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We study the impact of the QCD critical point (CP) on the spin polarization of Λ -hyperon generated by the thermal vorticity in viscous quark gluon plasma (QGP). The equations of the relativistic causal viscous hydrodynamics have been solved numerically in (3+1) dimensions to evaluate the thermal vorticity. The effects of the CP have been incorporated through the equation of state (EoS) and the scaling behavior of the transport coefficients. We observe a significant suppression of the spin polarization around mid-rapidity region induced by the CP and propose that such changed behaviour of the spin polarization can be used to detect the CP. To the best of our knowledge this is the first study on the effects of critical point on the spin polarization in QCD matter. [ABSTRACT FROM AUTHOR]

The power spectrum of the momentum distributions of particles have been estimated with Optical Glauber and Monte Carlo Glauber initial conditions for relativistic heavy ion collisions. The evaluation procedure adopted in this work is analogous to the one used in the calculation of power spectrum in Cosmic Microwave Background Radiation (CMBR). The power spectrum due to perturbations in the phase space distribution of the particles has also been evaluated. The perturbation in phase space has been evolved through the Boltzmann transport equation in an expanding quark–gluon plasma (QGP) background. The expansion of the QGP has been treated within the purview of (3 + 1)-dimensional relativistic hydrodynamics. We observe that the nonequilibrium effects introduced as perturbations in the phase space distributions can be traced through its variation with temperature which is distinctly different from the case of vanishing perturbation. A relation has been derived between the power spectrum and the flow harmonics. [ABSTRACT FROM AUTHOR]

Arsenic awareness plays a vital role in ensuring the sustainability of arsenic mitigation technologies. Thus far, however, few studies have dealt with the sustainability of such technologies and its associated socioeconomic dimensions. As a result, arsenic awareness prediction has not yet been fully conceptualized. Accordingly, this study evaluated arsenic awareness among arsenic-affected communities in rural India, using a structured questionnaire to record socioeconomic, demographic, and other sociobehavioral factors with an eye to assessing their association with and influence on arsenic awareness. First a logistic regression model was applied and its results compared with those produced by six state-of-the-art machine-learning algorithms (Support Vector Machine [SVM], Kernel-SVM, Decision Tree [DT], k -Nearest Neighbor [ k -NN], Naïve Bayes [NB], and Random Forests [RF]) as measured by their accuracy at predicting arsenic awareness. Most (63%) of the surveyed population was found to be arsenic-aware. Significant arsenic awareness predictors were divided into three types: (1) socioeconomic factors: caste, education level, and occupation; (2) water and sanitation behavior factors: number of family members involved in water collection, distance traveled and time spent for water collection, places for defecation, and materials used for handwashing after defecation; and (3) social capital and trust factors: presence of anganwadi and people's trust in other community members, NGOs, and private agencies. Moreover, individuals' having higher social network positively contributed to arsenic awareness in the communities. Results indicated that both the SVM and the RF algorithms outperformed at overall prediction of arsenic awareness—a nonlinear classification problem. Lower-caste, less educated, and unemployed members of the population were found to be the most vulnerable, requiring immediate arsenic mitigation. To this end, local social institutions and NGOs could play a crucial role in arsenic awareness and outreach programs. Use of SVM or RF or a combination of the two, together with use of a larger sample size, could enhance the accuracy of arsenic awareness prediction. [ABSTRACT FROM AUTHOR]

This study seeks to understand factors that guide the decision-making process to adopt and implement the available arsenic-mitigation technologies in rural areas in the middle-Ganga Plain in India. A total of 340 households comprising 2500 people were surveyed. Socio-economic and demographic factors, water and sanitation status, time spent and distance travelled to collect water, arsenic awareness, willingness to pay (WTP) for arsenic-free water, people's trust in others and in institutions, social capital in communities, and preferences for sustainable arsenic-mitigation options were investigated. Arsenic treatment units (filters) and piped water supply systems were the most preferred sustainable arsenic-mitigation options in the surveyed villages. Less preferred arsenic-mitigation options include deep tube wells, dug wells, and rainwater harvesting systems. Binary logistic regression models for each arsenic-mitigation option were produced. Arsenic awareness, WTP, trust in agencies, trust in institutions and social capital were found to be the most significant factors for decision-making for preferring one arsenic-mitigation technology over the others. We recommend a mixed model of two arsenicmitigation options for the studied individuals, which could be a sustainable arsenic-mitigation option for them, considering their socio-economic and demographic conditions. Existing institutions should be strengthened, agencies empowered, and communities enlightened about arsenic problems. [ABSTRACT FROM AUTHOR]

The article provides an analysis of global arsenic contamination, which is a category-I carcinogen that naturally occurs in the earth's crust. It mentions that arsenic gets easily dissolved in groundwater and can cause several incurable diseases. Other topics include impact of geogenic groundwater arsenic contamination on human health, development of arsenic filtration technologies, and need of chemical treatments.

The two component Monte-Carlo Glauber model predicts a knee-like structure in the centrality dependence of elliptic flow v 2 in Uranium + Uranium collisions at s NN = 193 GeV . It also produces a strong anti-correlation between v 2 and d N c h / d y in the case of top ZDC events. However, none of these features have been observed in data. We address these discrepancies by including the effect of nucleon shadowing to the two component Monte-Carlo Glauber model. Apart from addressing successfully the above issues, we find that the nucleon shadow suppresses the event by event fluctuation of various quantities, e.g. ε 2 which is in accordance with expectation from the dynamical models of initial condition based on gluon saturation physics. [ABSTRACT FROM AUTHOR]

We study the random field p -spin model with Ising spins on a fully connected graph using the theory of large deviations in this paper. This is a good model to study the effect of quenched random field on systems which have a sharp first order transition in the pure state. For p = 2 , the phase-diagram of the model, for bimodal distribution of the random field, has been well studied and is known to undergo a continuous transition for lower values of the random field ( h ) and a first order transition beyond a threshold, h t p ( ≈ 0.439 ) . We find the phase diagram of the model, for all p ≥ 2 , with bimodal random field distribution, using large deviation techniques. We also look at the fluctuations in the system by calculating the magnetic susceptibility. For p = 2 , beyond the tricritical point in the regime of first order transition, we find that for h t p < h < 0.447 , magnetic susceptibility increases rapidly (even though it never diverges) as one approaches the transition from the high temperature side. On the other hand, for 0.447 < h ≤ 0.5 , the high temperature behaviour is well described by the Curie–Weiss law. For all p ≥ 2 , we find that for larger magnitudes of the random field ( h > h o = 1 / p ! ), the system does not show ferromagnetic order even at zero temperature. We find that the magnetic susceptibility for p ≥ 3 is discontinuous at the transition point for h < h o . [ABSTRACT FROM AUTHOR]

The article discusses the contribution and role of India as keeper of Africa's peace and security under the United Nation peacekeeping operations (UNKPO). It states that troops from India have been deployed to take part in Africa's peril missions and have endured more casualties compared to other UN peacekeepers. It notes that India had made substantial contribution to retain peace at Mozambique and has been the vanguard of Africa's antiapartheid and anti-colonial movements.

Abstract Nowadays, a number of machine learning prediction methods are being applied in the field of landslide susceptibility modeling of the large area especially in the difficult hilly terrain. In the present study, hybrid machine learning approaches of Reduced Error Pruning Trees (REPT) and different ensemble techniques were used for the construction of four novel hybrid models namely Bagging based Reduced Error Pruning Trees (BREPT), MultiBoost based Reduced Error Pruning Trees (MBREPT), Rotation Forest-based Reduced Error Pruning Trees (RFREPT), Random Subspace-based Reduced Error Pruning Trees (RSREPT) for landslide susceptibility assessment and prediction. In total, ten topographical and geo-environmental landslide conditioning factors were considered for analyzing their spatial relationship with landslide occurrences. Receiver Operating Characteristic curve, Statistical Indexes, and Root Mean Square Error methods were used to validate performance of these models. Analysis of model results indicate that the BREPT is the best model for landslide susceptibility assessment in comparison to other models. Highlights • New hybrid models of REPT and its ensembles developed for landslide susceptibility modeling. • RMSE, accuracy, Chi square test, and ROC curve used for validation of the models. • Results indicate that BREPT is the best method for modeling of landslide susceptibility. [ABSTRACT FROM AUTHOR]

We study the initial conditions for Pb+Pb collisions at √sNN=2.76 TeV using the two-component Monte-Carlo Glauber model with shadowing of the nucleons in the interior by the leading ones. The model parameters are fixed by comparing them to the multiplicity data of p+Pb and Pb+Pb collisions at √sNN=5.02 and 2.76 TeV, respectively. We then compute the centrality dependence of the eccentricities up to the fourth order as well as their event-by-event distributions. The inclusion of shadowing brings the Monte-Carlo Glauber model predictions in agreement with data as well as with results from other dynamical models of initial conditions based on gluon saturation at high-energy nuclear collisions. Further, we find that the shadowed Glauber model provides the desired relative magnitude between the ellipticity and the triangularity of the initial energy distribution required to explain the data on the even and odd flow harmonics v2 and v3, respectively, at the CERN Large Hadron Collider. [ABSTRACT FROM AUTHOR]

Soil erosion is a major cause of damage to agricultural lands in many parts of the world and is of particular concern in semiarid parts of Iran. We use five machine learning techniques—Random Forest (RF), M5P, Reduced Error Pruning Tree (REPTree), Gaussian Processes (GP), and Pace Regression (PR)—under two scenarios to predict soil erodibility in the Dehgolan region, Kurdistan Province, Iran. Our models are based on a variety of soil properties, including soil texture, structure, permeability, bulk density, aggregates, organic matter, and chemical constituents. We checked the validity of the models with statistical metrics, including the coefficient of determination (R2 ), mean absolute error (MAE), root mean squared error (RMSE), T-tests, Taylor diagrams, and box plots. All five algorithms show a positive correlation between the soil erodibility factor (K) and silt, sand, fine sand, bulk density, and infiltration. The GP model has the highest prediction accuracy (R² ¼ 0.843, MAE ¼ 0.0044, RMSE ¼ 0.0050). It outperformed the RF (R² ¼ 0.812, MAE ¼ 0.0050, RMSE ¼ 0.0061), PR, (R² ¼ 0.794, MAE ¼ 0.0037, RMSE ¼ 0.0052), M5P (R² ¼ 0.781, MAE ¼ 0.0043, RMSE ¼ 0.0053), and REPTree (R² ¼ 0.752, MAE ¼ 0.0045, RMSE ¼ 0.0056) algorithms and thus is a useful complement to studies aimed at predicting soil erodibility in areas with similar climate and soil characteristics. [ABSTRACT FROM AUTHOR]

Landslide is a natural hazard which causes huge loss of properties and human life in many places of the world. Mapping of landslide susceptibility is an important task for preventing and combating the landslides problems. Main objective of this study is to use different artificial intelligence methods namely support vector machines (SVM), artificial neural networks (ANN), logistic regression (LR), and reduced error-pruning tree (REPT) in the development of models for landslide susceptibility mapping of Muong Lay district of Vietnam. In total data of 217 landslide locations of the study area was used for the development and evaluation of the models. Nine landslide-conditioning factors were used for generating the datasets for training and validating the models. Results show that the SVM outperformed all other methods namely ANN, LR and REPT. Thus, it can be suggested that the SVM method is more useful in developing accurate and robust landslide prediction model. [ABSTRACT FROM AUTHOR]

In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost and MultiBoost for landslide susceptibility assessment and prediction. For this, two hybrid models, namely, AdaBoost-based Voting Feature Intervals (ABVFIs) and MultiBoost-based Voting Feature Intervals (MBVFIs) were developed and validated using landslide data collected from one of the landslide affected districts of Vietnam, namely, Muong Lay. Quantitative validation methods including area under the ROC curve (AUC) were used to evaluate model performance. The results indicated that both the newly developed ensemble models ABVFI (AUC = 0.859) and MBVFI (AUC = 0.839) outperformed the single VFI (AUC = 0.824) model. Thus, ensemble framework-based VFI algorithms can be used for the accurate spatial prediction of landslides, which can also be applied in other landslide prone regions of the world. Landslide susceptibility maps developed by ensemble VFI models can be used for better landslide prevention and risk management of the area. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping has become one of the most important tools for the management of landslide hazards. In this study, we proposed a novel approach to improve the performance of Credal Decision Tree (CDT) by using four ensemble frameworks: Bagging, Dagging, Decorate, and Rotation Forest (RF) for landslide susceptibility mapping. A total number of 180 past and present landslides data of the Muong Lay district (Viet Nam) was analyzed and used for generating training and validation of the models. Several standard statistical performance evaluation metrics, such as negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, Kappa, Area Under the receiver operating Characteristic curve (AUC) were used to evaluate performance of the models. Results indicated that all the developed and applied models performed well (AUC: 0.842–0.886) but performance of the RF-CDT (AUC: 0.886) model is the best. Therefore, the RF-CDT ensemble model can be used for the correct landslide susceptibility mapping and for proper landslide management not only of the study area but also other hilly areas of the world. [ABSTRACT FROM AUTHOR]

In the present study, Rotation Forest ensemble was integrated with different base classifiers to develop different hybrid models namely Rotation Forest based Support Vector Machines (RFSVM), Rotation Forest based Artificial Neural Networks (RFANN), Rotation Forest based Decision Trees (RFDT), and Rotation Forest based Naïve Bayes (RFNB) for landslide susceptibility modelling. The validity of these models was evaluated using statistical methods such as Root Mean Square Error (RMSE), Kappa index, accuracy, and the area under the success rate and predictive rate curves (AUC). Part of the landslide prone area of Pithoragarh district, Uttarakhand, Himalaya, India was selected as the study area. Results indicate that the RFDT is the best model showing the highest predictive capability (AUC = 0.741) in comparison to RFANN (AUC = 0.710), RFSVM (AUC = 0.701), and RFNB (AUC = 0.640) models. The present study would be helpful in the selection of best model for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Recently the effect of nucleon shadowing on the Monte Carlo-Glauber initial condition was studied and its role on the centrality dependence of elliptic flow (v2) and fluctuations in initial eccentricity for different colliding nuclei were explored. It was found that the results with shadowing effects are closer to the QCD-based dynamical model as well as to the experimental data. Inspired by this outcome, in this work we study the transverse momentum (pT) spectra and elliptic flow of thermal photons for Au+Au collisions at the BNL Relativisitic Heavy Ion Collider and Pb+Pb collisions at the CERN Large Hadron Collider by incorporating the shadowing effects in deducing the initial energy density profile required to solve the relativistic hydrodynamical equations. We find that the thermal photon spectra remain almost unaltered; however, the elliptic flow of photons is found to be enhanced significantly due to shadowing effects. [ABSTRACT FROM AUTHOR]

Identifying and dating first human colonization of new places is challenging, especially when group sizes were small and material traces of their occupations were ephemeral. Generating reliable reconstructions of human colonization patterns from intact archaeological sites may be difficult to impossible given post-depositional taphonomic processes and in cases of island and coastal locations the inundation of landscapes resulting from post-Pleistocene sea-level rise. Paleoenvironmental reconstruction is proving to be a more reliable method of identifying small-scale human colonization events than archaeological data alone. We demonstrate the method through a sediment-coring project across the Lesser Antilles and southern Caribbean. Paleoenvironmental data were collected informing on the timing of multiple island-colonization events and land-use histories spanning the full range of human occupations in the Caribbean, from the initial forays into the islands through the arrival and eventual domination of the landscapes and indigenous people by Europeans. In some areas, our data complement archaeological, paleoecological, and historical findings from the Lesser Antilles and in others amplify understanding of colonization history. Here, we highlight data relating to the timing and process of initial colonization in the eastern Caribbean. In particular, paleoenvironmental data from Trinidad, Grenada, Martinique, and Marie-Galante (Guadeloupe) provide a basis for revisiting initial colonization models of the Caribbean. We conclude that archaeological programs addressing human occupations dating to the early to mid-Holocene, especially in dynamic coastal settings, should systematically incorporate paleoenvironmental investigations. [ABSTRACT FROM AUTHOR]

Groundwater is the most important natural resource in many parts of the world that requires advanced new technologies for monitoring and control. This study presents a comparative analysis of three novel hybrid computational intelligence models that consist of a base Decision Stump classifier and three ensemble learning techniques, i.e., Rotation Forest, MultiBoost, and Bagging, for the groundwater potential mapping. Ten influencing factors (i.e., slope, aspect, plan curvature, topographic wetness index, rainfall, river density, lithology, land use, and soil) and 34 groundwater wells from the Vadodara district, Gujarat, India, were used to prepare a geospatial database. Using this database, three hybrid groundwater models, i.e., Rotation Forest based Decision Stump, MultiBoost based Decision Stump, and Bagging based Decision Stump, were developed. Based on a variety of performance metrics, it is revealed that the Rotation Forest based Decision Stump model had the best performance, followed by the MultiBoost based Decision Stump and Bagging based Decision Stump models. However, all the novel hybrid computational models presented here provided improved estimates of groundwater potential compared to those in previous studies and are sufficiently general to be used in many different landscapes around the world. Unlabelled Image • Developing three hybrid intelligence models for groundwater potential mapping • Performance test of Forest Rotation, Multi Boost, and Bagging ensemble techniques • Highest reliability of mapping coupling Decision Stump with Forest Rotation [ABSTRACT FROM AUTHOR]