Preparation and Properties of a Chitosan–Hyaluronic Acid-Polypyrrole Conductive Hydrogel Catalyzed by Laccase

Electro conductive hydrogels, consisting of chitosan (CS), hyaluronic acid (HA), and polypyrrole (PPy), were prepared via an in situ enzymic polymerization of pyrrole in the CS–HA hydrogel, using laccase as the catalyst. This CS–HA–PPy composite hydrogel showed good conductivity. The chemical structure and morphology of this conductive hydrogel were studied by Fourier transform infrared spectroscopy, scanning electron microscopy, and X-ray diffraction technique. For CS–HA–PPy and CH–HA hydrogel, the temperature at which fastest decomposition occurred was 260 and 244 °C, respectively. That means the thermal stability of CS–HA–PPy is better than CS–HA hydrogel. The conductive hydrogel also showed excellent swelling and deswelling behaviors.     

Environmentally Friendly Oil-Modified Polyesters as Polymeric Plasticizers for Poly(vinyl chloride)

Oil-modified polyesters were synthesized to serve as polymeric plasticizers for PVC. A total of four polymeric plasticizers with different average molecular weights were prepared. Characterizations were done using Fourier-transformed infrared spectroscopy, proton nuclear magnetic resonance spectroscopy, and gel permeation chromatography. Some of the tests conducted on PVC films include thermal stability test using thermogravimetric analyser, determination of glass transition temperature (T_g), plasticizer migration and leaching resistance test, morphology study of plasticized PVC films using field emission scanning microscope, toxicity test, and tensile test. Owing to the plasticizing effect of the palm oil-based compound, T_g of the plasticized PVC has decreased to an average of 65 °C at 20 wt% loading. The polymeric plasticizer is also able to contribute positively to the thermal stability and mechanical properties of the PVC films. Some of the advantages of incorporating polymeric plasticizer with high molecular weight includes lower rate of leaching from plastic, and improved tensile strength and elongation at break. Besides, thermal stability of the plastic studied using Kissinger’s and Flynn–Wall–Ozawa’s approaches shows that PVC blended with high molecular weight oil-modified polyester is more thermally stable, evidenced by the increase in the activation energy of decomposition, E_d. Toxicity test using brine shrimp egg shows encouraging results, where the oil-based plasticizer is considerably less toxic compared to some of the commercial plasticizers.     

Optimization of Pellet Production from Agro-Industrial By-Products: Effect of Plasticizers on Properties of Pellets and Composite Pots

The present study aimed to optimize the pellets formulation (deoiled rice bran, potato peel powder and plasticizers) for the development of the injection molded pots. The maximum hardness and bulk density (desirable responses) were obtained for pellets having 100 g of deoiled rice bran, 100 g potato peel powder and 14 % of cashew nut shell liquid (CNSL) as well as 14 % of glycerol (GL) (on raw material basis). The optimized pellets and the pots developed from them were characterized for their physico-chemical, functional, rheological and morphological properties. Expansion ratio, pellet durability index and hardness of the pellets with 14 % CNSL were found to be 1.097, 98.647 % and 485.551 N, respectively. For pellets with 14 % GL expansion ratio, pellet durability index and hardness were found to be 1.150, 97.747 % and 462.949 N, respectively. The biodegradation analysis of the pots developed from optimized pellets with 14 % CNSL and GL degraded in 11 and 9 weeks, respectively. Porosity, puncture force, density and hardness of ‘AP’ pots were 27.473 %, 495.731 N, 1.549 g/ml and 542.641 N, respectively. However, for ‘BP’ pots, the porosity, puncture force, density and hardness were 32.548 %, 440.149 N, 1.191 g/ml and 507.841 N, respectively. Pots prepared from 14 % CNSL (AP) were better in physical and mechanical properties as compared to pots developed from glycerol.     

Nearest neighbor methods for testing reflexivity

Nearest neighbor (NN) methods are widely employed for drawing inferences about spatial point patterns of two or more classes. We introduce a method for testing reflexivity in the NN structure (i.e., NN reflexivity) based on a contingency table which will be called reflexivity contingency table (RCT) henceforth. The RCT is based on the NN relationships among the data points and was used for testing niche specificity in literature, but we demonstrate that it is actually more appropriate for testing the NN reflexivity pattern. We derive the asymptotic distribution of the entries of the RCT under random labeling and introduce tests of reflexivity based on these entries. We also consider Pielou’s approach on RCT and show that it is not appropriate for completely mapped spatial data. We determine the appropriate null hypotheses and the underlying conditions/assumptions required for all tests considered. We investigate the finite sample performance of the tests in terms of empirical size and power by extensive Monte Carlo simulations and illustrate the methods on two real-life ecological data sets.     

Fourth-corner correlation is a score test statistic in a log-linear trait–environment model that is useful in permutation testing

Ecologists wish to understand the role of traits of species in determining where each species occurs in the environment. For this, they wish to detect associations between species traits and environmental variables from three data tables, species count data from sites with associated environmental data and species trait data from data bases. These three tables leave a missing part, the fourth-corner. The fourth-corner correlations between quantitative traits and environmental variables, heuristically proposed 20 years ago, fill this corner. Generalized linear (mixed) models have been proposed more recently as a model-based alternative. This paper shows that the squared fourth-corner correlation times the total count is precisely the score test statistic for testing the linear-by-linear interaction in a Poisson log-linear model that also contains species and sites as main effects. For multiple traits and environmental variables, the score test statistic is proportional to the total inertia of a doubly constrained correspondence analysis. When the count data are over-dispersed compared to the Poisson or when there are other deviations from the model such as unobserved traits or environmental variables that interact with the observed ones, the score test statistic does not have the usual chi-square distribution. For these types of deviations, row- and column-based permutation methods (and their sequential combination) are proposed to control the type I error without undue loss of power (unless no deviation is present), as illustrated in a small simulation study. The issues for valid statistical testing are illustrated using the well-known Dutch Dune Meadow data set.     

A graphical assessment and spatial clustering of the Deepwater Horizon oil spill impact on Laughing Gulls

The Deepwater Horizon oil spill occurred in the Gulf of Mexico on April 20, 2010. Considered the largest accidental marine oil spill in history, oil flowed for three months and approximately five million barrels of oil spilled through by mid-July 2010. In this article, we analyze bird data to assess the impact of the oil spill on the Gulf wildlife. Particularly, we want to determine in which regions Laughing Gulls were mostly affected by the oil spill, and whether those regions spatially shifted throughout the year 2010. Though our data sets have some limitations to apply statistical analysis methods, we obtained very interesting results. Our analyses showed the general consistency of the results based on two population data sets (from the 2011 Data Expo and from the eBird community) and justified the use of both publicly available data sets. We showed that the closer the surface oil spill area approached to the Laughing Gulls habitats, the more significant clusters of bird cases were observed.     

Investigating the link between $$\hbox {PM}_{2.5}$$ and atmospheric profile variables via penalized functional quantile regression

Fine particulate matter (\(\hbox {PM}_{2.5}\)) events negatively affect the health of numerous persons globally each year. Previous works have described the association between air pollution and surface-level meteorological conditions; however, there has been less focus on the task of linking air pollution events with meteorological conditions at higher levels of the atmosphere. Working within the functional data framework, we develop a penalized functional quantile regression (PFQR) procedure to model conditional quantiles of a continuous response based on a functional covariate, with the ability to penalize selected derivatives of the estimated coefficient function. Our aim is to investigate the relationship between atmospheric profile variables (APVs), assumed to be functional, and key quantiles of the conditional distribution of surface-level \(\hbox {PM}_{2.5}\). Via a simulation study, we find that the performance of our PFQR procedure compares favorably to other related approaches. We conclude with an analysis of \(\hbox {PM}_{2.5}\) data at two Southeastern US locations, Columbia, SC and Tampa, FL, where we estimate the coefficient functions for the APVs corresponding to both ‘typical’ and ‘high’ \(\hbox {PM}_{2.5}\) events. As we believe that the true coefficient functions are smooth and may be exactly zero over subsets of their domains, we impose penalties on the 0th and 2nd derivatives. Our analysis indicates that the corresponding atmospheric conditions differ between the two locations, and that the conditions differ seasonally within location.     

Comparing canopy leaf temperature of three Central European tree species based on simultaneous confidence bands for penalized splines

Temperature is an important physical factor that is known to strongly affect biodiversity as well as ecosystems and their functioning. However, research in this area is still relatively limited; this may also be attributed to the multitude of influencing factors and the complexity of the statistics involved. This study analyzes the differences between the surface temperature of three Central European broadleaf tree species. A better understanding of these differences may help to elucidate the role of microclimate in biodiversity. We consider a time series of high-resolution thermal images taken from a meteorological observation tower and calculate mean canopy leaf temperatures for beech, ash and maple (Fagus silvatica, Fraxinus excelsior and Acer pseudoplatanus). In a first step, comparable image areas are extracted from the thermal image sections of the crown of each tree species avoiding shadow areas, branches, etc. We used an automatic segmentation technique, the Otsu thresholding. Extracted canopy leaf temperature values were then processed and the resulting temperature profiles estimated by O’Sullivan penalized splines. For comparing the differences in canopy leaf temperature over time, we propose the construction of simultaneous confidence bands. The analyses show that there are significant—though small—differences in canopy surface temperature between the three tree species.     

Estimation bias under model selection for distance sampling detection functions

Many simulation studies have examined the properties of distance sampling estimators of wildlife population size. When assumptions hold, if distances are generated from a detection model and fitted using the same model, they are known to perform well. However, in practice, the true model is unknown. Therefore, standard practice includes model selection, typically using model comparison tools like Akaike Information Criterion. Here we examine the performance of standard distance sampling estimators under model selection. We compare line and point transect estimators with distances simulated from two detection functions, hazard-rate and exponential power series (EPS), over a range of sample sizes. To mimic the real-world context where the true model may not be part of the candidate set, EPS models were not included as candidates, except for the half-normal parameterization. We found median bias depended on sample size (being asymptotically unbiased) and on the form of the true detection function: negative bias (up to 15% for line transects and 30% for point transects) when the shoulder of maximum detectability was narrow, and positive bias (up to 10% for line transects and 15% for point transects) when it was wide. Generating unbiased simulations requires careful choice of detection function or very large datasets. Practitioners should collect data that result in detection functions with a shoulder similar to a half-normal and use the monotonicity constraint. Narrow-shouldered detection functions can be avoided through good field procedures and those with wide shoulder are unlikely to occur, due to heterogeneity in detectability.     

Observations and analytical modeling of freshwater and rainwater lenses in coastal dune systems

Observations are reported on (i) groundwater recharge rates under various types of vegetation as measured with megalysimeters in the dunes, (ii) freshwater lenses along the Dutch North Sea coast in the early 1900s, and (iii) rainwater lenses that develop on top of laterally migrating, artificially recharged riverwater. Subsequently analytical methods are presented to estimate annual natural groundwater recharge as function of rainfall and vegetation, and to calculate the size, shape and transition zone of freshwater lenses on saline groundwater and rainwater lenses on infiltrated riverwater. An empirical correction factor, based on the hydraulic resistance of an aquitard within the freshwater lens, is proposed to account for the frequently observed reduction of the Ghyben-Herzberg ratio of 40. This factor raises the groundwater table, reduces the depth of the fresh/salt interface and increases the lens formation time. The suite of methods offers a tool box for knowledge based water management of dune systems, by rapidly predicting: (i) more or less autonomous changes due to sealevel rise, climate change and vegetation development; and (ii) the potential (side) effects of interventions. Knowing what happened or will happen to the fresh water lens or a rainwater lens is important, because changes impact on important natural habitat parameters such as salinity, depth to groundwater table, decalcification rate (and thus on pH, Ca/Al, PO₄, NH₄) and nutrient availability, and on drinking water supply. The analytical models are applied to predict effects of sealevel rise, coastal progradation, vegetation changes, and increased temperature of coastal air and river water to be infiltrated.