Proactive analysis of care systems undergoing or- ganizational change may help identify potential un- intended outcomes and facilitate up-front change to reduce the threat to patient safety. The 10 simplerules offered by the IOM report provide us with a template to review other areas of care that may have been similarly impacted and plagued by the unin- tended consequences of many and frequent changes in the health care delivery system. Taking less than a full-systems view of the impact of change leaves us vulnerable to additional gaps in quality and threats to patient safety. We must act independently, follow- ing clear, simplerules to meet these challenges to- gether.
At the outset of a research project, clarify contributor roles, acknowledgments, rewards, and code of conduct, e.g., (50). Use resources like "Ten SimpleRules for a Successful Collaboration" (51) and Collaboration and Team Science: A Field Guide (52) for guidance defining these roles. Also clarify when data or software can be released, and cite the resources you use (53). Think beyond the usual contributor acknowledgments of "author", "editor", "contributor", "acknowledgment", etc. (54), and reconsider author order. In other words, clearly define and state what contributions would lead to what acknowledgments or rewards (55). The International Committee of Medical Journal Editors provides guidance ("the Vancouver Recommendations") that many journals require for submissions and that are good practices to follow regardless of publisher requirements (56,57). The Committee on Publication Ethics also provides hundreds of guiding documents, including flowcharts, specifically relating to authorship and contributorship (58–60).
We have defined a set of simplerules that can be used to an- ticipate, and thus potentially reduce, exposure to earthquake- triggered landslides. We test a set of candidate predictors for their ability to reproduce mapped landslide distributions from six recent earthquakes. Landslide hazard, defined as the conditional probability of intersecting a landslide in one of the six earthquakes, increases exponentially with local slope. Landslide hazard on hillslopes also increases with upslope contributing area, suggesting that, while ridges may be ar- eas of preferential coseismic landslide initiation, they are not the locations of highest coseismic landslide hazard due to downslope movement of landslide material during run-out. When accounting for both slope and upslope contributing area, landslide hazard is highest for the largest upslope con- tributing area at a given slope or the highest slope at a given upslope contributing area. Landslide hazard can be reduced by decreasing local slope, even at the cost of increased up- slope contributing area and especially at high slopes. Land- slide hazard also increases exponentially with the skyline an- gle, and this simple, easily measured metric performs better than slope or upslope contributing area for four of the six inventories. Hazard area, which accounts for both landslide initiation and run-out, offers the best predictive skill for all six inventories but is more difficult to estimate in the field and requires estimation of two empirical parameters. Fortu- nately, hazard area calculated with parameters that are aver- aged across all six study sites (initiation angle of 40 ◦ and stopping angle of 10 ◦ ) performs almost as well as hazard area calculated with optimised site-specific parameters, sug- gesting that the average parameters can be applied to other inventories. These findings can be distilled into three simplerules:
Results: The framework for Successful Healthcare Improvement From Translating Evidence in complex systems (SHIFT- Evidence) positions the challenge of evidence translation within the dynamic context of the health system. SHIFT-Evidence is summarised by three strategic principles, namely (1) ‘ act scientifically and pragmatically ’ – knowledge of existing evidence needs to be combined with knowledge of the unique initial conditions of a system, and interventions need to adapt as the complex system responds and learning emerges about unpredictable effects; (2) ‘ embrace complexity ’ – evidence-based interventions only work if related practices and processes of care within the complex system are functional, and evidence- translation efforts need to identify and address any problems with usual care, recognising that this typically includes a range of interdependent parts of the system; and (3) ‘ engage and empower ’ – evidence translation and system navigation requires commitment and insights from staff and patients with experience of the local system, and changes need to align with their motivations and concerns. Twelve associated ‘ simplerules ’ are presented to provide actionable guidance to support evidence translation and improvement in complex systems.
However—and this is really the aim of all the rules—in order to make those decisions, you need to be reasonably well informed about what workshop organization will and could involve. The requirements and possibilities aren’t always obvious and are broader than these ten simplerules (see , , , and ). Even if you go for the “ simple ” option, it is worth being aware of what opportunities or problems you are excluding or encouraging; for instance, if you were to ignore the benefits of social engineering (Rule 3), seating (Rule 6), or communications (Rule 5) or by being unprepared for timetable changes (Rule 10) and the workshop closing (Rule 9). Good luck! I have never got everything right at the same time!
review the images and perform a diagnosis (diagnoses 2 and 3), respectively. They were asked to classify the masses into five groups, according to the ultrasonic fea- tures: benign, possibly benign, undetermined, possibly ma- lignant, and malignant. The two reviewers were blinded to the clinical and pathological information when they were assessing the cases. The diagnoses were locked as soon as they were made and could not be changed afterwards. Two months later, the two sonographers were asked to re- view the stored images (order disturbed) again and per- form a second diagnosis (diagnoses 4 and 5), respectively, after learning the simplerules by reading the original paper published by the IOTA group . In addition, they had no knowledge of the pathological or clinical informa- tion of the patients during evaluation. Only at this time they were encouraged to use diagnosis 1 as a reference. Diagnosis 2 and diagnosis 4 were made by an experienced sonographer before and after referencing the simple rule diagnosis. Diagnosis 3 and diagnosis 5 were made by a less-experienced sonographer before and after referencing the simple rule diagnosis.
In this paper we have introduced ten simple ‘rules’ for the teaching and learning of the language of statistics, consisting of four aspects of the landscape of tricky terms, and six signposts for navigating the landscape successfully. Just like its parent, English, the language of statistics is rich and draws from a variety of sources, including general, mathematical, statistical and discipline- specific English. We believe these rules offer statistics educators a way to welcome learners into the community of statistics speakers and enhance statistical literacy across the board.
Rough sets, a data-analysis method originally proposed by Pawlak in the early 1980s , has evolved into a widely accepted machine-learning and data-mining method . In [7-10], rough sets was applied for cancer classification and prediction based on an attribute reduction approach. In , we proposed a rough sets-based soft computing method to conduct cancer classification using single genes or gene pairs. In this article, we also explore the use of sin- gle genes and gene pairs in constructing cancer classifiers; however, in contrast to , we first aimed to use the con- cept of canonical depended degree, as proposed in rough sets for gene selection. In the cases that this approach was unsuccessful, we considered utilizing the α depended degree standard suggested in  for gene selection. In this work, the α depended degree was employed for a por- tion of the datasets. In addition, unlike the other rough sets-based methods, we did not carry out attribute reduc- tion for gene selection. Instead, we first implemented fea- ture ranking according to the depended degree or α depended degree of attributes, and then selected the top- ranked genes to create classifiers so as to avoid expensive computation for attribute reduction. Moreover, we made use of the decision rules induced by the chosen genes to build classifiers, whereas existing rough sets-based meth- ods only utilized rough sets for gene selection, and the classifier constructions depended upon other machine- learning algorithms such as SVMs, ANNs, GAs, NB, and k- NNs [7-10].
Interestingly, the same uncertainty remains about human abilities in artificial language learning tasks. The findings in (4) lacked the appropriate controls, but later replications claim that the AABB/ABAB task, which Fitch and Hauser (4) designed to obtain evidence for recursion, can be solved by humans using a simpler strategy instead (5, 9) or by a conscious counting strategy that seems unrelated to language (11). At present, there is thus no convincing demonstration of the use of recursive rules in artificial language learning in any species. It remains a challenge to design experiments on artificial rule learning and its under- lying mechanisms that unambiguously exclude simpler explana- Table 2. Average response to the two consecutive blocks of 30 probes of each probe type
Flipped classrooms modify how teaching is traditionally performed. Teachers need to explain the rules, for instance by providing detailed instructions in their syllabus. One way to enforce being explicit on what students are expected to do at home and in class is to clearly define pedagogical objectives also termed ‘learning outcomes’. In designing pedagogical objectives for flipped classrooms, teachers have to keep in mind that activities performed at home by the students should be related to cognitive categories ‘Remember’, ‘Understand’ or ‘Apply’ (see Table 1).
It is important to consider the cost of a workshop in terms of time, effort, and money when thinking about measuring its impact. The number of people, the duration, the venue (whether held in person or online), the price to the individual, the resources available within the organi- sation, and whether the workshop is one of a series or a stand-alone event can all affect how much effort is reasonable to put into measuring impact and which of the rules below are applied. For example, the impact of a free one-hour online workshop might be adequately ana- lysed using a few survey-type questions sent out at the end of the workshop. However, a multi- day, moderately expensive workshop that uses a mixture of learning and exploring and intends to enthuse people about changing practices may require more effort to be put into the impact analysis, so more of the rules would come into play (especially Rules 6–10, related to techniques).
Notably, our model simulations not only recapitulate the growth profiles of both the untreated and treated biofilm successfully (Figs. 1c and 2c), they also capture the spatial organization of the cells/biofilm over time (Figs. 1a, b and 2a, b). The model predictions suggest that adding the antibiotic agent inhibits the move- ment of certain single planktonic P. aeruginosa which retards their growth. However, subsequently, the inhi- bition succumbs due to the other rules (1 and 4) to form delayed biofilm. Thus, our model predictions indi- cate that AZM, on top of regulating bacterial quorum sensing mechanism and metabolism, is also regulating the cell movement mechanisms such as those involved in flagellar functioning. This delays the overall biofilm progression.
The purpose of this paper is to re-evaluate a recent paper by Quint and Rabanal (2014), which examined the optimal mix of monetary and macroprudential policies in an estimated model of the euro area. There are three additional issues that can most obviously be ad- dressed within the context of their DSGE model. Firstly, although their optimal policies, as evaluated via optimal simplerules, relative to the estimated simplerules show con- sumption equivalent welfare benefits or losses of the order of 0.6-0.7%, these are evaluated without consideration as to how frequently the zero lower bound (ZLB) for the nominal interest rate is violated. Here we correct for this by penalising deviations from the steady state of the nominal interest rate such that the likelihood of violating the ZLB occurs once every 400 quarters. Secondly, we evaluate whether there is any benefit from using optimal simplerules that include deviations of output from steady state - commonly viewed as countercyclical policy as advocated by by Goodhart; this can be viewed as a feedback that is additional to the standard feedback on either credit growth or on credit/GDP ratios. Finally we also examine the impact of reserve ratios for the financial intermediaries in the model. Quint and Rabanal (2014) assume that the lending-to-deposit ratio is 1 in steady state; although the model is not ideal for examining changes in this ratio, by allowing for the possibility that borrowers in the model have a discount factor that is lower than that assumed by Quint and Rabanal (2014) we are able to evaluate the costs and benefits of banks having a certain required reserve ratio.
Wieting et al., 2016; Verwimp et al., 2017, i.a.). In contrast to prior work, our model decouples the use of morphological information, now pro- vided in the form of inflectional and derivational rules transformed into constraints, from the actual training. This pipelined approach results in a sim- pler, more portable model. In spirit, our work is sim- ilar to Cotterell et al. (2016b), who formulate the idea of post-training specialisation in a generative Bayesian framework. Their work uses gold mor- phological lexicons; we show that competitive per- formance can be achieved using a non-exhaustive set of simplerules. Our framework facilitates the inclusion of antonyms at no extra cost and natu- rally extends to constraints from other sources (e.g., WordNet) in future work. Another practical differ- ence is that we focus on similarity and evaluate morph-fitting in a well-defined downstream task where the artefacts of the distributional hypothesis are known to prompt statistical system failures. 7 Conclusion and Future Work
This paper focuses precisely on bank lending and on whether monetary policy responded and should respond to credit exuberance. First, it provides Bayesian estimates of a DSGE model in which frictions in the bank-loan market arise due to the presence of lending relationships, and monetary policy is set according to a credit-growth-augmented Taylor-type rule. The model is otherwise standard and exhibits the real and nominal frictions commonly found in the mainstream literature. Then, the paper provides also a normative analysis via the computation of optimised simple interest-rate rules. We deem this strategy to be appropriate as (i) Bayesian estimation is suitable to empirically assess whether a leaning-against-the-wind policy can be detected in standard US macroeconomic data and to estimate the shocks, which we find to be key determinants of optimal policy, and (ii) optimised simplerules unveil whether the credit-growth-augmented Taylor- type rule is welfare-optimal.
Apart from the theoretical arguments, the results from the empirical studies are controversial as well. The issue regarding the role of exchange rate in the monetary policy framework for the open economies still open for debates. Focusing on the effects of exchange rate pass-through and trade openness in emerging market environment, this chapter seeks to compare the performances of various simple policy rules with the closed economy rule and if the augmented Taylor rules with exchange rate terms perform better compare to the other rules. Taking into account the economic characters for the emerging East-Asian countries, this chapter seeks to evaluate the role of exchange rate in the design of monetary policy for the emerging countries. This chapter applies two different approaches of analysis which divides it into two main parts. In the first part of this chapter, simulations are carried out to compare a battery of restricted optimized simple policy rules under different degrees of exchange rate pass-through and trade openness. For the robustness purpose, simulations are repeated by considering different persistency and variation of shocks and policy weighting. In the second part of this chapter, a different approach of analysis is conducted to evaluate the exchange rate regimes (flexible, managed floating and fixed exchange rate regimes). Simulations are based on several simplerules which represent different exchange rate regimes. Evaluations on the regimes are based on the source, the persistency and variation of shocks, given different cases of exchange rate pass-through. Evaluations are followed by robustness checking.
Abstract—In part 1 of this study, it was suggested that technical trading systems were capable of out performing the market. Part 2, of this empirical study, now being discussed, is based on the JSE All Share Index over the period of April 1988 to April 2007. The over-all data series is broken down and tested in four non-overlapping sub-periods. The results show that excess returns over a buy-and-hold strategy are possible using technical analysis, even in the presence of transactional costs. However, the statistical significance tests of the results obtained in this research are inconclusive as they fail to reject the null hypothesis that daily technical trading returns are equal to or less than zero. The VMA trading rule was found to outperform the other simplerules tested and shorter moving average time lengths were found to yield better results, even in the presence of transaction costs.