Isadore Breslau papers undated, 1938-1975

This collection consists primarily of correspondence, notes and memoranda relating to his work with the American Zionist Bureau (1939-40) and the Zionist Organization of America (1941). Also includes correspondence as U.S. Army Chaplain (1943) stationed in Daytona Beach, Florida. Among the more important correspondents are Louis D. Brandeis, Stephen S. Wise, Solomon Goldman and Robert Szold.

Factors affecting log traits and green rotary-peeled veneer recovery from temperate eucalypt plantations

High levels of percentage green veneer recovery can be obtained from temperate eucalypt plantations. Recovery traits are affected by site and log position in the stem. Of the post-felling log traits studied, out-of-roundness was the best predictor of green recovery.

Influence of site, storage and steaming on Eucalyptus nitens log-end splitting

Key message Log-end splitting is one of the single most important defects in veneer logs. We show that log-end splitting in the temperate plantation species Eucalyptus nitens varies across sites and within-tree log position and increases with time in storage. Context Log-end splitting is one of the single most important defects in veneer logs because it can substantially reduce the recovery of veneer sheets. Eucalyptus nitens can develop log-end splits, but factors affecting log-end splitting in this species are not well understood. Aims The present study aims to describe the effect of log storage and steaming on the development of log-end splitting in logs from different plantations and log positions within the tree. Methods The study was conducted on upper and lower logs from each of 41 trees from three 20–22-year-old Tasmanian E. nitens plantations. Log-end splitting was assessed immediately after felling, after transport and storage in a log-yard, and just before peeling. A pre-peeling steam treatment was applied to half the logs. Results Site had a significant effect on splitting, and upper logs split more than lower logs with storage. Splitting increased with tree diameter breast height (DBH), but this relationship varied with site. The most rapidly growing site had more splitting even after accounting for DBH. No significant effect of steaming was detected. Conclusion Log-end splitting varied across sites and within-tree log position and increased with time in storage.

Fine-tuning of unmanned aerial surveillance for ecological systems

Much of our understanding and management of ecological processes requires knowledge of the distribution and abundance of species. Reliable abundance or density estimates are essential for managing both threatened and invasive populations, yet are often challenging to obtain. Recent and emerging technological advances, particularly in unmanned aerial vehicles (UAVs), provide exciting opportunities to overcome these challenges in ecological surveillance. UAVs can provide automated, cost-effective surveillance and offer repeat surveys for pest incursions at an invasion front. They can capitalise on manoeuvrability and advanced imagery options to detect species that are cryptic due to behaviour, life-history or inaccessible habitat. UAVs may also cause less disturbance, in magnitude and duration, for sensitive fauna than other survey methods such as transect counting by humans or sniffer dogs. The surveillance approach depends upon the particular ecological context and the objective. For example, animal, plant and microbial target species differ in their movement, spread and observability. Lag-times may exist between a pest species presence at a site and its detectability, prompting a need for repeat surveys. Operationally, however, the frequency and coverage of UAV surveys may be limited by financial and other constraints, leading to errors in estimating species occurrence or density. We use simulation modelling to investigate how movement ecology should influence fine-scale decisions regarding ecological surveillance using UAVs. Movement and dispersal parameter choices allow contrasts between locally mobile but slow-dispersing populations, and species that are locally more static but invasive at the landscape scale. We find that low and slow UAV flights may offer the best monitoring strategy to predict local population densities in transects, but that the consequent reduction in overall area sampled may sacrifice the ability to reliably predict regional population density. Alternative flight plans may perform better, but this is also dependent on movement ecology and the magnitude of relative detection errors for different flight choices. Simulated investigations such as this will become increasingly useful to reveal how spatio-temporal extent and resolution of UAV monitoring should be adjusted to reduce observation errors and thus provide better population estimates, maximising the efficacy and efficiency of unmanned aerial surveys.

The Wieruszowski ancestry : from the mid 1700’s to the late 1900’s /

Family trees of the descendants of the Wieruszowski and of the Apt families from the mid 1700’s to the late 1900’s, from family sources and from other places.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

The averaging principle and the diagram technique

Rae and Davidson have found a striking connection between the averaging method generalised by Kruskal and the diagram technique used by the Brussels school in statistical mechanics. They have considered conservative systems whose evolution is governed by the Liouville equation. In this paper we have considered a class of dissipative systems whose evolution is governed not by the Liouville equation but by the last-multiplier equation of Jacobi whose Fourier transform has been shown to be the Hopf equation. The application of the diagram technique to the interaction representation of the Jacobi equation reveals the presence of two kinds of interactions, namely the transition from one mode to another and the persistence of a mode. The first kind occurs in the treatment of conservative systems while the latter type is unique to dissipative fields and is precisely the one that determines the asymptotic Jacobi equation. The dynamical equations of motion equivalent to this limiting Jacobi equation have been shown to be the same as averaged equations.

Quarantine and surveillance strategies for plant pathogen detection and control

Incursions of plant pests and diseases pose serious threats to food security, agricultural productivity and the natural environment. One of the challenges in confidently delimiting and eradicating incursions is how to choose from an arsenal of surveillance and quarantine approaches in order to best control multiple dispersal pathways. Anthropogenic spread (propagules carried on humans or transported on produce or equipment) can be controlled with quarantine measures, which in turn can vary in intensity. In contrast, environmental spread processes are more difficult to control, but often have a temporal signal (e.g. seasonality) which can introduce both challenges and opportunities for surveillance and control. This leads to complex decisions regarding when, where and how to search. Recent modelling investigations of surveillance performance have optimised the output of simulation models, and found that a risk-weighted randomised search can perform close to optimally. However, exactly how quarantine and surveillance strategies should change to reflect different dispersal modes remains largely unaddressed. Here we develop a spatial simulation model of a plant fungal-pathogen incursion into an agricultural region, and its subsequent surveillance and control. We include structural differences in dispersal via the interplay of biological, environmental and anthropogenic connectivity between host sites (farms). Our objective was to gain broad insights into the relative roles played by different spread modes in propagating an invasion, and how incorporating knowledge of these spread risks may improve approaches to quarantine restrictions and surveillance. We find that broad heuristic rules for quarantine restrictions fail to contain the pathogen due to residual connectivity between sites, but surveillance measures enable early detection and successfully lead to suppression of the pathogen in all farms. Alternative surveillance strategies attain similar levels of performance by incorporating environmental or anthropogenic dispersal risk in the prioritisation of sites. Our model provides the basis to develop essential insights into the effectiveness of different surveillance and quarantine decisions for fungal pathogen control. Parameterised for authentic settings it will aid our understanding of how the extent and resolution of interventions should suitably reflect the spatial structure of dispersal processes.

Social evolution - Has nature ever rewound the tape?

Social insects such as ants, bees, wasps and termites exhibit extreme forms of altruism where some individuals remain sterile and assist other individuals in reproduction. Hamilton's inclusive fitness theory provides a powerful framework for investigating the evolution of such altruism. Using the paper wasp Ropalidia marginata, we have quantified and delineated the role of ecological, physiological, genetic and demographic factors in social evolution. An interesting feature of the models we have developed is their symmetry so that either altruism or selfishness can evolve, depending on the numerical values of various parameters. This suggests that selfish/solitary behaviour must occasionally re-emerge even from the eusocial state, It is useful to contemplate expected intermediate states during such potential reversals. We can perhaps envisage three successive steps in such a hypothetical process: i) workers revolt against the hegemony of the queen and challenge her status as the sole reproductive, ii) workers stop producing queens and one or more of them function as egg layers (functional queen/s) capable of producing both haploid as well as diploid offspring and iii) social evolution reverses completely so that a eusocial species becomes solitary, at least facultatively. It appears that the third step, namely transition from eusociality to the solitary state, is rare and has been restricted to transitions from the primitively eusocial state only. The absence of transitions from the highly eusocial state to the solitary state may be attributed to a number of 'preventing mechanisms' such as (a) queen control of workers (b) loss of spermathecae and ability to mate (c) morphological specialization (d) caste polyethism and (e) homeostasis, which must each make the transition difficult and, taken together, perhaps very difficult. However, the discovery of a transition from the highly eusocial to the solitary state can hardly he ruled out, given that little or no effort has gone into its detection. In this paper I discuss social evolution and its possible reversal and cite potential examples of stages in the transition from the social to the solitary.

Keeping kids sun safe: exploring parents' beliefs about their young child's sun-protective behaviours

Objectives Melanoma of the skin is the third most commonly diagnosed cancer in Australia. Given the high incidence of sunburn in children and the level of sun protection provided by parents is often infrequent and/or insufficient, this research employed qualitative methodology to examine parents' beliefs about their young child's sun safe behaviour. Methods Parents (N = 21; n = 14 mothers, n = 7 fathers) of children aged 2–5 years participated in focus groups to identify commonly held beliefs about their decision to sun protect their child. Data were analysed using thematic content analysis. Results Parents generally had knowledge of the broad sun safe recommendations; however, the specific details of the recommendations were not always known. Parents reported adopting a range of sun-protective measures for their child, which depended on the time of year. A range of advantages (e.g. reducing the risk of skin cancer, developing good habits early and parental peace of mind), disadvantages (e.g. false sense of safety and preventing vitamin D absorption), barriers (e.g. child refusal) and facilitators (e.g. routine and accessibility) to performing sun safe practices were identified. Normative pressures and expectations also affected parents' motivation to be sun safe for their child. Conclusions These identified beliefs can be used to inform interventions to improve sun safe behaviours in young children who reside in a region that has the highest skin cancer incidence in the world.

A psychosocial analysis of parent's decisions for limiting their young child's screen time: An examination of attitudes, social norms and roles, and control perceptions

- Objectives Preschool-aged children spend substantial amounts of time engaged in screen-based activities. As parents have considerable control over their child's health behaviours during the younger years, it is important to understand those influences that guide parents' decisions about their child's screen time behaviours. - Design A prospective design with two waves of data collection, 1 week apart, was adopted. - Methods Parents (n = 207) completed a Theory of Planned Behaviour (TPB)-based questionnaire, with the addition of parental role construction (i.e., parents' expectations and beliefs of responsibility for their child's behaviour) and past behaviour. A number of underlying beliefs identified in a prior pilot study were also assessed. - Results The model explained 77% (with past behaviour accounting for 5%) of the variance in intention and 50% (with past behaviour accounting for 3%) of the variance in parental decisions to limit child screen time. Attitude, subjective norms, perceived behavioural control, parental role construction, and past behaviour predicted intentions, and intentions and past behaviour predicted follow-up behaviour. Underlying screen time beliefs (e.g., increased parental distress, pressure from friends, inconvenience) were also identified as guiding parents' decisions. - Conclusion Results support the TPB and highlight the importance of beliefs for understanding parental decisions for children's screen time behaviours, as well as the addition of parental role construction. This formative research provides necessary depth of understanding of sedentary lifestyle behaviours in young children which can be adopted in future interventions to test the efficacy of the TPB mechanisms in changing parental behaviour for their child's health.

The effect of exercise and light on mood

The aim of the study was to compare the effect physical exercise and bright light has on mood in healthy, working-age subjects with varying degrees of depressive symptoms. Previous research suggests that exercise may have beneficial effects on mood at least in subjects with depression. Bright light exposure is an effective treatment of winter depression, and possibly of non-seasonal depression as well. Limited data exist on the effect of exercise and bright light on mood in non-clinical populations, and no research has been done on the combination of these interventions. Working-age subjects were recruited through occupational health centres and 244 subjects were randomized into intervention groups: exercise, either in bright light or normal lighting, and relaxation / stretching sessions, either in bright light or normal gym lighting. During the eight-week intervention in midwinter, subjects rated their mood using a self-rating version of the Hamilton Depression Scale with additional questions for atypical depressive symptoms. The main finding of the study was that both exercise and bright-light exposure were effective in treating depressive symptoms. When the interventions were combined, the relative reduction in the Hamilton Depression Scale was 40 to 66%, and in atypical depressive symptoms even higher, 45 to 85%. Bright light exposure was more effective than exercise in treating atypical depressive symptoms. No single factor could be found that would predict a good response to these interventions. In conclusion, aerobic physical exercise twice a week during wintertime was effective in treating depressive symptoms. Adding bright light exposure to exercise increased the benefit, especially by reducing atypical depressive symptoms. Since this is so, this treatment could prevent subsequent major depressive episodes among the population generally.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

A detailed study is presented of the expected performance of the ATLAS detector. The reconstruction of tracks, leptons, photons, missing energy and jets is investigated, together with the performance of b-tagging and the trigger. The physics potential for a variety of interesting physics processes, within the Standard Model and beyond, is examined. The study comprises a series of notes based on simulations of the detector and physics processes, with particular emphasis given to the data expected from the first years of operation of the LHC at CERN.

Universal SU(2) gadget for polarization optics

We present a design of a universal gadget, consisting of two half-wave plates and two quarter-wave plates coaxially mounted, which can realize every SU (2) polarization optical transformation; to realize a given SU (2) element one simply has to rotate these plates about the common axis to angular positions characteristic of the element. The design is also geometrically interpreted in terms of Hamilton's theory of turns for the group SU (2).