Tartu, Estonia
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A system (140) and method (30) for actively managing Type 1 diabetes mellitus on a personalized basis is provided. Models of glycemic effect for a Type 1 diabetic patient (21) are established for both insulin time course (50) and digestive response (40). A rise (157) in postprandial blood glucose is estimated through food ingestion of a planned meal (71) in proportion to the digestive response model (40). An amount of insulin (158) necessary and timing of delivery (156) to mediate transport of blood glucose into cells in proportion to the postprandial blood glucose rise (157) is determined through the insulin time course model (51).


A system (180) and method for actively managing Type 2 diabetes mellitus on a personalized basis is provided. A model of glycemic effect for a Type 2 diabetic patient (31) for digestive response (61) is established. The digestive response model (61) is adjusted for a degree of insulin resistance (194) experienced by the patient (31). A rise in postprandial blood glucose (121) through food ingestion of a planned meal (102) is estimated in proportion to the adjusted digestive response model (61). The tool also allows for the avoidance of hypoglycemic episodes by medications.


A computer-implemented method for providing a personalized tool (230) for estimating 1,5-anhydroglucitol is provided. An electronically-stored history of empirically measured glucose (246) levels is maintained for a patient (11) over a set period of time in order of increasing age. A predictive model (350) of estimated glycated hemoglobin is built on a computer workstation (22). A decay factor is designated particularized to the patient (11). The decay factor is applied to each of the measured glucose (246) levels. The measured glucose (246) levels are scaled by a scaling coefficient. The measured glucose (246) levels are aggregated and scaled as decayed and scaled into an estimate (235) of glycated hemoglobin for the time period. The glycated hemoglobin estimate (235) is displayed to the patient (11) on the computer workstation (22).


A system (330) and method (280) for providing a personalized tool for estimating glycated hemoglobin is provided. An electronically-stored history of empirically measured glucose levels (240) for a patient (21) is maintained over a set period of time in order of increasing age. A decay factor (334) is applied to each of the measured glucose levels (240). The measured glucose levels (240) are aggregated and scaled as decayed into an estimate of glycated hemoglobin (337) for the time period. The glycated hemoglobin estimate (337) is displayed to the patient (21).


A computer-implemented method (280 for providing a tunable personalized tool for estimating glycated hemoglobin (337) is provided. An electronically-stored history (240) of empirically measured glucose levels (246) is maintained for a patient (51) over a set period of time in order of increasing age. A predictive model (236) of estimated glycated hemoglobin (337) is built on a computer workstation (330). A decay factor (314) is designated particularized to the patient (51). The decay factor (314) is applied (293) to each of the measured glucose levels (246). The measured glucose levels (246) are scaled (295) by a scaling coefficient (335). The measured glucose levels (246) are aggregated and scaled (298) as decayed and scaled into an estimate of glycated hemoglobin (337) for the time period. The glycated hemoglobin estimate (337) is displayed to the patient (51) on the computer workstation (330).


A system (330) and method (280) for providing a personalized tool for estimating glycated hemoglobin is provided. An electronically-stored history of empirically measured glucose levels (240) for a patient (21) is maintained over a set period of time in order of increasing age. A decay factor (334) is applied to each of the measured glucose levels (240). The measured glucose levels (240) are aggregated and scaled as decayed into an estimate of glycated hemoglobin (337) for the time period. The glycated hemoglobin estimate (337) is displayed to the patient (21).


Hessler D.M.,Clifton Inc. | Hessler Jr. G.F.,Clifton Inc.
Noise Control Engineering Journal | Year: 2011

Potential impacts from operational noise produced by wind turbines is a major issue during the project planning and permitting process, particularly for projects east of the Mississippi River in fairly populous areas. While still an issue farther west, more buffer space and lower population densities sometimes make noise less of a factor. In general, however, noise may be the principal obstacle, from an environmental impact standpoint, to the more rapid growth of this renewable energy source in the United States. Proposed projects are frequently opposed on noise concerns, if not outright fear, usually aroused by the highly biased misinformation found on numerous anti-wind websites. While significant noise problems have certainly been experienced at some newly operational projects, they are usually attributable to poor design (siting units too close to houses without any real awareness of the likely impact) or to unexpected mechanical noises, such as chattering yaw brakes or noisy ventilation fans. A common theme at sites with legitimate complaints is that no one-not the developer, their consultants or the regulatory authority-really understood the import and meaning of the sound levels predicted at adjacent homes in project environmental impact statement (EIS) noise modeling. This paper seeks to address this lack of knowledge with suggested design goals and regulatory limits for new wind projects based on experience with the design of nearly 60 large wind projects and field testing at a number of completed installations where the apparent reaction of the community can be compared to model predictions and measurements at complainant's homes. © 2011 Institute of Noise Control Engineering.


McCarthy K.H.,Clifton Inc.
Healthcare financial management : journal of the Healthcare Financial Management Association | Year: 2010

True health system integration can produce many direct and indirect financial benefits beyond operating cost savings through functional and service centralization or consolidation. These additional benefits of a strong integration strategy include: Improved market position. Expanded continuum of care. Increased scope of services. Improved healthcare quality and organizational performance.


Hessler G.,Clifton Inc.
Proceedings of Meetings on Acoustics | Year: 2013

Each of the two authors has developed single, 24-hour, constant wind turbine noise criteria; the criteria are constants because wind turbine noise is basically not adjustable. Hessler develops his criteria from his knowledge of how wind turbine noise is being regulated at the local, state, and national levels, from regulations in other countries, and from his extensive experience with numerous wind turbine projects. Schomer develops his recommended criteria on the basis of existing national and international standards, notably ISO 1996-1 and ANSI/ASA S12.9 parts 4 and 5. Ultimately, Hessler comes up with a single, 24-hour A-weighted average criterion of 40 dB, and Schomer comes up with a 24 hour, A-weighted average criterion of 39 dB. These two researchers have decidedly different backgrounds, different experience, and a slight difference in orientation towards the industry. Thus, it is remarkable that these two criteria, derived in such different ways result in nearly identical 24-hour A-weighted criteria levels. Although there is essential agreement in immissions criterion, there are variables debated herein for both modeling wind turbine emissions and certifying such emissions at far-off receptors that could result in a 10 dBA difference in the actual immissions level. © 2013 Acoustical Society of America.


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