Collection, seminal characteristics and chilled storage of spermatozoa from three species of free-range flying fox (Pteropus spp.)

This study reports observations on the collection and characteristics of semen from free-range populations of flying fox in Brisbane, Australia. Semen was successfully recovered by electroejaculation from 107 of 115 wild flying foxes (Pteropus alecto, Pteropus poliocephalus and Pteropus scapulatus). A proportion of ejaculates collected from all three species contained seminal vesicle secretions, the incidence of which appeared related to breeding season. Ejaculate volume was small (5-160 mu L), requiring a specialised collection vessel and immediate extension to avoid desiccation. Sperm morphological abnormalities and characteristics are described for the first time. In two species (P. scapulatus and P. alecto), sperm quality varied with breeding season. Dilution in Tris-citratefructose buffer and subsequent incubation (37 degrees C) of Pteropus semen for 2-3 h appeared to have a negative impact on sperm motility and the percentage of sperm with intact plasma membranes and acrosomes and represents a concern for the potential development and use of assisted breeding technology in these species. Preliminary attempts to develop a short-term chilled preservation protocol for flying fox semen revealed that spenn viability (percentage motility and percentage live sperm with intact acrosomes) was significantly reduced after 102 h chilled storage at 5 degrees C; nevertheless, approximately 40% of the spermatozoa were still motile and contained intact acrosomes. Glycerol was neither protective nor detrimental to sperm survival during chilled storage. Microbial flora of the prepuce, urethra and semen of all species were isolated and their antibiotic susceptibility tested. Tetracycline, penicillin, ciprofloxacin, and ceftazidime were the most effective antibiotics in preventing growth of all identified bacteria; however, their effects on sperm survival were not investigated. (c) 2005 Elsevier Inc. All rights reserved.

Lasers - An effective artificial source of radiation for the cultivation of anoxygenic photosynthetic bacteria

The laser diode (LD) is a unique light source that can efficiently produce all radiant energy within the narrow wavelength range used most effectively by a photosynthetic microorganism. We have investigated the use of a single type of LID for the cultivation of the well-studied anoxygenic photosynthetic bacterium, Rhodobacter capsulatus (Rb. capsulatus). An array of vertical-cavity surface-emitting lasers (VCSELs) was driven with a current of 25 mA, and delivered radiation at 860 nm with 0.4 nm linewidth. The emitted light was found to be a suitable source of radiant energy for the cultivation of Rb. capsulatus. The dependence of growth rate on incident irradiance was quantified. Despite the unusual nearly monochromatic light source used in these experiments, no significant changes in the pigment composition and in the distribution of bacteriochlorophyll between LHII and LHI-RC were detected in bacterial cells transferred from incandescent light to laser light. We were also able to show that to achieve a given growth rate in a light-limited culture, the VCSEL required only 30% of the electricity needed by an incandescent bulb, which is of great significance for the potential use of laser-devices in biotechnological applications and photobioreactor construction. (c) 2006 Wiley Periodicals, Inc.

Setting parameters in MRP for the effective management of bought-out inventory in a JIT assembly environment

In recent years, UK industry has seen an explosive growth in the number of `Computer Aided Production Management' (CAPM) system installations. Of the many CAPM systems, materials requirement planning/manufacturing resource planning (MRP/MRPII) is the most widely implemented. Despite the huge investments in MRP systems, over 80 percent are said to have failed within 3 to 5 years of installation. Many people now assume that Just-In-Time (JIT) is the best manufacturing technique. However, those who have implemented JIT have found that it also has many problems. The author argues that the success of a manufacturing company will not be due to a system which complies with a single technique; but due to the integration of many techniques and the ability to make them complement each other in a specific manufacturing environment. This dissertation examines the potential for integrating MRP with JIT and Two-Bin systems to reduce operational costs involved in managing bought-out inventory. Within this framework it shows that controlling MRP is essential to facilitate the integrating process. The behaviour of MRP systems is dependent on the complex interactions between the numerous control parameters used. Methodologies/models are developed to set these parameters. The models are based on the Pareto principle. The idea is to use business targets to set a coherent set of parameters, which not only enables those business targets to be realised, but also facilitates JIT implementation. It illustrates this approach in the context of an actual manufacturing plant - IBM Havant. (IBM Havant is a high volume electronics assembly plant with the majority of the materials bought-out). The parameter setting models are applicable to control bought-out items in a wide range of industries and are not dependent on specific MRP software. The models have produced successful results in several companies and are now being developed as commercial products.

Abnormally increased effective connectivity between parahippocampal gyrus and ventromedial prefrontal regions during emotion labeling in bipolar disorder

Emotional liability and mood dysregulation characterize bipolar disorder (BD), yet no study has examined effective connectivity between parahippocampal gyrus and prefrontal cortical regions in ventromedial and dorsal/lateral neural systems subserving mood regulation in BD. Participants comprised 46 individuals (age range: 18-56 years): 21 with a DSM-IV diagnosis of BD, type I currently remitted; and 25 age- and gender-matched healthy controls (HC). Participants performed an event-related functional magnetic resonance imaging paradigm, viewing mild and intense happy and neutral faces. We employed dynamic causal modeling (DCM) to identify significant alterations in effective connectivity between BD and HC. Bayes model selection was used to determine the best model. The right parahippocampal gyrus (PHG) and right subgenual cingulate gyrus (sgCG) were included as representative regions of the ventromedial neural system. The right dorsolateral prefrontal cortex (DLPFC) region was included as representative of the dorsal/lateral neural system. Right PHG-sgCG effective connectivity was significantly greater in BD than HC, reflecting more rapid, forward PHG-sgCG signaling in BD than HC. There was no between-group difference in sgCG-DLPFC effective connectivity. In BD, abnormally increased right PHG-sgCG effective connectivity and reduced right PHG activity to emotional stimuli suggest a dysfunctional ventromedial neural system implicated in early stimulus appraisal, encoding and automatic regulation of emotion that may represent a pathophysiological functional neural mechanism for mood dysregulation in BD.

Dehydroacetic acid based dioxaborine styryl dye:effective fluorescent probe for ammonia and amine detection

The newly synthesized dioxaborine dyes, derivatives of dehydroacetic acid, were tested for the detection of amines and ammonia. To discriminate the substance with efficient sensing parameters, series of ca. 20 dioxaborine dyes were synthesized and tested for reactivity with amines. The most promising one showed the fluorescent sensitivity to amines in the range of 1-4 ppm. © (2014) Trans Tech Publications.

A cost-effective steam-driven RO plant for brackish groundwater

Desalination is a costly means of providing freshwater. Most desalination plants use either reverse osmosis (RO) or thermal distillation. Both processes have drawbacks: RO is efficient but uses expensive electrical energy; thermal distillation is inefficient but uses less expensive thermal energy. This work aims to provide an efficient RO plant that uses thermal energy. A steam-Rankine cycle has been designed to drive mechanically a batch-RO system that achieves high recovery, without the high energy penalty typically incurred in a continuous-RO system. The steam may be generated by solar panels, biomass boilers, or as an industrial by-product. A novel mechanical arrangement has been designed for low cost, and a steam-jacketed arrangement has been designed for isothermal expansion and improved thermodynamic efficiency. Based on detailed heat transfer and cost calculations, a gain output ratio of 69-162 is predicted, enabling water to be treated at a cost of 71 Indian Rupees/m3 at small scale. Costs will reduce with scale-up. Plants may be designed for a wide range of outputs, from 5 m3/day, up to commercial versions producing 300 m3/day of clean water from brackish groundwater.

Writing online:a guide to effective digital communication at work

Online writing plays a complex and increasingly prominent role in the life of organizations. From newsletters to press releases, social media marketing and advertising, to virtual presentations and interactions via e-mail and instant messaging, digital writing intertwines and affects the day-to-day running of the company - yet we rarely pay enough attention to it. Typing on the screen can become particularly problematic because digital text-based communication increases the opportunities for misunderstanding: it lacks the direct audio-visual contact and the norms and conventions that would normally help people to understand each other. Providing a clear, convincing and approachable discussion, this book addresses arenas of online writing: virtual teamwork, instant messaging, emails, corporate communication channels, and social media. Instead of offering do and don’t lists, however, it teaches the reader to develop a practice that is observant, reflective, and grounded in the understanding of the basic principles of language and communication. Through real-life examples and case studies, it helps the reader to notice previously unnoticed small details, question previously unchallenged assumptions and practices, and become a competent digital communicator in a wide range of professional contexts.

Service Quality in Arts Events: Operations Management Strategies for Effective Delivery

Recent debate on the quality of arts events has concentrated on the requirement to deliver against a complex range of political, social, and cultural criteria with an emphasis on the external partnerships that are forged. Yet those aspects of quality over which event organizers have more direct control have been accorded minor examination. The authors believe that operational effectiveness is key to service quality in the cultural context, and seek to demonstrate that a balanced consideration of both process and product is vital to fully deliver quality arts events. This article identifies areas of emergent research and practice and focuses on issues in the front-of-house environment where the breakdown of service quality is a real concern, using the experience of one UK not-for-profit arts organization as a case study to illustrate potential management responses.

Utilisation of Glass Reinforced Plastics as Cost Effective Alternative to Traditional Materials in Access Panel Design for Waste Water Treatment Plants

Composites are fast becoming a cost effective option when considering the design of engineering structures in a broad range of applications. If the strength to weight benefits of these material systems can be exploited and challenges in developing lower cost manufacturing methods overcome, then the advanced composite systems will play a bigger role in the diverse range of sectors outside the aerospace industry where they have been used for decades.
This paper presents physical testing results that showcase the advantages of GRP (Glass Reinforced Plastics), such as the ability to endure loading with minimal deformation. The testing involved is a cross comparison of GRP grating vs. GRP encapsulated foam core. Resulting data gained within this paper will then be coupled with design optimization (utilising model simulation) to bring forward layup alterations to meet the specified load classifications involved.

Are coach education programmes the most effective method for coach development?

The purpose of this study is to establish whether coaches from a multi-sport context develop most effectively through coach education programmes and whether formal learning is fostering coach effectiveness. A sample of eight qualified male multi-sports’ coaches participated with an age range of 24 to 52 years (M = 32.6, ± = 8.9) and 9 to 18 years coaching experience (M = 12.6, ± = 3.8). Qualitative semi structured interviews were employed, lasting approximately 30 to 60 minutes. The data then underwent a thematic analysis process reducing the data into six overarching themes: values of the coach; the coach’s role on athlete development; forms of learning; barriers regarding coach education; role of governing bodies; coaches career pathway. The findings of the study indicated coaches access a wide range of sources to enhance their practice, but informal learning was preferred (interacting with other coaches and learning by doing). This resulted from numerous barriers experienced surrounding the delivery, cost and access to coach education programmes preventing coaches from progressing through the pathway. However, coaches in the study feel coach education should be a mandatory process for every coach. The findings have implications for policymakers and sport organisations in developing their coach education structure.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

Quantitative Derivation of Effective Evolution Equations for the Dynamics of Bose-Einstein Condensates

This thesis proves certain results concerning an important question in non-equilibrium quantum statistical mechanics which is the derivation of effective evolution equations approximating the dynamics of a system of large number of bosons initially at equilibrium (ground state at very low temperatures). The dynamics of such systems are governed by the time-dependent linear many-body Schroedinger equation from which it is typically difficult to extract useful information due to the number of particles being large. We will study quantitatively (i.e. with explicit bounds on the error) how a suitable one particle non-linear Schroedinger equation arises in the mean field limit as number of particles N → ∞ and how the appropriate corrections to the mean field will provide better approximations of the exact dynamics. In the first part of this thesis we consider the evolution of N bosons, where N is large, with two-body interactions of the form N³ᵝv(Nᵝ⋅), 0≤β≤1. The parameter β measures the strength and the range of interactions. We compare the exact evolution with an approximation which considers the evolution of a mean field coupled with an appropriate description of pair excitations, see [18,19] by Grillakis-Machedon-Margetis. We extend the results for 0 ≤ β < 1/3 in [19, 20] to the case of β < 1/2 and obtain an error bound of the form p(t)/Nᵅ, where α>0 and p(t) is a polynomial, which implies a specific rate of convergence as N → ∞. In the second part, utilizing estimates of the type discussed in the first part, we compare the exact evolution with the mean field approximation in the sense of marginals. We prove that the exact evolution is close to the approximate in trace norm for times of the order o(1)√N compared to log(o(1)N) as obtained in Chen-Lee-Schlein [6] for the Hartree evolution. Estimates of similar type are obtained for stronger interactions as well.

Finite difference calculations of permeability in large domains in a wide porosity range.

Determining effective hydraulic, thermal, mechanical and electrical properties of porous materials by means of classical physical experiments is often time-consuming and expensive. Thus, accurate numerical calculations of material properties are of increasing interest in geophysical, manufacturing, bio-mechanical and environmental applications, among other fields. Characteristic material properties (e.g. intrinsic permeability, thermal conductivity and elastic moduli) depend on morphological details on the porescale such as shape and size of pores and pore throats or cracks. To obtain reliable predictions of these properties it is necessary to perform numerical analyses of sufficiently large unit cells. Such representative volume elements require optimized numerical simulation techniques. Current state-of-the-art simulation tools to calculate effective permeabilities of porous materials are based on various methods, e.g. lattice Boltzmann, finite volumes or explicit jump Stokes methods. All approaches still have limitations in the maximum size of the simulation domain. In response to these deficits of the well-established methods we propose an efficient and reliable numerical method which allows to calculate intrinsic permeabilities directly from voxel-based data obtained from 3D imaging techniques like X-ray microtomography. We present a modelling framework based on a parallel finite differences solver, allowing the calculation of large domains with relative low computing requirements (i.e. desktop computers). The presented method is validated in a diverse selection of materials, obtaining accurate results for a large range of porosities, wider than the ranges previously reported. Ongoing work includes the estimation of other effective properties of porous media.

Construction of effective disposable biosensors for point of care testing of nitrite

© 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/