Characterization of cyclist behavior across built environments

Though many have studied cyclist behavior and factors influencing cyclist behavior, most of these studies have focused on intrinsic factors rather than the effect of the local environment. This thesis analyzed an observational study before the installation of bicycle signals to evaluate cyclist behavior in the existing local context. Observations were made throughout a 24-hour period at 11 intersection and cyclist behaviors were characterized as compliance/non-compliance and interaction with motorist/no-interaction with motorist. The non-compliance rate was computed as the total number of non-compliant cyclists observed at an intersection divided by the total number of cyclists observed at the intersection. Both time of day and Level of traffic stress (LTS) were considered factors relevant to the studied behaviors. LTS was assigned to each intersection based on a set of criteria. The study found that non-compliance rate of cyclist was inversely correlated with LTS, with higher non-compliance rate at lower LTS, and intersections with LTS 1 showed significantly higher non-compliance rate (25.33%) than other LTS groups (9.96%, 8.50% and 3.09% corresponding to LTS 2, 3 and 4 respectively). The study also pointed out that non-compliance rate was correlated with time. During morning off-peak (0:00 to 7:00), cyclist showed significantly higher non-compliance rate (27.74%) comparing to other time periods (12.91%, 14.93%, 10.14% and 10.87%, corresponding to morning peak, mid-day off-peak, mid-day peak and night off-peak respectively).Civil, Architectural, and Environmental Engineerin

Efficient Motion and Inspection Planning for Medical Robots with Theoretical Guarantees

Medical robots enable faster and safer patient care. Continuum medical robots (e.g., steerable needles) have great potential to accomplish procedures with less damage to patients compared to conventional instruments (e.g., reducing puncturing and cutting of tissues). Due to their complexity and degrees of freedom, such robots are often harder and less intuitive for physicians to operate directly. Automating robot-assisted medical procedures can enable physicians and patients to harness the full potential of medical robots in terms of safety, efficiency, accuracy, and precision.Motion planning methods compute motions for a robot that satisfy various constraints and accomplish a specific task, e.g., plan motions for a mobile robot to move to a target spot while avoiding obstacles. Inspection planning is the task of planning motions for a robot to inspect a set of points of interest, and it has applications in domains such as industrial, field, and medical robotics. With motion and inspection planning, medical robots would be able to automatically accomplish tasks like biopsy and endoscopy while minimizing safety risks and damage to the patient. Computing a motion or inspection plan can be computationally hard since we have to consider application-specific constraints, which come from the robotic system due to the mechanical properties of the robot or come from the environment, such as the requirement to avoid critical anatomical structures during the procedure.I develop motion and inspection planning algorithms that focus on efficiency and effectiveness. Given the same computing power, higher efficiency would shorten the procedure time, thus reducing costs and improving patient outcomes. Additionally, for the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the algorithms involved in procedure automation. Therefore, I focus on providing theoretical guarantees to certify the performance of planners. More specifically, it is important to certify if a planner is able to find a plan if one exists (i.e., completeness) and if a planner is able to find a globally optimal plan according to a given metric (i.e., optimality).Doctor of Philosoph

Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search

Inspection planning, the task of planning motions that allow a robot to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robotics. Inspection planning can be computationally challenging, as the search space over motion plans that inspect the points of interest grows exponentially with the number of inspected points. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of inspected points asymptotically converge to those of an optimal inspection plan. IRIS incrementally densifies a motion planning roadmap using sampling-based algorithms, and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We demonstrate IRIS's efficacy on a simulated planar 5DOF manipulator inspection task and on a medical endoscopic inspection task for a continuum parallel surgical robot in anatomy segmented from patient CT data. We show that IRIS computes higher-quality inspection paths orders of magnitudes faster than a prior state-of-the-art method.Comment: RSS 201

Asymptotically optimal inspection planning via efficient near-optimal search on sampled roadmaps

Inspection planning, the task of planning motions for a robot that enable it to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robotics. Inspection planning can be computationally challenging, as the search space over motion plans grows exponentially with the number of points of interest to inspect. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of successfully inspected points asymptotically converge to those of an optimal inspection plan. IRIS incrementally densifies a motion-planning roadmap using a sampling-based algorithm and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We prove the resulting algorithm is asymptotically optimal under very general assumptions about the robot and the environment. We demonstrate IRIS’s efficacy on a simulated inspection task with a planar five DOF manipulator, on a simulated bridge inspection task with an Unmanned Aerial Vehicle (UAV), and on a medical endoscopic inspection task for a continuum parallel surgical robot in cluttered human anatomy. In all these systems IRIS computes higher-quality inspection plans orders of magnitudes faster than a prior state-of-the-art method

Toward certifiable optimal motion planning for medical steerable needles

Medical steerable needles can follow 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steerable needle procedures can enable physicians and patients to harness the full potential of steerable needles by maximally leveraging their steerability to safely and accurately reach targets for medical procedures such as biopsies. For the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the planning algorithms involved in procedure automation. In this paper, we take an important step toward creating a certifiable optimal planner for steerable needles. We present an efficient, resolution-complete motion planner for steerable needles based on a novel adaptation of multi-resolution planning. This is the first motion planner for steerable needles that guarantees to compute in finite time an obstacle-avoiding plan (or notify the user that no such plan exists), under clinically appropriate assumptions. Based on this planner, we then develop the first resolution-optimal motion planner for steerable needles that further provides theoretical guarantees on the quality of the computed motion plan, that is, global optimality, in finite time. Compared to state-of-the-art steerable needle motion planners, we demonstrate with clinically realistic simulations that our planners not only provide theoretical guarantees but also have higher success rates, have lower computation times, and result in higher quality plans

Stress-oriented structural optimization for frame structures

To fabricate a virtual shape into the real world, the physical strength of the shape is an important consideration. We introduce a framework to consider both the strength and complexity of 3D frame structures. The key to the framework is a stress-oriented analysis and a semi-continuous condition in the shape representation that can both strengthen and simplify a structure at the same time. We formulate a novel semi-continuous optimization and present an elegant method to solve this optimization. We also extend our framework to general solid shapes by considering them as skeletal structures with non-uniform beams. We demonstrate our approach with applications such as topology simplification and structural strengthening

Mg3(Bi,Sb)2 single crystals towards high thermoelectric performance

The rapid growth of the thermoelectric cooler market makes the development of novel room temperature thermoelectric materials of great importance. Ternary n-type Mg3(Bi,Sb)2 alloys are promising alternatives to the state-of-the-art Bi2(Te,Se)3 alloys but grain boundary resistance is the most important limitation. n-type Mg3(Bi,Sb)2 single crystals with negligible grain boundaries are expected to have particularly high zT but have rarely been realized due to the demanding Mg-rich growth conditions required. Here, we report, for the first time, the thermoelectric properties of n-type Mg3(Bi,Sb)2 alloyed single crystals grown by a one-step Mg-flux method using sealed tantalum tubes. High weighted mobility ∼140 cm2 V−1 s−1 and a high zT of 0.82 at 315 K are achieved in Y-doped Mg3Bi1.25Sb0.75 single crystals. Through both experimental angle-resolved photoemission spectroscopy and theoretical calculations, we denote the origin of the high thermoelectric performance from a point of view of band widening effect and electronegativity, as well as the necessity to form high Bi/Sb ratio ternary Mg3(Bi,Sb)2 alloys. The present work paves the way for further development of Mg3(Bi,Sb)2 for near room temperature thermoelectric applications

Autonomous Medical Needle Steering In Vivo

The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safe and accurate navigation of a needle through living tissue to an intra-tissue target is currently often challenging or infeasible due to the presence of anatomical obstacles in the tissue, high levels of uncertainty, and natural tissue motion (e.g., due to breathing). Medical robots capable of automating needle-based procedures in vivo have the potential to overcome these challenges and enable an enhanced level of patient care and safety. In this paper, we show the first medical robot that autonomously navigates a needle inside living tissue around anatomical obstacles to an intra-tissue target. Our system leverages an aiming device and a laser-patterned highly flexible steerable needle, a type of needle capable of maneuvering along curvilinear trajectories to avoid obstacles. The autonomous robot accounts for anatomical obstacles and uncertainty in living tissue/needle interaction with replanning and control and accounts for respiratory motion by defining safe insertion time windows during the breathing cycle. We apply the system to lung biopsy, which is critical in the diagnosis of lung cancer, the leading cause of cancer-related death in the United States. We demonstrate successful performance of our system in multiple in vivo porcine studies and also demonstrate that our approach leveraging autonomous needle steering outperforms a standard manual clinical technique for lung nodule access.Comment: 22 pages, 6 figure

A High-Throughput Screen Indicates Gemcitabine and JAK Inhibitors May be Useful for Treating Pediatric AML

Improvement in survival has been achieved for children and adolescents with AML but is largely attributed to enhanced supportive care as opposed to the development of better treatment regimens. High risk subtypes continue to have poor outcomes with event free survival rates \u3c 40% despite the use of high intensity chemotherapy in combination with hematopoietic stem cell transplant. Here we combine high-throughput screening, intracellular accumulation assays, and in vivo efficacy studies to identify therapeutic strategies for pediatric AML. We report therapeutics not currently used to treat AML, gemcitabine and cabazitaxel, have broad anti-leukemic activity across subtypes and are more effective relative to the AML standard of care, cytarabine, both in vitro and in vivo. JAK inhibitors are selective for acute megakaryoblastic leukemia and significantly prolong survival in multiple preclinical models. Our approach provides advances in the development of treatment strategies for pediatric AML

Sensing Characteristics of Fiber Fabry-Perot Sensors Based on Polymer Materials

A simple optic-fiber Fabry-Perot (FP) sensing technique was proposed and experimentally investigated by using polymer material to connect the ends of two singlemode fibers. Four different polymer materials (benzocyclobutene (BCB), UV88 (Relentless, China), Loctite3525 (HenKel, Germany), and NOA68 (Norland, USA)) filling the FP cavity were used to comparatively study the sensing performance of temperature, strain and refractive index. The result shows that the FP sensor with BCB has excellent repeatability with good linear response to temperature in a wide range from room temperature to 250 ◦C, which is much larger than that of other three materials (<90 ◦C), while UV88 with a cost of less than 1/10 of the other three polymer materials has the best sensitivity to strain and temperature. In addition, the FP sensor was firstly applied to measure ultraviolet (UV) light ntensity. The test results demonstrate that the proposed FP sensor structure has a good linear response and repeatability to UV intensity for all four polymer materials, and Loctite3525 has the highest sensitivity (0.0087 nm/(mw/cm2)) and the best repeatability among the four polymer materials