Time, place and space – student group collaboration using Google Drive

Virtual working environments are intrinsic to the contemporary workplace and collaborative skills are a vital graduate capability. To develop students’ collaborative skills, first year medical laboratory science students undertake a group poster project, based on a blended learning model. Learning is scaffolded in lectures, workshops in collaborative learning spaces, practitioner mentoring sessions, and online resources. Google Drive provides an online collaborative space for students to realise tangible outcomes from this learning. A Google Drive document is created for each group and shared with members. In this space, students assign tasks and plan workflow, share research, progressively develop poster content, reflect and comment on peer contributions and use the messaging functions to ‘talk’ to group members. This provides a readily accessible, transparent record of group work, crucial in peer assessment, and a communication channel for group members and the lecturer, who can support groups if required. This knowledge creation space also augments productivity and effectiveness of face-to-face collaboration. As members are randomly allocated to groups and are often of diverse backgrounds and unknown to each other, resilience is built as students navigate the uncertainties and complexities of group dynamics, learning to focus on the goal of the team task as they constructively and professionally engage in team dialogue. Students are responsible and accountable for individual and group work. The use of Google Drive was evaluated in a survey including Likert scale and open ended qualitative questions. Statistical analysis was carried out. Results show students (79%) valued the inclusion of online space in collaborative work and highly appreciated (78%) the flexibility provided by Google Drive, while recognising the need for improved notification functionality. Teaching staff recognised the advantages in monitoring and moderating collaborative group work, and the transformational progression in student collaborative as well as technological skill acquisition, including professional dialogue.

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.

Design and flight testing of an integrated solar powered UAV and WSN for greenhouse gas monitoring emissions in agricultural farms

There is an increased interest in measuring the amount of greenhouse gases produced by farming practices . This paper describes an integrated solar powered Unmanned Air Vehicles (UAV) and Wireless Sensor Network (WSN) gas sensing system for greenhouse gas emissions in agricultural lands. The system uses a generic gas sensing system for CH4 and CO2 concentrations using metal oxide (MoX) and non-dispersive infrared sensors, and a new solar cell encapsulation method to power the unmanned aerial system (UAS)as well as a data management platform to store, analyze and share the information with operators and external users. The system was successfully field tested at ground and low altitudes, collecting, storing and transmitting data in real time to a central node for analysis and 3D mapping. The system can be used in a wide range of outdoor applications at a relatively low operational cost. In particular, agricultural environments are increasingly subject to emissions mitigation policies. Accurate measurements of CH4 and CO2 with its temporal and spatial variability can provide farm managers key information to plan agricultural practices. A video of the bench and flight test performed can be seen in the following link: https://www.youtube.com/watch?v=Bwas7stYIxQ

In vivo mri-based estimation of time-dependent elastic modulus in healthy arteries

The elastic properties of the arterial wall have been the subject of physiological, clinical and biomedical research for many years. There is convincing evidence that the elastic properties of the large arteries are seriously impaired in the presence of cardiovascular disease (CVD), due to alterations in the intrinsic structural and functional characteristics of vessels [1]. Early detection of changes in the elastic modulus of arteries would provide a powerful tool for both monitoring patients at high cardiovascular risk and testing the effects of pharmaceuticals aimed at stabilizing existing plaques by stiffening them or lowering the lipids.

Perylene-based profluorescent nitroxides for the rapid monitoring of polyester degradation upon weathering: An assessment

A profluorescent nitroxide possessing an isoindoline nitroxide moiety linked to a perylene fluorophore was developed to monitor radical mediated degradation of melamine-formaldehyde crosslinked polyester coil coatings in an industry standard accelerated weathering tester. Trapping of polyester-derived radicals (most likely C-radicals) that are generated during polymer degradation leads to fluorescent closed-shell alkoxy amines, which was used to obtain time-dependent degradation profiles to assess the relative stability of different polyesters towards weathering. The nitroxide probe couples excellent thermal stability and satisfactory photostability with high sensitivity and enables detection of free radical damage in polyesters under conditions that mimic exposure to the environment on a time scale of hours rather than months or years required by other testing methods. There are indications that the profluorescent nitroxide undergoes partial photo-degradation in the absence of polymer-derived radicals. Unexpectedly, it was also found that UV-induced fragmentation of the NO–C bond in closed-shell alkoxy amines leads to regeneration of the profluorescent nitroxide and the respective C-radical. The maximum fluorescence intensity that could be achieved with a given probe concentration is therefore not only determined by the amount of polyester radicals formed during accelerated weathering, but also by the light-driven side reactions of the profluorescent nitroxide and the corresponding alkoxy amine radical trapping products. Studies to determine the optimum probe concentration in the polymer matrix revealed that aggregation and re-absorption effects lowered the fluorescence intensity at higher concentrations of the profluorescent nitroxide, but too low probe concentrations, where these effects would be avoided, were not sufficient to trap the amount of polyester radicals formed upon weathering. The optimized experimental conditions were used to assess the impact of temperature and UV irradiance on polymer degradation during accelerated weathering.

Evaluation of aerial photography for predicting trends in structural attributes of Australian woodland including comparison with ground-based monitoring data

The accurate assessment of trends in the woody structure of savannas has important implications for greenhouse accounting and land-use industries such as pastoralism. Two recent assessments of live woody biomass change from north-east Australian eucalypt woodland between the 1980s and 1990s present divergent results. The first estimate is derived from a network of permanent monitoring plots and the second from woody cover assessments from aerial photography. The differences between the studies are reviewed and include sample density, spatial scale and design. Further analyses targeting potential biases in the indirect aerial photography technique are conducted including a comparison of basal area estimates derived from 28 permanent monitoring sites with basal area estimates derived by the aerial photography technique. It is concluded that the effect of photo-scale; or the failure to include appropriate back-transformation of biomass estimates in the aerial photography study are not likely to have contributed significantly to the discrepancy. However, temporal changes in the structure of woodlands, for example, woodlands maturing from many smaller trees to fewer larger trees or seasonal changes, which affect the relationship between cover and basal area could impact on the detection of trends using the aerial photography technique. It is also possible that issues concerning photo-quality may bias assessments through time, and that the limited sample of the permanent monitoring network may inadequately represent change at regional scales

Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring

This paper addresses the following predictive business process monitoring problem: Given the execution trace of an ongoing case,and given a set of traces of historical (completed) cases, predict the most likely outcome of the ongoing case. In this context, a trace refers to a sequence of events with corresponding payloads, where a payload consists of a set of attribute-value pairs. Meanwhile, an outcome refers to a label associated to completed cases, like, for example, a label indicating that a given case completed “on time” (with respect to a given desired duration) or “late”, or a label indicating that a given case led to a customer complaint or not. The paper tackles this problem via a two-phased approach. In the first phase, prefixes of historical cases are encoded using complex symbolic sequences and clustered. In the second phase, a classifier is built for each of the clusters. To predict the outcome of an ongoing case at runtime given its (uncompleted) trace, we select the closest cluster(s) to the trace in question and apply the respective classifier(s), taking into account the Euclidean distance of the trace from the center of the clusters. We consider two families of clustering algorithms – hierarchical clustering and k-medoids – and use random forests for classification. The approach was evaluated on four real-life datasets.

Real-time wide-area loading margin sensitivity (WALMS) in power systems

Loading margin sensitivity (LMS) has been widely used in applications in the realm of voltage stability assessment and control. Typically, LMS is derived based on system equilibrium equations near bifurcation and therefore requires full detailed system model and significant computation effort. Availability of phasor measurement units (PMUs) due to the recent development of wide-area monitoring system (WAMS) provides an alternative computation-friendly approach for calculating LMS. With such motivation, this work proposes measurement-based wide-area loading margin sensitivity (WALMS) in bulk power systems. The proposed sensitivity, with its simplicity, has great potential to be embedded in real-time applications. Moreover, the calculation of the WALMS is not limited to low voltage near bifurcation point. A case study on IEEE 39-bus system verifies the proposed sensitivity. Finally, a voltage control scenario demonstrates the potential application of the WALMS.

Specific detection of the Old World screwworm fly, Chrysomya bezziana, in bulk fly trap catches using real-time PCR.

The Old World screwworm fly (OWS), Chrysomya bezziana Villeneuve (Diptera: Calliphoridae), is a myiasis-causing blowfly of major concern for both animals and humans. Surveillance traps are used in several countries for early detection of incursions and to monitor control strategies. Examination of surveillance trap catches is time-consuming and is complicated by the presence of morphologically similar flies that are difficult to differentiate from Ch. bezziana, especially when the condition of specimens is poor. A molecular-based method to confirm or refute the presence of Ch. bezziana in trap catches would greatly simplify monitoring programmes. A species-specific real-time polymerase chain reaction (PCR) assay was designed to target the ribosomal DNA internal transcribed spacer 1 (rDNA ITS1) of Ch. bezziana. The assay uses both species-specific primers and an OWS-specific Taqman MGB probe. Specificity was confirmed against morphologically similar and related Chrysomya and Cochliomyia species. An optimal extraction protocol was developed to process trap catches of up to 1000 flies and the assay is sensitive enough to detect one Ch. bezziana in a sample of 1000 non-target species. Blind testing of 29 trap catches from Australia and Malaysia detected Ch. bezziana with 100% accuracy. The probability of detecting OWS in a trap catch of 50 000 flies when the OWS population prevalence is low (one in 1000 flies) is 63.6% for one extraction. For three extractions (3000 flies), the probability of detection increases to 95.5%. The real-time PCR assay, used in conjunction with morphology, will greatly increase screening capabilities in surveillance areas where OWS prevalence is low.

A hidden Markov model approach for determining vessel activity from vessel monitoring system data.

Many fisheries worldwide have adopted vessel monitoring systems (VMS) for compliance purposes. An added benefit of these systems is that they collect a large amount of data on vessel locations at very fine spatial and temporal scales. This data can provide a wealth of information for stock assessment, research, and management. However, since most VMS implementations record vessel location at set time intervals with no regard to vessel activity, some methodology is required to determine which data records correspond to fishing activity. This paper describes a probabilistic approach, based on hidden Markov models (HMMs), to determine vessel activity. A HMM provides a natural framework for the problem and, by definition, models the intrinsic temporal correlation of the data. The paper describes the general approach that was developed and presents an example of this approach applied to the Queensland trawl fishery off the coast of eastern Australia. Finally, a simulation experiment is presented that compares the misallocation rates of the HMM approach with other approaches.

Fisheries Queensland Long Term Monitoring Program Underwater Visual Census of twenty-four reefs on the Great Barrier Reef between 1999 and 2004.

In 1999, the Department of Employment, Economic Development and Innovation (DEEDI), Fisheries Queensland undertook a new initiative to collect long term monitoring data of various important stocks including reef fish. This data and monitoring manual for the reef fish component of that program which was based on Underwater Visual Census methodology of 24 reefs on the Great Barrier Reef between 1999 and 2004. Data was collected using six 50m x 5m transects at 4 sites on 24 reefs. Benthic cover type was also recorded for 10m of each transect. The attached Access Database contains 5 tables being: SITE DETAILS TABLE Survey year Data entry complete REF survey site ID Site # (1-4) Location (reef name) Site Date (date surveyed) Observer 1 (3 initials to identify who estimated fish lengths and recorded benthic cover) TRANSECT DETAILS Survey ID Transect Number (1-6) Time (the transect was surveyed) Visibility (in metres) Minimum Depth surveyed (m) Maximum Depth surveyed (m) Percent of survey completed (%) Comments SUBSTRATE Survey ID Transect Number (1-6) then % cover of each of eth following categories of benthic cover types Dead Coral Live Coral Soft Coral Rubble Sand Sponge Algae Sea Grass Other COORDINATES (over survey sites) from -14 38.792 to -19 44.233 and from 145 21.507 to 149 55.515 SIGHTINGS ID Survey ID Transect Number (1-6) CAAB Code Scientific Name Reef Fish Length (estimated Fork Length of fish; -1 = unknown or not recorded) Outside Transect (if a fish was observed outside a transect -1 was recorded) Morph Code (F = footballer morph for Plectropomus laevis, S = Spawning colour morph displayed)

The potential for monitoring and control of insect pests in Southern Hemisphere forestry plantations using semiochemicals

Southern Hemisphere plantation forestry has grown substantially over the past few decades and will play an increasing role in fibre production and carbon sequestration in future. The sustainability of these plantations is, however, increasingly under pressure from introduced pests. This pressure requires an urgent and matching increase in the speed and efficiency at which tools are developed to monitor and control these pests. To consider the potential role of semiochemicals to address the need for more efficient pest control in Southern Hemisphere plantations, particularly by drawing from research in other parts of the world. Semiochemical research in forestry has grown exponentially over the last 40 years but has been almost exclusively focussed on Northern Hemisphere forests. In these forests, semiochemicals have played an important role to enhance the efficiency of integrated pest management programmes. An analysis of semiochemical research from 1970 to 2010 showed a rapid increase over time. It also indicated that pheromones have been the most extensively studied type of semiochemical in forestry, contributing to 92% of the semiochemical literature over this period, compared with research on plant kairomones. This research has led to numerous applications in detection of new invasions, monitoring population levels and spread, in addition to controlling pests by mass trapping or disrupting of aggregation and mating signals. The value of semiochemicals as an environmentally benign and efficient approach to managing forest plantation pests in the Southern Hemisphere seems obvious. There is, however, a lack of research capacity and focus to optimally capture this opportunity. Given the pressure from increasing numbers of pests and reduced opportunities to use pesticides, there is some urgency to develop semiochemical research capacity.

A Bayesian hurdle model for analysis of an insect resistance monitoring database

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models. © 2014 Springer Science+Business Media New York.

Effect of Broadcast Baiting on Abundance Patterns of Red Imported Fire Ants (Hymenoptera: Formicidae) and Key Local Ant Genera at Long-Term Monitoring Sites in Brisbane, Australia

In 2001, the red imported fire ant (Solenopsis invicta Buren) was identified in Brisbane, Australia. An eradication program involving broadcast bait treatment with two insect growth regulators and a metabolic inhibitor began in September of that year and is currently ongoing. To gauge the impacts of these treatments on local ant populations, we examined long-term monitoring data and quantified abundance patterns of S. invicta and common local ant genera using a linear mixed-effects model. For S. invicta, presence in pitfalls reduced over time to zero on every site. Significantly higher numbers of S. invicta workers were collected on high-density polygyne sites, which took longer to disinfest compared with monogyne and low-density polygyne sites. For local ants, nine genus groups of the 10 most common genera analyzed either increased in abundance or showed no significant trend. Five of these genus groups were significantly less abundant at the start of monitoring on high-density polygyne sites compared with monogyne and low-density polygyne sites. The genus Pheidole significantly reduced in abundance over time, suggesting that it was affected by treatment efforts. These results demonstrate that the treatment regime used at the time successfully removed S. invicta from these sites in Brisbane, and that most local ant genera were not seriously impacted by the treatment. These results have important implications for current and future prophylactic treatment efforts, and suggest that native ants remain in treated areas to provide some biological resistance to S. invicta.

Monitoring healing progression and characterizing the mechanical environment in preclinical models for bone tissue engineering

The treatment of large segmental bone defects remains a significant clinical challenge. Due to limitations surrounding the use of bone grafts, tissue-engineered constructs for the repair of large bone defects could offer an alternative. Before translation of any newly developed tissue engineering (TE) approach to the clinic, efficacy of the treatment must be shown in a validated preclinical large animal model. Currently, biomechanical testing, histology, and microcomputed tomography are performed to assess the quality and quantity of the regenerated bone. However, in vivo monitoring of the progression of healing is seldom performed, which could reveal important information regarding time to restoration of mechanical function and acceleration of regeneration. Furthermore, since the mechanical environment is known to influence bone regeneration, and limb loading of the animals can poorly be controlled, characterizing activity and load history could provide the ability to explain variability in the acquired data sets and potentially outliers based on abnormal loading. Many approaches have been devised to monitor the progression of healing and characterize the mechanical environment in fracture healing studies. In this article, we review previous methods and share results of recent work of our group toward developing and implementing a comprehensive biomechanical monitoring system to study bone regeneration in preclinical TE studies.