A rock bolt installed in field has only one short exposed end on the rock surface. This condition has posed challenges in field instrumentation and made it difficult to use the ultrasonic guided wave method for rock bolt monitoring. In rock bolt laboratory tests using ultrasonic guided waves, the input and receiving transducers are typically installed at the two exposed ends of a bolt. This is suitable to laboratory conditions but not practical in the field because one of the ends of a rock bolt is embedded in the rock mass. A method needs to be found to install the receiving transducer at a suitable location in the field for receiving valid wave data. In this thesis, a practical approach is proposed for conducting field tests with the installation of the receiving transducer on the grout surface near the exposed end of the bolt. The effects of the installation location of the receiving transducer are studied with numerical modeling. Experiments are conducted to verify the numerical modeling results. The results indicate that the data obtained from the receiving transducer installed on the grout surface at a proper location are representative and can be analyzed through the established correlations to determine the required parameters. Previous researches have mostly focused on the feasibility of the ultrasonic guided wave method for rock bolt tests and on the behavior of ultrasonic guided waves of fully grouted rock bolts in laboratory conditions. No further study has been performed to identify the grout defects in grouted rock bolts. Adequate understanding of the behaviour of ultrasonic guided waves in rock bolts with defects is therefore prerequisite for this method to be applied in practice. This thesis investigates the effects of some typical defects (e.g., an insufficient rebar length, a missing grout at the ground end, and a void in grout) in grouted rock bolts using the developed field measurement method and numerical modeling. The results are verified by laboratory tests using the equipment set-up established in this research. The results indicate that it is practically possible to identify those grout defects using ultrasonic guided waves.

No description available.

No description available.

No description available.

No description available.

The Kingston group's engagement in an International Cooperative Biodiversity Group (ICBG) program and a collaborative research project established between Virginia Tech and the Institute for Hepatitis and Virus Research (IHVR) has focused on the search for bioactive natural products from tropical forests in both Madagascar and South Africa. As a part of this research, a total of four antiproliferative extracts were studied, leading to the isolation of fourteen novel compounds with antiproliferative activity against the A2780 human ovarian cancer line. One extract with antimalarial activity was studied, which led to the isolation of two new natural products with antiplasmodial activity against a drug-resistant Dd2 strain of Plasmodium falciparum. The plants and their secondary metabolites are discussed in the following order: two new antiproliferative acetogenins from a Uvaria sp. (Annonaceae); two new antiproliferative calamenene-type sesquiterpenoids from Sterculia tavia (Malvaceae); two new antiproliferative triterpene saponins from Nematostylis anthophylla (Rubiaceae); six new antiproliferative homoisoflavonoids and two new bufatrienolides from Urginea depressa (Asparagaceae); and two new antiplasmodial anthraquinones from Kniphofia ensifolia (Asphodelaceae). The structures of all these compounds were determined by analysis of their mass spectrometric, 1D and 2D NMR, UV and IR spectroscopic and optical rotation data. Other than structural elucidation, this work also involved bioactivity evaluations of all the isolates, as well as total synthesis of the two antiproliferative sesquiterpenoids, and a structure-activity relationship (SAR) studies on the antiplasmodial anthroquinones. / Ph. D.

The ever increasing use of computer systems in society brings emergent challenges to companies and system designers. The reliability of software and hardware can be financially critical, and lives can depend on it. The growth in size and complexity of software, and increasing concurrency, compounds the problem. The potential for errors is greater than ever before, and the stakes are higher than ever before. Formal methods, particularly model checking, is an approach that attempts to prove mathematically that a model of the behaviour of a product is correct with respect to certain properties. Certain errors can therefore be proven never to occur in the model. This approach has tremendous potential in system development to provide guarantees of correctness. Unfortunately, in practice, model checking cannot handle the enormous sizes of the models of real-world systems. The reason is that the approach requires an exhaustive search of the model to be conducted. While there are exceptions, in general model checkers are said not to scale well. In this thesis, we deal with this scaling issue by using a guiding technique that avoids searching areas of the model, which are unlikely to contain errors. This technique is based on a process of model abstraction in which a new, much smaller model is generated that retains certain important model information but discards the rest. This new model is called a heuristic. While model checking using a heuristic as a guide can be extremely effective, in the worst case (when the guide is of no help), it performs the same as exhaustive search, and hence it also does not scale well in all cases. A second technique is employed to deal with the scaling issue. This technique is based on the concept of random walks. A random walk is simply a `walk' through the model of the system, carried out by selecting states in the model randomly. Such a walk may encounter an error, or it may not. It is a non-exhaustive technique in the sense that only a manageable number of walks are carried out before the search is terminated. This technique cannot replace the conventional model checking as it can never guarantee the correctness of a model. It can however, be a very useful debugging tool because it scales well. From this point of view, it relieves the system designer from the difficult task of dealing with the problem of size in model checking. Using random walks, the effort goes instead into looking for errors. The effectiveness of model checking can be greatly enhanced if the above two techniques are combined: a random walk is used to search for errors, but the walk is guided by a heuristic. This in a nutshell is the focus of this work. We should emphasise that the random walk approach uses the same formal model as model checking. Furthermore, the same heuristic technique is used to guide the random walk as a guided model checker. Together, guidance and random walks are shown in this work to result in vastly improved performance over conventional model checking. Verification has been sacrificed of course, but the new technique is able to find errors far more quickly, and deal with much larger models.

The ever increasing use of computer systems in society brings emergent challenges to companies and system designers. The reliability of software and hardware can be financially critical, and lives can depend on it. The growth in size and complexity of software, and increasing concurrency, compounds the problem. The potential for errors is greater than ever before, and the stakes are higher than ever before. Formal methods, particularly model checking, is an approach that attempts to prove mathematically that a model of the behaviour of a product is correct with respect to certain properties. Certain errors can therefore be proven never to occur in the model. This approach has tremendous potential in system development to provide guarantees of correctness. Unfortunately, in practice, model checking cannot handle the enormous sizes of the models of real-world systems. The reason is that the approach requires an exhaustive search of the model to be conducted. While there are exceptions, in general model checkers are said not to scale well. In this thesis, we deal with this scaling issue by using a guiding technique that avoids searching areas of the model, which are unlikely to contain errors. This technique is based on a process of model abstraction in which a new, much smaller model is generated that retains certain important model information but discards the rest. This new model is called a heuristic. While model checking using a heuristic as a guide can be extremely effective, in the worst case (when the guide is of no help), it performs the same as exhaustive search, and hence it also does not scale well in all cases. A second technique is employed to deal with the scaling issue. This technique is based on the concept of random walks. A random walk is simply a `walk' through the model of the system, carried out by selecting states in the model randomly. Such a walk may encounter an error, or it may not. It is a non-exhaustive technique in the sense that only a manageable number of walks are carried out before the search is terminated. This technique cannot replace the conventional model checking as it can never guarantee the correctness of a model. It can however, be a very useful debugging tool because it scales well. From this point of view, it relieves the system designer from the difficult task of dealing with the problem of size in model checking. Using random walks, the effort goes instead into looking for errors. The effectiveness of model checking can be greatly enhanced if the above two techniques are combined: a random walk is used to search for errors, but the walk is guided by a heuristic. This in a nutshell is the focus of this work. We should emphasise that the random walk approach uses the same formal model as model checking. Furthermore, the same heuristic technique is used to guide the random walk as a guided model checker. Together, guidance and random walks are shown in this work to result in vastly improved performance over conventional model checking. Verification has been sacrificed of course, but the new technique is able to find errors far more quickly, and deal with much larger models.

The ever increasing use of computer systems in society brings emergent challenges to companies and system designers. The reliability of software and hardware can be financially critical, and lives can depend on it. The growth in size and complexity of software, and increasing concurrency, compounds the problem. The potential for errors is greater than ever before, and the stakes are higher than ever before. Formal methods, particularly model checking, is an approach that attempts to prove mathematically that a model of the behaviour of a product is correct with respect to certain properties. Certain errors can therefore be proven never to occur in the model. This approach has tremendous potential in system development to provide guarantees of correctness. Unfortunately, in practice, model checking cannot handle the enormous sizes of the models of real-world systems. The reason is that the approach requires an exhaustive search of the model to be conducted. While there are exceptions, in general model checkers are said not to scale well. In this thesis, we deal with this scaling issue by using a guiding technique that avoids searching areas of the model, which are unlikely to contain errors. This technique is based on a process of model abstraction in which a new, much smaller model is generated that retains certain important model information but discards the rest. This new model is called a heuristic. While model checking using a heuristic as a guide can be extremely effective, in the worst case (when the guide is of no help), it performs the same as exhaustive search, and hence it also does not scale well in all cases. A second technique is employed to deal with the scaling issue. This technique is based on the concept of random walks. A random walk is simply a `walk' through the model of the system, carried out by selecting states in the model randomly. Such a walk may encounter an error, or it may not. It is a non-exhaustive technique in the sense that only a manageable number of walks are carried out before the search is terminated. This technique cannot replace the conventional model checking as it can never guarantee the correctness of a model. It can however, be a very useful debugging tool because it scales well. From this point of view, it relieves the system designer from the difficult task of dealing with the problem of size in model checking. Using random walks, the effort goes instead into looking for errors. The effectiveness of model checking can be greatly enhanced if the above two techniques are combined: a random walk is used to search for errors, but the walk is guided by a heuristic. This in a nutshell is the focus of this work. We should emphasise that the random walk approach uses the same formal model as model checking. Furthermore, the same heuristic technique is used to guide the random walk as a guided model checker. Together, guidance and random walks are shown in this work to result in vastly improved performance over conventional model checking. Verification has been sacrificed of course, but the new technique is able to find errors far more quickly, and deal with much larger models.

Thesis (M.S.)--Ohio University, June, 1985. / Title from PDF t.p.