The structure of the mineral leogangite Cu10(OH)6(SO4)(AsO4)4•8H2O–implications for arsenic accumulation and removal

The objective of this research is to determine the molecular structure of the mineral leogangite. The formation of the types of arsenosulphate minerals offers a mechanism for arsenate removal from soils and mine dumps. Raman and infrared spectroscopy have been used to characterise the mineral. Observed bands are assigned to the stretching and bending vibrations of (SO4)2- and (AsO4)3- units, stretching and bending vibrations of hydrogen bonded (OH)- ions and Cu2+-(O,OH) units. The approximate range of O-H...O hydrogen bond lengths is inferred from the Raman spectra. Raman spectra of leogangite from different origins differ in that some spectra are more complex, where bands are sharp and the degenerate bands of (SO4)2- and (AsO4)3- are split and more intense. Lower wavenumbers of  H2O bending vibration in the spectrum may indicate the presence of weaker hydrogen bonds compared with those in a different leogangite samples. The formation of leogangite offers a mechanism for the removal of arsenic from the environment.

The molecular structure of the mineral sarmientite Fe2(AsO4,SO4)2(OH)6•5H2O : implications for arsenic accumulation and removal

Sarmientite is an environmental mineral; its formation in soils enables the entrapment and immobilisation of arsenic. The mineral sarmientite is often amorphous making the application of X-ray diffraction difficult. Vibrational spectroscopy has been applied to the study of sarmientite. Bands are attributed to the vibrational units of arsenate, sulphate, hydroxyl and water. Raman bands at 794, 814 and 831 cm−1 are assigned to the ν3 (AsO4)3− antisymmetric stretching modes and the ν1 symmetric stretching mode is observed at 891 cm−1. Raman bands at 1003 and 1106 cm−1 are attributed to vibrations. The Raman band at 484 cm−1 is assigned to the triply degenerate (AsO4)3− bending vibration. The high intensity Raman band observed at 355 cm−1 (both lower and upper) is considered to be due to the (AsO4)3−ν2 bending vibration. Bands attributed to water and OH stretching vibrations are observed.

Parental responsibility and removal of life sustaining treatment : Re Baby D

The case of Re Baby D (No. 2) has been described as a “landmark decision” as to whether parents themselves can authorise medical staff to withdraw life-sustaining treatment from their child or are required to seek the permission of a court or tribunal. The reasons for the decision that the removal of an endotracheal tube from the airway of Baby D was to treat “a bodily malfunction or disease” and therefore could be authorised by the parents will be explored.

Implementation and removal of an affirmative-action quota : the impact on task assignment and workers’ skill acquisition

Both the United States and Canada have federal legislation that attempts to address employment inequities across specific target groups. The US has a long tradition of affirmative action, dating back to President Kennedy’s 1961 Executive Order; Canada enacted its Employment Equity Act in 1986. Employment Equity/Affirmative Action policy has attracted significant controversy, with high profile court cases and the repeal of state/provincial legislation. Coate and Loury (1993) examine the theoretical impact of introducing affirmative action. Unfortunately the theoretical impact of affirmative action is ambiguous. The current paper employs a laboratory experiment to shed empirical light on this theoretical ambiguity.

Iron and manganese in groundwater in Finland : Occurrence in glacifluvial aquifers and removal by biofiltration

Yhteenveto: Rauta ja mangaani Suomen glasifluviaalisten akviferien pohjavedessä ja poisto biosuodatuksella

Electrochemical detection and removal of lead in water using poly(propylene imine) modified re-compressed exfoliated graphite electrodes

Modification of exfoliated graphite (EG) electrode with generation 2 poly(propylene imine) dendrimer by electrodeposition resulted in an electrochemical sensor which was used to detect lead ions in water to a limit of 1 ppb and a linear response between 2.5 and 40 ppb using square wave anodic stripping voltammetry (SW-ASV). Pb(II) was also removed from spiked water sample using a 40-mm diameter unmodified EG electrode with an applied potential of -1,000 mV for 180 min. A removal efficiency of 99% was calculated from a 150 mL sample. The results obtained in both cases using SW-ASV, correlated with atomic absorption spectroscopy.

On-demand generation and removal of alginate biocompatible microvalves for flow control in microfluidics

[EN] This paper describes, for the first time, the use of alginate hydrogels as miniaturised microvalves within microfluidic devices. These biocompatible and biodegradable microvalves are generated in situ and on demand, allowing for microfluidic flow control. The microfluidic devices were fabricated using an origami inspired technique of folding several layers of cyclic olefin polymer followed by thermocompression bonding. The hydrogels can be dehydrated at mild temperatures, 37◦C, to slightly open the microvalve and chemically erased using an ethylenediaminetetraacetic acid disodium salt (EDTA) solution, to completely open the channel, ensuring the reusability of the whole device and removal of damaged or defective valves for subsequent regeneration.

Flocculation and removal of the brown tide organism, Aureococcus anophagefferens (Chrysophyceae), using clays

Previous attempts to remove the brown tide organism, Aureococcus anophagefferens, through flocculation with clays have been unsuccessful, in spite of adopting concentrations and dispersal protocols that yielded excellent cell removal efficiency (RE>90%) with other species, so a study was planned to improve cell removal. Four modifications in clay preparation and dispersal were explored: 1) varying the salinity of the clay suspension; 2) mixing of the clay-cell suspension after clay addition; 3) varying of concentration of the initial clay stock; 4) pulsed loading of the clay slurry. The effect of salinity was dependent on the clay mineral type: phosphatic clay (IMC-P2) had a higher RE than kaolinite (H-DP) when seawater was used to disperse the clay, but H-DP removed cells more efficiently when suspended in distilled water prior to application. Mixing after dispersal approximately doubled RE for both clays compared to when the slurry was layered over the culture surface. Lowering the concentration of clay stock and pulsing the clay loading increased RE, regardless of mineral type. However, this increase was more apparent for clays dispersed in seawater than in distilled water. In general, application procedures that decrease the rate of self-aggregation among the clay particles and increase the collision frequency between clay particles and A. anophagefferens achieve higher cell removal efficiency. These empirical studies demonstrated that clays might be an important control option for the brown tide organism, given the proper attention to preparation, dispersal methods, environmental impacts, and the hydrodynamic properties of the system being treated. Implications for the treatment of brown tides in the field are discussed.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.

Two-stage no-wait scheduling models with setup and removal times separated

This paper studies two models of two-stage processing with no-wait in process. The first model is the two-machine flow shop, and the other is the assembly model. For both models we consider the problem of minimizing the makespan, provided that the setup and removal times are separated from the processing times. Each of these scheduling problems is reduced to the Traveling Salesman Problem (TSP). We show that, in general, the assembly problem is NP-hard in the strong sense. On the other hand, the two-machine flow shop problem reduces to the Gilmore-Gomory TSP, and is solvable in polynomial time. The same holds for the assembly problem under some reasonable assumptions. Using these and existing results, we provide a complete complexity classification of the relevant two-stage no-wait scheduling models.