Various Degrees Of Correlation Research Articles

Correlated noise enhancement of coherence and fidelity in coupled qubits

ABSTRACT It is generally assumed that environmental noise arising from thermal fluctuations are detrimental to preserving coherence and entanglement in a quantum system. In the simplest sense, dephasing and decoherence are tied to energy fluctuations driven by coupling between the system and the normal modes of the bath. Here, we explore the role of noise correlation in an open-loop model quantum communication system whereby the ‘sender’ and the ‘receiver’ are subject to local environments with various degrees of correlation or anti-correlation. We introduce correlation within the spectral density by solving multidimensional stochastic differential equations and introduce these into the Redfield equations of motion for the system density matrix. We find that correlation can enhance both the fidelity and purity of a maximally entangled (Bell) state. Moreover, by comparing the evolution of different initial Bell states, we show that one can effectively probe the correlation between two local environments. These observations may be useful in the design of high-fidelity quantum gates and communication protocols.

Analysis and Validation of Critical Signatures and Immune Cell Infiltration Characteristics in Doxorubicin-Induced Cardiotoxicity by Integrating Bioinformatics and Machine Learning.

Doxorubicin-induced cardiotoxicity (DIC) is a severe side reaction in cancer chemotherapy that greatly impacts the well-being of cancer patients. Currently, there is still an insufficiency of effective and reliable biomarkers in the field of clinical practice for the early detection of DIC. This study aimed to determine and validate the potential diagnostic and predictive values of critical signatures in DIC. We obtained high-throughput sequencing data from the GEO database and performed data analysis and visualization using R software, GO, KEGG and Cytoscape. Machine learning methods and weighted gene coexpression network (WGCNA) were used to identify key genes for diagnostic model construction. Receiver operating characteristic (ROC) analysis and a nomogram were used to assess their diagnostic values. A multiregulatory network was built to reveal the possible regulatory relationships of critical signatures. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) analysis was used to investigate differential immune cell infiltration. Additionally, a cell and animal model were constructed to investigate the relationship between the identified genes and DIC. Among the 3713 differentially expressed genes, three key genes (CSGALNACT1, ZNF296 and FANCB) were identified. A nomogram and ROC curves based on three key genes showed excellent diagnostic predictive performance. The regulatory network analysis showed that the TFs CREB1, EP300, FLI1, FOXA1, MAX, and MAZ modulated three key genes. An analysis of immune cell infiltration indicated that many immune cells (activated NK cells, M0 macrophages, activated dendritic cells and neutrophils) might be related to the progression of DIC. Furthermore, there may be various degrees of correlation between the three critical signatures and immune cells. RT‒qPCR demonstrated that the mRNA expression of CSGALNACT1 and ZNF296 was significantly upregulated, while FANCB was significantly downregulated in DOX-treated cardiomyocytes in vitro and in vivo. Our study suggested that the differential expression of CSGALNACT1, ZNF296 and FANCB is associated with cardiotoxicity and is also involved in immune cell infiltration in DIC. They might be potential biomarkers for the early occurrence of DIC.

Assessment of Right Ventricular-Arterial Coupling by Echocardiography in Patients with Right Ventricular Pressure and Volume Overload.

Right ventricle-pulmonary arterial (RV-PA) coupling is considered the gold standard for assessing right ventricular (RV) function and can be evaluated noninvasively by echocardiography. The ratios of tricuspid annular plane systolic excursion/pulmonary artery systolic pressure (TAPSE/PASP), RV global longitudinal strain (G-RVLS)/PASP, and stroke volume/end-systolic volume (SV/ESV) have been proposed as surrogates of RV-PA coupling. The relationship of these parameters remains incompletely understood in patients with volume and pressure loading conditions. We aimed to compare these parameters and evaluate their relationship with 3D RV data in patients with RV pressure and volume overload. This study was performed on 110 individuals who underwent 2D and 3D echocardiography. Fifty-four patients had RV volume overload (atrial septal defect (ASD) group), 34 patients had RV pressure overload (pulmonary hypertension (PH) group), and 22 were controls. TAPSE/PASP, G-RVLS/PASP and SV/ESV ratios were calculated. Correlations between parameters of RV-PA coupling and 3D data were assessed using general linear mixed models. Compared with the ASD group, the PH group had lower TAPSE/PASP and G-RVLS/PASP ratios. The SV/ESV ratio had a strong correlation with right ventricle ejection fraction (RVEF) in both ASD and PH patients (r = 0.8703, p 0.001 and r = 0.9388, p 0.001, respectively). The G-RVLS/PASP ratio showed a strong or moderately negative relationship with end-diastolic volume (EDV), ESV and SV (r = -0.7768, p = 0.001; r = -0.7327, p = 0.0005 and r = -0.6816, p = 0.0018, respectively) in PH patients. The TAPSE/PASP ratio showed moderately negative correlations with EDV and ESV (r = -0.5712, p = 0.0012 and r = -0.5594, p = 0.0016, respectively) in PH patients. Non-invasive RV-PA coupling parameters derived from echocardiography appear similar, but not identical to profiles in pressure-overloaded and volume-overloaded patients. The correlations between non-invasive RV-PA coupling parameters and 3D data displayed various degrees of correlation.

Maximizing the Accuracy of Adolescent Concussion Diagnosis Using Individual Elements of Common Standardized Clinical Assessment Tools.

Multiple clinical evaluation tools exist for adolescent concussion with various degrees of correlation, presenting challenges for clinicians in identifying which elements of these tools provide the greatest diagnostic utility. To determine the combination of elements from 4 commonly used clinical concussion batteries that maximize discrimination of adolescents with concussion from those without concussion. Cross-sectional study. Suburban school and concussion program of a tertiary care academic center. A total of 231 participants with concussion (from a suburban school and a concussion program) and 166 participants without concussion (from a suburban school) between the ages of 13 and 19 years. Individual elements of the visio-vestibular examination (VVE), Sport Concussion Assessment Tool, fifth edition (SCAT5; including the modified Balance Error Scoring System), King-Devick test (K-D), and Postconcussion Symptom Inventory (PCSI) were evaluated. The 24 subcomponents of these tests were grouped into interpretable factors using sparse principal component analysis. The 13 resultant factors were combined with demographic and clinical covariates into a logistic regression model and ranked by frequency of inclusion into the ideal model, and the predictive performance of the ideal model was compared with each of the clinical batteries using the area under the receiver operating characteristic curve (AUC). A cluster of 4 factors (factor 1 [VVE saccades and vestibulo-ocular reflex], factor 2 [modified Balance Error Scoring System double-legged stance], factor 3 [SCAT5/PCSI symptom scores], and factor 4 [K-D completion time]) emerged. A model fit with the top factors performed as well as each battery in predicting concussion status (AUC = 0.816 [95% CI = 0.731, 0.889]) compared with the SCAT5 (AUC = 0.784 [95% CI = 0.692, 0.866]), PCSI (AUC = 0.776 [95% CI = 0.674, 0.863]), VVE (AUC = 0.711 [95% CI = 0.602, 0.814]), and K-D (AUC = 0.708 [95% CI = 0.590, 0.819]). A multifaceted assessment for adolescents with concussion, comprising symptoms, attention, balance, and the visio-vestibular system, is critical. Current diagnostic batteries likely measure overlapping domains, and the sparse principal component analysis demonstrated strategies for streamlining comprehensive concussion assessment across a variety of settings.

What exactly is the relationship between plasma cytokines and the clinical phenotype of primary sjögren's syndrome? a single-centre retrospective study.

The pathogenesis of primary Sjögren's syndrome (pSS) remains unclear. Accumulating evidence suggests that an imbalance of multiple cytokines contributes to the occurrence and development of pSS. To our knowledge, there are few studies on the relationship between plasma cytokines and pSS clinical phenotype (including disease activity), and the available results are controversial. Cytokine-targeted therapy failed to achieve satisfactory effects. We collected the demographic and clinical characteristics (laboratory indicators and clinical presentation) of pSS patients and calculated the European League Against Rheumatism SS disease activity index (ESSDAI) scores and ClinESSDAI. Associations between plasma cytokines and pSS continuous and categorical variables, and between various cytokines were analysed separately. 348 patients were finally included in the analysis, with a female to male ratio of 13.5:1. The disease activity was mild to moderate in 86.78% of patients, with the most and least involved organs being the exocrine glands and neurological system respectively. Among the various cytokines analysed, plasma interleukin(IL)-6 levels were elevated and correlated with a variety of inflammatory indicators and clinical manifestations. A weak positive correlation was found between IL - 10 and ESSDAI. Various degrees of correlation were observed between cytokines and clinical manifestations of pSS and between multiple cytokines. Our study shows that different cytokines are closely associated with the clinical phenotype of pSS. Plasma IL-10 can be used to monitor pSS disease activity. Multiple cytokines form a systemic network and participate in the pathological process of pSS. This study provides a solid foundation for further exploring the pathogenesis of pSS and establishing more effective cytokine-targeted therapeutic regimens.

Factors Influencing Stakeholders’ Decision to Invest in Residential Properties: A Perceptual Analysis of Flood-Risk Areas

The ground can become saturated during prolonged downpours. If sewers and drains are unable to cope, overflows will be inevitable. This situation could affect properties that are not designed to cope with flood hazards. It is pertinent that property investors should consider flooding and the likelihood of its occurrence when making investment decisions. The question is, “do they?” This study investigates the factors that influence residential property investment decisions in flood-risk areas of the Lagos metropolis. This is achieved by evaluating a range of locational, neighbourhood, structural, market/economic, behavioural, and risk characteristics in the determination of residential property investment choices in areas that are susceptible to flood risk. The data were sourced from private investors and registered real estate agents in the risk areas of Lagos State, Nigeria. Structured questionnaires were used for data collection purposes, and only valid responses were used for the data analysis. The results show that the availability of infrastructural supply, the crime rate in the neighbourhood, and the property location could significantly motivate investors’ decisions with respect to property investment in flood-risk areas. The perception plot shows that behavioural factors have the highest bearing on the investment decisions for private investors, at 4.4, followed by economic factors at 4.0 and locational factors at 3.6. The plot also shows that neighbourhood factors have the highest bearing on the investment decisions for estate agents, at 4.6, followed by economic factors at 4.0 and locational factors at 3.6. This implies that there are various degrees of correlation between the factors examined in this study. It is important to highlight the relevance of the findings for the field and pave the way for future scientific development in flood-risk management.

Calcium transients in nNOS neurons underlie distinct phases of the neurovascular response to barrel cortex activation in awake mice

Neuronal nitric oxide (NO) synthase (nNOS), a Ca2+ dependent enzyme, is expressed by distinct populations of neocortical neurons. Although neuronal NO is well known to contribute to the blood flow increase evoked by neural activity, the relationships between nNOS neurons activity and vascular responses in the awake state remain unclear. We imaged the barrel cortex in awake, head-fixed mice through a chronically implanted cranial window. The Ca2+ indicator GCaMP7f was expressed selectively in nNOS neurons using adenoviral gene transfer in nNOScre mice. Air-puffs directed at the contralateral whiskers or spontaneous motion induced Ca2+ transients in 30.2 ± 2.2% or 51.6 ± 3.3% of nNOS neurons, respectively, and evoked local arteriolar dilation. The greatest dilatation (14.8 ± 1.1%) occurred when whisking and motion occurred simultaneously. Ca2+ transients in individual nNOS neurons and local arteriolar dilation showed various degrees of correlation, which was strongest when the activity of whole nNOS neuron ensemble was examined. We also found that some nNOS neurons became active immediately prior to arteriolar dilation, while others were activated gradually after arteriolar dilatation. Discrete nNOS neuron subsets may contribute either to the initiation or to the maintenance of the vascular response, suggesting a previously unappreciated temporal specificity to the role of NO in neurovascular coupling.

Anomaly detection with correlation laws

Datasets from different domains usually contain data defined over a wide set of attributes among which various degrees of correlation exist. The identification of data objects not complying with these hidden correlations is a formidable task. Moreover, often attributes may play different roles in applications. Specifically, some features can be perceived as independent variables which are responsible for the definition of a context in which a dependent variable exhibits anomalous behaving values. Hence, in this work we focus on the detection of data objects showing an anomalous behavior on a subset of attributes, called behavioral, w.r.t. some other ones, called contextual. As a main contribution, we design a model to describe the correlation laws hidden in data distributions over pairs of behavioral–contextual attributes. We introduce a probability measure aimed at scoring subsequently observed objects based on how much their behavior deviates from the detected correlation laws. We test our method on both synthetic and real dataset to demonstrate its effectiveness and show its ability in outperforming some competitors. Moreover, we discuss a case study in the field of gene expression data analysis to prove that it can provide a valuable contribution when dealing with those scenarios in which the features are much more abundant than the samples available for the analysis.

Application Of Machine Learning For Prediction Of Normal Organ Dose: Feasibility Study In Treatment Planning For Total Marrow Irradiation

Machine learning is an application of artificial intelligence that allows a software system to automatically learn and improve from existing data and make intelligent predictions. We hypothesize that machine learning is applicable in treatment planning for external beam radiotherapy by predicting critical organ dose based on target dose distribution and anatomical data. In this study, we evaluated the feasibility of critical organ dose prediction in treatment planning for total marrow irradiation (TMI). We retrospectively retrieved over 300 historical TMI treatment plans from our clinical database, all of which were generated on treatment planning systems for helical tomotherapy with heterogeneous prescription dose levels. Forty cases were randomly selected as the test dataset, while the other cases were used as the training dataset. Target volumes included skeletal bones, rib cage, lymph nodes, and might optionally include liver, brain, spleen, and testes. Representative dosimetric parameters for over 20 normal organs were used consecutively as the variable for regression modeling. Using the supervised machine learning technique, regression models were built with training feature data that included representative DVH points for targets, anatomical/geometrical parameters of each structure, and values for the variable in the training dataset. Predicted values for the given variable were compared with actual values in the test dataset. Accuracy of the regression models was evaluated with linear regression, correlation analysis, and mean absolute errors (MAE) between predicted and actual values. In the TMI plans, the average bone PTV dose was 16.1±3.6 Gy (range: 9.2 – 21.7 Gy), and the average mean lung dose was 7.3±1.3 Gy (range: 4.2 – 10.0 Gy). Various degrees of correlation were found between dosimetric parameters for the targets and those for the normal organs. Statistically significant correlation (p < 0.05) was found between model predictions and actual plan data for mean dose of the lens, bladder, kidneys, and rectum; however, the Pearson’s correlation coefficient (CE) was < 0.6 or the MAE > 1 Gy. For the average bowel dose, the CE between the predicted and actual values was > 0.75 with statistical significance (p < 0.05) and the MAE < 1 Gy. The CE was > 0.75 with statistical significance (p < 0.05) and the MAE was < 0.7 Gy for most dosimetric parameters of the lung including mean dose, median dose, and D30 to D85 (dose that covers 30 to 85% lung volume), but the MAE was > 1 Gy for dose levels that cover less than 25% of the lung volume. This feasibility study indicated that given representative DVH points for the targets in a TMI plan, the machine learning technique could make predictions for certain dosimetric variables of some normal organs, especially those of the lung, that correlate with actual plan values with statistical significance. This technique can be readily ported to other disease sites and used to aide treatment planning.

Improved crop forecasts for the Australian macadamia industry from ensemble models

Annual forecasts for the Australian macadamia crop have been issued since 2001, with varying (and not always improving) degrees of accuracy. Regression models using climate variables have formed the basis for these forecasts, with general linear model (GLM) ensembles being adopted more recently. This has proven to be a challenging task, as there are only a small number of observations (18) combined with a large number (90+) of independent variables – these being different climate measures for different times of the year (representing ‘key physiological periods for macadamia trees’). Also, these ‘assumedly-independent’ variables contain various degrees of correlation. This study uses cross-validation, with the most recent data for the two dominant production regions of Australia (Lismore and Bundaberg), to investigate the relative performance of alternate modelling methods. These modelling methods were GLMs, partial least squares (PLS) regression and LASSO (least absolute shrinkage and selection operator) penalised regression. Model ensembles, which have been shown to be beneficial in many alternate disciplines, are used to advantage. Both GLMs and PLS produced quite-disappointing results, failing to meet the project's benchmarked accuracy of ±10% error. The optimal LASSO models performed notably better, with a further improvement when ensembles were incorporated. The lowest mean absolute error (MAE) rates here were 9.0% for Lismore and 5.9% for Bundaberg. Hence LASSO ensembles will be adopted for future forecasts of the Australian macadamia crop.

Assessing Equality of Means Using the Overlap of Range-Preserving Confidence Intervals

ABSTRACTHypothesis testing procedures where equality of means is assessed at a prespecified level based on the (non-)overlap of confidence intervals are discussed. Assessing statistical significance via the (non-)overlap of two confidence intervals with an appropriate confidence level provides a simple and effective way of visually understanding statistical results. This article extends previous approaches by considering range-preserving confidence intervals where the values in such intervals are in the allowable range of the parameter of interest. To obtain reliable procedures, appropriate effective degrees of freedom are suggested by considering the Welch-Satterthwaite equation for both independent two-sample and paired-sample cases. The proposed procedures also allow users to express results in terms of commonly used scale-free effect sizes, which are highly useful for interpreting parameters of interest. Simulation results suggest that the proposed procedures may be robust to unequal or small sample sizes, nonnormal distributions, heterogeneous variances, and various degrees of correlation. A real-life application from a study in cognitive psychology illustrates the effectiveness of the proposed procedures.

Unbiased Metabolomic Investigation of Alzheimer's Disease Brain Points to Dysregulation of Mitochondrial Aspartate Metabolism.

Alzheimer's disease (AD) is the most common cause of adult dementia. Yet the complete set of molecular changes accompanying this inexorable, neurodegenerative disease remains elusive. Here we adopted an unbiased lipidomics and metabolomics approach to surveying frozen frontal cortex samples from clinically characterized AD patients (n = 21) and age-matched controls (n = 19), revealing marked molecular differences between them. Then, by means of metabolomic pathway analysis, we incorporated the novel molecular information into the known biochemical pathways and compared it with the results of a metabolomics meta-analysis of previously published AD research. We found six metabolic pathways of the central metabolism as well as glycerophospholipid metabolism predominantly altered in AD brains. Using targeted metabolomics approaches and MS imaging, we confirmed a marked dysregulation of mitochondrial aspartate metabolism. The altered metabolic pathways were further integrated with clinical data, showing various degrees of correlation with parameters of dementia and AD pathology. Our study highlights specific, altered biochemical pathways in the brains of individuals with AD compared with those of control subjects, emphasizing dysregulation of mitochondrial aspartate metabolism and supporting future venues of investigation.

Diffusion-Weighted Imaging of the Normal Placenta, Correlation of ADC Values With Granuum Classification

Background: Imaging evaluation of the placenta is of prime importance in evaluation of IUGR. This has conventionally been performed with ultrasound, and more recently with MRI. To our knowledge, however, DWI of the normal placenta has seldom been studied. Objectives: In this study, we aim to correlate ADC values of the normal placenta with Grannum classification. Patients and Methods: From December 2010 to December 2011, 66 pregnant mothers with normal ultrasound and MRI (with the exception of borderline ventriculomegaly), and no evidence of IUGR at time of ultrasound were referred for fetal MRI (indications including borderline ventriculomegaly and family history of anomalies). After excluding patients who did not provide consent, technically inadequate images and mothers who gave birth to small for gestational age fetuses, 38 mothers with singleton malignancies were enrolled into the study. In addition to the usual fetal MRI sequences including the HASTE images, two sets of DWI images with b values of 0,1000 and b = 50,400,800 were obtained. ADC values were calculated using manual free-hand ROI drawn over the largest visible placenta. ADC values were correlated with Grannum classification using Kruskal-Wallis test. Also the ADC values obtained from the two sets of ADC values (b = 0, 1000 and b = 50,400,800) correlated with the gestational age using linear regression. Results: ADC values obtained from b values of 0,1000 show significant correlation with gestational age using linear regression (P = 0.000), however, the ADC values obtained from the b values of 50,400,800, failed to show statistically significant correlation with GA (P = 0.076). Also ADC values obtained from b values of 0,1000 show significant correlation with Grannum classification using Kruskal-Wallis (P = 0.004) while the ADC values obtained from the b values of 50,400,800 failed to show statistically significant correlation with Grannum class (P = 0.236). Using linear regression analysis, ADC values obtained from b values of 0, 1000 show a better fit correlated with gestational age (R = 0.60) than the ADC values obtained from b values of 50,400,800 (R = 0.087). Conclusions: Various sets of b values result in ADC values with various degrees of correlation with Granuum classification and gestational age. Comparing the two sets of b value in our study, the set including b = 0 resulted in a better fit both with gestational age and with Granuum classification.

The ontogeny of endothelial progenitor cells through flow cytometry

Since the discovery of endothelial progenitor cells (EPCs), there have been conflicting reports as to the precise phenotypic identity, and thus an accurate description of the function of these cells in disease pathology is lacking. This review will detail the protocols that have been published within 2010 to help decipher the true identity of the various cells that have been reported as EPCs in numerous clinical trials. Throughout 2010, three protocols have been published alleging to identify EPCs, yet only one provides a true nonhematopoietic origin for a cell that is classified as an EPC. In addition to the protocols published to try to establish a consensus definition, 10 studies involving EPCs across disease pathologies were published with various degrees of correlation to disease phenotype and cellular level. A true phenotypic definition of a circulating EPC capable of becoming an endothelial colony forming cell with proliferative potential has been given. It is now time the EPC field drops this ambiguous term (i.e. EPCs), as many studies purporting to measure EPCs are in fact still quantifying cells of a hematopoietic origin. It is necessary for cross study comparisons that a uniform phenotypic definition be adhered to when using the term EPC.

Mechanisms of Double Proton Transfer. Theory and Applications

Abstract An analytical two-dimensional (2D) potential-energy surface based on two equal hydrogen bonds coupled by a correlation term, recently introduced [J. Chem. Phys. 127 (2007) 174513] to describe the dynamics of double proton transfer, is reviewed and generalized. It is then applied to the evaluation of proton transfer dynamics in a number of realistic systems, namely several molecules and dimers that exhibit various degrees of correlation between the motions of the two protons. The three parameters required to generate this 2D potential are derived from electronic structure and force field calculations, such that they include implicitly the effect of coupled skeletal modes. It follows that explicit introduction of such coupled modes is not required to obtain the basic relations that define the stationary points of the 2D surface, and thereby the reaction mechanism. Based on these relations, a detailed analysis is reported of a variety of systems exhibiting double proton transfer, including, apart from previously investigated porphine and porphycene, representing weak correlation, and the formic and benzoic acid dimers, representing strong correlation, two newly investigated systems which shed light on the hitherto not represented intermediate correlation category, namely naphthazarin, and the 4-bromopyrazole dimer.

Correlation Between Genetic Polymorphism of Cytokine Genes, Plasma Protein Levels and Bronchial Asthma in the Han People in Northern China

Background and objective. A variety of cytokines are involved in the incidence and development of bronchial asthma. This study was designed to reveal the relationship among genetic polymorphism of cytokine genes, expression levels of cytokines and incidence of bronchial asthma. Methods. We analyzed 14 single nucleotide polymorphism loci in the 10 major cytokine genes plus plasma protein levels of 7 proteins in the bronchial asthma patients (n = 108) and the healthy population (n = 88) of the Han people in northern China. The polymorphism-based genotypes were identified by the sequence-specific primer-polymerase chain reaction. The plasma protein levels were determined by enzyme-linked immunoassay. Bioinformatics analysis was applied to further data processing.Results. Data presented here showed that the 6 polymorphism loci were significantly correlated with the incidence of asthma (p < 0.05). Two of them, IL-2 (−330) and IL-2 (+166), constituted a linkage disequilibrium block. The GG haplotype of this block gave a relatively higher correlation with asthma (p = 0.0767). The plasma protein levels of IgE, IL-6 and IL-1 β correlated with a number of polymorphism loci tested (p < 0.05), of which IgE gave the most significant correlation. The plasma IL-10 and IL-12 levels of the patients in the asthma group were significantly lower than those of the healthy adults in the control group (p < 0.05), while IgE gave the opposite result (p < 0.0001). We constructed a prototype of the metabolic and regulatory network composed of bronchial asthma-related proteins. In this network, IL-6 and TNF-α, were found with a high degree (D = 343 and 235, respectively) and IL-1β with a moderate degree of connection (D = 155). Conclusions.We have found that various degrees of correlation with cytokine genes and protein expression of single nucleotide polymorphism in bronchial asthma. IL-6 and IL-1β appear to play important biological roles in the pathogenesis of asthma. During the analysis of correlation between genetic polymorphism and a complex disease, the effects of environmental factors should be taken into account. The information at the protein level should be fully developed and the bioinformatics techniques can be used for the comprehensive analysis, to have a deep understanding of molecular mechanisms of incidence and development of diseases.

Hybrid Population-Based Algorithms for the Bi-Objective Quadratic Assignment Problem

We present variants of an ant colony optimization (MO-ACO) algorithm and of an evolutionary algorithm (SPEA2) for tackling multi-objective combinatorial optimization problems, hybridized with an iterative improvement algorithm and the robust tabu search algorithm. The performance of the resulting hybrid stochastic local search (SLS) algorithms is experimentally investigated for the bi-objective quadratic assignment problem (bQAP) and compared against repeated applications of the underlying local search algorithms for several scalarizations. The experiments consider structured and unstructured bQAP instances with various degrees of correlation between the flow matrices. We do a systematic experimental analysis of the algorithms using outperformance relations and the attainment functions methodology to asses differences in the performance of the algorithms. The experimental results show the usefulness of the hybrid algorithms if the available computation time is not too limited and identify SPEA2 hybridized with very short tabu search runs as the most promising variant.

The Viability of Metapopulations: Individual Dispersal Behaviour Matters

Metapopulation models are frequently used for analysing species–landscape interactions and their effect on structure and dynamic of populations in fragmented landscapes. They especially support a better understanding of the viability of metapopulations. In such models, the processes determining metapopulation viability are often modelled in a simple way. Animals’ dispersal between habitat fragments is mostly taken into account by using a simple dispersal function that assumes the underlying process of dispersal to be random movement. Species-specific dispersal behaviour such as a systematic search for habitat patches is likely to influence the viability of a metapopulation. Using a model for metapopulation viability analysis, we investigate whether such specific dispersal behaviour affects the predictions of ranking orders among alternative landscape configurations rated regarding their ability to carry viable metapopulations. To incorporate dispersal behaviour in the model, we use a submodel for the colonisation rates which allows different movement patterns to be considered (uncorrelated random walk, correlated random walk with various degrees of correlation, and loops). For each movement pattern, the landscape order is determined by comparing the resulting mean metapopulation lifetime Tm of different landscape configurations. Results show that landscape orders can change considerably between different movement patterns. We analyse whether and under what circumstances dispersal behaviour influences the ranking orders of landscapes. We find that the ‘competition between patches for migrants’ – i.e. the fact that dispersers immigrating into one patch are not longer available as colonisers for other patches – is an important factor driving the change in landscape ranks. The implications of our results for metapopulation modelling, planning and conservation are discussed.

Dispersal behaviour in fragmented landscapes: Deriving a practical formula for patch accessibility

Dispersal has been shown to be a key determinant of spatially structured populations. One crucial aspect is predicting patch accessibility: the probability rij of a certain patch j being reached by individuals starting at another patch i. Patch accessibility rij depends on both the landscape structure and the individuals’ dispersal behaviour. To investigate the effects of these factors on rij, we developed a simulation model focusing on animal dispersal. Our model analyses show that there is an important intrinsic effect of the interplay between landscape structure and dispersal behaviour on patch accessibility: the competition between patches for migrants. We derive a formula for patch accessibility. This formula is very simple because it just takes distances into account: not only the distance between start patch and target patch, but also between the start patch and all the other patches in the landscape. Despite its simplicity, the formula is able to cover effects such as the competition for migrants. The formula was found to have high predictive power for a variety of movement behaviours (random walk with various degrees of correlation, Archimedean spirals and loops) in any given landscape. The formula can be interpreted as a generic function for patch accessibility for further population dynamics analyses. It also delivers insights into the consequences of dispersal in fragmented landscapes.

Performance of a spatio-temporal error model for raster datasets under complex error patterns

The CLC (Combined Location Classification) error model provides indices for overall data uncertainty in thematic spatio-temporal datasets. It accounts for the two major sources of error in such datasets, location error and classification error. The model assumes independence between error components, while recent studies revealed various degrees of correlation between error components in actual datasets. The goal of this study is to determine if the likely violation of model assumptions biases model predictions. A comprehensive algorithm was devised to simulate the entire process of error formation and propagation. Time series thematic maps were constructed, and modified maps were derived as realizations of underlying error patterns. Error rate and pattern (positive autocorrelation) were controlled for location error and for classification error. The magnitude of correlation between errors from different sources and correlation between error at different time steps was also controlled. A very good agreement between model predictions and simulation results was found in the absence of correlation in error between time steps and between error types, while the inclusion of such correlations was shown to affect model fit slightly. Given our current knowledge of spatio-temporal error patterns in real data, the CLC error model can be used reliably to assess the overall uncertainty in thematic change detection analyses.