Consensual Sex Crimes in the Armed Forces: A Primer for the Uninformed

Though not specifically mentioned in this chapter, all disorders and neglects to the prejudice of good order and discipline in the armed forces, all conduct of a nature to bring discredit upon the armed forces, and crimes and offenses not capital, of which persons subject to this chapter may be guilty, . . . shall be punished at the discretion of that court.5 To implement this congressionally-enacted prohibition, the President, as Commander-in-Chief of the Armed Forces, has deemed by executive order that a number of acts are punishable under this Article

Semantic and Structural Difficulties with the Unified Modeling Language Use-Case Notation version 1.3

A case study was undertaken to examine and apply the UML use-case notation version 1.3. This study shows that the notation is open to interpretation and the semantics of the use-case relationships are confusing. The attempt to bolt on the object-oriented inheritance structure to the use-case notation is shown to cause problems with users

A profiled structure with improved low frequency absorption.

It is possible to obtain good absorption from Schroeder diffusers if suitable alterations to the design are made. Interestingly, previous work has shown that good absorption appears possible below the design frequency when the diffusers are poorly constructed. This has inspired the design of a profiled absorber using perforated plates in some wells; the absorber has extended bass response. The paper presents a theory for the enhanced absorption and the important design parameters are discussed. Good agreement is shown between the prediction model and impedance tube measurements. The design of this absorber was first carried out using a numerical optimization, although a simplified design procedure is also outlined which is almost as good. The results clearly show that this type of profiled absorber extends the absorption at low frequencies while maintaining the good absorption at mid frequencies as well

Implications of Hydrocarbon and Helium Gas Analyses of Springs from the Ouachita Mountains, Arkansas

One hundred and three ground water samples (predominantly springs) were analyzed for headspace light hydrocarbon gases and helium. Four of the formations (Arkansas Novaculite, Bigfork Chert, Stanley Shale, and Womble) having the highest mean methane values are the only Ouachita Mountain facies to produce petroleum or exhibit marginally commercial production. This observation suggests that the mean methane values are useful as an indication of the relative hydrocarbon content of these formations Anomalous helium values are generally associated with mapped faults

From a profiled diffuser to an optimized absorber

The quadratic residue diffuser was originally designed for enhanced scattering. Subsequently, however, it has been found that these diffusers can also be designed to produce exceptional absorption. This paper looks into the absorption mechanism of the one-dimensional quadratic residue diffuser. A theory for enhanced absorption is presented. Corresponding experiments have also been done to verify the theory. The usefulness of a resistive layer at the well openings has been verified. A numerical optimization was performed to obtain a better depth sequence. The results clearly show that by arranging the depths of the wells properly in one period, the absorption is considerably better than that of a quadratic residue diffuser. © 2000 Acoustical Society of America

Application of methods for central statistical monitoring in clinical trials

Background On-site source data verification is a common and expensive activity, with little evidence that it is worthwhile. Central statistical monitoring (CSM) is a cheaper alternative, where data checks are performed by the coordinating centre, avoiding the need to visit all sites. Several publications have suggested methods for CSM; however, few have described their use in real trials. Methods R-programs were created to check data at either the subject level (7 tests within 3 programs) or site level (9 tests within 8 programs) using previously described methods or new ones we developed. These aimed to find possible data errors such as outliers, incorrect dates, or anomalous data patterns; digit preference, values too close or too far from the means, unusual correlation structures, extreme variances which may indicate fraud or procedural errors and under-reporting of adverse events. The methods were applied to three trials, one of which had closed and has been published, one in follow-up, and a third to which fabricated data were added. We examined how well the methods work, discussing their strengths and limitations. Results The R-programs produced simple tables or easy-to-read figures. Few data errors were found in the first two trials, and those added to the third were easily detected. The programs were able to identify patients with outliers based on single or multiple variables. They also detected (1) fabricated patients, generated to have values too close to the multivariate mean, or with too low variances in repeated measurements, and (2) sites which had unusual correlation structures or too few adverse events. Some methods were unreliable if applied to centres with few patients or if data were fabricated in a way which did not fit the assumptions used to create the programs. Outputs from the R-programs are interpreted using examples. Limitations Detecting data errors is relatively straightforward; however, there are several limitations in the detection of fraud: some programs cannot be applied to small trials or to centres with few patients (<10) and data falsified in a manner which does not fit the program’s assumptions may not be detected. In addition, many tests require a visual assessment of the output (showing flagged participants or sites), before data queries are made or on-site visits performed. Conclusions CSM is a worthwhile alternative to on-site data checking and may be used to limit the number of site visits by targeting only sites which are picked up by the programs. We summarise the methods, show how they are implemented and that they can be easy to interpret. The methods can identify incorrect or unusual data for a trial subject, or centres where the data considered together are too different to other centres and therefore should be reviewed, possibly through an on-site visit