Experimental Methods In Rf Design Pdf Download
Notes are concise accounts describing novel observations, new methods of wide applicability or interest, or focused studies of general interest. Notes differ from Articles in having a narrower scope. The level of experimental rigor, including compound characterization, required for a Note is the same as that for an Article. The length of a Note is limited to 3,000 words, which includes the abstract, introductory paragraph, results and discussion, and space occupied by tables and graphics; the word count limit does not include the experimental section, acknowledgments, supporting information availability statement, and list of references. Tables and graphics count toward the word-count limits at the rate of 50 words per vertical inch for one-column items (8.4 cm/3.3 inches wide or less), and 100 words per vertical inch for wider items up to two-columns (17.8 cm/7.0 inches). Authors are reminded that any graphics that are reduced in size to help adhere to the above length limits need to be fully legible when the page is printed at 100% scale.
experimental methods in rf design pdf download
Experimental Section/Computational Methods. For Notes and Articles, manuscripts reporting the results of experimental work must include all experimental procedures, compound characterization data, and any associated literature citations. Authors have the flexibility to place the experimental content in the main text (Experimental Section), in the Supporting Information, or a combination of both as it best supports the manuscript, so long as the information is accurate and complete. As needed, authors may substitute or also include a section on Computational Methods. These sections should describe methods in sufficient detail to permit repetition of the work by others. The Data Requirements section should be consulted for guidance on reporting synthetic experimental, compound characterization, spectroscopic, crystallographic, computational, and bioassay data in the Experimental Section, Computational Methods, and Supporting Information. A general Experimental Methods paragraph may be optionally provided to document procedures, such as purification methods, solvent removal, and spectroscopic and chromatographic analyses, that are common to most of the individual procedures, and should be placed at the beginning of the Experimental Section.
Doug Denison received his BS and MS degrees in Electrical Engineering from the University of Alabama, Tuscaloosa, AL, and the PhD degree in EE from the Massachusetts Institute of Technology (MIT), Cambridge, MA. In 1999 he served as a post-doctoral fellow in the MIT Plasma Science and Fusion Center where he performed numerical and experimental studies on field shaping in highly-overmoded waveguides. Since 1999 he has been on the research staff of GTRI, where his general research interests are in the areas of numerical methods in electromagnetics and radio frequency (RF) systems. His research includes wideband antenna design and characterization, electrode simulation and design for RF atom trapping experiments, and photoconductive THz emitter design and experimental characterization. Prior to his appointment as ACL Director, Dr. Denison has served consecutively as Chief of the ACL Electromagnetics Division, Chief Engineer of ACL, and ACL Associate Director.
Second, it is essential to plan and conduct studies that limit the biases so that the outcome rightfully may be attributed to the effect under observation of the study. The difference observed at the end of an experiment between two treatments is the sum of the effect of chance, of the treatment or characteristic studied, and of other confounding factors or biases. Therefore, it is essential to minimize the effect of confounding factors by adequately planning and conducting the study so we know the difference observed can be inferred to be the treatment studied, considering we are willing to reject the effect of chance (when the p value or equivalently the test statistic engages us to do so). Randomization, when adequate, for example, when comparing the 1-month HHS after miniincision and standard incision hip arthroplasty, is the preferred experimental design to control on average known and unknown confounding factors. The same principles should apply to other experimental designs. For instance, owing to the rare and late occurrence of certain events, a retrospective study rather than a prospective study is preferable to judge the association between the existence of a radiolucent line in Zone 1 on the postoperative radiograph in cemented cups and the risk of acetabular loosening. Nonetheless researchers should ensure there is no systematic difference regarding all known confounding factors between patients who have a radiolucent line in Zone 1 and those who do not. For instance, they should retrieve both groups over the same period of time and the acetabular components used and patients under study should be the same in both groups. If the types of acetabular components differ markedly between groups, the researcher will not be able to say whether the difference observed in aseptic loosening between groups is attributable to the existence of a radiolucent line in Zone 1 or to differences in design between acetabular components.
There is considerable debate about the usefulness of ranking the quality of evidence in management research (e.g., Learmonth & Harding, 2006; Morrell, 2012). Our intention here is not to argue for the pre-eminence of one method over another, but to highlight the importance of method choice, and the need for so-called triangulation of methods (McGrath, 1982) to evaluate internal, external, construct, and statistical conclusion validity of our work (Cook & Campbell, 1976). For readers without a deep grounding in experimental methods we summarize the basics in Box 1.
Another limitation of all experiments is that the observed covariation between independent and dependent variables may be disturbed by the research environment itself, such as demand characteristics or researcher expectations. It is beyond the scope of this editorial to describe the numerous experimental designs created to handle various threats, but when planning and designing experiments extreme care must be taken to choose and correctly implement the design elements that best manage potential threats to internal validity.
Quasi-experiments (sometimes called natural experiments) are often thought of as a special case of field experiments. Like true experiments, quasi-experiments involve research where the independent variable (treatment) is not determined by or controlled by the cases being studied. In a quasi-experiment, the independent variable might be (1) controlled by the experimenter, (2) be produced by an exogenous event, or (3) vary exogenously across groups. The key distinction between true experiments and quasi-experiments is that in quasi-experiments, assignment to the treatment condition is not random. In quasi-experiments, researchers design methods other than random assignment to improve evidence of internal validity (Schwab, 2013).
There may be a number of reasons for the paucity of true experimental studies, including that much of the research published on JIBS is multidisciplinary and examines macro, often long-term phenomena. Many researchers in IB phenomena cannot engage in random assignment into experimental or control groups; assigning countries to political economies, companies to globalization strategies, or country of origin to individuals would be impossible to say the least. In cases like these, we do not have IB experiments because random assignment is not possible. Yet while not right for all areas of IB, there are some IB theories and applications that would benefit from experimental designs, from quasi-experimental designs, and from experimental thinking.
While experimental designs are somewhat rare in IB research, and particularly in JIBS, there are subfields within IB where experiments are more common, especially those examining individual-level or team-level outcomes. These areas are good places to start in looking for how experiments can be conducted on IB topics. Consider the following examples within international business subfields where controlled experiments have made contributions that would have been impossible or where other methods would have provided weaker evidence.
One obvious opportunity for additional experimental studies in IB is to encourage more experimental work in the three domains identified above. However, in keeping with improving the evidence for causal relationships more broadly across IB, there are several other opportunities to apply experimental approaches. These are the control of possible alternative causes, designing longitudinal field experiments, employing experiments as part of a multiple methods approach, and thinking experimentally.
A number of experimental design methods can be used to control nuisance variables, or alternative causal explanations. Two examples are matching and identifying comparable groups of cases, then varying the treatment level across the groups. Matching equates cases on a number of dimensions (determined by theory). Done correctly, matching on nuisance variables can strongly rule out alternative causal explanations. For example, Earley (1989) matched an American sample to a harder to obtain sample from the P.R.C. on age, gender, job tenure, education, job duties, and career aspirations in a study of the effect of social loafing across cultures. Matching can be effective but becomes difficult to correctly execute as the number of cases required increases and/or as the number of nuisance variables needing control increases (Schwab, 2013). An alternative method of control involves identifying comparable groups of cases for each level of the independent variable. A recent example is provided in the previously mentioned article by Wan et al. (2014) in JIBS, in which they test the effect of modernization on consumer responses to advertising. In that study several Chinese cities at various stages of modernization serve as an independent variable, while the context of a single country controlled for a wide range of societal-level variables. Similarly, Meyer and Peng (2005) suggest that changes in Central and Eastern Europe since the 1990s provide unique societal quasi-experiments that offer an opportunity to test the applicability of existing theories and develop new ones in the areas of (1) organizational economics; (2) resource-based theories and (3) institutional theories.