https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:15391 Wed 11 Apr 2018 16:58:28 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:15390 Wed 11 Apr 2018 11:35:59 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:28623 Wed 11 Apr 2018 10:36:00 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:15372 Wed 11 Apr 2018 10:19:10 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:20350 Wed 04 Sep 2019 12:35:15 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:21703 a) _a∈A. We permit parallel arcs, i.e. there may exist more than one arc in A having the same start and end node. By dδ¯(v) and dδ⁺ (v) we denote the set of incoming and outgoing arcs of node v, respectively. We consider this network over a set of T time periods indexed by the set [T] := {1, 2, . . . ,T }, and our objective is to maximize the total flow from s to t. In addition, we are given a subset J ⊆ A of arcs that have to be shut down for exactly one time period in the time horizon. In other words, there is a set of maintenance jobs, one for each arc in J, each with unit processing time. Our optimization problem is to choose these outage time periods in such a way that the total flow from s to t is maximized. More formally, this can be written as a mixed binary program as follows: (formula could not be replicated: see full text) where x_ai ≥ 0 for a ∈ A and i ∈ [T] denotes the flow on arc a in time period i, and y_ai ∈ {0, 1} for a ∈ J and i ∈ [T] indicates when the arc a is not shut down for maintenance in time period i. We present a branch-and-bound algorithm called the "Partial-State algorithm" to solve the problem for single transhipment node networks i.e. networks with |V| = 3. Unit processing time of each job leads to formation of symmetries in the solution space. We include powerful symmetry breaking rules in the algorithm to make it more efficient. We provide an easily-computer combinatorial expression that is proved to give the value of LP-relaxation of the problem at each node of the branch-and-bound tree. We also provide another upper bound which is even stronger than the LP value at each node of the tree, and show how this improves the run time of the algorithm.]]> Sat 24 Mar 2018 08:06:24 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:21704 Sat 24 Mar 2018 08:06:23 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:28860 Sat 24 Mar 2018 07:40:27 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:27463 Eucalyptus leucoxylon and E. tricarpa) from the Maryborough region of Victoria between 1940 and 1962. Monthly behaviour (start, peak, finish, monthly intensity, duration and success) in budding and flowering was assessed using the indices of Keatley et al. (1999) and Keatley & Hudson (2007). Although E. tricarpa buds are significantly (P < 0.01) positively and linearly related to higher minimum temperature (≥ 9°C) both flowering and buds decrease significantly with maximum temperature (>21°C) (P < 0.01). Models of flowering including current bud status and climate show that E. tricarpa flowering is positively related to current budding intensities (buds > 4.5) (P = 0.0000) and increases with elevated rainfall (from 40 to approximately 88 mm) (P=0.045) (R²=60.8%). Inclusion of current budding as well as budding intensity 1 to 3 months prior to flowering in the models show E. tricarpa’s flowering to significantly decrease and cease above 7.7°C minimum temperature, and increase with increased rainfall between appropriately 44 and 93 mm. Budding 2 months prior is a positive influence (P < 0.007), combined current budding and budding 2 months prior indicate flowering commences within the budding range of 4 to 6 (R²=71.4%). For E. tricarpa minimum temperature is shown to drive increased budding but is associated with decreased flowering. Maximum temperature is associated with both increased budding and increased flowering for E. tricarpa; and flowering increases non-linearly both with elevated rainfall (from 40 -90 mm) and with increased buds. For E. leucoxylon buds are significantly (P < 0.01) negatively and linearly related to elevated maximum temperature (> 23°C) (Z = -3.2, P < 0.0001) and buds increase with increasing minimum temperature ((≥ 9°C) (Z =1.92, P < 0.08, 10% sig). Budding is significantly but nonlinearly influenced by rainfall: rain up to 40 mm has a positive influence and 40 to 80 negative. Models of E. leucoxylon flowering, which include current bud status and climate, show that E. leucoxylon’s flowering is positively and nonlinearly related to current buds (buds > 5.5) (P = 0.000001) and decreases significantly with elevated minimum temperature (≥ 8.5°C) (Z = - 2.38, P < 0.0001) (R² = 42.6%). Inclusion of budding 1 to 3 months in the models show E. leucoxylon flowering to significantly increase with higher current bud quantity (Z = 2.57, P < 0.0001) and nonlinearly with respect to bud quantity 2 months prior (P < 0.005) - with flowering commencing with bud intensity above 4.5 and decreasing when buds reach 7.0 (R²=68.9%). This study has confirmed that for flowering to start, buds must have reached a particular maturity, before flowering occurs. For E. tricarpa this seems to occur when bud intensity has reached greater than 4.5, with a slightly lower value for E. leucoxylon, indicating that this species buds need longer to mature - this in turn further assists in separating the temporal flowering peaks between the two species. Additionally, a maximum flowering intensity is indicated with the inclusion of lagged budding: 6.0 for E. tricarpa and 7.0 for E. leucoxlyon. The inclusion of lagged budding found that budding two months prior was influential on flowering. Noteworthy is that 2 months is the most common period when temperature has the greatest influence on flowering (Hudson and Keatley, 2010a; Hudson et al., 2011a; Hudson et al., 2011c; Menzel and Sparks, 2006). These results indicate that it might not just be temperature, but temperature influencing the development of buds, which in turns influences flowering. This needs further work and the examination of additional species, but given that flowering is dependent on budding, this postulate makes sense (Primack, 1987).]]> Sat 24 Mar 2018 07:32:42 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:28931 wl denote the number of letters, l, in word w. One approach to measuring the similarity of the two sets is to compare the average value of the attribute across the sets, i.e. measure based on the difference |1/B₁∑w∈B₁ f,sub>wl - 1/B₂∑w∈B₂ f,sub>wl|. However it is well known that very different distributions can have the same average value. For example, defining the attribute value count vectors ^ηB_ic = |{w ∈ B_i : f,sub>wl = c}| for each i = 1, 2 and each positive integer value c that could be the length of a word, say ranging from 1 to 5 letters. This approach would consider two sets with ^ηB_i = (3, 3, 3, 3, 3) and ^ηB₂ = (0, 0, 15, 0, 0) to be very similar, whereas clearly the experience of a human subject to these two sets might be very different: the former has an even spread of word lengths whereas the latter has all words of identical length. The existing metaheuristics address this issue by using group characteristics, such as average or standard deviation, which take into account the relative values of the heuristics. However, as we have shown, these group characteristics do not adequately measure the similarity of the sets. Recent MIP approaches measure similarity between sets using the entire histogram, i.e. they measure based on the difference |^ηB₁c − ^ηB₂c| for each c. Whilst this provides a richer measure of similarity than simple averages, it does not take into account the relationships between attribute values. To return to the word length illustration, the length count vectors (0, 3, 3, 3, 6) and (3, 4, 5, 0, 3) are “equally” different from (3, 3, 3, 3, 3) component-wise. But it is common sense that words of length 2 or 3 are more similar to words of length 4 than words of length 1 are to words of length 5, so the vector (0, 3, 3, 3, 6) “replacing” three words of length 1 with three of length 5 is less similar to (3, 3, 3, 3, 3) than is (3, 4, 5, 0, 3), which “replaces” three words of length 4 with two of length 3 and one of length 2. The component-wise histogram measure does not take into account similarities and differences between attribute values. This paper briefly reviews the existing approaches to automate picking items for experimental sets, and then discusses new MIP approaches that address the entire distribution of attribute values across sets while also taking into account the relationships between attribute values. Numerical results on psycholinguistic data sets are analysed, and the alternative approaches compared.]]> Sat 24 Mar 2018 07:31:28 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:28927 Sat 24 Mar 2018 07:31:26 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:23591 40K concentration of the soil samples determined. Relationships between the field ⁴⁰K and NARM ⁴⁰K were investigated using a digital elevation model, the national soil atlas model and the national geology model. Our results showed that the NARM and field data are correlated and that this correlation extends across changing soil types and geology. A complex relationship with topographical features was also determined which needs further investigation.]]> Sat 24 Mar 2018 07:13:22 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:23491 Sat 24 Mar 2018 07:13:05 AEDT ]]>