Process optimization by decoupled control of key microbial populations: distribution of activity and abundance of polyphosphate-accumulating organisms and nitrifying populations in a full-scale IFAS-EBPR plant

This study investigated the abundance and distribution of key functional microbial populations and their activities in a full-scale integrated fixed film activated sludge-enhanced biological phosphorus removal (IFAS-EBPR) process. Polyphosphate accumulating organisms (PAOs) including Accumulibacter and EBPR activities were predominately associated with the mixed liquor (>90%) whereas nitrifying populations and nitrification activity resided mostly (>70%) on the carrier media. Ammonia oxidizer bacteria (AOB) were members of the Nitrosomonas europaea/eutropha/halophila and the Nitrosomonas oligotropha lineages, while nitrite oxidizer bacteria (NOB) belonged to the Nitrospira genus. Addition of the carrier media in the hybrid activated sludge system increased the nitrification capacity and stability; this effect was much greater in the first IFAS stage than in the second one where the residual ammonia concentration becomes limiting. Our results show that IFAS-EBPR systems enable decoupling of solid residence time (SRT) control for nitrifiers and PAOs that require or prefer conflicting SRT values (e.g. >15 days required for nitrifiers and <5 days preferred for PAOs). Allowing the slow-growing nitrifiers to attach to the carrier media and the faster-growing phosphorus (P)-removing organisms (and other heterotrophs, e.g. denitrifiers) to be in the suspended mixed liquor (ML), the EBPR-IFAS system facilitates separate SRT controls and overall optimization for both N and P removal processes.     

Congenital diarrheal disorders: an updated diagnostic approach

Congenital diarrheal disorders (CDDs) are a group of inherited enteropathies with a typical onset early in the life. Infants with these disorders have frequently chronic diarrhea of sufficient severity to require parenteral nutrition. For most CDDs the disease-gene is known and molecular analysis may contribute to an unequivocal diagnosis. We review CDDs on the basis of the genetic defect, focusing on the significant contribution of molecular analysis in the complex, multistep diagnostic work-up.     

Conventional,organic and biodynamic farming: differences in polyphenol content and antioxidant activity of Batavia lettuce

BACKGROUND: Lactuca sativa L. ssp. acephala L., cv. Batavia red Mohican plants were cultivated under intensive conventional, organic and biodynamic farming and were analyzed for their polyphenol content and antiradical activity in order to demonstrate the influence of farming on yield, polyphenol content and antiradical activity. RESULTS: The yield of plants from conventional farming was the highest (2.89 kg m^?2), while polyphenol content, measured by spectrophotometry, of these plants was lower at P < 0.05 (1.36 mg g^?1) than the content of plants from organic and biodynamic farming (1.74 and 1.85 mg g^?1, respectively). The antiradical activity, measured by DPPH · assay, was positively correlated to flavonoid and hydroxycinnamic acid contents. CONCLUSION: Flavonoid, hydroxycinnamic acid and anthocyan patterns were not affected by the type of cultivation, while quantitative differences were demonstrated and some differences were found between conventional farming and organic or biodynamic farming. The yield of conventionally grown salads was the highest. Copyright © 2011 Society of Chemical Industry     

P&;P: an asynchronous and distributed protocol for mobile sensor deployment

The use of wireless mobile sensors is of great relevance for a number of strategic applications devoted to monitoring critical areas where sensors can not be deployed manually. Mobile sensors can adapt their position on the basis of a local evaluation of coverage, thus permitting an autonomous deployment. Several algorithms have been proposed to deploy mobile sensors over an area of interest. The applicability of these approaches largely depends on a proper formalization of rigorous rules to coordinate sensor movements, solve local conflicts and manage possible failures of communications and devices. In this paper we introduce P&P, a communication protocol that permits a correct and efficient coordination of sensor movements in agreement with the Push & Pull algorithm. We deeply investigate and solve the problems that may occur when coordinating asynchronous local decisions in the presence of an unreliable transmission medium and possibly faulty devices such as in the typical working scenario of mobile sensor networks. Simulation results show the performance of our protocol under a range of operative settings, including conflict situations and irregularly shaped target areas. Furthermore, a performance comparison between the P&P protocol and one of the best solutions based on the virtual force approach, shows the superiority of our proposal in terms of deployment time, message exchanges and energy consumption.     

Triplet Formation in Fullerene Multi‐Adduct Blends for Organic Solar Cells and Its Influence on Device Performance

In organic solar cells, high open circuit voltages may be obtained by choosing materials with a high offset between the donor highest occupied molecular orbital (HOMO) and acceptor lowest unoccupied molecular orbital (LUMO). However, increasing this energy offset can also lead to photophysical processes that compete with charge separation. In this paper the formation of triplet states is addressed in blends of polyfluorene polymers with a series of PCBM multi‐adducts. Specifically, it is demonstrated that the formation of such triplets occurs when the offset energy between donor ionization potential and acceptor electron affinity is ～1.6 eV or greater. Spectroscopic measurements support a mechanism of resonance energy transfer for triplet formation, influenced by the energy levels of the materials, but also demonstrate that the competition between processes at the donor–acceptor interface is strongly influenced by morphology.     

The role of interface DoFs in decoupling of substructures based on the dual domain decomposition

The paper considers the decoupling problem, i.e. the identification of the dynamic behaviour of a structural subsystem, starting from the known dynamic behaviour of the coupled system, and from information about the remaining part of the structural system (residual subsystem). Typically, the FRF matrix of the coupled system is assumed to be known at the coupling DoFs (standard interface). To circumvent ill-conditioning around particular frequencies, some authors suggest the use of FRFs at some internal DoFs of the residual subsystem. In this paper, the decoupling problem is revisited in the general framework of frequency based substructuring. Specifically, the dual domain decomposition is used by adding a fictitious subsystem, which is the negative of the residual subsystem, to the coupled system. In this framework, the use of internal DoFs of the residual subsystem, in addition to coupling DoFs, appears quite natural (extended interface). The effects of using an extended interface are widely discussed: the main drawback is that the problem becomes singular at any frequency. However, this singularity is easily removed by using standard smart inversion techniques. The approach is tested on a discrete system describing a two-speed transmission, using simulated data polluted by noise. Results are compared with those obtained from existing approaches.     

Incremental template updating for face recognition in home environments

The extreme variability of faces in smart environment applications, due to continuous changes in terms of pose, illumination and subject appearance (hairstyle, make-up, etc.), requires the relevant mode of variations of the subject's faces to be encoded in the templates and to be continuously updated based on new inputs. This work proposes a new video-based template updating approach suitable for home environments where the image acquisition process is totally unconstrained but a large amount of face data is available for continuous learning. A small set of labeled images is initially used to create the templates and the updating is then totally unsupervised. Although the method is here presented in conjunction with a subspace-based face recognition approach, it can be easily adapted to deal with different kinds of face representations. A thorough performance evaluation is carried out to show the efficacy and reliability of the proposed technique.     

Dose-effect study on the antioxidant properties of leaves and outer bracts of extracts obtained from Violetto di Toscana artichoke

Artichoke (Cynara scolymus L.) is an edible vegetable largely used in the Mediterranean diet and in folk medicine. The present paper discusses the analysis of the polyphenol content of leaves and outer bracts of Violetto di Toscana artichoke using different extraction procedures with the aim of establishing a correlation between polyphenol subclasses and antioxidant activity measured on human LDL oxidized by copper ions. HPLC/DAD and HPLC/MS analyses revealed that both the matrixes contain identical polyphenol subclasses, with mainly quantitative differences. The antioxidant effect of four artichoke extracts decreases in the following order when the sum of total phenolic compounds was considered: ethanolic extract from leaves (IC₅₀ = 2.92 ± 0.46 μM); ethanolic extract from outer bracts (IC₅₀ = 4.04 ± 0.21 μM); ethyl acetate extract from leaves (IC₅₀ = 4.91 ± 0.11 μM); ethyl acetate extract from outer bracts (IC₅₀ = 10.18 ± 1.6 μM). IC₅₀ were also calculated considering the concentrations of single polyphenol subclasses. In both cases, the potency of antioxidant properties was not related to the amount of total polyphenols or the single subclasses.     

Multi-output learning via spectral filtering

In this paper we study a class of regularized kernel methods for multi-output learning which are based on filtering the spectrum of the kernel matrix. The considered methods include Tikhonov regularization as a special case, as well as interesting alternatives such as vector-valued extensions of L2 boosting and other iterative schemes. Computational properties are discussed for various examples of kernels for vector-valued functions and the benefits of iterative techniques are illustrated. Generalizing previous results for the scalar case, we show a finite sample bound for the excess risk of the obtained estimator, which allows to prove consistency both for regression and multi-category classification. Finally, we present some promising results of the proposed algorithms on artificial and real data.     

Introduction to the special issue of the ECML PKDD 2021 journal track

Data Mining and Knowledge Discovery -