Performance evaluation of a biotrickling filter degrading mixtures of hydrophobic and hydrophilic compounds

The removal of a mixture of painting solvents from waste air using a biofiltration process was evaluated in this project. The pollutants removed included hydrophobic (aromatic hydrocarbons) and hydrophilic (water soluble ketones and esters) compounds. A trickle bed reactor with a defined immobilized mixed culture on polyethylene Pall rings was utilized in this biodegradation study. The removal efficiencies (RE) of the individual groups of pollutants during loading experiments were determined. An increase of the aromatic hydrocarbons loading resulted in a drop of their RE_AROM with no effect on the RE value of ketones. The overloading of ketones caused a rapid drop in RE_AROM and a small drop in RE_KET. To achieve a restoration of the biocatalyst degradation properties after the increase in loading, an addition of phosphate to the aqueous medium was implemented which successfully restored the removal efficiency.     

An empirical investigation of bias and variance in time series forecasting: modeling considerations and error evaluation

Bias and variance play an important role in understanding the fundamental issue of learning and generalization in neural network modeling. Several studies on bias and variance effects have been published in classification and regression related research of neural networks. However, little research has been done in this area for time-series modeling and forecasting. We consider modeling issues related to understanding error components given the common practices associated with neural-network time-series forecasting. We point out the key difference between classification and time-series problems in consideration of the bias-plus-variance decomposition. A Monte Carlo study on the role of bias and variance in neural networks time-series forecasting is conducted. We find that both input lag structure and hidden nodes are important in contributing to the overall forecasting performance. The results also suggest that overspecification of input nodes in neural network modeling does not impact the model bias, but has significant effect on the model variance. Methods such as neural ensembles that focus on reducing the model variance, therefore, can be valuable and effective in time-series forecasting modeling.     

Preparation of water‐repellent silks by a reaction with octadecenylsuccinic anhydride

Silk fibers and membranes were acylated with octadecenylsuccinic anhydride (ODSA) at 75°C for different times. Swelling [N,N‐dimethylformamide (DMF) and dimethyl sulfoxide (DMSO)] and nonswelling (xylene) solvent media were used for the reaction. Silk membranes that reacted in DMF or DMSO displayed faster reaction kinetics and attained higher weight‐gain values than fibers. The effect of the solvent on the reaction yield was in the following order: DMSO > DMF ? xylene. The Fourier transform infrared spectra of acylated silk samples showed the characteristic absorption bands of the anhydride at 2990, 2852, 1780–1700, and 1170 cm^?1. The intensity of the latter band, which increased linearly with the weight gain, was used as a marker for evaluating the reaction kinetics of the samples acylated in the nonswelling medium. The moisture regain and water retention of silk fibers acylated with ODSA decreased significantly, regardless of the solvent system used. Accordingly, the water repellency increased. Acylation induced an increase in the thermal stability of the silk fibers and membranes. Fine particles adhering to the surfaces of the silk fibers acylated in xylene were detected by scanning electron microscopy. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 89: 324–332, 2003     

Formal verification of security protocol implementations: a survey

Automated formal verification of security protocols has been mostly focused on analyzing high-level abstract models which, however, are significantly different from real protocol implementations written in programming languages. Recently, some researchers have started investigating techniques that bring automated formal proofs closer to real implementations. This paper surveys these attempts, focusing on approaches that target the application code that implements protocol logic, rather than the libraries that implement cryptography. According to these approaches, libraries are assumed to correctly implement some models. The aim is to derive formal proofs that, under this assumption, give assurance about the application code that implements the protocol logic. The two main approaches of model extraction and code generation are presented, along with the main techniques adopted for each approach.     

An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes

Constraint-based modeling is largely used in computational studies of metabolism. We propose here a novel approach that aims to identify ensembles of flux distributions that comply with one or more target phenotype(s). The methodology has been tested on a small-scale model of yeast energy metabolism. The target phenotypes describe the differential pattern of ethanol production and O₂ consumption observed in “Crabtree-positive” and “Crabtree-negative” yeasts in changing environment (i.e., when the upper limit of glucose uptake is varied). The ensembles were obtained either by selection among sampled flux distributions or by means of a search heuristic (genetic algorithm). The former approach provided indication about the probability to observe a given phenotype, but the resulting ensembles could not be unambiguously partitioned into “Crabtree-positive” and “Crabtree-negative” clusters. On the contrary well-separated clusters were obtained with the latter method. The cluster analysis further allowed identification of distinct groups within each target phenotype. The method may thus prove useful in characterizing the design principles underlying metabolic plasticity arising from evolving physio-pathological or developmental constraints.     

Aromatic bisphenol A-based elastomers via hydrosilylation chemistry

The preparation of highly aromatic elastomers from a bisphenol A-based divinyl-terminated resin and polymerization with various aromatic silane containing compounds utilizing a room temperature hydrosilylation reaction is demonstrated. The polymers exhibit high thermal and oxidative stability with 5% weight losses around 430 and 350°C and char yields ranging from 35% to 40%. The thermosets maintained their elastomeric properties with good hardness and mechanical properties as measured by elongation measurements. The toughness of the thermosets was not improved with the inclusion of aromatic moieties but the hardness did appear to increase with the addition of more aromatic groups.     

Multi-target tracking on confidence maps: An application to people tracking

We propose a generic online multi-target track-before-detect (MT-TBD) that is applicable on confidence maps used as observations. The proposed tracker is based on particle filtering and automatically initializes tracks. The main novelty is the inclusion of the target ID in the particle state, enabling the algorithm to deal with unknown and large number of targets. To overcome the problem of mixing IDs of targets close to each other, we propose a probabilistic model of target birth and death based on a Markov Random Field (MRF) applied to the particle IDs. Each particle ID is managed using the information carried by neighboring particles. The assignment of the IDs to the targets is performed using Mean-Shift clustering and supported by a Gaussian Mixture Model. We also show that the computational complexity of MT-TBD is proportional only to the number of particles. To compare our method with recent state-of-the-art works, we include a postprocessing stage suited for multi-person tracking. We validate the method on real-world and crowded scenarios, and demonstrate its robustness in scenes presenting different perspective views and targets very close to each other.