Decision theory and real estate development: a note on uncertainty

Real estate development appraisal is a quantification of future expectations. The appraisal model relies upon the valuer/developer having an understanding of the future in terms of the future marketability of the completed development and the future cost of development. In some cases the developer has some degree of control over the possible variation in the variables, as with the cost of construction through the choice of specification. However, other variables, such as the sale price of the final product, are totally dependent upon the vagaries of the market at the completion date. To try to address the risk of a different outcome to the one expected (modelled) the developer will often carry out a sensitivity analysis on the development. However, traditional sensitivity analysis has generally only looked at the best and worst scenarios and has focused on the anticipated or expected outcomes. This does not take into account uncertainty and the range of outcomes that can happen. A fuller analysis should include examination of the uncertainties in each of the components of the appraisal and account for the appropriate distributions of the variables. Similarly, as many of the variables in the model are not independent, the variables need to be correlated. This requires a standardised approach and we suggest that the use of a generic forecasting software package, in this case Crystal Ball, allows the analyst to work with an existing development appraisal model set up in Excel (or other spreadsheet) and to work with a predetermined set of probability distributions. Without a full knowledge of risk, developers are unable to determine the anticipated level of return that should be sought to compensate for the risk. This model allows the user a better understanding of the possible outcomes for the development. Ultimately the final decision will be made relative to current expectations and current business constraints, but by assessing the upside and downside risks more appropriately, the decision maker should be better placed to make a more informed and “better”

Robustness of complex networks to node and cluster damage

Copyright @ 2009 Universtiy of WarwickThe goal of this investigation is to assess the robustness of two popular network structures – random networks and scale-free networks – to node and cluster damage. There is no previous work on the latter. For node damage, we remove nodes iteratively and for cluster damage, we first build a network of clusters and then remove the nodes (clusters)

Noise reduction of dental drill noise

Dental drills produce a characteristic noise that is uncomfortable for patients and is also known to be harmful to dentists under prolonged exposure. It is therefore desirable to protect the patient and dentist whilst allowing two-way communication, which will require a headphone - type system. Re-establishing good communication between the dentist and patient will be achieved through a combination of three noise cancellation technologies, namely, Passive Noise Control (PNC), Adaptive Filtering (AF) and Active Noise Control (ANC). This paper describes how far a test-rig has been developed to achieve sufficient noise reduction that the uncomfortable noise can no longer be heard

Community structure detection in the evolution of the United States airport network

This is the post-print version of the Article. Copyright © 2013 World Scientific PublishingThis paper investigates community structure in the US Airport Network as it evolved from 1990 to 2010 by looking at six bi-monthly intervals in 1990, 2000 and 2010, using data obtained from the Bureau of Transportation Statistics of the US Department of Transport. The data contained monthly records of origin-destination pairs of domestic airports and the number of passengers carried. The topological properties and the volume of people traveling are both studied in detail, revealing high heterogeneity in space and time. A recently developed community structure detection method, accounting for the spatial nature of these networks, is applied and reveals a picture of the communities within. The patterns of communities plotted for each bi-monthly interval reveal some interesting seasonal variations of passenger flows and airport clusters that do not occupy a single US region. The long-term evolution of the network between those years is explored and found to have consistently improved its stability. The more recent structure of the network (2010) is compared with migration patterns among the four US macro-regions (West, Midwest, Northeast and South) in order to identify possible relationships and the results highlight a clear overlap between US domestic air travel and migration

Active noise control on high frequency narrow band dental drill noise: Preliminary results

Dental drills produce a characteristic noise that is uncomfortable for patients and is also known to be harmful to dentists under prolonged exposure. It is therefore desirable to protect the patient and dentist whilst allowing two-way communication. A solution is to use a combination of the three main noise cancellation methods, namely, Passive Noise Control, Adaptive Filtering and Active Noise Control. Dental drill noise occurs at very high frequency ranges in relation to conventional ANC, typically 2kHz to 6kHz and it has a narrow band characteristic due to the direct relation of the noise to the rotational speed of the bearing. This paper presents a design of an experimental rig where first applications of ANC on dental drill noise are executed using the standard filtered reference Least Mean Square (FXLMS) algorithm. The secondary path is kept as simple as possible, due to the high frequency range of interest, and hence is chosen as the space between headphone loudspeaker and error microphone placed in the ear (input of the headphone loudspeaker and the output of the error microphone). A standard headphone loudspeaker is used for the control source and the microphone inside of an “Ear and Cheek Simulator Type 43AG” is used as the error microphone. The secondary path transfer function is obtained and preliminary results of the application of ANC are discussed

Testing medical drills for noise emissions

Copyright @ 2007 TUT PressA comparative study of drills from a range of manufacturers was carried out in the laboratory. These tests were made under a variety of loading conditions to assess the characteristics of the noise likely in the dentist’s surgery. Details of the test setup are given, and some of the issues in analysing the results considered. Simultaneous Recordings were made of the noise on a minidisk recorder, a hand held sound meter, and a sound analyser. The results from these are considered. These are significant for dentist’s hearing, and the patient’s perception of visiting the dentists. The performance of different designs of pneumatic drill varies much with type and load

Dental drill noise reduction using a combination of active noise control, passive noise control and adaptive filtering

Dental drills produce a characteristic high frequency, narrow band noise that is uncomfortable for patients and is also known to be harmful to dentists under prolonged exposure. It is therefore desirable to protect the patient and dentist whilst allowing two-way communication. A solution is to use a combination of the three main noise control methods, namely, Passive Noise Control (PNC), Adaptive Filtering (AF) and Active Noise Control (ANC). This paper discusses the application of the three methods to reduce dental drill noise while allowing two-way communication. Experimental setup for measuring the noise reduction by PNC is explained and results from different headphones and headphone types are presented. The implementation and results of an AF system using the Least Mean Square (LMS) algorithm are shown. ANC requires a modification of the LMS algorithm due to the introduction of the electro-acoustical cancellation path transfer function to compensate for the delays introduced by the control system. Therefore a cancellation path transfer function modeling method based on the filtered reference LMS (FXLMS) algorithm is presented along with preliminary results of the implementation

Active noise control for high frequencies

There are many applications that can benefit from Active Noise Control (ANC) such as in aircraft cabins and air conditioning ducts, i.e. in situations where technology interferes with human hearing in a harmful way or disrupts communication. Headsets with analogue ANC circuits have been used in the armed forces for attenuating frequencies below 1 kHz, which when combined with passive filtering offers protection across the whole frequency range of human hearing. A dental surgery is also a noisy environment; in which dental drill noise is commonly off-putting for many patients and is believed to harm the dentist’s hearing over a long period of time. However, dealing with dental drill noise is a different proposition from the applications mentioned above as the frequency range of the peak amplitudes goes from approximately 1.5 kHz to 12 kHz, whereas conventional ANC applications consider a maximum of 1.5 kHz. This paper will review the application of ANC at low frequencies and justify an approach for dealing with dental noise using digital technologies at higher frequencies. The limits of current ANC technologies will be highlighted and the means of improving performance for this dental application will be explored. In particular, technicalities of implementing filtering algorithms on a Digital Signal Processor will be addressed

Real-time adaptive filtering of dental drill noise using a digital signal processor

The application of noise reduction methods requires the integration of acoustics engineering and digital signal processing, which is well served by a mechatronic approach as described in this paper. The Normalised Least Mean Square (NLMS) algorithm is implemented on the Texas Instruments TMS320C6713 DSK Digital Signal Processor (DSP) as an adaptive digital filter for dental drill noise. Blocks within the Matlab/Simulink Signal Processing Blockset and the Embedded Target for TI C6000 DSP family are used. A working model of the algorithm is then transferred to the Code Composer Studio (CCS), where the desired code can be linked and transferred to the target DSP. The experimental rig comprises a noise reference microphone, a microphone for the desired signal, the DSK and loudspeakers. Different load situations of the dental drill are considered as the noise characteristics change when the drill load changes. The result is that annoying drill noise peaks, which occur in a frequency range from 1.5 kHz to 10 kHz, are filtered out adaptively by the DSP. Additionally a schematic design for its implementation in a dentist’s surgery will also be presented

L.E. Atherton to Professor James Silver, undated

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