Automating condition monitoring of vegetation on railway trackbeds and embankments

Vegetation growing on railway trackbeds and embankments present potential problems. The presence of vegetation threatens the safety of personnel inspecting the railway infrastructure. In addition vegetation growth clogs the ballast and results in inadequate track drainage which in turn could lead to the collapse of the railway embankment. Assessing vegetation within the realm of railway maintenance is mainly carried out manually by making visual inspections along the track. This is done either on-site or by watching videos recorded by maintenance vehicles mainly operated by the national railway administrative body. A need for the automated detection and characterisation of vegetation on railways (a subset of vegetation control/management) has been identified in collaboration with local railway maintenance subcontractors and Trafikverket, the Swedish Transport Administration (STA). The latter is responsible for long-term planning of the transport system for all types of traffic, as well as for the building, operation and maintenance of public roads and railways. The purpose of this research project was to investigate how vegetation can be measured and quantified by human raters and how machine vision can automate the same process. Data were acquired at railway trackbeds and embankments during field measurement experiments. All field data (such as images) in this thesis work was acquired on operational, lightly trafficked railway tracks, mostly trafficked by goods trains. Data were also generated by letting (human) raters conduct visual estimates of plant cover and/or count the number of plants, either on-site or in-house by making visual estimates of the images acquired from the field experiments. Later, the degree of reliability of(human) raters’ visual estimates were investigated and compared against machine vision algorithms. The overall results of the investigations involving human raters showed inconsistency in their estimates, and are therefore unreliable. As a result of the exploration of machine vision, computational methods and algorithms enabling automatic detection and characterisation of vegetation along railways were developed. The results achieved in the current work have shown that the use of image data for detecting vegetation is indeed possible and that such results could form the base for decisions regarding vegetation control. The performance of the machine vision algorithm which quantifies the vegetation cover was able to process 98% of the im-age data. Investigations of classifying plants from images were conducted in in order to recognise the specie. The classification rate accuracy was 95%.Objective measurements such as the ones proposed in thesis offers easy access to the measurements to all the involved parties and makes the subcontracting process easier i.e., both the subcontractors and the national railway administration are given the same reference framework concerning vegetation before signing a contract, which can then be crosschecked post maintenance.A very important issue which comes with an increasing ability to recognise species is the maintenance of biological diversity. Biological diversity along the trackbeds and embankments can be mapped, and maintained, through better and robust monitoring procedures. Continuously monitoring the state of vegetation along railways is highly recommended in order to identify a need for maintenance actions, and in addition to keep track of biodiversity. The computational methods or algorithms developed form the foundation of an automatic inspection system capable of objectively supporting manual inspections, or replacing manual inspections.

Photocatalytic reduction of carbon dioxide

Carbon dioxide reforming happens all the time in nature by photosynthesis of plants. It thus provides a great challenge to equal or surpass this photosynthesis in an artificial system. This paper presents a literature review of using semi-conductor to assist photocatalytic reduction of carbon dioxide under UV irradiation. It analyses some key factors influencing the reaction rates which have been studied worldwide in respective areas. Special interest is taken in recommending possible improvements for the heterogeneous photocatalysis involving gas-solid interfaces, particularly in relation to the influencing factors affecting product concentrations and the reduction rate.

Biotic effects of climate change in urban environments: The case of the grey-headed flying-fox (Pteropus poliocephalus) in Melbourne, Australia

Urban climates are known to differ from those of the surrounding rural areas, as human activities in cities lead to changes in temperature, humidity and wind regimes. These changes can in turn affect the geographic distribution of species, the behaviour of animals and the phenology of plants. The grey-headed flying-fox (Pteropus poliocephalus) is a large, nomadic bat from eastern Australia that roosts in large colonies known as camps. Historically a warm temperate to tropical species, P. poliocephalus recently established a year-round camp in the Royal Botanic Gardens Melbourne. Using a bioclimatic analysis, we demonstrated that on the basis of long-term data, Melbourne does not fall within the climatic range of other P. poliocephalus camp sites in Australia. Melbourne is drier than other summer camps, and cooler and drier than other winter camps. The city also receives less radiation, in winter and annually, than the other summer and winter camps of P. poliocephalus. However, we found that temperatures in central Melbourne have been increasing since the 1950s, leading to warmer conditions and a reduction in the number of frosts. In addition, artificial watering of parks and gardens in the city may contribute the equivalent of 590 mm (95% CI: 450–720 mm) of extra rainfall per year. It appears that human activities have increased temperatures and effective precipitation in central Melbourne, creating a more suitable climate for camps of the grey-headed flying-fox. As demonstrated by this example, anthropogenic climate change is likely to complicate further the task of conserving biological diversity in urban environments.

Evaluation of fixed sample-size plans for Plutella xylostella (Lepidoptera: Plutellidae) on broccoli crops in Australia

Fixed sample-size plans for monitoring Plutella xylostella (L.) (Lepidoptera: Plutellidae) on broccoli and other Brassica vegetable crops are popular in Australia for their simplicity and ease of application. But the sample sizes used are often small, ≈10–25 plants per crop, and it may be that they fail to provide sufficient information upon which to base pest control decisions. We tested the performance of seven fixed sample-size plans (10, 15, 20, 30, 35, 40, and 45 plants) by resampling a large data set on P. xylostella in commercial broccoli crops. For each sample size, enumerative and presence-absence plans were assessed. The precision of the plans was assessed in terms of the ratio of the standard error to the mean; and at least 45 and 35 samples were necessary for the enumerative and presence-absence plans, respectively, to attain the generally accepted benchmark of ≤0.3. Sample sizes of 10–20 were highly imprecise. We also assessed the consequences of classifications based on action thresholds (ATs) of 0.2 and 0.8 larvae per plant for the enumerative case, and 0.15 and 0.45 proportion of plants of infested for the presence-absence case. Operating characteristic curves and investigations of the frequency of correct decisions suggest improvements in the performance of plans with increased sample size. In both the enumerative and presence-absence cases, the proportion of incorrect decisions was much higher for the lower of the two ATs assessed, and type II errors (i.e., failure to suggest pest control upon the AT is exceeded) generally accounted for the majority of this error. Type II errors are the most significant from a producer’s standpoint. Further consideration is necessary to determine what is an acceptable type II error rate.

Purchase commitments : Big business bias or solution to the 'neglected diseases' dilemma?

Miniscule research resources are allocated to researching the diseases of developing countries such as malaria, tuberculosis (TB), dengue fever, river blindness, Chagas disease and leishmaniasis, and the strains of HIV prevalent in Africa. Plainly, the patent system and the commercial model of drug research fail to respond to the needs of the poor for the simple reason that the poor exercise little purchasing power. But pressures are mounting on governments and corporations to tackle the ‘neglected diseases’ calamity. An important argument in an intense global debate is that corporations would respond to the needs of developing countries if the diseases of the poor could be made profitable. This is the idea developed by Kremer and Glennerster in a crisply written book, Strong Medicine: Creating Incentives for Pharmaceutical Research on Neglected Diseases.