This book is just chock full of goodness. Not only does the Book of CSS3 by Peter Gasston provide a thoughtful introduction to the latest and greatest power of CSS3 (the history and background of which is covered superbly), it offers clear, thorough and well illustrated examples built on tips, tricks and real world experience.

This book is aimed at those with some experience with CSS in general but does not demand an in-depth knowledge. It jumps in and demonstrates by doing and provides a logical path through the various facets of CSS3.

The material covered in the book is extensive and thorough and moves with a logical progression allowing this (unlike some more reference oriented pieces) to be passed through linearly. It also contains an elabourative table of contents retaining its use as a solid reference source.

The section on the Future of CSS is of particular note, wherein the author explores some of the emerging directions and areas of interest for the next go around of CSS even including a short discussion of how haptics may well be integrated into CSS. Quite fascinating and certainly new to me.

Gasston provides a useful browser compatibility cart with extensive granularity detailing where CSS3 currently works and where web developers may run into cross-browser issues. This is a great reference tool. Additionally extensive links are provided to online resources that will provide the most current information about compatibility and experience with CSS3.

I would highly recommend this volume for those working beyond off the shelf web tools and desiring to harnessing the new abilities present in CSS3.

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Yes, another tutorial based guide to JavaScript. Does the world need another one? Do you need another one? Well, it’s made it to a second edition, there must be something to that. At least that was what made it worth checking out for me. In fact, after having spent some time with this volume, I come away very impressed. Suehring’s JavaScript Step by Step in a superb ‘missing manual’ for novices and those already somewhat acquainted with aspects of JavaScript.

This is a well crafted guidebook that has clearly adopted a proven progression. A very basic introduction that familiarises someone new to JS with a couple handy development environments (Visual Studio and Eclipse — hey it’s a Microsoft Press book what do you expect…at least an open source alternative was suggested), and moves to the basic grammar and the philosophy behind the language. It gradually builds on your working knowledge moving from syntax to operators and variables and then to expressions and control mechanisms. The approach is in very well constructed and steps are scoped and paced for an absolute beginner, but also allow someone with moderate experience to move quicker through the material and refresh their knowledge. Hands-on exercises are sprinkled throughout and the companion downloadable files allow you to avoid extensive typing and pick and choose where you really want to go hands-on. There are even specific exercises at the end of each section for those used to a little self-evaluation. All of which means that this book will suit a variety of learning styles.

The steps continue beyond the language basics to apply the building blocks of JavaScript into the important aspects of how it ties into the DOM and interacts with the browser — aspects that obviously are very much to the raison d’etre of JavaScript specifically. This builds into AJAX and Server-side integration, which was very much what I was most interested in seeing the handling of. For someone that played around with JavaScript in the more distant past, getting up to speed with how it is being used more recently (a wonderfully subjective reference ;-) is a real plus and I feel that Suehring accomplishes this well in this volume.

There are obvious limitations to how far one can go in an introductory (or even in a single) volume and exposure to the more advanced applications of JavaScript (Such as AJAX and JQuery) are more lightly handled, but enough of a taste to give the novice an basic understanding and at least know where to go next and be able to be more critically understand what is needed to know to move on, i.e. when shopping for the next volume of instruction.

The only critical issue I can raise, and it is more preferential than a defect, is the choice of Microsoft products largely exclusively, whether Visual Studio or integration with Bing and LiveMaps. All fine products and in widespread use. I do tip my hat to the suggestion of other OS products such as Eclipse, but am not used to seeing a volume aimed as particularly at a Microsoft-certified audience. Just me I suppose.

I am actually very impressed with this book and preceding comment aside I think this is a particularly notable volume for covering such a wide amount of information, in a very well considered and effective level of detail. For a novice to JavaScript, or someone that perhaps explored it within the last decade and wants to brush up on how it has been extended and functions well with new API’s and frameworks, this is a great choice.

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Faced with finding a place to live in Dublin, I decided to quickly attempt to create a mashup of potential properties mapped throughout the city. This is, I sense, an increasingly common sort of mashup. But when I did a quick scan, I couldn’t find anything that accomplished this for the area I wanted. Yes, you could plot each place manually in either the My Maps section of Google Maps or create a KML overlay for Google Earth. Viable, but I wanted a tool that would allow the list to be dynamically generated and capture the list current to when I was looking at it. I would note that many sites have their own spatial displays, and there are a lot of mashups involving craigslist, but none for me and the Dublin scene. The site I was using to look for rental opportunities is daft.ie. It’s got a great search engine, and it will map your selections on a rental by rental basis, or will present all (unfiltered) listings in the area of the listing you have chosen. You can additionally select a particular agency and have their listings plotted on a Google Map, but this was not quite what I wanted. My objective was to create a custom search, take the detailed results, identify the location, geocode it, and then pipe it into Google Maps. In concept simple — in practise subject to the vagaries of daft and the tools used.

So, what tools do we need to build such a map without worrying about code?:

• Daft.ie for data
• Dapper.com to create a dynamic RSS feed
• Yahoo Pipes to geocode and output a further feed
• Yahoo Maps because that’s what Pipes works with

The first step is to construct the target properties from daft.ie. To do this, use their search form and have it return a paged series of search results matching your criteria. I have not been able to crack their paging code and am limited to the default 10 results per page. I tried to aggregate the paged results in Dapper, but to no avail. The work around for this is to create an account on daft and have it add listings to this as they are created. Viewing the My Daft page then provides non-paged view of your properties and gives the data (albeit in a relatively unstructured form) needed to build a custom feed from.

The next step is to log into your Dapper account and make a new Dapp. For those who have not used Dapper, it allows you to take unstructured data on a web page and turn it into structured data through some pattern matching exercises. It’s entirely graphical and calls for no knowledge of what’s going on behind the scenes. What you do is choose example pages containing the data you want to grab, teach Dapper how to recognize different types of data, provide this data with descriptors and then choose the format for the resulting structured data. Dapper is very slick and consistently reliable.

I pointed Dapper at ‘My Daft’ and it displayed the page with a list of target properties. I clicked on all the location phrases for each entry and then told it that these contained location info. I added separate fields for details about the property, a picture, the contact number and most importantly — the price. Once satisfied with the constructed feed, I told Dapper I wanted this as a public filtered RSS feed. Dapper then provides a custom URL containing this information linked back to the original source. Thus, when new things are added at the Daft end, they are picked up by Dapper and fed through to our next step.

Armed with a feed of structured data I turned to Yahoo PIpes to pick out the locational information, geocode this and plot it on a map. Yahoo Pipes provides a graphical editing environment into which you drag objects that represent your input, the operations you perform on this input and the way in which you want to view the output. So, I chose a URL feed input widget and pointed it at the custom Dapper feed. Now I have the property information flowing in from Daft. I then choose the widget ’ location extractor’ that will scan any feed and attempt to locate geospatial references. In this case, I don’t even have to specify that the locational information is enclosed by a <location> bracket. When it finds this information, it then attempts to geocode it. Thus if we supply a location of “Long Boat Quay North, Sir Rogerson Quay, Dublin Ci, Dublin 2, Dublin City Centre — Apartment to let” it will take the information, access a geocoding service and return a longitude and latitude: 53.339688,-6.216545. This is very cool. Note that it is not stripping out information, but adding new information to the existing property reference. Now we can point this output at a map and each packet of information is plotted using the precise spatial information that has been determined. Working in Yahoo PIpes is quite cool. The graphical editor allows you to pipe your information between sources and operators by dragging pipes between them. Great for visual thinkers.

When you run the Yahoo Pipes, it pulls all these pieces together and presents a map cleverly dotted with pinpoints indicating the properties that you are interested in. Clicking on any property marker then brings up the details about that property. This works very well, but its not perfect and some times the geodcoder simply can’t find the location from the text provided. Sometimes it also guesses rather badly and you can end up with a property marker on the other side of the world. However, it works in most cases and gives you a great up to the minute view of rental opportunities meeting your search criteria in the city of choice.

A next step would be to start to provide some logic to deal with locations which cannot be geocoded. It might also be useful to colour the pins by price range, number of bedrooms, etc. This is all accomplishable within PIpes once we have this great custom feed coming from Dapper/Daft.

If you would like to experiment further with map mashups, here’s a couple good references:
Erle, Schuyler and Rich Gibson, Google Maps Hacks, O’Reilly, 2006 — 0–596-10161–9
Lewis, Andre, Michael Purvis, Jeffrey Sambells and Cameron Turner, Beginning Google Maps Applications with Rails and Ajax From Novice to Professional , Apress, 2007 — 1–59059-787–7
Lenssen , Philipp, Google Apps Hacks, O’Reilly, 2008 — 978–0-596–51588-1
Shu-Wai Chow, PHP Web 2.0 Mashup Projects, PAckt, 2007 — 978–1-847190–88-8

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The folks at Many Eyes recently introduced their new comparison cloud tool. Basically, it lets you visualise two fragments of text displaying word frequency for each in the same cloud. It’s an interesting addition to the more familiar word cloud. Using a standard word cloud you get a matrix of words with relative size, weight or colour highlighting frequency in a selected text. This quickly allows you to visually perceive an author or speaker’s emphasis on a particular theme or style of writing or speaking. With Many Eyes hybrid tool, words which occur in both text are abutted. You can now visually compare two texts from the same author for similar empahsis or quickly determine a difference between texts. In the example presented at Many Eyes, they compare the US presidential State of the Union addresses from 2002 and 2003. In this example they note the less frequent mention of Afghanistan and the increase in mention of Saddam. Whether this allows one to conclude a change in policy or not, it does demonstrate the use of the tool for provoking questions for further exploration.

On Saturday, the Ontario government officially announced how much funding each university in Ontario is to receive for maintenance and renewal of facilities. I just happened to see announcements from a few institutions appear simultaneously in my RSS reader and was struck by the rather different ways in which they presented this news.

At McMaster, there was a relatively terse announcement that provided very little detail on how the money would be spent. Western on the other hand had a pretty picture and a complete list of how much was being distributed to each institution. The University of Guelph was more detailed than McMaster and provided very precise details of what the money would be spent on. I was struck by the differences, so I thought I’d see how I might quickly use a text analysis tools to compare the announcements.

I rely on two sources for tools such as these TAPoRWare and ManyEyes. For industrial strength analysis and fast results, I use TAPoRWare tools. By simply choosing the URLs of the announcements from two universities I receive a wealth of information about the announcements. I am particularly interested in extremes in this case. What makes each announcement similar and what apparent differences are there. Taking a look at a chart of common words and their frequency of use is a first attempt at this.

A similar chart was created showing me words that appeared only in one or the another and I was immediately struck by the fact that campus didn’t occur at all in the McMaster announcement, where it was the most frequent word at Guelph. On the other hand, McMaster emphasized engineering and psychology. Yet, neither word occurred in the text of announcements. The reason for this was my use of the the web addresses of the announcements, as opposed to the text of the announcements themselves. The TAPoRWare tool analysed all the text on the page and McMaster’s announcement page contained summaries of a variety of other announcements, thus ‘polluting’ my analysis. Thankfully there is an easy way to fix this. By choosing to upload only the text of the announcements themselves (And thus help the tool know just what is important to me) I can get the results I want to consider.

Voila! Now I can see that Guelph emphasizes the future and campus, whereas McMaster emphasizes renewal. Interesting. I want to consider this further, but I am far more a visual thinker, and while these bar charts are pleasing, and take a wealth of data and distill it to a very nice summary, I want to take it one step further. Word clouds are a way of accomplishing this as I mentioned above.

ManyEyes new tool gives me a way to quickly accomplish this comparative analysis. Unfortunately, I can’t just point ME at the web pages and have it capture the text. I had grabbed the text files above to better focus TAPoRWare, and so it was a matter of copy and pasting the text from each of the university web pages and inserting a short comment line between each fragment. Then you simply upload it to Many Eyes by pasting it into a text box, applying some meta information (a title, source and description) and clicking the upload button. Once uploaded, you can choose from a variety of available visualization tools. Choosing the word cloud tool immediately presents you with a default cloud display. In this case, Many Eyes noted the fragment dividers and automatically selected the comparison cloud type. I could have overridden this option if the fragment dividers were actually part of the text I was analysing.

The texts that I was analysing are somewhat shorter than the examples that the Many Eyes blog featured and one thing that became apparent was that shorter text may demonstrate a far fewer number of comparable words. Nonetheless for the ones that are identified, one might be inspired to consider whether lack of emphasis is reflective of institutional priorities. In the word cloud in this post, I compare the announcements from York and Western.

York seems to be emphasizing campus renewal, where Western seems to focus on funding as a concept.

To further refine the analysis, you can also choose word pairs on the display and change the cloud to most frequent pairs of words. Unfortunately in my samples, campus renewal and facilities renewal are the only two repeated pairs, York favouring both.

If we consider the announcement from Guelph versus the one from York, the word future features very large in the Guelph announcement. Does this mean they have vision for the future or that they fear the future? The word York in their own announcement is the single most frequent word, where references to Guelph in theirs is rare. Does this suggest that York University is far more interested in self-promotion? These are the sort of questions that calls for further investigation and underlies the danger of trying to use word clouds on their own. They are all the rage and can be very powerful, but as with any visualization tool, they call for consideration of shortcomings as well as strengths.

By the way, thanks to Western’s comprehensive list of where all the funding went, it begged creating a bar chart of distribution amongst institutions. Click on the chart below to go to ManyEyes to see it.

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I use both GraphViz and OmniGraffle to construct charts involving relationships and processes. Over the last few days I was noodling my way through a schematic of sectarian associations in Northern Ireland. Trying to get the players and organizations straight was simply impossible for me without some sort of visual aid. I did a quick scan of the usual suspects to determine whether anyone already had something that would suit my needs, but only found textual compilations. Although comprehensive, these required more than casual scans to get an immediate sense of who fits where. I put the chart before the horse this time and started drawing on a napkin. I presupposed that I would need to visually distinguish between political organizations and paramilitary ones, and also between religio/political affiliations. The colours green and orange sprang to mind as good visual cues ;-) I was able to access the CAIN database which provides a superbly authoritative compendium of organizations on on ‘the Troubles’ and politics in Northern Ireland from 1968 to the present. Chronology was also a factor and I had an additional temporal dimension to consider. The napkin was overwhelmed.

In many cases these days when I have a nice tidy little dataset, I like to throw it online at Many Eyes and see how it looks. I have blogged a couple times in the past about Many Eyes and remain a fan. Unfortunately at times, their tools don’t quite give you what you were looking for — although they do often give you a great starting point. I did actually throw a subset of the data at Many Eyes to see the result. The really usefult thing about Many Eyes is that I could also take the same dataset and with a couple clicks try visualizing it in different chart formats, such as a word cloud for example. However, the network chart was limited in my judgement (and I know they have a very cool visualization coming, but it doesn’t seem to have shown yet).

My weapon of choice for free-form digital charting on my own machine is OmniGraffle. It’s powerful, intuitive, creates instantly aesthetically pleasing charts and is wonderfully extensible. There is an ever growing library of user contributed templates and charting processes available as plug-ins. Perhaps most importantly, OmniGraffle plays well with others. It can import graphic and text as inputs and similarly output a variety of formats. Omnigroup as well offer educational pricing on their products. However, as I laid out the map, I was in manual mode and although aided by the visual, the growing complexity of the chart suggested that my free-form approach had really skipped the possibility that all this wonderful graph theory that I am vaguely aware of might actually have a role to play.

So, I took a big step back and started mapping the relationships textually before I went too far. I will note at this stage that Bill Turkel published a wonderful post last week on how he used similar tools to visual the emergence of a strategic knowledge cluster — a great real-world application for relationship mapping. His post builds a wonderful case study of an expanding network and how graph theory can be applied to appreciate dynamics that might be salient to actual participants. So how do you star this process?

I wanted to have raw materials that I could repurpose easily to use with multiple tools and also be able to massage as the data itself suggested avenue for further exploration Working in a simple text editor, I created a file using the DOT language. There are a number of ‘standards’ for representation of relationships between data, but I find DOT to be rich and as intuitive as a high level encoding ‘language’ might be. DOT symbolizes a relationship as simply as A -> B and then allows you to elabourate on the relationship and the way in which it is presented. I didn’t want to code in the presentation, but instead, just to have a simple file defining all the parent -> child relationships that needed to be mapped. Social Network conventions term the entities being related as ‘nodes’ and the relationship the ‘edge’. The resulting list in DOT looked something like this:

digraph unix {
node [shape=rectangle, color=orange, style=filled];
"Ulster Unionist Party (UUP) 1921-1972" -> "Unionist Party of Northern Ireland (UPNI) 1974 - 1981" ;
"Ulster Unionist Party (UUP) 1921-1972" -> "Alliance Party of Northern Ireland (APNI) 1974 - 1981";
"Ulster Unionist Party (UUP) 1921-1972" -> "Protestant Unionist Party (PUP) 1960s - 1971" ;
"Ulster Volunteer Force (UVF) 1966 -" -> "Shankhill Defense Association (SDA) 1969 -" ;
"Ulster Loyalist Central Co-ordinating Committee (1974 -)" -> "Ulster Special Constabulary Association (USCA)" ;
"Ulster Loyalist Central Co-ordinating Committee (1974 -)" -> "Ulster Volunteer Service Corps (UVSC)" ;
}


A simple start and as you can see, DOT is not too complex. You put your data between two curlies, keep node labels between quotes and end each line with semi-colon. I eventually added all the organizations that I wanted to connect and saved it as a .dot file.

This file format is the standard input for a product called Graphviz. It was developed in the AT&T research labs and is available as open source. If your relationships have all been coded correctly, you can simply open the .dot in Graphviz and it will immediately render the web of associations that you have created as a chart. Most of the time, the chart will be just what you need and concentration of relationships, or key nodes in the web will be immediately apparent. Depending on the version of Graphviz that you are using, you will be able to choose the way in which the programme should interpret your relationships and re-render it as you instruct. The default view of this dataset for example looks like this:

It is actually very close to what I wanted. However, the beauty of a programme such as Graphviz is that I can change the chart from hierarchical to circular or to a radial arrangement with a click and see what happens. A radial chart of the same info looks like this:

Which is actually rather cool as you can immediately see apparent clustering of groups and this is the primary reason that automated relationship visualisation tools are useful.

As I mentioned, in most cases you’ll be happy very quickly with what you need from Graphviz and it has the huge bonus of being free. Unfortunately, as a graphic editor, its is less powerful than other dedicated programmes out there. It is also very focussed on demonstrating relationships for your own analysis as opposed to making them ravishingly pretty for further presentation.

And so we come back to OmniGraffle. As I had simply started drawing objects and not approached the original task by defining my data as consisting of a series of relationships, I was limited to the haphazard construction of objects I drew. Now that I was armed with a documented list of relationships conforming to a standard for interchange, I had a greatly enhanced the range of presentation options open to me. Omnigraffle reads .dot files natively, so I was able to save the few changes I made in Graphviz as a .dot file and read this file directly into Omnigraffle. In OG, I am able to select one of a series of nodes and can radically alter their appearance. I am also able to drag nodes about, all the while maintaining their relationships. I can operate on nodes and edges independently and most important to my needs, be able do ll this while retaining the ability to apply automatic transformations to the chart as a whole for analytical purposes. Thus, I was able to embellish the chart I was interested in having by adding additional summary graphics such as creating a box around suspected related groups of organizations that aren’t easily represented in standardized graph theory. The resulting chart looks like this:

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Bill Turkel has written a thought provoking post at Digital History Hacks calling for a re-conception of how we ‘do’ history. He summarizes his understanding of the conventional process involving measured, concrete steps, that unfortunately presuppose that time essentially stands still as we practise our craft. Here I learn about Parmenides. Turkel proposes a radical new model that accounts, not only for the aspect of continual change, but to my mind also suggests that the future of our practise is in greater real-time collective research products. I would normally point directly at a notable post, but this one warranted particular acknowledgement in hope that you will check it out. Great points to ponder.

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I seem to have been posting much on the subject of note-taking as of late. It’s the seasonal thing.
What I suddenly realized amongst my plaudits for techniques and for tools was a gem of a concept that Geoffrey and I have been ruminating over for the past year or so: TiddlyWiki. In case you have missed the Tiddly thing, it is a tiny, entirely self-contained information storage mechanism that uses a wiki-style of interlinked and tagged entries. Unlike the more traditional wiki’s, it is entirely local. While this poses some backup and access issues, it also means that you don’t need an internet connection to edit data, it is blazing fast and very secure. Its extremely easy to use and if you think about what is going on, its an amazing concept.
The TiddlyWiki comes from Jeremy Rushton. I blogged earlier about Tiddly’s, but probably didn’t do so with the enthusiastic recommendation that you must try this out and see if it works for you. I do so now. TiddlyWiki is free. It takes virtually no space on a hard drive and can be easily carried on a memory stick. There is no installation procedure. You simply open it with your favourite browser. You don’t have to remember to save your work. It does it for you by virtue of its construction. And you can throw any information you could want into your TiddlyWiki for search, retrieval and re-purposing. It makes an incredible note-taking tool.

1. Go to Jeremy Rushton’s Page and simply choose ‘Save Page As…” in your browser. Rushton has all the instructions at this page as well.
2. Reopen the file you just saved. Hint: If you do this in a separate tab from the one you have Rushton’s page open in, you can see the instructions as you do play.
3. Enter your name in the author’s name Box.
4. Click the Options » in the menu to the right. Enable the autosave option.

As an alternative to a commercial product such as OneNote, the TiddlyWiki is a surprisingly strong alternative. Its fast, free and its simplicity belies its power. There are countless uses for the TiddlyWiki, but at this time of the year, it may be an opportune time to explore its possibilities for your note-taking needs.

Update: Here a good tutorial site in case you need/want one.

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Last week I pointed readers to the excellent article by Dustin Wax comparing note-taking methodologies and weighing the pros and cons of a couple techniques. I was not personally aware of the Cornell method. I am however a big fan of Microsoft OneNote for organization not just of notes, but of research materials of all kinds. I use blogs, wikis and OneNote together to manage my data and happened upon a Cornell template for One Note users this morning. If you are a laptop note taker and you are not aware of OneNote I recommend giving the trial version a test drive. You may find it useful. Note taking however is a very personal thing and we all tend to develop a process that works for us. Here’s a collection of MS’ Four Ways to Improve note-taking with One Note.
I don’t mean to be a shill for Microsoft in this and those that know me certainly know that I don’t approach the use of technology religiously. Its a pragmatic thing. However, if perchance you have missed the ‘The Ultimate Steal” promotion that Microsoft is running until the end of the month its worth considering. Students are able to acquire Office 2007 Ultimate for $US60. Compared to the retail cost of any of their products this is a steal.
I will however also offer a little caveat. OneNote 2003 and 2007 do not play well together. I am comfortable using 2003, but tried the 2007 version. Its very conveniently converted all my notes to a new (not backwards compatible) format. When I went to launch 2003 after the end of the 2007 imagine my shock and horror when it told me that the note I wanted to read was in a later and unreadable format. Luckily there was a backup folder with 2003 version notes, but the rather less than intuitive organizational structure of OneNote left me scrambling for a time to reconstitute my working structure. The authors of OneNote have gone to great lengths to make this software as seamless with your work routine that they do away with the manual save process…you don’t even directly choose where notes are stored. Good for usability, but bad for manual backup processes. My notes were stored in a hidden folder not readily backed up if you rely on your documents folder for this routine.

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I threw this link into an aside, and then thought better of it. It might get lost there, and this latest post from the consistently pragmatic Dustin Wax on Taking Better Notes has a plethora of great tips. He also introduces the Cornell System for notetaking which I find quite intriguing and well worth a look.
As I mentioned in comment to this post, these hints and tips should remind the lecturer to also consider this process from the lecturees’ side and imagine how media such as Powerpoint or overheads can tie into the most effective delivery and retention of materials. My humble contribution to this was ensure that when using technology try to ensure that the link is made between where the lecture is and how this ties into the bigger picture. I have seen many different examples of this ranging from the simplest of periodically referring to the lecture outline and reminding where the material is at, to keeping a rolling outline always visible in the top corner of a presentation.
My contribution here is rather weak, but the article by Dustin is quite strong and I recommend it for consideration.

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The tentative title for post this was how to get ahead by really trying, but I thought this might actually sounds a little too preachy. Without making this too much of a habit (stealing from another’s hard work), Dustin Wax has compiled a great set of suggestions on how to ‘Make this your best Semester Yet’. I previously noted his excellent article on how to put a wiki to personal use and he has taken some time to assemble a set of thoughtful and powerful reminders on how to tackle the semester.
I draw particular attention to his suggestions to ‘know your professor’ and ‘speak up’. These are oft overlooked aspects of the learning environment, but crucial ones. This list reminds us all (I have a student hat on myself as well as TA) that your education calls for strategy and operates at a variety of levels. Ones that I sure which someone highlighted for me at the outset, as opposed to having discover on my own. Even if only a couple of these remind or instruct, they can make a huge difference on your effectiveness this semester.

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