Contemporary nursing in Australia and internationally is challenging, complex, dynamic and very rewarding. Many of the people we care for, both in the community and in hospitals, are older and sicker than they were a decade ago, often with complex health and psychosocial needs. This means that nurses today must be clinically competent, flexible and knowledgeable. T hey must have a broad and deep understanding of physiology, pathophysiology, pharmacology, epidemiology, therapeutics , culture, ethics and law, as well as a commitment to evidence-based practice . Today 's nurses have many roles and functions-clinician, educator, leader, researcher, to name just a few. They must be highly skilled with the ability to problem solve and they must possess sophisticated critica l thinking skills. Nurses must be lifelong learners and confident in the use of information and communication technology. They must be able to communicate effectively, with their clients , with each other and with other members of the health care team. Above all, they must care for people in ways that signify respect , acceptance, empathy, connectedness , cultural sensitivity and genuine concern.
Earning per share (EPS) is statistically significant in the first three models but after controlling for time variant effects, earning per share (EPS) turn statistically insignificant in model 4 and model 5. It shows that EPS is a time variant ratio. Stockholders may appreciate higher EPS in some years but in other years it may not be fully reflected in stock rate of return. Reported earnings that are used in formulation of EPS are quite sensitive to standards imposed by accounting bodies. We know that Australia adopted international accounting standards during our study period thus Australian investors may not have paid much attention to the change in this ratio over the years.
Augmenting hill climbing with memory rather than randomness turns out to be a more effective approach. The basic idea is to store a “current best estimate” H(s) of the cost to reach the goal from each state that has been visited. H(s) starts out being just the heuristic estimate h(s) and is updated as the agent gains experience in the state space. Figure 4.23 shows a simple example in a one-dimensional state space. In (a), the agent seems to be stuck in a flat local minimum at the shaded state. Rather than staying where it is, the agent should follow what seems to be the best path to the goal given the current cost estimates for its neighbors. The estimated cost to reach the goal through a neighbor s is the cost to get to s plus the estimated cost to get to a goal from there—that is, c(s, a, s ) + H(s ). In the example, there are two actions, with estimated costs 1 + 9 and 1 + 2, so it seems best to move right. Now, it is clear that the cost estimate of 2 for the shaded state was overly optimistic. Since the best move cost 1 and led to a state that is at least 2 steps from a goal, the shaded state must be at least 3 steps from a goal, so its H should be updated accordingly, as shown in Figure 4.23(b). Continuing this process, the agent will move back and forth twice more, updating H each time and “flattening out” the local minimum until it escapes to the right.
Probably the most common criticism of college textbooks is that they are too long. With most popular texts, the number of pages often increases with each new edition. This leads instructors and students to complain that it is impossible to cover all the topics in the text in a single term. After struggling with this concern (trying to decide what to delete without limiting the value of the text), we decided to divide the text into two components. The first is a set of ‘‘core’’ topics—sections of the text that are most commonly covered in an introductory materials course, and second, ‘‘supplementary’’ topics—sections of the text covered less frequently. Fur- thermore, we chose to provide only the core topics in print, but the entire text (both core and supplementary topics) is available on the CD-ROM that is included with the print component of Fundamentals. Decisions as to which topics to include in print and which to include only on the CD-ROM were based on the results of a recent survey of instructors and confirmed in developmental reviews. The result is a printed text of approximately 525 pages and an Interactive eText on the CD- ROM, which consists of, in addition to the complete text, a wealth of additional resources including interactive software modules, as discussed below.
S ome devices run all the time — a refrigerator, for example. Other devices must be turned on and off, which is done easily by using a common switch: The toaster’s large handle turns the thing on and automatically turns it off after producing toast of the desired toasti- ness. The oven, the dishwasher, the dryer, and other devices have dials and timers to turn themselves on and off. The water heater heats water automatically and turns itself on and off as needed. With a computer, you can just toss out the window all the on–off knowledge you’ve gained throughout your life. Although nothing could be simpler than flipping a light switch, few things in this life are as complex as turning a computer on — and fewer things are more daunting than turning the blasted computer off!
M AXIMUM -S UBARRAY returns a tuple containing the indices that demarcate a maximum subarray, along with the sum of the values in a maximum subarray. Line 1 tests for the base case, where the subarray has just one element. A subar- ray with just one element has only one subarray—itself—and so line 2 returns a tuple with the starting and ending indices of just the one element, along with its value. Lines 3–11 handle the recursive case. Line 3 does the divide part, comput- ing the index mid of the midpoint. Let’s refer to the subarray AŒlow : : mid as the left subarray and to AŒmid C 1 : : high as the right subarray. Because we know that the subarray AŒlow : : high contains at least two elements, each of the left and right subarrays must have at least one element. Lines 4 and 5 conquer by recur- sively ﬁnding maximum subarrays within the left and right subarrays, respectively. Lines 6–11 form the combine part. Line 6 ﬁnds a maximum subarray that crosses the midpoint. (Recall that because line 6 solves a subproblem that is not a smaller instance of the original problem, we consider it to be in the combine part.) Line 7 tests whether the left subarray contains a subarray with the maximum sum, and line 8 returns that maximum subarray. Otherwise, line 9 tests whether the right subarray contains a subarray with the maximum sum, and line 10 returns that max- imum subarray. If neither the left nor right subarrays contain a subarray achieving the maximum sum, then a maximum subarray must cross the midpoint, and line 11 returns it.
The continuous variables will be summarized as mean ± standard deviation (SD) and the categorical variables will be described in frequency and percent. The baseline characteristics will be compared by either the Student t- test for the continuous variables or χ 2 -test for the categorical data. All analyses will initially be done on an intention-to-treat basis (ITT) and all participants will be included in the analyses if the relevant outcome vari- ables have been collected. A per-protocol analysis will also be conducted for the primary and secondary outcome if there are a considerable number of protocol violators. For the primary outcome, a one-way ANCOVA will be used comparing differences in post- intervention UIC between the intervention group and the control group with the pre-intervention UIC as a co- variate, as it is believed that the post-intervention UIC will depend, to some degree, on the pre-intervention UIC. For the secondary outcome, we will use linear re- gression to compare the mean Bayley-III cognitive score, the language scores (receptive and expressive separately and as a composite score) and the motor scores (fine and gross motor separately and as a composite score) between the two groups. The Bayley-III scores will be used on a continuous scale and is expected to be normally distributed. We will also investigate potential effect modification by other nutrients and confounding factors. In the case of unanticipated differences occur- ring between the intervention and control group, we will adjust for that/those factor(s) in the analyses for both the primary and secondary outcome. For the other out- comes, we will use a variety of statistical approaches where the data may be used as predictors, mediators or moderators. These analyses will be based on plans of analyses for the specific research questions that are addressed. Multilevel linear modeling will be applied to assess longitudinally data. Statistically significance will be set at p < 0.05.
As I watched these trends, it had been in the back of my mind for about a decade to write a new edition of Managing Projects with make. But I sensed that someone with a broader range of professional experience than mine was required. Finally, Robert Mecklenburg came along and wowed us all at O’Reilly with his expertise. I was happy to let him take over the book and to retire to the role of kibitzer, which earns me a mention on the copyright page of this book. (Incidentally, we put the book under the GNU Free Documentation License to mirror the GPL status of GNU make .) Robert is too modest to tout his Ph.D., but the depth and precision of thinking he must have applied to that endeavor comes through clearly in this book. Perhaps more important to the book is his focus on practicality. He’s committed to making make work for you, and this commitment ranges from being alert about efficiency to being clever about making even typographical errors in makefiles self-documenting. This is a great moment: the creation of a new edition of one of O’Reilly’s earliest and most enduring books. Sit back and read about how an unassuming little tool at the background of almost every project embodies powers you never imagined. Don’t set- tle for creaky and unsatisfying makefiles—expand your potential today.