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Computational engineering of metallic nanostructures and nanomachines.

Small structures with dimensions in the nanometer regime play an important role within a lot of modern technological branches like, for example, genetics, chip fabrication, material science, medicine, or chemistry. While highly sophisticated characterization methods would be necessary to study such nanostructures, computational methods and models have made their entrance into the field of nanotechnology. The present work gives an overview of the problems connected with quantum mechanics, many-particle systems, and nanophysical models. Further, the application of molecular dynamics (MD)--a typical computational method suitable for modelling at the nanolevel--is introduced and outlined. The setup and use of specific MD models, advanced computation techniques, and efficient algorithms are discussed, while the focus is laid on the subjects nanodesign and nanoengineering which are demonstrated for the example of metallic nanostructures. Finally, the introduced techniques and methods are applied to stability studies of theoretical nanomachines.

Computer Simulation↗

An assessment of effectiveness of PLATO basic medical science lessons.

The present study investigated the effectiveness of PLATO basic medical science lessons. Evidence was found that users of the PLATO lessons earned higher mean scores than non-users on subtests of basic science examinations. The findings offer encouragement that PLATO materials may be viable educational tools within a first-year basic science program.

Analysis of Variance↗

Construction of a model demonstrating cardiovascular principles.

We developed a laboratory exercise that involves the construction and subsequent manipulation of a model of the cardiovascular system. The laboratory was designed to engage students in interactive, inquiry-based learning and to stimulate interest for future science study. The model presents a concrete means by which cardiovascular mechanics can be understood as well as a focal point for student interaction and discussion of cardiovascular principles. The laboratory contains directions for the construction of an inexpensive, easy-to-build model as well as an experimental protocol. From this experience students may gain an appreciation fo science that cannot be obtained by reading a book or interacting with a computer. Students not only learn the significant physiological concepts but also appreciate the importance of laboratory experimentation for understanding complex concepts. Model construction provides a hands-on experience that may substantially improve performance in science processes. We believe that model construction is an appropriate method for teaching advanced concepts.

Cardiovascular Physiological Phenomena↗

Technological trends in clinical laboratory science.

OBJECTIVE: This article will review the advancements and new developments being made in (1) advanced computers, (2) microtechnology, (3) advanced immunodiagnostics, (4) neural networks, and (5) molecular biology. The influence of these technologies and their products on clinical laboratories is also discussed. CONCLUSION: Significant evolutionary and revolutionary technological changes are occurring in a number of scientific and engineering disciplines that impact the medical profession. As a consequence of this era of rapid change, the discipline of Clinical Laboratory Science is undergoing a "technological explosion" which is having a significant and profound effect on how clinical laboratories of today and tomorrow are and will be staffed, equipped, and operated.

Chemistry, Clinical↗

Computer-assisted interpretation in forensic toxicology: morphine-involved deaths.

Case data from 200 morphine-involved deaths (Spiehler, V. and Brown, R., Journal of Forensic Sciences, Vol. 32, No. 4, July 1987, pp. 906-916) were analyzed for patterns and relationships using artificial intelligence (AI) computer software. Case parameters were blood unconjugated morphine, blood, brain, and liver total morphine, sex, age, frequency of use, time of death after injection, cause of death, and presence of other drugs. The programs used were Expert 4 (Biosoft-Cambridge), BEAGLE (Warm Boot, Ltd.), and KnowledgeMaker (Knowledge Garden Inc.). Interpretation was defined as estimating the dose, response, and time after drug dosing. The AI programs were used to advise on time and response outcomes for cases, to calculate the probability of the estimate being true, to develop rules for interpretation of morphine-involved cases, and to diagram a decision tree. On known cases the AI programs were successful 70 to 90% of the time in classifying the cases as to response and time. No data on dose were available in this database. The success rate in individual cases was proportional to the program-estimated probability. All three programs found the case parameters of most value in predicting response to be blood unconjugated morphine, blood total morphine, and liver total morphine. The case data most useful in estimating time of death since drug injection were blood unconjugated morphine, percent unconjugated morphine in blood, and brain total morphine. The rule induction programs found that morphine overdoses were characterized by blood unconjugated morphine greater than 0.24 micrograms/mL, liver morphine greater than 0.50 to 0.75 micrograms/g, brain morphine greater than 0.08 micrograms/g or greater than blood unconjugated morphine, and percent blood unconjugated morphine greater than 37%. Rapid deaths were characterized by percent unconjugated morphine greater than 44 to 50%; blood unconjugated morphine, as a function of other drugs present, greater than 0.09 to 0.21 micrograms/mL; and brain total morphine greater than 0.16 to 0.22 micrograms/g. This work demonstrates that inexpensive AI programs commercially available for personal computers can be useful in interpretation in forensic toxicology.

Cause of Death↗

[Assessment of technology in intensive care medicine].

In recent years intensive care medicine has been accused of becoming a purely technological medicine. Those of this opinion, however, fail to realize that automatic ECG and blood pressure monitoring have improved the safety and validity of vital parameters and have relieved qualified personnel from unnecessary tasks. The controversial discussion of invasive monitoring, particularly the use of pulmonary artery catheters, led to new and improved technology. Infusion technology and artificial ventilation are characteristic examples which demonstrate the essential need for technology in intensive care medicine. The same is true for extracorporeal CO2 elimination, pacemaker technology, haemofiltration, etc. Computer technology will lead to further improvement in safety and patient care, protecting the patient from human and technical errors.

Critical Care↗

Which algorithm for scheduling add-on elective cases maximizes operating room utilization? Use of bin packing algorithms and fuzzy constraints in operating room management.

BACKGROUND: The algorithm to schedule add-on elective cases that maximizes operating room (OR) suite utilization is unknown. The goal of this study was to use computer simulation to evaluate 10 scheduling algorithms described in the management sciences literature to determine their relative performance at scheduling as many hours of add-on elective cases as possible into open OR time. METHODS: From a surgical services information system for two separate surgical suites, the authors collected these data: (1) hours of open OR time available for add-on cases in each OR each day and (2) duration of each add-on case. These empirical data were used in computer simulations of case scheduling to compare algorithms appropriate for "variable-sized bin packing with bounded space." "Variable size" refers to differing amounts of open time in each "bin," or OR. The end point of the simulations was OR utilization (time an OR was used divided by the time the OR was available). RESULTS: Each day there were 0.24 +/- 0.11 and 0.28 +/- 0.23 simulated cases (mean +/- SD) scheduled to each OR in each of the two surgical suites. The algorithm that maximized OR utilization, Best Fit Descending with fuzzy constraints, achieved OR utilizations 4% larger than the algorithm with poorest performance. CONCLUSIONS: We identified the algorithm for scheduling add-on elective cases that maximizes OR utilization for surgical suites that usually have zero or one add-on elective case in each OR. The ease of implementation of the algorithm, either manually or in an OR information system, needs to be studied.

Algorithms↗