果树缺铁矫正新技术—高渗液注射法

本文概述了植物的铁素营养、果材缺铁黄化病的诊断治疗方法，论述了高涌液注射法矫治果树缺铁黄化病的理论、假说、树木微型注射器、克黄灵高渗液、树干穴位注射法、树干穴位插管法及其应用前景。     

Predicting the frequency of accidents in port areas by developing event trees from historical analysis

The historical analysis of 828 accidents in port areas, which have been selected from a database, has been used to identify the sequences of the accidents. Processing these data has allowed the event trees and the probability of the various accident scenarios to be determined. By using these event trees and figures detailing the frequency of the events that initiated the accidents, as taken from various authors, the frequency of the accidents has been determined.     

花卉无土栽培技术的研究(Ⅰ)

花卉无土栽培是在土壤栽培的基础上发展起来的现代农业高新技术,本文作者经多年来的无土栽培试验,根据当地条件,改良了霍格兰和阿农(Hogagland&Arnon)配方,改良配方效果显示:无土栽培的花卉其叶色浓艳,叶绿素含量较有土栽培一般高10~20%;黄枯叶较土壤栽培少60~80%左右,耗水量、病虫害较土壤栽培分别少40~60%、75~100﹪,且无杂草,可观期延长.     

秋色叶树种在园林造景中的应用

秋色叶树种是园林造景中重要的观赏树种之一，我国秋色叶树木资源十分丰富，开发利用秋色叶树木资源对我国园林建设具有重要意义．作者分析了秋色叶树种内涵，主要树种的性状、特征、观赏价值及在我国自然秋色景观中的应用等，初步探讨了它们在城市园林造景中应用方式、应用前景，提出了秋色叶树种选择应用的原则及应注意的问题．     

Fire and Green-Tree Retention in Conservation of Red-Listed and Rare Deadwood-Dependent Beetles in Finnish Boreal Forests

Abstract:  Habitat loss, fragmentation, and declining habitat quality have created an extinction debt in boreal forests, which could be partly reversed by deliberately improving the habitat quality in managed areas outside reserves. We studied the effects of green-tree retention and controlled burning on red-listed and rare, deadwood-dependent (saproxylic) beetles in a large-scale field experiment in eastern Finland. Our factorial study design included 24 sites dominated by Scots pine ( Pinus sylvestris L.) and with three levels of green-tree retention (0, 10, and 50 m³/ha) and uncut controls. Twelve of the 24 sites were burned in 2001. We sampled beetles with 10 flight-intercept traps on each site during the years 2000–2002 (i.e., 1 pretreatment and 2 post-treatment years). A total sample of 153,449 individuals representing 1,160 beetle species yielded 2,107 specimens of 84 red-listed or rare saproxylic species. The richness of these species was higher on the burned than on the unburned sites, and higher levels of green-tree retention promoted species richness, but there were clear differences between the years. The richness of red-listed and rare saproxylic species increased in the first post-treatment year, evidently due to the treatments, continued to increase on the burned sites in the second post-treatment year, but decreased on the unburned sites. Our results showed that the living conditions of many red-listed and rare saproxylic species could be improved significantly with rather simple alterations to forest management methods. Controlled burning with high levels of green-tree retention creates resources for many saproxylic species, but increasing the levels of green-tree retention in unburned areas can also be beneficial.     

袋装控释铁肥压根法矫治桃树黄叶病的效果

本文介绍了袋装控释铁肥压根法矫治桃树缺铁黄叶病的效果。该法具有大幅度增加叶绿素含量．提高果实产量,改善果实品质,降低矫治成本,安全可靠,效果好的特点。     

A classification tree based modeling approach for segment related crashes on multilane highways

Introduction

This study presents a classification tree based alternative to crash frequency analysis for analyzing crashes on mid-block segments of multilane arterials.

Method

The traditional approach of modeling counts of crashes that occur over a period of time works well for intersection crashes where each intersection itself provides a well-defined unit over which to aggregate the crash data. However, in the case of mid-block segments the crash frequency based approach requires segmentation of the arterial corridor into segments of arbitrary lengths. In this study we have used random samples of time, day of week, and location (i.e., milepost) combinations and compared them with the sample of crashes from the same arterial corridor. For crash and non-crash cases, geometric design/roadside and traffic characteristics were derived based on their milepost locations. The variables used in the analysis are non-event specific and therefore more relevant for roadway safety feature improvement programs. First classification tree model is a model comparing all crashes with the non-crash data and then four groups of crashes (rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes) are separately compared to the non-crash cases. The classification tree models provide a list of significant variables as well as a measure to classify crash from non-crash cases. ADT along with time of day/day of week are significantly related to all crash types with different groups of crashes being more likely to occur at different times.

Conclusions

From the classification performance of different models it was apparent that using non-event specific information may not be suitable for single vehicle/off-road crashes.

Impact on Industry

The study provides the safety analysis community an additional tool to assess safety without having to aggregate the corridor crash data over arbitrary segment lengths.     

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.