Biodegradation of agricultural herbicides in sequencing batch reactors under aerobic or anaerobic conditions

This study investigated the biodegradability of the herbicides isoproturon and 2,4-dichlorophenoxyacetic acid (2,4-D) in sequencing batch reactors (SBRs). Two laboratory-scale (2L liquid volume) SBRs were employed: one reactor performing under aerobic and the other under anaerobic conditions. The aerobic SBR was operated at an ambient temperature (22+/-2 degrees C), while the anaerobic SBR was run in the lower mesophilic range (30+/-2 degrees C). Each bioreactor was seeded with a 3:1 mixture (by weight) of fresh sludge and biomass that had been previously exposed to both herbicides. The effect of herbicide concentration on either treatment process was explored at a hydraulic retention time (HRT) of 48 h, using glucose as a supplemental carbon substrate. Although no isoproturon degradation was observed in either system during the study, complete 2,4-D removal occurred after an acclimation period of approximately 30 d (aerobic SBR) and 70 d (anaerobic SBR). The aerobic reactor achieved complete 2,4-D utilization at feed concentrations up to 500 mg/L. A further increase to 700 mg/L, however, proved to be inhibitory since 2,4-D biodegradation was negligible. On the other hand, the anaerobic SBR was able to degrade 120 mg/L of 2,4-D, which corresponds to 40% of the maximum feed concentration applied. Moreover, glucose was consumed first throughout the experiment in a sequential utilization pattern relating to 2,4-D, with biodegradation of both substrates following closely first-order kinetics.     

When false recognition is unopposed by true recognition: Gist-based memory distortion in Alzheimer's disease.

The authors examined false recognition of semantic associates in patients with probable Alzheimer's disease (AD), older adults, and young adults using a paradigm that provided rates of false recognition after single and multiple exposures to word lists. Using corrected false recognition scores to control for unrelated false alarms, the authors found that (a) the level of false recognition after a single list exposure was lower in AD patients than in controls; (b) across 5 trials, false recognition increased in AD patients, decreased in young adults, and showed a fluctuating pattern in older adults; and (c) all groups showed an increase in true recognition over the 5 trials. Analyses suggested that AD patients built up semantic gist across trials, whereas both control groups were able to use increased item-specific recollection and more conservative response criteria to suppress gist-based false alarms. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Low-Power, Battery-Free Tag for Body Sensor Networks

The authors describe a low-power, battery-free tag for use in pervasive sensing applications such as wearable patient-monitoring systems and body sensor networks. The tag consists of a custom integrated circuit, an antenna for RF energy harvesting, and several sensors for monitoring important physiological parameters and generating alarms when necessary. They also describe experimental results with phonocardiogram and photoplethysmogram signals and demonstrate tag localization within 0.6 m by using an audio localization scheme.     

Efficient prediction of drug–drug interaction using deep learning models

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill‐posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over‐fitting issue. Therefore, these models are not so‐effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.Inspec keywords: cancer, learning (artificial intelligence), drugs, recurrent neural nets, convolutional neural nets, drug delivery systemsOther keywords: drug synergy, drug–drug interaction score, drug–drug interaction prediction, deep learning, cancer treatment, machine learning, convolutional mixture density recurrent neural network     

Effect of packaging under vacuum or under nitrogen on the keeping quality of Danedar khoa

A khoa variety popularly known as Danedar khoa, which has a very short life under ambient conditions, was studied for its shelf life at room and refrigerated temperatures under controlled conditions after packaging in a flexible material (poster paper/aluminium foil/low-density polyethylene [LDPE]). The samples were observed periodically for characteristics such as moisture content, peroxide value, titratable acidity and overall acceptability. Samples packed under nitrogen and stored under refrigeration were found to be distinctly superior in all respects.     

Editor's Page

Abstract

The various steps of mine planning and control in open pits are described in connection with the appropriate mathematical models. Modern hardware for control and communication 18 summarily described. The gains which are to be realized by the various degrees of truck dispatching are discussed as well as the potential for future developments.     

Applications of Genetic Algorithm in Polymer Science and Engineering

In the last several years, genetic algorithm (GA) has gained wide acceptance as a robust optimization algorithm in almost all areas of science and engineering. Polymer science and engineering is no exception. Researchers in this field have devoted considerable attention to the optimization of polymer productionusing objective functions and constraints that lead to products having desired material properties. Multiple-objective functions have been optimized simultaneously. An example is the minimization of the reaction time in a reactor (lower costs) while simultaneously minimizing the concentration of side products (that affect the properties of the product adversely). Several end-point constraints (equality or inequality) may also be present, as, e.g., obtaining polymer of a desired molecular weight. Again, this requirement stems from producing polymer having desired strength. Solving such problems usually result in Pareto sets. A variety of adaptations of GA have been developed to obtain optimal solutions for such complex problems. These adaptations can be used to advantage in other fields too. The applications of GA in areas of polymer science and engineering other than polymerization systems are few and far between, but this field is now maturing, and it is hoped that the future will see several newer applications.     

Fair queueing with service envelopes (FQSE): a cousin-fair hierarchical scheduler for subscriber access networks

In this paper, we propose and investigate the characteristics of a fair queueing with service envelopes (FQSE) algorithm-a hierarchical fair-share scheduling algorithm for access networks based on a remote scheduling system such as Ethernet passive optical networks (EPON) or cable TV network. FQSE is designed to overcome the limiting factors of a typical remote scheduling system such as large control-plane delay, limited control-plane bandwidth, and significant queue switch-over overhead. The algorithm is based on a concept of service envelope-a function representing the fair allocation of resources based on a global network condition called satisfiability parameter (SP). We define properties of cousin-fairness and sibling-fairness and show the FQSE to be cousin-fair. FQSE is unique in that it is the only hierarchical algorithm that is simultaneously cousin-fair. Furthermore, we show the necessary techniques to adapt FQSE to variable-sized packet-based networks. We analyze FQSE performance in EPON serving 1024 independent queues and demonstrate FQSE's ability to provide guaranteed bandwidth to each queue and to share the excess bandwidth fairly.     

Protecting multicast sessions in WDM optical mesh networks

Recent advances in wavelength-division-multiplexing (WDM) technology are expected to facilitate bandwidth-intensive multicast applications. However, a single fiber (bundle) cut on such a network can disrupt the transmission of information to several destination nodes on a "light tree"-based multicast session. Thus, it is imperative to protect multicast sessions e.g., by reserving resources along backup trees. We show that, if a backup tree is directed-link-disjoint to its primary counterpart, then data loss can be prevented in the event of any single link failure. We provide mathematical formulations for efficient routing and wavelength assignment (RWA) of several multicast sessions (including their backup trees for dedicated protection) at a globally optimum cost. We present these formulations for networks equipped with two kinds of multicast-capable switch architectures: one using the opaque (O-E-O) approach and the other using transparent (all-optical) approach. We expand our formulations to accommodate sparse splitting constraints in a network, in which an optical splitter has limited splitting fanout and each node has a limited number of such splitters. We develop a profit-maximizing model that would enable a network operator to be judicious in selecting sessions and simultaneously routing the chosen ones optimally. We illustrate the solutions obtained from solving these optimization problem formulations for a representative-size network.     

Reliability of large periphery GaN-on-Si HFETs

GaN devices exhibit excellent potential for use in many RF applications. However, commercial acceptance of the technology has been hindered by the scarcity and non-statistical nature of reliability results. In this work we present a full device level reliability study of GaN-on-Si HFETs. Reliability results on this technology include three-temperature DC data that show an activation energy of 1.7 eV and an average failure time >10⁷ h at 150 °C. Additionally, long duration DC lifetest (30 000 device hours) and RF lifetest (4000 device hours) results demonstrate a repeatable low drift process. Environmental tests such as autoclave and ESD demonstrate the ruggedness of the material system and technology. Finally, initial failure analysis is discussed.