A toy model of food production in a connected landscape

The drive to maximize food production in a sustainable manner is a paramount concern for farmers and governments. The aim of food producers is to maximize their production yield employing actions such as application of fertilizer or pesticide they believe help to achieve this aim. However, farms do not exist in isolation, but rather share a landscape with neighbors forming networks where any action taken by any one farmer affects their neighbors who are forced to take mitigating actions creating a complicated set of interactions. Understanding these [non-]cooperative interactions and their effect on the shared ecosystem is important to develop food security strategies while protecting the environment and allowing farmers to make a living. We introduce a simple competitive agent based model in which agents produce food that is sold at a fixed price (we ignore market dynamics and do not include explicit punishment on any agent). We analyzed agent's profits in several simple scenarios allowing us to identify the most advantageous set of actions for maximizing the yield (and thus profit) for each farmer. We show that the effect of the structure of the network on each farm has implications on the actions taken by agents. These results have implications for the understanding of the effects of farming practices on the environment and how different levels of cooperation between farmers, taking into account the local terrain, can be used to incentivise producers to minimise the effects on the environment while maximizing yields

Guidelines for type 2 diabetes: keeping a finger on the pulse

Cardiovascular disease remains the biggest cause of morbidity and mortality in patients with type 2 diabetes.1 Individual drugs from two classes of glucose-lowering agents, glucagon-like peptide-1 (GLP-1)receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors, have been shown in recent clinical trials to improve cardiovascular outcomes in patients with type 2 diabetes at high risk of cardiovascular disease. These new data are reflected in guidelines from several professional associations, but not in the National Institute for Health and Care Excellence (NICE) guidelines in the UK.2 We believe that NICE and other national and international health authorities should respond rapidly to new data, particularly when there is potential to improve outcomes and save lives

Two-Dimensional Ising Model with Competing Interactions as a Model for Interacting pi-Rings

We investigate the Ising model on a square lattice with antiferromagnetic exchange between nearest and next-nearest neighbors and show that at low temperatures stripe-like and droplet-like superstructures appear. We show that the competing interactions introduce a strong frustration that could plausibly describe the systems with dipole{dipole type interactions and pay particular attention to arrays of interacting Josephson ¼-rings

Two-dimensional Ising model with competing interactions: phase diagram and low-temperature remanent disorder

The two-dimensional Ising model with competing nearest-neighbor and diagonal interactions on the square lattice is studied by the transfer-matrix technique and by the Monte Carlo simulations. The phase diagram of this model is constructed with a special emphasis to the analysis of a glassy state arising as an order to disorder transition at low temperatures. Evidence of the glassy state (based, in particular, on the calculation of the average length of domain walls and on the Edwards-Anderson order parameter) and its characteristics are presented. It was shown that, in the frustrated Ising model, the domain-wall length correlates to the onset of the glassy state, that is, it may play the role of the order parameter for the Ising glass or for glasslike states in other frustrated magnetic systems

Investigating Causal links from Observed Features in the first COVID-19 Waves in California

Determining who is at risk from a disease is important in order to protect vulnerable subpopula- tions during an outbreak. We are currently in a SARS-COV-2 (commonly referred to as COVID-19) pandemic which has had a massive impact across the world, with some communities and individuals seen to have a higher risk of severe outcomes and death from the disease compared to others. These risks are compounded for people of lower socioeconomic status, those who have limited access to health care, higher rates of chronic diseases, such as hypertension, diabetes (type-2), obesity, likely due to the chronic stress of these types of living conditions. Essential workers are also at a higher risk of COVID-19 due to having higher rates of exposure due to the nature of their work. In this study we determine the important features of the pandemic in California in terms of cumulative cases and deaths per 100,000 of population up to the date of 5 July, 2021 (the date of analysis) using Pearson correlation coefficients between population demographic features and cumulative cases and deaths. The most highly correlated features, based on the absolute value of their Pearson Correlation Coefficients in relation to cases or deaths per 100,000, were used to create regression models in two ways: using the top 5 features and using the top 20 features filtered out to limit interactions between features. These models were used to determine a) the most significant features out of these subsets and b) features that approximate different potential forces on COVID- 19 cases and deaths (especially in the case of the latter set). Additionally, co-correlations, defined as demographic features not within a given input feature set for the regression models but which are strongly correlated with the features included within, were calculated for all features. The five features which had the highest correlations to cumulative cases per 100,000 were found to be the following: Overcrowding (% of households), Average Household Size, Hispanic ethnicity (% of population), Ages 0-19 (% population), education level of 9th to 12th with no high school diploma (% of population older than 25 years), and incidence rates of Long-term Diabetes Compli- cations (per 100,000 population). For cumulative deaths per 100,000, the feature set was similar except Overcrowding (% of households) replaced Long-term Diabetes Complications. The feature set for uncorrelated features was the same for both cases and deaths. This set was comprised of Overcrowding (% of households), Wholesale trade (% of workforce employed in), ‘Transportation, warehousing, and utilities’ (% of workforce employed in), and ‘Graduate or professional degree’ (% of population older than 25 years)

Spatio-temporal evaluation of social media as a tool for livestock disease surveillance

Recent outbreaks of Avian Influenza across Europe have highlighted the potential for syndromic surveillance systems that consider other modes of data, namely social media. This study investigates the feasibility of using social media, primarily Twitter, to monitor illness outbreaks such as avian flu. Using temporal, geographical, and correlation analyses, we investigated the association between avian influenza tweets and officially verified cases in the United Kingdom in 2021 and 2022. Pearson correlation coefficient, bivariate Moran's I analysis and time series analysis, were among the methodologies used. The findings show a weak, statistically insignificant relationship between the number of tweets and confirmed cases in a temporal context, implying that relying simply on social media data for surveillance may be insufficient. The spatial analysis provided insights into the overlaps between confirmed cases and tweet locations, shedding light on regionally targeted interventions during outbreaks. Although social media can be useful for understanding public sentiment and concerns during outbreaks, it must be combined with traditional surveillance methods and official data sources for a more accurate and comprehensive approach. Improved data mining techniques and real-time analysis can improve outbreak detection and response even further. This study underscores the need of having a strong surveillance system in place to properly monitor and manage disease outbreaks and protect public health.</p

A scholarship program for academic staff to develop exemplary online learning tasks

There is a strong impetus for blended learning approaches to be more widely adopted in higher education but finding an effective model for professional development of teaching staff can be problematic. In 2009, Curtin University developed an eTeaching and Learning Scholarship program for academic staff to develop exemplary online learning tasks that could be shared with the university community and inform future online teaching within their disciplines. This paper describes the design of the professional learning program together with early encouraging results that indicate both the willingness of the eScholars to incorporate additional learning technologies to extend the affordances of the university provisioned systems and to embrace authentic learner-centred tasks

Risk of Foot-and-Mouth Disease Spread Due to Sole Occupancy Authorities and Linked Cattle Holdings

Livestock movements in Great Britain are well recorded, have been extensively analysed with respect to their role in disease spread, and have been used in real time to advise governments on the control of infectious diseases. Typically, livestock holdings are treated as distinct entities that must observe movement standstills upon receipt of livestock, and must report livestock movements. However, there are currently two dispensations that can exempt holdings from either observing standstills or reporting movements, namely the Sole Occupancy Authority (SOA) and Cattle Tracing System (CTS) Links, respectively. In this report we have used a combination of data analyses and computational modelling to investigate the usage and potential impact of such linked holdings on the size of a Foot-and-Mouth Disease (FMD) epidemic. Our analyses show that although SOAs are abundant, their dynamics appear relatively stagnant. The number of CTS Links is also abundant, and increasing rapidly. Although most linked holdings are only involved in a single CTS Link, some holdings are involved in numerous links that can be amalgamated to form "CTS Chains" which can be both large and geographically dispersed. Our model predicts that under a worst case scenario of "one infected - all infected", SOAs do pose a risk of increasing the size (in terms of number of infected holdings) of a FMD epidemic, but this increase is mainly due to intra-SOA infection spread events. Furthermore, although SOAs do increase the geographic spread of an epidemic, this increase is predominantly local. Whereas, CTS Chains pose a risk of increasing both the size and the geographical spread of the disease substantially, under a worse case scenario. Our results highlight the need for further investigations into whether CTS Chains are transmission chains, and also investigations into intra-SOA movements and livestock distributions due to the lack of current data

Intelligent middleware for adaptive sensing of tennis coaching sessions

In professional tennis training matches, the coach needs to be able to view play from the most appropriate angle in order to monitor players activities. In this paper, we present a system which can adapt the operation of a series of cameras in order to maintain optimal system performance based on a set of wireless sensors. This setup is used as a testbed for an agent based intelligent middleware that can correlate data from many different wired and wireless sensors and provide effective in-situ decision making. The proposed solution is flexible enough to allow the addition of new sensors and actuators. Within this setup we also provide details of a case study for the embedded control of cameras through the use of Ubisense data

An Improved linearity ring oscillator-based current-to-digital converter

Many biosensors produce single-ended current outputs. Lab-on-chip applications demand parallel readout channels requiring low area current-to-digital converters. High HD2 has limited the current controlled ring oscillator’s (CCROs) adoption as a low area, single-ended converter. This work improves CCRO open loop linearity by 10 dB. A wide-bandwidth current buffer is also designed. A low area (0.0025 mm 2 ), low power ( 357 μW ), single-ended, and 1 MHz bandwidth converter suitable for array readout is presented with the measured performance