Odors treatment : biological technologies

Physical-chemical waste gas cleaning techniques have proven their effi ciency and reliability and will continue to occupy their niche, but several disadvantages remain. Among them are high investment and operation costs and the possible generation of secondary waste streams. With biological waste treatment techniques, reactor engineering is often less complicated and consequently costs are less. In addition, usually no secondary wastes are produced. Biological methods are nonhazardous and benign for the environment. Possible drawbacks are restricted knowledge about the biodegradation processes, limited process control, and relatively slow reaction kinetics. Anyway, the biological methods for the removal of odors and volatile organic compounds (VOCs) from waste gases are cost-effective technologies, when low concentrations (below 1-10 g/m -3 ) are to be dealt with (Kosteltz et al., 1996). Therefore, decision making can be based merely on economical analysis. Like the treatment of liquid effl uents, gaseous streams will be more often considered for biological treatment. For organic compounds, the biological reaction can be described as: CHO + O 2 + nutrients C 5 H 7 O 2 N (cell dry weight) + CO 2 + H 2 O + heat When heteroatoms are present (e.g., chlorine, sulfur), end-products like HCl or H 2 SO 4 can be formed. For effi cient pollutant removal, target pollutants have to be suffi ciently biodegradable and bioavailable. A major advantage in the case of odor treatment is that biocatalysts have high affi nity for the substrates, which allows effi cient treatment of low infl uent concentrations. Biocatalysts also operate at room temperature and they have innocuous fi nal products (e.g., carbon dioxide and water). Provided that you have the right inocula, microorganisms can metabolize almost every compound there is. In general, odors consist of a very complex mixture of volatile organic as well as inorganic compounds. The most relevant compounds regarding odors in the food industry are nitrogencontaining compou(undefined

Stocks, Bonds, T-Bills and Inflation Hedging

We analyze the inflation-hedging properties of US stocks, bonds, and T-bills at the subindex level during the 1983 “ 2012 period, for investment horizons between 1 month and 10 years. Bonds other than T-bills turn out poor inflation hedges during the entire sample period, regardless of the investment horizon. Stocks in both cyclical and non-cyclical industries have virtually no hedging ability until the fall of Lehman Brothers in September 2008. From that moment on, equity subindices particularly in the cyclical industries started to develop statistically significant hedging ability, even in the short run. Hence, the extent to which investors can benefit from the hedging ability of stocks and bonds varies over time and across industries, maturities and investment horizons

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals

Characterization of Coastal Urban Watershed Bacterial Communities Leads to Alternative Community-Based Indicators

BACKGROUND: Microbial communities in aquatic environments are spatially and temporally dynamic due to environmental fluctuations and varied external input sources. A large percentage of the urban watersheds in the United States are affected by fecal pollution, including human pathogens, thus warranting comprehensive monitoring. METHODOLOGY/PRINCIPAL FINDINGS: Using a high-density microarray (PhyloChip), we examined water column bacterial community DNA extracted from two connecting urban watersheds, elucidating variable and stable bacterial subpopulations over a 3-day period and community composition profiles that were distinct to fecal and non-fecal sources. Two approaches were used for indication of fecal influence. The first approach utilized similarity of 503 operational taxonomic units (OTUs) common to all fecal samples analyzed in this study with the watershed samples as an index of fecal pollution. A majority of the 503 OTUs were found in the phyla Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria. The second approach incorporated relative richness of 4 bacterial classes (Bacilli, Bacteroidetes, Clostridia and alpha-proteobacteria) found to have the highest variance in fecal and non-fecal samples. The ratio of these 4 classes (BBC:A) from the watershed samples demonstrated a trend where bacterial communities from gut and sewage sources had higher ratios than from sources not impacted by fecal material. This trend was also observed in the 124 bacterial communities from previously published and unpublished sequencing or PhyloChip- analyzed studies. CONCLUSIONS/SIGNIFICANCE: This study provided a detailed characterization of bacterial community variability during dry weather across a 3-day period in two urban watersheds. The comparative analysis of watershed community composition resulted in alternative community-based indicators that could be useful for assessing ecosystem health

Small Drains, Big Problems: The Impact of Dry Weather Runoff on Shoreline Water Quality at Enclosed Beaches

Enclosed beaches along urban coastlines are frequent hot spots of fecal indicator bacteria (FIB) pollution. In this paper we present field measurements and modeling studies aimed at evaluating the impact of small storm drains on FIB pollution at enclosed beaches in Newport Bay, the second largest tidal embayment in Southern California. Our results suggest that small drains have a disproportionate impact on enclosed beach water quality for five reasons: (1) dry weather surface flows (primarily from overirrigation of lawns and ornamental plants) harbor FIB at concentrations exceeding recreational water quality criteria; (2) small drains can trap dry weather runoff during high tide, and then release it in a bolus during the falling tide when drainpipe outlets are exposed; (3) nearshore turbulence is low (turbulent diffusivities approximately 10(-3) m(2) s(-1)), limiting dilution of FIB and other runoff-associated pollutants once they enter the bay; (4) once in the bay, runoff can form buoyant plumes that further limit vertical mixing and dilution; and (5) local winds can force buoyant runoff plumes back against the shoreline, where water depth is minimal and human contact likely. Outdoor water conservation and urban retrofits that minimize the volume of dry and wet weather runoff entering the local storm drain system may be the best option for improving beach water quality in Newport Bay and other urban-impacted enclosed beaches

Promoting Intercultural Competence in Bilingual Programs in Indonesia

Most studies of intercultural competence in bilingual programs have been conducted in non-Asian contexts resulting in little information on how Indonesian educators promote intercultural values within bilingual programs. This research contributes to the body of literature that examines the promotion of intercultural values within bilingual higher education in Indonesia. It focuses on lecturers’ perceptions of their strategies to teach and promote intercultural values in Indonesian higher education institutions (IHEIs). Data collection employed in-depth semi-structure interviews of eight lecturers that teach in three different programs: Math; Economic and Political Science bilingual programs. The findings of this study show that intercultural competent components include open-minded attitudes, reciprocal interaction, and respecting differences. In addition, two important strategies to promote intercultural competence are in-country programs and international intercultural programs. This study has implications for the success of similar programs and the ways to gain intercultural competence within higher education contexts