Applications of passive sampling techniques in monitoring organic pollutants in the environment

As there are a myriad of micro organic pollutants that can affect the well-being of human and other organisms in the environment the need for an effective monitoring tool is eminent. Passive sampling techniques, which have been developed over the last decades, could provide several advantages to the conventional sampling methods including simpler sampling devices, more cost-effective sampling campaign, providing time-integrated load as well as representative average of concentrations of pollutants in the environment. Those techniques have been applied to monitor many pollutants caused by agricultural activities, i.e. residues of pesticides, veterinary drugs and so on. Several types of passive samplers are commercially available and their uses are widely accepted. However, not many applications of those techniques have been found in Japan, especially in the field of agricultural environment. This paper aims to introduce the field of passive sampling and then to describe some applications of passive sampling techniques in environmental monitoring studies related to the agriculture industry.

Underlying knowledge of construction management consultants in China

To meet clients/owners’ multidimensional and changing requirements, construction management consultants (CMCs) ought to possess a diverse and dynamic knowledge structure. In China, although the population of CMCs has grown to the point of their being indispensable in the industry, their knowledge structure has not been explored explicitly. The study presented in this paper investigated this by first conducting a comprehensive content analysis of the curricula of the highest ranked construction management university courses in China. This was followed by in-depth interviews with experts, resulting in the identification of 22 main knowledge areas that can be grouped into technology, economy, management and law. A questionnaire survey was then conducted among 115 experienced CMCs to evaluate the current level of knowledge in these areas together with their importance and need-for-improvement. The main findings demonstrate the significance of the identified 22 knowledge areas, and they also need substantial improvement in practice. The research has practical implications for China's CMCs to develop necessary knowledge and the extent to which they need to be improved to provide a better quality of services in future.

Raman spectroscopy techniques for detection of energetic materials at high and low concentration

This thesis presents the development of a rapid, sensitive and reproducible spectroscopic method for the detection of TNT in forensic and environmental applications. Simple nano sensors prepared by cost effective methods were utilized as sensitive platforms for the detection of TNT by surface enhanced Raman spectroscopy. The optimization of the substrate and the careful selection of a suitable recognition molecule contributed to the significant improvements of sensitive and selective targeting over current detection methods. The work presented in this thesis paves the way for effective detection and monitoring of explosives residues in law enforcement and environmental health applications.

Designing informal learning experiences for early career academics using a knowledge ecosystem model

This article presents a ‘knowledge ecosystem’ model of how early career academics experience using information to learn while building their social networks for developmental purposes. Developed using grounded theory methodology, the model offers a way of conceptualising how to empower early career academics through 1) agency (individual and relational) and 2) facilitation of personalised informal learning (design of physical and virtual systems and environments) in spaces where developmental relationships are formed including programs, courses, events, community, home and social media. It is suggested that the knowledge ecosystem model is suitable for use in designing informal learning experiences for early career academics.

Application of image processing techniques for frog call classification

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

Building instance knowledge network for word sense disambiguation

In this paper, a new high precision focused word sense disambiguation (WSD) approach is proposed, which not only attempts to identify the proper sense for a word but also provides the probabilistic evaluation for the identification confidence at the same time. A novel Instance Knowledge Network (IKN) is built to generate and maintain semantic knowledge at the word, type synonym set and instance levels. Related algorithms based on graph matching are developed to train IKN with probabilistic knowledge and to use IKN for probabilistic word sense disambiguation. Based on the Senseval-3 all-words task, we run extensive experiments to show the performance enhancements in different precision ranges and the rationality of probabilistic based automatic confidence evaluation of disambiguation. We combine our WSD algorithm with five best WSD algorithms in senseval-3 all words tasks. The results show that the combined algorithms all outperform the corresponding algorithms.

Place making facilitators of knowledge and innovation spaces: Insights from European best practices

Knowledge economy seeks its nourishment from diversity and dissemination of ideas and creativity of its talent base. This has led to the acknowledgement of place making as a major strategy to attract and retain the knowledge base into the emerging knowledge and innovation spaces. The study seeks to explore the adoption of place making in this context. Literature and practice provide information to understand the evolution of various spatial typologies and the specialised role of place making in such locations. This helps in determining the key facilitators of place making. The paper takes an interdisciplinary approach and develops an integrated conceptual framework considering dimensions and facilitators of place making. Through the lens of the framework, best practices across Europe—i.e., Cambridge Science Park (UK), 22@Barcelona (Spain), Arabianranta (Finland), Strijp-S (Netherlands), and Digital Hub (Ireland)—are scrutinised to highlight various approaches to place making. The findings provide insights and a discussion into the interplay of form, function, image and underlying processes in globally emerging spatial typologies of contemporary knowledge and innovation spaces.

Engineering cellulose nanofibre suspensions to control filtration resistance and sheet permeability

This study examines and quantifies the effect of adding polyelectrolytes to cellulose nanofibre suspensions on the gel point of cellulose nanofibre suspensions, which is the lowest solids concentration at which the suspension forms a continuous network. The lower the gel point, the faster the drainage time to produce a sheet and the higher the porosity of the final sheet formed. Two new techniques were designed to measure the dynamic compressibility and the drainability of nanocellulose–polyelectrolyte suspensions. We developed a master curve which showed that the independent variable controlling the behaviour of nanocellulose suspensions and its composite is the structure of the flocculated suspension which is best quantified as the gel point. This was independent of the type of polyelectrolyte used. At an addition level of 2 mg/g of nanofibre, a reduction in gel point over 50 % was achieved using either a high molecular weight (13 MDa) linear cationic polyacrylamide (CPAM, 40 % charge), a dendrimer polyethylenimine of high molecular weight of 750,000 Da (HPEI) or even a low molecular weight of 2000 Da (LPEI). There was no significant difference in the minimum gel point achieved, despite the difference in polyelectrolyte morphology and molecular weight. In this paper, we show that the gel point controls the flow through the fibre suspension, even when comparing fibre suspensions with solids content above the gel point. A lower gel point makes it easier for water to drain through the fibre network,reducing the pressure required to achieve a given dewatering rate and reducing the filtering time required to form a wet laid sheet. We further show that the lower gel point partially controls the structure of the wet laid sheet after it is dried. Halving the gel point increased the air permeability of the dry sheet by 37, 46 and 25 %, when using CPAM, HPEI and LPEI, respectively. The resistance to liquid flow was reduced by 74 and 90 %, when using CPAM and LPEI. Analysing the paper formed shows that sheet forming process and final sheet properties can be engineered and controlled by adding polyelectrolytes to the nanofibre suspension.

Feasibility of autologous bone marrow mesenchymal stem cell-derived extracellular matrix scaffold for cartilage tissue engineering

Extracellular matrix (ECM) materials are widely used in cartilage tissue engineering. However, the current ECM materials are unsatisfactory for clinical practice as most of them are derived from allogenous or xenogenous tissue. This study was designed to develop a novel autologous ECM scaffold for cartilage tissue engineering. The autologous bone marrow mesenchymal stem cell-derived ECM (aBMSC-dECM) membrane was collected and fabricated into a three-dimensional porous scaffold via cross-linking and freeze-drying techniques. Articular chondrocytes were seeded into the aBMSC-dECM scaffold and atelocollagen scaffold, respectively. An in vitro culture and an in vivo implantation in nude mice model were performed to evaluate the influence on engineered cartilage. The current results showed that the aBMSC-dECM scaffold had a good microstructure and biocompatibility. After 4 weeks in vitro culture, the engineered cartilage in the aBMSC-dECM scaffold group formed thicker cartilage tissue with more homogeneous structure and higher expressions of cartilaginous gene and protein compared with the atelocollagen scaffold group. Furthermore, the engineered cartilage based on the aBMSC-dECM scaffold showed better cartilage formation in terms of volume and homogeneity, cartilage matrix content, and compressive modulus after 3 weeks in vivo implantation. These results indicated that the aBMSC-dECM scaffold could be a successful novel candidate scaffold for cartilage tissue engineering.

Mathematical modelling of the impaction and spreading of spray droplets on leaves

This thesis concerns the development of mathematical models to describe the interactions that occur between spray droplets and leaves. Models are presented that not only provide a contribution to mathematical knowledge in the field of fluid dynamics, but are also of utility within the agrichemical industry. The thesis is presented in two parts. First, thin film models are implemented with efficient numerical schemes in order to simulate droplets on virtual leaf surfaces. Then the interception event is considered, whereby energy balance techniques are employed to instantaneously predict whether an impacting droplet will bounce, splash, or adhere to a leaf.

Special Issue on Spatial Moment Techniques for Modelling Biological Processes

Over the last two decades, there has been an increasing awareness of, and interest in, the use of spatial moment techniques to provide insight into a range of biological and ecological processes. Models that incorporate spatial moments can be viewed as extensions of mean-field models. These mean-field models often consist of systems of classical ordinary differential equations and partial differential equations, whose derivation, at some point, hinges on the simplifying assumption that individuals in the underlying stochastic process encounter each other at a rate that is proportional to the average abundance of individuals. This assumption has several implications, the most striking of which is that mean-field models essentially neglect any impact of the spatial structure of individuals in the system. Moment dynamics models extend traditional mean-field descriptions by accounting for the dynamics of pairs, triples and higher n-tuples of individuals. This means that moment dynamics models can, to some extent, account for how the spatial structure affects the dynamics of the system in question.