When we think of physics, we often think of abstract, mind-bending intellectual pursuits that have little to do with the real world.

Well, think again. How does physics and money-making sound? Or physics and smoke detectors? Or even physics and coffee beans?
These are some of the unlikely connections that are being made by one of ICTP's newest activities, the Joint Master's Degree Programme on Modelling and Simulation of Complex Realities.
Launched in May 2001, the programme is co-sponsored by the International School for Advanced Studies (SISSA). It is modelled after the successful ICTP Diploma Course programme (see News from ICTP, Summer 2001, pp. 4-5).
In the programme's first year (2001-2002), 10 students from 8 different countries were selected from a pool of more than 100 candidates. Reflecting the programme's multidisciplinary nature, the students had earned undergraduate degrees in pure and applied mathematics, environmental physics and biophysics before arriving in Trieste.
"The first six months of study," explains Riccardo Zecchina, co-ordinator of the Joint Master's Degree Programme, "were devoted to course work in a variety of subjects, including probability and game theory, stochastic processes, financial mathematics, fluid dynamics, and combinatorial optimisation. The purpose was to provide students with a strong analytical background in modelling and simulation."
With six months of course work behind them, this fall the students turned their attention to projects with real-world challenges and potential applications. Local and regional businesses were contacted to see if any would be interested in having students work, free-of-charge, on projects that might help companies better understand an important aspect of their businesses or even improve the efficiency of what they do and how they do it.

"Most importantly," Zecchina adds, "we wanted to make sure that the projects would be intellectually stimulating, allowing students to fully utilise the concepts in physics and mathematics and the modelling techniques that they had just been exposed to in the classroom."
After several months of surveying and speaking to members of the business community in Trieste and the surrounding area, some private firms agreed to participate: Assicurazioni Generali, Pittway Tecnologica, and Demus.
Once these companies agreed to welcome students into their work world, the next step was to divide students into three groups and to assign them specific tasks.
Financial Management. "Financial institutions like Assicurazioni Generali," explains Zecchina, "always seek to optimise the return that their clients earn on their investments--that is, financial institutions want to make as much money as possible for their customers."
Yet both financial institutions and the people they serve also know that the higher the return, the higher the risk. The recent high-tech stock market bubble, which began in the 1990s and burst in 2000, is an example of the 'ups and downs' inherent in the stock market.
As a result, the issue for financial institutions and individuals is how to create a portfolio of assets that optimises financial returns while minimising risks.
In the past, financial managers relied on experience and intuition to serve their clients. More recently, they have relied on idealised mathematical models to project market behaviour.
In the real world, however, financial managers must deal with a number of constraints related to the number of assets a client is willing to hold and the size of the investment he or she is willing to make. Combinatorial optimisation and probability theory are the tools that scientists have crafted to devise more analytical--and more accurate--assessments of stock performance.
"We turned Assicurazioni Generali's financial management challenge into a math problem," explains Zecchina. "We did this by putting together and analysing a portfolio of stocks to determine the optimal combination for maximising returns and minimising risks. In mathematical terms, we designed an algorithm to find an optimal portfolio composed of a minimal number of stocks."
This could prove an important finding because fewer stocks in the portfolio require less computational time to analyse. And less computational time means reduced costs for the financial management firm.
Smoke Detectors. Moving from financial management to the manufacture of smoke detectors, such as those made by Pittway Tecnologica, Silvio Franz, of the ICTP condensed matter physics group, explains that the potential contribution of physics and mathematics here is based on this fact: "Smoke detector manufacturers want to create an alarm system that rings when sensing smoke caused by 'real' fire but is not falsely set off by such factors as traces of smoke caused by lighting a cigarette or turning on a gas stove."
Current smoke detectors rely either on an interruption in light or a rise in temperature to signal an alarm. "The devices are good but by no means perfect," says Franz. "Light-ray detectors are fast but somewhat unreliable; temperature-dependent detectors, on the other hand, are more reliable but slower to signal a problem."
"To improve the performance of smoke detectors," Franz notes, "our students have investigated the possibility of relying on neural networks, that could reduce the number of false positives without undermining the detector's reliability. The devices would have the added benefit of costing a lot less to manufacture."
The system would work like this: Instead of relying on one parameter, the detector's sensors would rely on a range of parameters to trigger an alarm--for example, the amount of smoke, its density and composition, and rising room temperature.
For ICTP and SISSA, neural networks represent an abstract model; for manufacturers of smoke detectors and many other electronic devices, neural networks ultimately mean wires and circuits.
Theoretically, researchers may be on to something, but additional study and time will be required for their preliminary insights to find their way into the manufacturing process.
Coffee Beans. As Demus, one of Italy's largest and most prestigious coffee processors, can attest, public demand for decaffeinated coffee is on the rise. Yet, meeting this demand involves a costly, time-consuming process in which beans are steamed to raise their moisture content bringing the dissolved caffeine to the surface. The steamed beans are then washed in an alkaline solution consisting of methilene chloride to drain them of 99.9 percent of their original caffeine content.
"To achieve this international standard, which is necessary if a company hopes to participate in the international coffee market, each bean must be washed some 10 times during the production process," says Matteo Marsili, staff member of the ICTP condensed matter physics group.
The problem is that when the alkaline solution becomes saturated with caffeine, it loses its absorption capabilities. At that point, the solution must be discarded and replaced.
"Knowing when best to change the solution is no trivial matter," Marsili notes. "The chemicals are expensive and the process can take several hours each time. As a result, both money and time are at stake."
"The bathing process, moreover, is by no means a simple one," adds Marsili. The flow of the solution through the beans, the concentration of caffeine in the beans, the size of the bean pores, and the temperature of the water all have an impact on how efficiently the caffeine is removed and how long the solution will last.
But what if researchers could develop a computer model that illustrates how to optimise the decaffeination of a single bean? Could Demus extrapolate data and information from this model to develop a more efficient decaffeination process saving both time and money?
Like the other ICTP/SISSA student projects, investigations into coffee decaffeination have shown interesting results for Demus. Students pinpointed the optimal solution-replacement schedule for their 'synthetic coffee' composed of modellised beans. If this schedule proves a good approximation of the optimal solution-replacement schedule for real coffee beans undergoing a real decaffeination process, then Demus will have acquired a powerful tool for making its decaffeination more efficient.
"Yet," as Zecchina cautions, "the field work that is part of the ICTP/SISSA Joint Master's Degree Programme in Modelling and Simulation of Complex Realities is not designed to provide companies with cost-saving strategies for their businesses. In fact, the major criteria that we use in selecting activities is the intellectual challenge that the research questions pose and, equally important, whether something can be learned by students during their three months of field work."
"That's not to say that the experience won't someday help companies become more efficient," he adds. "But ICTP and SISSA are research and training institutions and the education we provide--whether in the classroom, laboratory or field--is intended first and foremost to create top-flight scientists."
"We are indeed thankful to the firms for giving our students opportunities to test their knowledge and skills in unusual settings," Zecchina says. "It's another way to put mathematics and physics to work in a world that increasingly needs the formidable analytical abilities that only these disciplines can provide."

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