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A crucial challenge in engineering modern, integrated systems is to produce robust designs. However, quantifying the robustness of a design is less straightforward than quantifying the robustness of products. For products, in particular engineering materials, intuitive, plain language terms of strong versus weak and brittle versus ductile take on precise, quantitative meaning in terms of stress–strain relationships. Here, we show that a “systems physics” framing of integrated system design produces stress–strain relationships in design space. From these stress–strain relationships, we find that both the mathematical and intuitive notions of strong versus weak and brittle versus directly characterize the robustness of designs. We use this to show that the relative robustness of designs against changes in problem objectives has a simple graphical representation. This graphical representation, and its underlying stress–strain foundation, provide new metrics that can be applied to classes of designs to assess robustness from feature- to system-level.

CAP Virtual Congress 2021

Robust Design from Systems Physics

complex systems

distributed systems

statistical physics

systems physics

## Dr. Nishikant (Nish) Sonwalkar: Academician, Scientist, Musician

![](https://assets.underline.io/uploads/markdown_image/1/image/0f9b63a0b3deba4a355d98e2c87af68a.png)

Dr. Sonwalkar is regarded as a leading expert in the application of computers in education. He has extensive research and development experience in hypermedia authoring, mobile learning, adaptive learning, and brain-based synaptic learning, advanced scientific visualization, database management systems, and computer simulation.

After receiving doctoral degree from MIT in the area of Molecular Dynamics of Nano-interfaces, he joined MIT as faculty in Mechanical Engineering, but his passion for technology enabled education led him become the founding director of Hypermedia Teaching Facility (Hypermedia Lab) at MIT and later he was appointed as the Principal Educational Architect of MIT.

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NISH 2021

technical paper

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Crowded soft-matter and biological systems organize locally into preferred motifs. Locally-organized motifs in soft systems can, paradoxically, arise from a drive to maximize overall system entropy. Entropy-driven local order has been directly confirmed in model, synthetic colloidal systems, however similar patterns of organization occur in crowded biological systems ranging from the contents of a cell to collections of cells. In biological settings, and in soft matter more broadly, it is unclear whether entropy generically promotes or inhibits local organization. Resolving this is difficult because entropic effects are intrinsically collective, complicating efforts to isolate them. Here, we employ minimal models that artificially restrict system entropy to show that entropy drives systems toward local organization, even when the model system entropy is below reasonable physical bounds. By establishing this bound, our results suggest that entropy generically promotes local organization in crowded soft and biological systems of rigid objects.

When does entropy promote local organization?

Accelerator Mass Spectrometry (AMS) provides high sensitivity measurements (typically at or below 1 part in $10^{12}$) for rare, long-lived radioisotopes when isobars (other elements with the same atomic weight as the isotope of interest) can be eliminated. In AMS laboratories, established techniques are used for the removal of the interfering isobars of some light isotopes. However, for smaller, lower-energy AMS systems separating the abundant isobars of many isotopes, such as the sulfur-36 in measurements of chlorine-36, remains a challenge. For some heavy isotopes, such as strontium-90 and cesium-135,137, even high energy accelerators are unable to separate the interfering isobars. 

The Isobar Separator for Anions (ISA), which has been integrated into a second injection line of the 3 MeV tandem accelerator system at the A. E. Lalonde AMS Laboratory, will provide a universal way to measure rare radioisotopes without the interference of abundant isobars. The ISA is a radiofrequency quadrupole (RFQ) reaction cell system, including a DC deceleration region, a combined cooling and reaction cell, and a DC acceleration region. The deceleration region accepts a mass analyzed beam from the ion source (with energy 20-35 keV) and reduces the energy to a level that the reaction cell can accept. RFQ segments along the length of the cell create a potential well which limits the divergence of the traversing ions. DC rod offset voltages on these RFQ segments maintain a controlled ion velocity through the cell. The cell is filled with an inert cooling gas that has been experimentally selected to provide the lowest ion energy and the highest transmission, and with nitrogen dioxide, a reaction gas chosen to preferentially react with the interfering isobar. In the case of chlorine-36, the sulfur-36 isobar has been shown to be reduced by over $10^{6}$. Preliminary characterization of the ISA and its incoming and outgoing ion beams will be presented.

Characterization of the Isobar Separator for Anions for Accelerator Mass Spectrometry measurements

The Feynman quantum-classical isomorphism between classical statistical mechanics in 3+1 dimensions and quantum statistical mechanics in 3 dimensions is used to relate classical polymer self-consistent field theory to quantum density functional theory. This allows the theorems of density functional theory, which connect single particle density descriptions of quantum systems to wave function descriptions, to relate non-relativistic quantum mechanics back to a classical statistical mechanical derivation of polymer self-consistent field theory for ring polymers. In turn, this allows for a quantum-classical isomorphic interpretation of quantum foundations which may require fewer postulates than standard approaches to quantum mechanics, while preserving all quantum predictions.

A Quantum-Classical Isomorphic Interpretation of Quantum Foundations Based on Density Functional Theory and Polymer Self-Consistent Field Theory

Objective: Dose distribution index (DDI) is a dose-volume parameter used in the treatment planning evaluation. DDI provides the dosimetric estimates on the target coverage, sparings of all organs-at-risk and remaining healthy tissue in the treated organ in a single parameter. In this study, the DDI value was predicted by machine learning model using different algorithms. 

Methods: The DDI were calculated using its original formula by definition. On the other hand, machine training was carried out to determine the DDI using the same data of 50 prostate volumetric modulated arc therapy (VMAT) plans from the Grand River Regional Cancer Centre, Kitchener, Ontario. Machine learning algorithms such as linear regression, tree regression, support vector machine (SVM) and Gaussian process regression (GPR) were used to predict the DDI value for each prostate VMAT treatment plan. For comparing the performance of the machine learning algorithms, root mean square error (RMSE), prediction time of the machine learning and training time were determined and compared.

Results: Comparing the RMSE values among all algorithms, only the DDI predicted by the medium and coarse tree regression algorithms showed a relatively large RMSE values in the range of 0.021 – 0.034. For other algorithms such as SVM and GRP, they all performed very well in predicting the DDI with smaller RMSE values ranging from 0.0038 to 0.0193. By considering other factors such as prediction speed and training time, the square exponential GPR algorithm had the smallest RMSE value of 0.0038, a relatively high prediction speed of 4,100 observation per second and a short machine training time of 0.18 second.

Conclusion: It is concluded that the family of GPR algorithms performed best in the dose distribution index prediction. It is expected that the accuracy of DDI prediction will increase with more plan data trained using such algorithm.

Evaluating Machine Learning Models in Predicting the Dose Distribution Index in Radiation Treatment Planning QA

Objective: We built a RT Bot, a chatbot with characterization for the patient, general public and radiation staff to provide educational information regarding radiotherapy using the artificial intelligence. The Bot was personalized by machine learning to detect the user’s temperament and intent in order to provide the best guidance to the user with a human-like response. 

Methods: The Bot was developed using the IBM Watson Assistant functionalities on the IBM cloud. Dataset of information was prepared for different user groups such as descriptions of all processes in radiotherapy, promotion of cancer screening especially high fatality and popular cancer sites, and basic cancer preventive measures such as how to maintain healthy life with suitable diet and exercise. To ensure correct information can be understood and digested by the users with their background (patient, general public and radiation staff), the Bot character was personalized through the IBM Watson Assistant functionalities such as natural language understanding, entities and slots.

Results: The Bot can be operated in a front-end window on any Internet-of-things such as smartphone, tablet, laptop and desktop. In the beginning, the Bot will communicate with the user intentionally with an introduction. The user can then type in any text to answer concerning their enquiry. The Bot usually begins by answering simple questions regarding radiotherapy and providing related information. If the Bot cannot understand the user’s wording, it will provide a guidance to help the user. 

Conclusion: A chatbot was built for interdisciplinary educational purpose for the patient, general public and radiation staff using artificial intelligence and machine learning. The Bot may be used by a cancer centres or some private sectors such as high school, community centres, volunteer group and charities, which promote cancer preventive measures and screening for a healthy life or educate user what is cancer and radiotherapy.

An AI-assisted Chatbot for Education in Radiotherapy

Negative Ion Source Development for Accelerator Mass Spectrometry
CJ Tiessen, WE Kieser, and XL Zhao
Accelerator mass spectrometry (AMS) is a highly sensitive technique used for the analysis of long-lived radioisotopes. While carbon-14 dating is the most well known application, AMS can be used to measure other isotopes such as beryllium-10, aluminum-26, iodine-129, and uranium-236 which are useful in geology, archeology, environmental tracer and chronology studies, nuclear waste monitoring, and nuclear forensics. The technique uses a combination of electrostatic analyzers, mass-separation magnets, electrostatic lenses, as well as a tandem accelerator. In the accelerator, an electron stripping gas canal is used to convert incoming negative ions to positive ions while simultaneously disintegrating molecular isobars. Ions from the samples are injected into the accelerator using a cesium sputter negative-ion source. The focus of this work is to model the electrodynamics within the ion source, including the effects of the more intense positive cesium ion beam and the sputtered sample negative-ion beam. Simulations using Integrated Engineering Software’s Lorentz 2E ion optics software will guide the design of a new ion source with the goal of increasing the emitted sample ion current while also improving the emittance of this beam. Following a short overview of the AMS system, details of the ion source, including the mutual space-charge interaction of the two beams, will be presented.

Negative Ion Source Development for Accelerator Mass Spectrometry

The symmetries of unconventional superconductors may be classified by the locations of their gap nodes. Recently, the role of spin-orbit coupling (SOC) has become important, as sufficiently strong SOC generates novel mixed-parity superconductivity. In this talk, I show that the nodal structure of unconventional superconductors may be determined by angle-dependent magneto-thermal conductivity measurements, provided the SOC is larger than the quasiparticle scattering rate. This effect is complementary to vortex-induced magneto-thermal oscillations identified previously, and is dominant in strongly anisotropic materials. As an application, I present results for the magneto-thermal conductivity of the "Rashba bilayer" YBa$_2$Cu$_3$O$_{6.5}$, which possesses a so-called "hidden spin-orbit coupling." We find that the SOC endows $\kappa_{xx}/T$ with a characteristic field-angle dependence that should be easily observed experimentally.

Magneto-Thermal Conductivity Oscillations in Spin-Orbit-Coupled Nodal Superconductors

We report a formal analogy between cosmology and earth science. The history of a closed universe is analogous to an equilibrium beach profile (i.e., the depth of the water as one recedes from a beach moving seaward). A beach profile reaches equilibrium in summer and in winter and is described by a variational principle that minimizes energy dissipation. The oceanography side of the analogy gains much needed information from the cosmology side. 

[Based on V. Faraoni, Phys. Rev. Research 1, 033002.]

Cosmology on the Beach: a formal analogy

The quest to develop a fundamental understanding of the nuclear interaction is aided greatly through measurements made with complex, multi-detector systems. As these systems allow for both an increase in total efficiency due to greater angular coverage as well as correlation measurements in both time and space, the information which can be probed using these detectors is greatly increased over single-detector systems.

With an increase in both number of detectors and complexity of detector systems comes challenges in data acquisition. As each detector can obtain data independently, the required throughput of the data acquisition system also increases. To address this problem, real-time event filtering can be performed on the data to only process and store events with certain characteristics. Traditional methods for event filtering require large arrays of electronics to perform logic on unprocessed data. This approach comes with a large up front cost both in money and effort in order to purchase, set up, and test electronics. Finally, for small laboratories, the scaling of these approaches can be a limiting factor as an increasing number of detectors also increases the number of electronic modules, as well as the space and power consumption of the data acquisition system.

At the Nuclear Science Lab (NSL) at Simon Fraser University (SFU), we implement a single-unit, computer controlled XLM72S universal logic module to perform real-time event filtering. The XLM72S contains a field programmable gate array with two operational firmware developed for the NSL at SFU. The first of these performs both anti-coincidence logic and multiplicity filtering for up to twenty independent Compton Suppressed Spectrometers, as well as coincidence logic with up to four auxiliary detectors. The second is designed to perform coincidence logic on up to 128 individual inputs through the daisy-chaining of multiple XLM72S logic modules, a feature which can be achieved with both firmware. Through daisy-chaining of XLM72S modules, scaling real-time event filtering to large-scale, complex, multi-detector system is easily achievable.

Details of the module and the two firmware developed for and implemented in the NSL at SFU will be discussed in this talk.

Real-Time Event Filtering in Complex Multi-Detector Systems

This is a report on a project to interview the student body of the Concordia University Physics Department concerning teacher-students interaction. The goal of this research is to establish an easily implemented guideline of teaching methods for a professor to use in the classroom. Students were asked to complete a questionnaire that included a request to describe the teaching style of their favourite physics professors. They were asked to describe what makes them excellent to the student. A variety of known and successful pedagogic techniques were also presented to ascertain their appeal to students. Follow-up interviews were conducted in order to fully develop the given answers.

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When does entropy promote local organization?

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