A practical real option methodology for the evaluation of farm-in/out joint venture agreements in mineral exploration

Although the “real option valuation” (ROV) methodology offers extremely valuable insights in optimising investment decisions in the face of uncertainty, its practical acceptance in the mining industry has until recently been slow because of its perceived computational complexity. Recent conceptual advances in the use of binomial lattices and software developments in the areas of decision trees and dynamic programming, have significantly simplified ROV analysis and made it of practical application in day-to-day financial evaluations and decisions involving uncertainty. This paper provides general background on the ROV methodology and an example of how a typical farm-in/out agreement, as a preliminary to the establishment of a joint venture (JV), can be valued as a series of sequential and compound real options. For illustrative and quality assurance purposes, a simple farm-in/out agreement is valued from the point of view of the party acquiring equity in the project (the farm-inee) using two distinct methods, i.e. (a) a binomial lattice and (b) a decision tree in combination with binomial stochastic processes, in both cases neutralising risk using the user-friendly “risk-neutral probability”. The fact that exactly the same ROV is obtained by both methods provides confidence in the modified decision tree approach, which opens up the capacity to value the more complex sequential/compound real options inherent in real-life farm-in/out agreements. The model is then modified to incorporate a number of realistic contractual conditions often encountered in typical exploration and mining farm-in/out deals. The paper demonstrates how the increased complexity of the model can be relatively easily addressed using a decision tree with dynamic programming capability.     

Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods to the problem of predicting the condition or quality of the remnant indigenous vegetation across an extensive area of south-eastern Australia—the state of Victoria. The field data were obtained using the ‘habitat hectares’ approach. This rapid assessment technique produces multiple scores that describe the condition of various attributes of the vegetation at a given site. Multiple sites were assessed and subsequently circumscribed with GIS and remote-sensed data.     

The Future of Scattered Trees in Agricultural Landscapes

Abstract: Mature trees scattered throughout agricultural landscapes are critical habitat for some biota and provide a range of ecosystem services. These trees are declining in intensively managed agricultural landscapes globally. We developed a simulation model to predict the rates at which these trees are declining, identified the key variables that can be manipulated to mitigate this decline, and compared alternative management proposals. We used the initial numbers of trees in the stand, the predicted ages of these trees, their rate of growth, the number of recruits established, the frequency of recruitment, and the rate of tree mortality to simulate the dynamics of scattered trees in agricultural landscapes. We applied this simulation model to case studies from Spain, United States, Australia, and Costa Rica. We predicted that mature trees would be lost from these landscapes in 90–180 years under current management. Existing management recommendations for these landscapes—which focus on increasing recruitment—would not reverse this trend. The loss of scattered mature trees was most sensitive to tree mortality, stand age, number of recruits, and frequency of recruitment. We predicted that perpetuating mature trees in agricultural landscapes at or above existing densities requires a strategy that keeps mortality among established trees below around 0.5% per year, recruits new trees at a rate that is higher than the number of existing trees, and recruits new trees at a frequency in years equivalent to around 15% of the maximum life expectancy of trees. Numbers of mature trees in landscapes represented by the case studies will decline before they increase, even if strategies of this type are implemented immediately. This decline will be greater if a management response is delayed.     

华北山区温度推算模式和应用

利用华北山区气象站观测的温度与其所在纬度、经度和高度，分山区建立宏观地面气温场方程。采用１：２０万地形图，以１０＇经、纬度为步长，判读其高度值，代入已建立好的方程中，经地形项订正，推算华北山区年、月平均温度、年较差和ΣＴ＇≥１０℃，其精度达极显著水平。计算结果使我们对华北山区热量资源有了新的认识，最后求算华北山区的主要水果最优品质种植上限高度。     

稀有鮈鲫细胞凋亡相关基因的克隆及序列分析

细胞凋亡是调控机体发育和维护内环境稳定的重要机制。在克隆了稀有鮈鲫(Gobiocypris rarus)肝脏组织中凋亡相关的bcl-2、apaf-1、caspase-3和caspase-9基因的部分cDNA片段之后,进行了序列分析。结果表明,caspase-3和caspase-9基因片段与唐鱼(Tanichthys albonubes)相对应的核苷酸序列同源性最高,而bcl-2、apaf-1基因片段则分别与鮈鱼(Gobio gobio)和鲤鱼(Cyprinus carpio)相对应的核苷酸序列同源性最高,为99%和89%。基于稀有鮈鲫和已知物种的caspase-3基因核苷酸序列构建了系统发育树,发现稀有鮈鲫与唐鱼、斑马鱼(Danio rerio)的亲缘关系最近,而与金头鲷(Sparus aurata)、红鳍东方鲀(Takifugu rubripes)的亲缘关系较远。本研究为深入理解外源性化学物质对鱼类的毒理学机制及其生态健康风险评价提供科学数据,同时也为稀有鮈鲫在鲤科鱼类的分类及进化地位的研究提供分子水平的重要参考。     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.     

酸性土壤改良对不同种源的柚木生长的影响

在强酸性土壤上，柚木不能正常生长；用石灰、有机肥和复合肥改良土壤后．树木生长正常，但如不继续追肥．生长将显著下降；7794、8508号种源的柚木在改良后的土壤上生长最好；7794．8508和3054号种源的柚木表现出对酸性土壤有一定的抗性；来自同一种源的个体之间存在着显著的生长差异．     

UV-C辐射增强对绿化树种膜质过氧化及抗氧化酶的影响

采用叶片试验法研究了模拟紫外辐射(UV-C)胁迫对3种绿化树种叶片膜质过氧化及抗氧化酶产生的影响。实验结果表明:UV-C辐射处理使连翘ForsythiasuspensaVahl和垂柳Salixbabylonica叶片的叶绿素质量分数增加,但使水蜡树FraxinuschinesisRoxb.叶片的叶绿素质量分数降低。UV-C辐射处理增加连翘和垂柳叶片的SOD活性,而水蜡的SOD活性则表现为先升高随后降低的变化趋势;辐射处理对水蜡树和垂柳POD活性影响不大,但使连翘POD活性先升高随后降低;辐射处理明显降低水蜡树和连翘的CAT活性,而垂柳的则先升高后降低。辐射处理导致MDA含量增加。     

Use of genetic algorithms to select input variables in decision tree models for the prediction of benthic macroinvertebrates

Predicting freshwater organisms based on machine learning is becoming more and more reliable due to the availability of appropriate datasets, advanced modelling techniques and the continuously increasing capacity of computers. A database consisting of measurements collected at 360 sampling sites in non-navigable watercourses in Flanders was applied to predict the absence/presence of benthic macroinvertebrate taxa by means of decision trees. The measured variables were a combination of physical–chemical (temperature, pH, dissolved oxygen concentration, conductivity, total organic carbon, Kjeldahl nitrogen and total phosphorus), structural (granulometric analysis of the sediment, width, depth and flow velocity of the river) and two ecotoxicological variables. The predictive power of decision trees was assessed on the basis of the number of Correctly Classified Instances (CCI). A genetic algorithm was introduced to compare the predictive power of different sets of input variables for the decision trees. The number of input variables was reduced from 15 to 2–8 variables without affecting the predictive power of the decision trees significantly. Furthermore, reducing the number of input variables allowed to ease the identification of general data trends.     

西双版纳热带雨林榕树种群变化与环境的关系

植物物种的多样性在生态环境保护中具有重要的地位。西双版纳热带雨林生物多样性极为丰富,是我国生物多样性研究的关键地区。榕树是热带雨林中的关键植物类群之一,关于该地区榕树种群变化与环境关系的研究结果表明：①西双版纳热带雨林内已知榕属植物有69种、变种和亚种,其中榕亚属有23个种和变种;白肉榕亚属有4个种;聚果榕亚属有2个种和变种;无花果亚属有41个种、亚种和变种;西双版纳是中国榕树种群最丰富的地区。②榕树的分布与环境有着密切的关系,榕树种类和数量以保护完好的热带原始雨林最为丰富,在该类环境中的榕以高大乔木、乔木为主;被破坏林地和次生林内的榕树种群则以小乔木、灌木、灌丛、木质藤本为主;而热带雨林环境破坏严重的地区中则以一部分先锋榕树种为主。③保护好热带雨林中的关键植物类群榕属,是保护和恢复热带雨林生态环境的重要措施之一。