

Prevalence of albuminuria and cardiovascular risk profile in a referred cohort of patients with type 2 diabetes: An Asian perspective

Background: Microalbuminuria (MA) is a risk marker for diabetic nephropathy and cardiovascular (CV) disease (CVD) in patients with diabetes. This study aimed to describe the prevalence of albuminuria, CV risk factors, and treatments for renal and CV protection in an Asian population with type 2 diabetes. Methods: This cross-sectional study conducted in eight Asian countries enrolled normotensive/hypertensive adults with type 2 diabetes without known proteinuria and/or non-diabetic kidney disease. Exclusion criteria were type 1 diabetes, menstruation, pregnancy, and acute fever. A single random urinary albumin/creatinine test was carried out in all patients. Results: Of 8,561 patients, 14% had diabetic retinopathy, and 17% and 21% had history of CV disease and smoking, respectively. Normoalbuminuria was seen in 44%, MA in 44%, and macroalbuminuria in 12%. Target glycosylated hemoglobin (HbA1c) (<7%) was reached in only 37% of 3,834 patients with available values. Diabetes was managed by diet alone in 6%, while others received oral hypoglycemic drugs and/or insulin. In total, 75% did not reach target blood pressure (BP) of ≤130/80 mm Hg. Antihypertensive drugs were prescribed to 52%, with the number of drugs increasing as the level of systolic BP increased. Drugs blocking the renin-angiotensin system were most commonly prescribed, followed by calcium channel blockers. Lipid-lowering drugs and anticoagulant/antiplatelet agents were used in about 30% and 25% of patients, respectively. Conclusions: Asian patients with type 2 diabetes had a high prevalence of MA and reduced kidney function. Furthermore, BP and HbA1c control was only achieved in a minority of patients. Aggressive risk management by administration of reno- and cardioprotective treatments is urgently needed. © 2008 Mary Ann Liebert, Inc.published_or_final_versio

Recent progress in organic-based radiative cooling materials: fabrication methods and thermal management properties

Organic-based materials capable of radiative cooling have attracted widespread interest in recent years due to their ease of engineering and good adaptability to different application scenarios. As a cooling material for walls, clothing, and electronic devices, these materials can reduce the energy consumption load of air conditioning, improve thermal comfort, and reduce carbon emissions. In this paper, an overview is given of the current fabrication strategies of organic-based radiative cooling materials, and of their properties. The methods and joint thermal management strategies including evaporative cooling, phase-change materials, fluorescence, and light-absorbing materials that have been demonstrated in conjunction with a radiative cooling function are also discussed. This review provides a comprehensive overview of organic-based radiative cooling, exemplifying the emerging application directions in this field and highlighting promising future research directions in the field

Seroprevalence of Mycoplasma bovis infection in dairy cows in subtropical southern China

The seroprevalence of Mycoplasma bovis infection in dairy cows in Guangxi Zhuang Autonomous Region (GZAR) in subtropical southern China was surveyed between June 2009 and March 2010. A total of 455 serum samples of dairy cows were collected from 6 districts in 4 different cities, and examined for M. bovis antibodies with the indirect enzyme-linked immunosorbent assay (ELISA) using a commercially available kit. The overall seroprevalence of M. bovis infection in dairy cows was 7.69% (35/455). Three year-old dairy cows had the highest seroprevalence (15.0%), followed by dairy cows of 4 year-old (11.1%). Dairy cows with the history of 5 pregnancies had the highest seroprevalence (33.3%). However, no statistically significant association was found between M. bovis infection and age or number of pregnancies (p &gt; 0.05). All the aborting dairy cows were negative for M. bovis antibodies, suggesting that bovine abortion may have no association with M. bovis infection in GZAR. These results indicate that M. bovis infection in dairy cows was widespread in GZAR, and integrated strategies and measures should be performed to control and prevent M. bovis infection and disease outbreak.Key words: Mycoplasma bovis, seroprevalence, dairy cows, Guangxi Zhuang Autonomous Region (GZAR), China, enzyme-linked immunosorbent assay (ELISA)

Stimulated emission of polarization-entangled photons

Entangled photon pairs -- discrete light quanta that exhibit non-classical correlations -- play a crucial role in quantum information science (for example in demonstrations of quantum non-locality and quantum cryptography). At the macroscopic optical field level non-classical correlations can also be important, as in the case of squeezed light, entangled light beams and teleportation of continuous quantum variables. Here we use stimulated parametric down-conversion to study entangled states of light that bridge the gap between discrete and macroscopic optical quantum correlations. We demonstrate experimentally the onset of laser-like action for entangled photons. This entanglement structure holds great promise in quantum information science where there is a strong demand for entangled states of increasing complexity.Comment: 5 pages, 4 figures, RevTeX

Elevated CO₂ does not increase eucalypt forest productivity on a low-phosphorus soil

Rising atmospheric CO2 stimulates photosynthesis and productivity of forests, offsetting CO2 emissions. Elevated CO2 experiments in temperate planted forests yielded ~23% increases in productivity over the initial years. Whether similar CO2 stimulation occurs in mature evergreen broadleaved forests on low-phosphorus (P) soils is unknown, largely due to lack of experimental evidence. This knowledge gap creates major uncertainties in future climate projections as a large part of the tropics is P-limited. Here,we increased atmospheric CO2 concentration in a mature broadleaved evergreen eucalypt forest for three years, in the first large-scale experiment on a P-limited site. We show that tree growth and other aboveground productivity components did not significantly increase in response to elevated CO2 in three years, despite a sustained 19% increase in leaf photosynthesis. Moreover, tree growth in ambient CO2 was strongly P-limited and increased by ~35% with added phosphorus. The findings suggest that P availability may potentially constrain CO2-enhanced productivity in P-limited forests; hence, future atmospheric CO2 trajectories may be higher than predicted by some models. As a result, coupled climate-carbon models should incorporate both nitrogen and phosphorus limitations to vegetation productivity in estimating future carbon sinks