Search

Accurate forecasts of daily COVID-19 admissions are critical for healthcare planners and decision-makers to better manage scarce resources during and around infection peaks. Numerous studies have focused on forecasting COVID-19 admissions at the national or global levels. Localised predictions are vital, as they allow for resource planning redistribution, but also scarce and harder to get right. Several possible indicators can be used to predict COVID-19 admissions. The inherent variability in the admissions necessitates the generation and evaluation of the forecast distribution of admissions, as opposed to producing only a point forecast. In this study, we propose a quantile regression forest (QRF) model for probabilistic forecasting of daily COVID-19 admissions for a local hospital trust (aggregation of 3 hospitals), up to 7-days ahead, using a multitude of different predictors. We evaluate point forecast accuracy as well as the accuracy of the forecast distribution using appropriate measures. We provide evidence that QRF outperforms univariate time series methods and other more sophisticated benchmarks. Our findings also show that lagged admissions, total positive cases, daily tests performed, and Google grocery and Apple driving are the most salient predictors. Finally, we highlight areas where further research is needed.

BackgroundAn unmet need remains for sensitive outcome measures in neuroprotective trials. The study aims to determine whether a composite clinical motor score, combining the Movement Disorders Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) III motor examination score, Purdue Pegboard Test, and Timed Up and Go, provides greater sensitivity in detecting motor change in early disease than the MDS-UPDRS III alone.MethodsThe Oxford Discovery longitudinal cohort study involves individuals with isolated rapid eye movement sleep behaviour disorder (iRBD) (n=272, confirmed polysomnographically, median follow-up: 1.6 years), idiopathic Parkinson’s disease (PD) (n=909, median follow-up: 3.5 years, baseline: ResultsCompared with the MDS-UPDRS III, the composite clinical motor score demonstrated a wider score distribution in iRBD and controls, lower coefficient of variation (37% vs 67%), and higher correlation coefficients with self-reported measures of motor severity (0.65 vs 0.61) and overall health status (−0.40 vs −0.33). Greater score range in mild to moderate PD, higher magnitude of longitudinal change in iRBD and longitudinal score linearity suggest better sensitivity in detecting subtle motor change. The composite clinical motor score was more accurate than the MDS-UPDRS III in predicting clinical outcomes, requiring 64% fewer participants with PD and 51% fewer participants with iRBD in sample size estimations for a hypothetical 18-month placebo-controlled clinical trial.ConclusionThe composite clinical motor score may offer greater consistency and sensitivity in detecting change than the MDS-UPDRS III.

Over 23 million software developers in 2018, this number is expected to reach 26.4 million by the end of 2019 and 27.7 million by 2023 according to Evans Data Corporation. The number of programmers continues to grow to this day as technology is the Forthcoming, especially in the AI field. Where there are 300,000 “AI researchers and practitioners” in the world, but the market demand is for millions of roles, so many people Siding to this field. Nowadays, most people learn the programming field as inquisitiveness but for their interest, however, they delve deeper into this field, which enhances their passion for and leaves their work to practice programming as occupation due to the availability of jobs and the most request for it. Over time, new languages have emerged, it has evolved to meet human needs in the form of programming languages. You can instruct the computer in the human-readable form where programming will enable you to learn the significance of clarity of expression, many determinations can be achieved, importantly, relationships, semantics, and grammar can be defined.

PURPOSE A COVID-19 lockdown in India posed significant challenges to the continuation of radiotherapy (RT) and systemic therapy services. Although several COVID-19 service guidelines have been promulgated, implementation data are yet unavailable. We performed a comprehensive audit of the implementation of services in a clinical oncology department. METHODS A departmental protocol of priority-based treatment guidance was developed, and a departmental staff rotation policy was implemented. Data were collected for the period of lockdown on outpatient visits, starting, and delivery of RT and systemic therapy. Adherence to protocol was audited, and factors affecting change from pre-COVID standards analyzed by multivariate logistic regression. RESULTS Outpatient consults dropped by 58%. Planned RT starts were implemented in 90%, 100%, 92%, 90%, and 75% of priority level 1-5 patients. Although 17% had a deferred start, the median time to start of adjuvant RT and overall treatment times were maintained. Concurrent chemotherapy was administered in 89% of those eligible. Systemic therapy was administered to 84.5% of planned patients. However, 33% and 57% of curative and palliative patients had modifications in cycle duration or deferrals. The patient’s inability to come was the most common reason for RT or ST deviation. Factors independently associated with a change from pre-COVID practice was priority-level allocation for RT and age and palliative intent for systemic therapy. CONCLUSION Despite significant access limitations, a planned priority-based system of delivery of treatment could be implemented.

The detailed assessment of water resources and hydrological cycle component are very critical for attaining sustainable development goals (SDGs) in a region. One of the critical challenges in water cycle science is to understand the source of water in a river which vary seasonally and spatially. Here we deal with the spatial pattern of a river in the North Eastern Himalayas, which receives groundwater and snow meltwater supply to meet the perennial flow regime. We used stable water isotopes of oxygen & hydrogen in the river water sampled during pre-Monsoon season. This study focusses on the region of West Kameng District of Arunachal Pradesh, India covering drainage basin of 2 tributaries of Kameng river basin viz. Tenga & Dirang as shown in Figure 1. Our study used isotopic method to trace the sources of which forms river water at the headwater regions.The Local Meteoric Water line (LMWL) for the region is defined by δD = (8.046 ± 0.03). δ18O + (11.644 ± 0.5‰) as derived from analysis of water samples collected in March 2021. This LMWL is almost identical to the signature of precipitation collected during April – October, 2007 from station at Cherrapunjee Meghalaya, India as obtained from IAEA-GNIP data [δD = 8.068 δ18O + 12.347‰] as shown in Figure 2. The d-excess signatures from the two set of observations near similar, 11.21± 1.79 ‰ & 11.41± 2.21‰ respectively implying that river and rainwater in the study region are indistinguishable. Based on the observation we commented on the baseflow contribution of the river. Result suggests places where groundwater contribution to the river water is significantly high during dry time and overall enrichment of river water composition due to this process. We will provide estimate of base flow contribution based on mass balance equation, which is prominent more than 90% at higher elevation. The volume of the aquifer is large given the long residence time & can be estimated by timebound analysis of stable isotopes and tritium isotope.

Peer reviewed: True, BACKGROUND: Inclusion of cerebrospinal fluid (CSF) oligoclonal IgG bands (OCGB) in the revised McDonald criteria increases the sensitivity of diagnosis when dissemination in time (DIT) cannot be proven. While OCGB negative patients are unlikely to develop clinically definite (CD) MS, OCGB positivity may lead to an erroneous diagnosis in conditions that present similarly, such as neuromyelitis optica spectrum disorders (NMOSD) or neurosarcoidosis. OBJECTIVE: To identify specific, OCGB-complementary, biomarkers to improve diagnostic accuracy in OCGB positive patients. METHODS: We analysed the CSF metabolome and proteome of CDMS (n=41) and confirmed non-MS patients (n=64) comprising a range of CNS conditions routinely encountered in neurology clinics. RESULTS: OCGB discriminated between CDMS and non-MS with high sensitivity (85%), but low specificity (67%), as previously described. Machine learning methods revealed CCN5 levels provide greater accuracy, sensitivity, and specificity than OCGB (79%, +5%; 90%, +5%; and 72%, +5% respectively) while glial fibrillary acidic protein (GFAP) identified CDMS with 100% specificity (+33%). A multiomics approach improved accuracy further to 90% (+16%). CONCLUSION: The measurement of a few additional CSF biomarkers could be used to complement OCGB and improve the specificity of MS diagnosis when clinical and radiological evidence of DIT is absent.

Polymer nanocomposites are highly apricated for the sensor and actuator applications. As they are soft and flexible and can produce higher cyclic loading with good repeatability. But when conductive fillers are woven in the polymer matrix it loses flexibility and enhances the conductivity. Therefore, studying the loading behavior of the nanocomposite becomes important for determining the stability and load bearing capacity of the conducting polymer nanocomposite membranes (CPNC). Therefore, the intent was to design a flexible piezoresistive strain sensor. Finite element analysis (FEA) technique was used for investigating the deformation behavior with a change in stress and strain by applying loads of 0.4 N, 0.6 N, 0.8 N and 1N. Displacement was taken as one parameter for determining the stress intensities in CPNC local regions.

The relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

Progress in exploring speech and Parkinson’s Disease (PD) has been hindered due to the use of different protocols across research labs/countries, single-site studies with relatively small numbers, and no external validation. We had recently reported on the Parkinson’s Voice Initiative (PVI), a large study where we collected 19,000+ sustained vowel phonations (control and PD groups) across seven countries, under acoustically non-controlled conditions. In this study, we explored how well findings generalize in the three English-speaking PVI cohorts (data collected in Boston, Oxford, and Toronto). We acoustically characterized each sustained vowel /a/ phonation using 307 dysphonia measures which had previously been successfully employed in speech-PD applications. We used the previously identified feature subset from the Boston cohort and explored hierarchical clustering with Ward’s linkage combined with 2D-data projections using t-distributed stochastic neighbor embedding to facilitate visual exploration of PD subgroups. Furthermore, we computed feature weights using LOGO to assess feature selection consistency towards differentiating PD from controls. Overall, findings are very consistent across the three cohorts, strongly suggesting the presence of four main PD clusters, and consistent identification of key contributing features. Collectively, these findings support the generalization of sustained vowels and robustness of the presented methodology across the English-speaking PVI cohorts.

Eighty-five percent of multiple sclerosis cases begin with a discrete attack termed clinically isolated syndrome, but 37% of clinically isolated syndrome patients do not experience a relapse within 20 years of onset. Thus, the identification of biomarkers able to differentiate between individuals who are most likely to have a second clinical attack from those who remain in the clinically isolated syndrome stage is essential to apply a personalized medicine approach. We sought to identify biomarkers from biochemical, metabolic and proteomic screens that predict clinically defined conversion from clinically isolated syndrome to multiple sclerosis and generate a multi-omics-based algorithm with higher prognostic accuracy than any currently available test. An integrative multi-variate approach was applied to the analysis of cerebrospinal fluid samples taken from 54 individuals at the point of clinically isolated syndrome with 2–10 years of subsequent follow-up enabling stratification into clinical converters and non-converters. Leukocyte counts were significantly elevated at onset in the clinical converters and predict the occurrence of a second attack with 70% accuracy. Myo-inositol levels were significantly increased in clinical converters while glucose levels were decreased, predicting transition to multiple sclerosis with accuracies of 72% and 63%, respectively. Proteomics analysis identified 89 novel gene products related to conversion. The identified biochemical and protein biomarkers were combined to produce an algorithm with predictive accuracy of 83% for the transition to clinically defined multiple sclerosis, outperforming any individual biomarker in isolation including oligoclonal bands. The identified protein biomarkers are consistent with an exaggerated immune response, perturbed energy metabolism and multiple sclerosis pathology in the clinical converter group. The new biomarkers presented provide novel insight into the molecular pathways promoting disease while the multi-omics algorithm provides a means to more accurately predict whether an individual is likely to convert to clinically defined multiple sclerosis., Graphical Abstract Graphical Abstract

INTRODUCTION: A COVID19 lockdown in India posed significant challenges to the continuation of radiotherapy (RT) and systemic therapy services. While several COVID19 service guidelines have been promulgated, implementation data is yet unavailable. We performed a comprehensive audit of the implementation of services in a clinical oncology department.METHODS AND MATERIALS: A departmental protocol of priority-based treatment guidance was developed and a departmental staff rotation policy was implemented. Data was collected for the period of lockdown on outpatient visits, starting, and delivery of RT and systemic therapy. Adherence to protocol was audited, and factors affecting change from pre-COVID standards analyzed by multivariate logistic regression.RESULTS: Outpatient consults dropped by 58%. Planned RT starts were implemented in 90%, 100%, 92%, 90% and 75% of priority level 1-5 patients. While 17% had a deferred start, the median time to start of adjuvant RT and overall treatment times were maintained. Concurrent chemotherapy was administered in 89% of those eligible. Systemic therapy was administered to 84.5% of planned patients with 33% and 57% of curative and palliative patients receiving modified or deferred cycles. The patient’s inability to come was the commonest reason for RT or ST deviation. Factors independently associated with a change from pre-COVID practice was priority level allocation for RT and age and palliative intent for systemic therapy.CONCLUSIONS: Despite significant access limitations a planned priority-based system of delivery of treatment could be implemented.

Feature selection is the important technique which plays a pivotal role in the process of data mining. This is mainly needed to deal with the extreme number of features, normally which can turn into a computational over burden on the process of learning algorithms. Such type of algorithm is also necessary, even when the computational resources are not panic, since it improves the accuracy of the machine learning tasks. In the field of medical science there are various kinds of problem in the area of medical imaging as like extraction, classification, segmentation, selection and etc. On the other side Medical datasets are normally categorized by gigantic extent of disease measurements and comparatively little quantity of patient records. Such feature selection are not appropriate, where all such immaterial and redundancy features are extremely typical to evaluate. On the contrary, the huge number of features may cause the problem of memory storage in order to represent the data set. In such a situation various kinds of data mining algorithms or techniques can be implemented for dealing with such a huge amount of data with precision. This paper basically represents a brief review about the medical image feature selection for the diagnosis of the tumor by the help of data mining techniques.

The use of high-quality speech signals has led to considerable breakthroughs in Parkinson's Disease (PD) in the last decade. These include accurate differentiation of PD versus Healthy Controls (HC) and monitoring longitudinal PD symptom severity. We recently concluded the Parkinson's Voice Initiative (PVI) study collecting data from a very large cohort under non-controlled acoustic conditions. We acoustically characterized 11,942 recordings from 6531 US-based participants using 307 dysphonia measures. We selected a robust subset of 30 dysphonia measures using Gram-Schmidt Orthogonalization (GSO). We projected the data onto a two-dimensional representation using t-distributed stochastic neighbor embedding to facilitate visual exploration, and used hierarchical clustering to understand data homogeneity. We demonstrate that there is considerable overlap in the projected feature space between PD and HC, making the binary classification task particularly challenging. The data was grouped into nine clusters using hierarchical clustering which was in broad agreement with the projected two-dimensional representation. These results provide some new insights into understanding the new challenges posed in the PVI project where acoustic recordings conditions were not controlled.

Recent studies have demonstrated that analysis of laboratory-quality voice recordings can be used to accurately differentiate people diagnosed with Parkinson's disease (PD) from healthy controls (HC). These findings could help facilitate the development of remote screening and monitoring tools for PD. In this study, we analyzed 2759 telephone-quality voice recordings from 1483 PD and 15321 recordings from 8300 HC participants. To account for variations in phonetic backgrounds, we acquired data from seven countries. We developed a statistical framework for analyzing voice, whereby we computed 307 dysphonia measures that quantify different properties of voice impairment, such as, breathiness, roughness, monopitch, hoarse voice quality, and exaggerated vocal tremor. We used feature selection algorithms to identify robust parsimonious feature subsets, which were used in combination with a Random Forests (RF) classifier to accurately distinguish PD from HC. The best 10-fold cross-validation performance was obtained using Gram-Schmidt Orthogonalization (GSO) and RF, leading to mean sensitivity of 64.90% (standard deviation, SD 2.90%) and mean specificity of 67.96% (SD 2.90%). This large-scale study is a step forward towards assessing the development of a reliable, cost-effective and practical clinical decision support tool for screening the population at large for PD using telephone-quality voice., Comment: 43 pages, 5 figures, 6 tables

Short term load forecasts will play a key role in the implementation of smart electricity grids. They are required for optimising a wide range of potential network solutions on the low voltage (LV) grid, including the integration of low carbon technologies (such as photovoltaics) and the utilisation of battery storage devices. Despite the need for accurate LV level load forecasts, much of the literature has focused on the individual household or building level using data from smart meters, or on aggregates of such data. This study provides a detailed analysis of several state-of-the-art methods for both point and probabilistic LV load forecasts. We evaluate the out-of-sample forecast accuracies of these methodologies on 100 real LV feeders, for horizons from one to four days ahead. In addition, we also test the effect of the temperature (both actual and forecast) on the accuracy of load forecasts. We present some important results on the drivers of forecasts accuracy as well as on the empirical comparison of point and probabilistic forecast measures.

The recent advent of smart meters has led to large micro-level datasets. For the first time, the electricity consumption at individual sites is available on a near real-time basis. Efficient management of energy resources, electric utilities, and transmission grids, can be greatly facilitated by harnessing the potential of this data. The aim of this study is to generate probability density estimates for consumption recorded by individual smart meters. Such estimates can assist decision making by helping consumers identify and minimize their excess electricity usage, especially during peak times. For suppliers, these estimates can be used to devise innovative time-of-use pricing strategies aimed at their target consumers. We consider methods based on conditional kernel density (CKD) estimation with the incorporation of a decay parameter. The methods capture the seasonality in consumption, and enable a nonparametric estimation of its conditional density. Using 8 months of half-hourly data for 1000 meters we evaluate point and density forecasts, for lead times ranging from one half-hour up to a week ahead. We find that the kernel-based methods outperform a simple benchmark method that does not account for seasonality, and compare well with an exponential smoothing method that we use as a sophisticated benchmark. To gauge the financial impact, we use density estimates of consumption to derive prediction intervals of electricity cost for different time-of-use tariffs. We show that a simple strategy of switching between different tariffs, based on a comparison of cost densities, delivers significant cost savings for the great majority of consumers.

Comet 49P/Arend-Rigaux, thought to be a low activity comet since the 1980’s was found to be active in its recent apparitions. Recent analysis of the data obtained from Spitzer observation of the comet in 2006 compared with laboratory spectra had revealed amorphous water ice on the surface. In addition, in 2012 a jet was found to appear during its subsequent perihelion passage as witnessed during an observation carried out on 26th March 2012 using the Physical Research Laboratory (PRL) telescope at Mt. Abu. This confirms recent activity of comet 49P/Arend-Rigaux. Our result confirms the presence of amorphous water ice on the surface of the comet and insists for more observations for a better understanding.

We investigate the potential association between leucine-rich repeat kinase 2 (LRRK2) mutations and voice. Sustained phonations ('aaah' sounds) were recorded from 7 individuals with LRRK2-associated Parkinson's disease (PD), 17 participants with idiopathic PD (iPD), 20 non-manifesting LRRK2-mutation carriers, 25 related non-carriers, and 26 controls. In distinguishing LRRK2-associated PD and iPD, the mean sensitivity was 95.4% (SD 17.8%) and mean specificity was 89.6% (SD 26.5%). Voice features for non-manifesting carriers, related non-carriers, and controls were much less discriminatory. Vocal deficits in LRRK2-associated PD may be different than those in iPD. These preliminary results warrant longitudinal analyses and replication in larger cohorts.

Background: Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinson's disease (PD) are lacking. Methods: Participants underwent baseline in-clinic assessments, including the Unified Parkinson's Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS. Results: Twenty participants performed an average of 2.7 tests per day (68.9% adherence) for the study duration (average of 34.4 days) in a home and community setting. The analyses of the five tasks differed between those with Parkinson disease and those without. In discriminating participants with PD from controls, the mean sensitivity was 96.2% (SD 2%) and mean specificity was 96.9% (SD 1.9%). The mean error in predicting the modified motor component of the UPDRS (range 11-34) was 1.26 UPDRS points (SD 0.16). Conclusion: Measuring PD symptoms via a smartphone is feasible and has potential value as a diagnostic support tool.

This paper presents a case study on short-term load forecasting for France, with emphasis on special days, such as public holidays. We investigate the generalisability to French data of a recently proposed approach, which generates forecasts for normal and special days in a coherent and unified framework, by incorporating subjective judgment in univariate statistical models using a rule-based methodology. The intraday, intraweek, and intrayear seasonality in load are accommodated using a rule-based triple seasonal adaptation of a seasonal autoregressive moving average (SARMA) model. We find that, for application to French load, the method requires an important adaption. We also adapt a recently proposed SARMA model that accommodates special day effects on an hourly basis using indicator variables. Using a rule formulated specifically for the French load, we compare the SARMA models with a range of different benchmark methods based on an evaluation of their point and density forecast accuracy. As sophisticated benchmarks, we employ the rule-based triple seasonal adaptations of Holt-Winters-Taylor (HWT) exponential smoothing and artificial neural networks (ANNs). We use nine years of half-hourly French load data, and consider lead times ranging from one half-hour up to a day ahead. The rule-based SARMA approach generated the most accurate forecasts., Comment: 11 figures, 3 tables

Detailed description of statistical methods, supplementary OPLS-DA scores plots, and box plots of discriminatory metabolites. (PDF 1136 kb)

We apply the method of variable feedback to obtain complete synchronization in a coupled map lattice. The conditions under which such a synchronization is possible are obtained analytically. We show that synchronization is robust against noise and parameter mismatches. This method leads to synchronized state quite rapidly and we discuss its applications for near-real-time multi-channel communications.

Numerous methods have been proposed for forecasting load for normal days. Modeling of anomalous load, however, has often been ignored in the research literature. Occurring on special days, such as public holidays, anomalous load conditions pose considerable modeling challenges due to their infrequent occurrence and significant deviation from normal load. To overcome these limitations, we adopt a rule-based approach, which allows incorporation of prior expert knowledge of load profiles into the statistical model. We use triple seasonal Holt-Winters-Taylor (HWT) exponential smoothing, triple seasonal autoregressive moving average (ARMA), artificial neural networks (ANNs), and triple seasonal intraweek singular value decomposition (SVD) based exponential smoothing. These methods have been shown to be competitive for modeling load for normal days. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model load for special days, when used in conjunction with a rule-based approach. The proposed rule-based method is able to model normal and anomalous load in a unified framework. Using nine years of half-hourly load for Great Britain, we evaluate point forecasts, for lead times from one half-hour up to a day ahead. A combination of two rule-based methods generated the most accurate forecasts., 8 Pages, 11 Figures

We study the spectral features of the polarized fluorescence spectra of normal and cancerous human breast tissues through continuous wavelet transform, which clearly identifies distinguishing features between the tissue types. After pinpointing these robust features in the wavelet scalogram, we systematically study the autocorrelation property of the wavelet coefficients of the fluorescence spectra, which is found to differentiate normal and malignant tissues with high sensitivity. The intensity difference of parallel and perpendicularly polarized fluorescence spectra is subjected to investigation, since the same is relatively free of the diffusive background.

Numerous time series models are available for forecasting economic output. Autoregressive models were initially applied to US gross national product (GNP), and have been extended to nonlinear structures, such as the self-exciting threshold autoregressive (SETAR) and Markov-switching autoregressive (MS-AR) models. We show that while these nonlinear models fit the in-sample data better than linear models, the out- of-sample forecast performance of extremely simple methods, such as the unconditional mean is competi- tive compared with both previously published linear and nonlinear models for GNP time series. Motivated by Occams razor and the forecasting competitiveness of the unconditional mean, we seek parsimonious models which are based on simple assumptions, incorporate few parameters, and can generate accurate forecasts. We propose nonlinear and nonparametric models based on nearest neighbor and kernel regression by forming a state-space of time delayed GNP observations. The rationale of the proposed methodology lies in identification of local regions of state-space known as nearest neighbor sub-spaces, whereby we utilize future trajectories of the neighboring states and weight them using a double kernel estimator to generate density forecasts. The models proposed in this work incorporate only one or two parameters, and the model estima- tion framework relies on optimizing the performance of in-sample density forecasts. The proposed modeling approach does not require prior assumptions regarding the form of the error distribution, and can provide transition between regimes of growth and contraction. We compare the forecasts from proposed models with classical time series models for GNP and a range of benchmarks, and validate our results on two post-war GNP time series using different performance scores, such as the root mean squared error (RMSE), mean abso- lute error (MAE), and the continuous ranked probability score (CRPS). We find that proposed models consist- ently outperformed previously published models for point and density forecasts of GNP at varying horizons.

A systematic investigation of the polarization characteristics of the auto-fluorescence of normal and benign human breast tissues is carried out complementing our earlier studies on normal and cancer tissues. Co- and cross-polarized auto-fluorescence are collected in the 500 to 700nm range through excitation at 488nm using laser as excitation source. A number of parameters, capturing spectral variations are extracted in the co- and cross-polarized channels through singular value decomposition and wavelet decomposition, which differentiate normal and benign tissues. The correlation matrix differs significantly in normal and benign tissues reflecting the presence of different fluorophores. The eigenvectors corresponding to the dominant eigenvalues reveal differences between tissue types. The co-polarized component being sensitive to intrinsic fluorescence shows different behavior for normal and benign tissues in the emission domain of known fluorophores. Interestingly, the benign tissue samples show correlation properties intermediate to malignant and normal cases. In the wavelet domain the standard deviation of percentage fluctuation reveal differences between tissues type. The correlation characteristics manifest prominently in the wavelet low pass (average) domain.

Effects of parameter mismatches in synchronized time series are studied first for an analytical non-linear dynamical system (coupled logistic map, CLM) and then in a real system (Electroencephalograph (EEG) signals). The internal system parameters derived from GP analysis are shown to be quite effective in understanding aspects of synchronization and non-synchronization in the two systems considered. In particular, GP is also successful in generating the CLM coupled equations to a very good accuracy with reasonable multi-step predictions. It is shown that synchronization in the above two systems is well understood in terms of parameter mismatches in the system equations derived by GP approach.

We show that two coupled map lattices that are mutually coupled to one another with a delay can display zero delay synchronization if they are driven by a third coupled map lattice. We analytically estimate the parametric regimes that lead to synchronization and show that the presence of mutual delays enhances synchronization to some extent. The zero delay or isochronal synchronization is reasonably robust against mismatches in the internal parameters of the coupled map lattices and we analytically estimate the synchronization error bounds., Comment: 9 pages, 9 figures ; To appear in Phys. Rev. E

We present an algorithm that enables one to perform locally adaptive block thresholding, while maintaining image continuity. Images are divided into sub-images based some standard image attributes and thresholding technique is employed over the sub-images. The present algorithm makes use of the thresholds of neighboring sub-images to calculate a range of values. The image continuity is taken care by choosing the threshold of the sub-image under consideration to lie within the above range. After examining the average range values for various sub-image sizes of a variety of images, it was found that the range of acceptable threshold values is substantially high, justifying our assumption of exploiting the freedom of range for bringing out local details., Comment: 12 Pages, 4 figures, 1 Table

A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance. Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a new sub-range for the next step, until no significant improvement in image quality can be achieved. The method makes use of the fact that a number of distributions tend towards Dirac delta function, peaking at the mean, in the limiting condition of vanishing variance. The procedure naturally provides for variable size segmentation with bigger blocks near the extreme pixel values and finer divisions around the mean or other chosen value for better visualization. Experiments on a variety of images show that the new algorithm effectively segments the image in computationally very less time., Comment: 9 pages, 3 figures

Using the multiresolution ability of wavelets and effectiveness of singular value decomposition (SVD) to identify statistically robust parameters, we find a number of local and global features, capturing spectral correlations in the co- and cross-polarized channels, at different scales (of human breast tissues). The copolarized component, being sensitive to intrinsic fluorescence, shows different behavior for normal, benign, and cancerous tissues, in the emission domain of known fluorophores, whereas the perpendicular component, being more prone to the diffusive effect of scattering, points out differences in the Kernel-Smoother density estimate employed to the principal components, between malignant, normal, and benign tissues. The eigenvectors, corresponding to the dominant eigenvalues of the correlation matrix in SVD, also exhibit significant differences between the three tissue types, which clearly reflects the differences in the spectral correlation behavior. Interestingly, the most significant distinguishing feature manifests in the perpendicular component, corresponding to porphyrin emission range in the cancerous tissue. The fact that perpendicular component is strongly influenced by depolarization, and porphyrin emissions in cancerous tissue has been found to be strongly depolarized, may be the possible cause of the above observation.

We study the spectral correlation properties of the polarized fluorescence spectra of normal and cancerous human breast tissues, corresponding to patients belonging to diverse age groups and socioeconomic backgrounds. The emission range in the visible wavelength regime of 500 to 700 nm is analyzed, with the excitation wavelength at 488 nm, where flavin is one of the active fluorophores. The correlation matrices for parallel and perpendicularly polarized fluorescence spectra reveal correlated domains, differing significantly in normal and cancerous tissues. These domains can be ascribed to different fluorophores and absorbers in the tissue medium. The spectral fluctuations in the perpendicular component of the cancerous tissue clearly reveal randomization not present in the normal channel. Random matrix-based predictions for the spectral correlations match quite well with the observed behavior. The eigenvectors of the correlation matrices corresponding to large eigenvalues clearly separate out different tissue types and identify the dominant wavelengths, which are active in cancerous tissues.