Inclusive Technology is the New “IT”

by P. G. Ramachandran, program director, Advanced Technology, IBM Accessibility

It's been 25 years since the Americans with Disabilities Act was signed into law. Organizations should be creating a holistic strategy for embedding accessibility across the entire enterprise - from processes, to product development, to education and training. But it's often still decentralized, and managed disparately across a business, often resulting in solutions that, instead, create barriers to information and don’t meet mandated levels of compliance.
To help organizations have better visibility and management over accessibility initiatives, IBM launched the IBM AbilityLab Compliance System. The solution helps establish and document accessibility standards compliance for all information and communication technologies (ICT), such as employee systems, customer-facing mobile and web applications, hardware, kiosks, and telecommunications.

Organizations can now better manage accessibility with a self-service reporting system that records and tracks ICT compliance over time as standards, techniques and tools change. The system allows executives to get reports that can track the accessibility of products and services across the organization. With business dynamics changing rapidly enterprises can get a good view of long term trends on how accessibility is impacting their overall business.

This system includes leading industry accessibility checklists, an extensive library of education and training modules, and a web assessment testing tool that examines and provides recommendations on improving the usability of web applications by ensuring compliance with accessibility standards, such as alternative text, proper tabbing and keyboard navigation, and color contrast.

Finally, the new solution includes a centralized process and business workflow that tracks accessibility, assigns responsibility and enables a broad group of internal stakeholders – beyond those who may manage ICT accessibility quality assurance (QA) tools – to have access to detailed reporting and auditing capabilities. This helps organizations prioritize resources, be more agile with product development, and accurately respond to requirements from customers and employees.

As part of this rollout, IBM is collaborating with Freedom Scientific to offer organizations a complete portfolio of enterprise accessibility training and eLearning to ensure that all employees – designers, developers, testers, quality assurance, and program managers – are following best practices in accessibility and are educated on current regulations and industry standards.

For more information on the IBM AbilityLab Compliance System and other technologies, visit IBM Accessibility.

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Crazy Science: Easing the Strain on the Energy Grid with Desiccant Packs

Scientists in Switzerland have kicked off a three-year project which uses a substance, similar to the silica gel desiccant packs often found in leather shoe boxes and electronics, to convert wasted heat from cloud data centers to cool air. The potential result, cloud data centers of the future might be able to cool themselves using their own waste heat.

The project is called THRIVE and it's goal is simple - to develop a heat pump powered by waste heat. If you have ever felt the hot air from an air conditioning unit or from the back of PC, this is waste heat. Waste heat can be very valuable and there is plenty of it coming from factories, power stations, data centers or other renewable sources such as solar power. It just needs to be harnessed and put to work efficiently.

A coated adsorber heat exchanger
tested at the Institute for Solar Technologies
at University of Applied Sciences Rapperswil.
Heat Pumps 101

Chances are you use a heat pump every day. They can be found in heating, ventilating, and air conditioning units to to convert environmental heat, the temperature of which lies between -5 and 15°C, into thermal heat for rooms or processes.

Traditional heat pumps draw warmth from the surroundings, such as from the earth or air, to vaporize a refrigerant in an evaporator. The vapor produced in the process rises into an electrically powered compressor, which condenses it and thus heats it up.

The vapor then turns back into liquid in an adjoining condenser and releases the heat into a heating cycle. This process can be used to produce both heat to air-condition rooms and cool air, like in a refrigerator.

Adsorption heat pump

A thermally powered adsorption heat pump works in a similar way – the major difference being that, in place of a compressor, it has an adsorption heat exchanger that uses heat at temperatures from 60°C as its driving energy instead of electricity.

During the so-called adsorption process, the adsorption heat exchanger adsorbs considerable amounts of vapor from the evaporator and compresses it inside the heat exchanger, thereby releasing heat. The refrigerant adsorbed beforehand is forced (desorbed) back out of the adsorption heat exchanger by the supply of driving heat from an external source.

The hot vapor released as a result turns back into liquid in the condenser and the corresponding condensation heat is released into the heating cycle. The adsorption heat pump can also be use to heat and cool.

These processes are supported by the desiccant or silca gel which will be filled in between the fins of the adsorber heat exchanger.

However, as the cooling or heat production takes place intermittently, at least two adsorption heat exchangers working in parallel are needed for it to run uninterrupted. Due to their low energy consumption, adsorption heat pumps achieve a much higher cooling or heat output in relation to the wattage used than conventional heat pumps.

In addition, pure water can be used as a coolant instead of refrigerants, which can sometimes be harmful for the environment. Another advantage of the technology is the fact that renewable heat sources can be used, such as solar-thermal systems, which typically generate temperatures of up to 90°C.

“Through the extensive use of the adsorption heat pumps we are looking to develop in THRIVE, it could theoretically be possible to reduce the electricity demand for heating and cooling purposes by up to 65% and the consumption of fossil fuels for heat production by up to 18% by 2040.” This would correspond to savings of around 1.8 million tons of CO2," says Dr. Bruno Michel, one of the THRIVE project leaders at IBM Research - Zurich.

Using a data center to heat and cool buildings  

By using heat, the adsorption heat pump is the ideal solution for many interesting applications where conventional heat pumps don’t make any sense. It could, for instance, use the waste heat from future, actively cooled, concentrated photovoltaic plants or cloud data centers that are cooled with hot water to provide air-conditioning for offices or residential buildings.

The Aquasar computer system developed by IBM researchers in collaboration with ETH Zurich in 2010 is a pioneer of hot-water cooling for computer systems, which not only massively reduces the energy demand for cooling in computer centers, but also enables the reuse of waste heat.

For the IBM researchers, THRIVE is the next step to make this a reality. Hot water-cooled data centers could then practically cool themselves using their own waste heat. 

THRIVE is a interdisciplinary team of partners including IBM Research – Zurich, the Hochschule für Technik Rapperswil, Empa, ETH Zurich, HEIG-VD, PSI, Zeochem AG, Danfoss, ETS Energie Technik Systeme AG, ewz, InfraWatt and MOF Technologies. The project is funded by the Swiss National Science Foundation (SNSF).

”In the THRIVE project, we have a unique opportunity to combine the latest findings from materials science, the technological optimization of heat exchangers and the merging of system and plant engineering from different disciplines,“ says Elimar Frank from the Hochschule für Technik Rapperswil and co-leader of the THRIVE project.


Cognitive Computing for better decision making

Editor's note: This article is by Léa Deleris, manager of Risk Management at IBM Research-Ireland. Additional contributions made by Charles Jochim, research staff member at IBM Research-Ireland.

Alice: `Would you tell me, please, which way I ought to go from here?'

Cheshire Cat: `That depends a good deal on where you want to get to.'

Alice: `I don't much care where—‘

Cheshire Cat: `Then it doesn't matter which way you go.’

Alice: ‘So long as I get somewhere.’

Cheshire Cat: `Oh, you're sure to do that, if you only walk long enough.'

This dilemma, and Stanford’s Decision Analysis Professor Dr. Rob Howard, who presented the Alice in Wonderland concept of indifference in a lecture, inspired me to study how mathematics can apply to the risk, uncertainty, and personal preferences that influence the decisions we make every day, about everything. What Alice and the Cheshire Cat so eloquently illustrate is that preferences are not as obvious as they may seem. I wanted to know, as a PhD student sitting among my fellow classmates, could natural language processing and cognitive computing be applied to web applications that could in turn, help us make more logical decisions?

Fast forward to today, and from student to IBM research scientist, and I’m applying artificial intelligence to our human intelligence – and how we can debate with machines to help us make good decisions when facing uncertainty. Here’s how I explained it to a TEDxParis audience in February.

Now my team in Dublin is using machines to help medical professionals make more rational decisions. The tool we developed, called MedicalRecap, extracts information from PubMed’s 24 million online citations to create a risk model for doctors.

MedicalRecap’s semantic module allows doctors to cluster the extracted terms (variables of the risk model) by grouping similar or related terms into concepts. It also has an aggregation module, which allows the user to combine the extracted dependence and probability statements into a dependence graph, also known as a Bayesian network. 

Imagine an instance of a doctor needing to understand the role of tea and coffee consumption on the incidence of endometrial cancer. Currently, doctors would address this task manually by searching for relevant papers, reading them, taking notes (by hand or copy-pasting on a spreadsheet), and aggregating this data.

MedicalRecap, instead, presents extracted and aggregated data in an intuitive graphical format, providing a way for the user to trace back through the summarised information, to the original input. The tool also allows users to edit the output of the algorithms if they encounter an error, which is fed back into the system to improve its knowledge and performance over time. 

MedicalRecap also relies on the doctor's expertise, so ideally, errors are reduced by combining the doctor's knowledge with the inferences the tool makes in finding dependency relationships. The assumption is that the doctor does not have all the input required, but is exploring the space. The tool helps the practitioner look for answers, but does not provide them. Ideally, it will reach the same conclusions that the doctor already has made so that he or she will trust the system more.

We also want MedicalRecap to provide evidence for new conclusions to be drawn. For example, if the doctor sees that coffee consumption is linked to some cancers, which she already knew, the tool could show that in fact this is primarily for certain populations, which she didn't know. 

As similar as it may sound, MedicalRecap is different to IBM Watson Health. Our tool is a web-based GUI focused only on published medical literature and is not designed for personalized medicine, but instead to make more global inferences between diseases and related risk factors. But like Watson, MedicalRecap’s Extractor, Clusterer, and Aggregator services are available on IBM’s SoftLayer cloud infrastructure as a service.

Our risk information extraction models, like in MedicalRecap, can be applied to other domains. In the future, oil and gas experts could use the tool to extract information from academic papers about factors influencing reservoir capacity and shape. As long as we have the main ingredient of a large body of literature related to a profession or domain, our decision support system tool might even be able to offer Alice somewhere to go, no matter how unsure she is. 


Better roads are paved with big data analytics

by Aisha Walcott, mobile engineer at IBM Research-Africa

Evans Ondieki, Executive Committee Member,
Nairobi City County (L) with Aisha (R)
Arriving at IBM Research - Africa in Nairobi, Kenya, I knew this was going to be my dream job. As a research scientist, you see the continent of Africa as a huge breeding ground for innovation, and an opportunity to make a tangible impact. As most residents and visitors to Nairobi would say, the bustle of the city paired with a flourishing tech and innovation scene provides an experience unmatched.

Unfortunately, those same residents and visitors are severely impacted by a tense traffic issue that challenges the city's infrastructure. In fact, the Nairobi government estimates that traffic jams and roadway problems result in a loss of more than $500,000 USD every day, when measuring lost productivity, fuel consumption, accidents and fatalities and emergency response.

My role at the Research lab in Nairobi focuses on mobility: environment, water, roadways, and the overall city ecosystem. In a meeting this month with the Executive Committee Member, Nairobi City County Evans Ondieki, my team learned that, in parallel to the imminent traffic issue, the city's waste management system was operating inefficiently: Nairobi's 3 million citizens generate 2,200 tons of waste each day, but less than half is collected. 

In fact, the city's waste management truck fleet was increased by 300 percent to accommodate the overwhelming amount of waste generated across the city and countryside, but the current systems are hand-written and riddled with inconsistencies like equipment failures, manual reporting that takes a day to process, and traffic jams that slow the pace of collection so much that many locations are missed. At a pace and volume that was too much for the county's fleet to manage, our team, along with colleagues from IBM Research-Ireland and IBM Watson, signed on to help.

IBM Research-Africa's Tierra Bills working with Nairobi city officials.
We applied our expertise in big data, analytics and mobile technology to design a first-of-a-kind solution to tackle these problems. Using an unconventional approach, we developed a pilot program in which the benefit was two-fold: by mounting smart devices to the city's waste management trucks, we could, for the first time, collect important data about the fleet, trucks and drivers, while also tracking problems on the roadways.

We became immersed in the work, driving our own cars, sensor devices in-hand, up and down the streets of Nairobi's South Ward C to test and learn how the data was being collected - comparing the readings to what was actually happening in real time. Once we fine-tuned the smart devices, the sensor were installed onto 10 trucks, or as we call them, our "data-collecting ants," gathering and transmitting data, via Safaricom's mobile network, about the truck's location, altitude, speed, acceleration, orientation, vibration levels, among other readings. 

IBM Research-Africa engineers Reginald Bryant (L) and Peter Maina (R)
installing sensor on a Nairobi Waste Management truck
The application sends data in near real-time to our backend where it's processed, then relevant information is sent to a tablet or mobile device that the fleet supervisor can monitor. It provides analytics-based indicators and alerts to improve performance of the entire fleet, as well as maintenance of individual vehicles; assist the supervisory team on driver and truck tracking; and provide information about the storage depots and facilities within the city. The insights will help the city design a more efficient system for picking up waste, so that, for example, areas that are less frequently attended to can be serviced, ultimately helping to improve the ensuing issues of poor sanitation and theft.

In the bigger picture, road blockages, accidents, detours, even unmarked speed bumps and hazardous potholes, could be reported back to city officials for tracking and response. Besides the improvements to waste management, the ultimate goal is to condition Nairobi's streets and related urban infrastructure more efficiently. We hope that the overall economic and social impact of this work will be realized by all residents of Nairobi, and that our solution can scale to surrounding cities, regions and, foundationally, across industries.

Read more about the Silicon Savannah on the Smarter Planet.


Discovered: high-temperature remoldable gels

Mareva Fevre, IBM Research-Almaden
by Mareva Fevre, research post-doc at IBM Research-Almaden 

My team at IBM’s research lab in the Silicon Valley just discovered a new class of “self-healing” organogels that have unique recyclable properties – they are the first class of chemically-crosslinked gels that can be cooled to a solid, but then re-heated back to a liquid state. Imagine filling a mold with this liquid material, cooling it and discovering there was a mistake – with this material and its dynamic properties, we can start over until we get the desired shape, and then cure it to a permanent, hardened object. We describe how this process works in the paper, Melt-Processable Dynamic-Covalent Poly(Hemiaminal) Organogels as Scaffolds for UV-Induced Polymerization, published in the journal, Advanced Materials. 

What is a gel? 

Gels are a peculiar family of materials. They exhibit properties between solids and liquids, and are composed of long polymer chains which link together and can trap some other smaller molecules. They are like a tiny (think nanoscale) fishing net that can retain liquids. The small molecules, which are trapped in the organogels that we developed, are monomers (the initial molecules which are used to prepare polymers). In our experiment, we also added some molecules called initiators in the gels, which under UV light transform the monomers into polymers. Thus, our gels have unique properties that allow them to behave at first like jello but after UV exposure become hard like plastic.

The elasticity of our organogel on display
After dozens of tests to measure the strength of the gels and figuring out the right compositions to obtain these properties, we created a modified gel that could melt at higher temperatures (80C) but recover its gel-like behavior when cooled down to 20C. The result is the first type of gel that could be molded, unmolded, and remolded several times before reaching that perfect final shape fixed by UV exposure.

Think of our gels like caulk used to repair cracks in objects, but liquid enough to penetrate into small cracks, and solid enough not to leak. A subsequent UV-curing step would allow for the material filling the crack to solidify and for the object to recover properties close to its original shape and strength. In the future, those gels could be used as a material for 3D-printing. If you think about today’s 3D-printing process, it requires layers of polymers stacked on top of each other. This leads to imperfect shapes that can have weakly bonded interfaces between its layers. Our new “self-healing” materials are not only fluid enough to be printed but also solid enough to hold their shape. By precisely controlling the printing temperature, we could ultimately get rid of the layers’ interface problem.

IBM’s gel (B), once heated, returns to its original state demonstrating
recyclable, remoldable properties. A typical gel (A) retains its form with heat.
A lab legacy 

This gel discovery stems from work by IBM scientists Jim Hedrick and Jeannette Garcia two years ago, when they discovered how to synthesize industrial polymers. We applied this chemistry to our gels using computational chemistry – co-author and IBMer Gavin Jones simulated the affinity our organogels’ crosslinks with the different monomers we used, and showed that they bound in a similar way as the original molecules used by Jim and Jeannette. Our team’s rheology expert, Nancy Zhang, also measured the gel’s ability to flow, and its strength. Her data explained how to perceive the gel’s hardness and softness, and also proved that the gel could be remolded multiple times before losing its strength.

Here’s Nancy explaining the gel’s ability to be molded multiple times: