https://nova.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:39900 Wed 28 Feb 2024 14:54:52 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45920 Wed 22 Mar 2023 17:37:52 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:15434 Wed 11 Apr 2018 17:16:15 AEST ]]> 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:12754 T, discount rate r, and time T are considered. The results are given in terms of mean cumulative annual loss. The discount rate was found to be a key parameter affecting cumulative damage prediction. Since damage often only occurs once a threshold level of hazard has occurred (such as a flood level exceeding the floor level of a house), then the influence of this threshold value on damage is significant. Damage/impact models utilising probabilistic hazard were used to verify the robustness of results. Results are very sensitive to threshold. Time-dependent predictions of damage can help decisionmakers assess the impact of climate change and the economic viability of climate change adaptation strategies.]]> Wed 11 Apr 2018 13:07:55 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:25979 Wed 04 Sep 2019 12:23:32 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:33560 Wed 04 Sep 2019 12:18:53 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:13506 Tue 27 Aug 2024 18:14:06 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45944 Tue 08 Nov 2022 10:07:15 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45917 Tue 08 Nov 2022 09:32:08 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:37389 d), regional wind speeds (V_R), and other relevant considerations to assist in designing and building structures. AS/NZS 1170.2:2011 identify eight individual wind regions for Australia, with regionally specific M_d and V_R values which can be applied to calculate the directional wind speed. Given the vast expanse of Australia and various wind hazards in both cyclonic and non-cyclonic wind areas, it is essential to accurately quantify current and future wind risk that is representative of the local wind climate. As such, this study compares the M_d component of the AS/NZS 1170.2:2011 standards for four wind regions (A1, A4, B and C), with observed wind data (4 stations per wind region) - see Figure 1 for more details. Findings suggest that the wind regions analysed do not adequately represent the wind climates of the stations considered within each wind region. For example, while AS/NZS 1170.2:2011 assume that the same directional wind multipliers should apply for Perth and Adelaide, we show that the prevailing direction of the strongest wind gusts (≥99.9^th percentile) varies considerably between stations. Using station data to model M_d suggests that AS/NZS 1170.2:2011 can underestimate wind risk for region A1 (70% of cases) and overestimate between 62-77% of cases for regions A4, B and C. These results highlight some inadequacies with AS/NZS 1170.2:2011 and suggests more regionally-specific wind direction multipliers that are more indicative of local wind climates are required.]]> Thu 29 Oct 2020 13:27:02 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:40500 Thu 14 Jul 2022 09:13:17 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:40477 Thu 14 Jul 2022 08:52:12 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:45860 Thu 08 Dec 2022 09:57:56 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:42737 Thu 01 Sep 2022 13:39:41 AEST ]]> 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: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:22937 Sat 24 Mar 2018 07:16:49 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:23128 ij). Note that each row (column) of D is associated with a demand (facility) location. We say that column k dominates column l if d_ik ≤ d_il for all i ≠ k . We use the term strongly dominates in the case of strict inequalities. Observe that locating a facility at a dominated location l would provide no advantage to locating a facility at k except possibly in serving the demands of customers in location l. Further, strongly dominated columns would only be used for ‘self-serve’. Consequently, dominated column can be dropped to generate a feasible solution and the location can later be considered as a possible ‘self-service’ facility. We extend the concept of dominance somewhat further as follows. We say columns k and l dominate column j if d_ij ≤min{d_ik, d_il} for all i ≠ j . In this case there is no advantage in using location j (except for serving customers in location j) when locations k and l are used. So again we can drop the dominated column j if columns k and l are used. The term strongly is used as before. We further extend this concept of dominance as follows. We say that column k partially dominates column l if d_ik ≤ d_il for at least half or more of the entries for which i ≠ k . Similarly, we say columns k and l partially dominate column j if d_ij ≥ min{d_ik, d_il} for at least half or more of the entries for which i ≠ j. Partially dominated columns correspond to nodes which may be assigned ‘self-serve’ facilities in the original and the reduced matrix. In this paper, we developed a new greedy algorithm based on a concept known as dominance to obtain solutions for the p-median problem. This concept reduces the number of columns of a distance matrix by considering potential facilities that are near and those that are far from the population or demand. We illustrate our ideas and the algorithm with an example. We further applied the new algorithm to effectively locate additional ambulance stations in the Central and South East metropolitan areas of Perth to complement the existing ones. We also compare the performance of our new Greedy Reduction Algorithm (GRA) with the existing greedy algorithm of the p-median problem.]]> Sat 24 Mar 2018 07:16:36 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:38994 Mon 28 Mar 2022 14:20:40 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:38969 Mon 21 Mar 2022 14:48:36 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:49251 Mon 08 May 2023 10:42:38 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:49252 Mon 08 May 2023 10:42:08 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:40427 p-center problem which addresses the difficulty of minimizing the maximum distance that a demand or population is from its closet facility given that p facilities are to be located. The third category refers to those designed to minimize the average weighted distance or time. This objective leads to a location problem known as the p-median problem. The p-median problem finds the location of p facilities to minimize the demand weighted average or total distance between demand or population and their closest facility. The objective of this study is to discuss the importance of the application of optimization models (maximal covering location and the p-median models) to locate health care facilities. We apply the p-median models and the maximal covering location models to real data from Mackay metropolitan area in Queensland, Australia. We compare the two models using the real data and with existing ambulance stations. The study shows that the p-median model gives a better solution than the maximal covering location model. We also noted that the results of the maximal covering location model depend on the pre-determined weighted coverage distance.]]> Fri 22 Jul 2022 14:30:27 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:40369 Fri 08 Jul 2022 16:09:37 AEST ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:53574 Fri 08 Dec 2023 15:46:55 AEDT ]]> https://nova.newcastle.edu.au/vital/access/ /manager/Repository/uon:53578 Fri 08 Dec 2023 15:39:50 AEDT ]]>