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Removal of per- and polyfluoroalkyl substances (PFAS) from water has become a research topic of interest in recent times. However, it is very challenging to remove short-chain ( 

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A spring in parallel with an effort source (e.g., electric motor or human muscle) can reduce its energy consumption and effort (i.e., torque or force) depending on the spring stiffness, spring preload, and actuation task. However, selecting the spring stiffness and preload that guarantees effort or energy reduction for an arbitrary set of tasks is a design challenge. This work formulates a convex optimization problem to guarantee that a parallel spring reduces the root-mean-square source effort or energy consumption for multiple tasks. Specifically, we guarantee the benefits across multiple tasks by enforcing a set of convex quadratic constraints in our optimization variables, the parallel spring stiffness and preload. These quadratic constraints are equivalent to ellipses in the stiffness and preload plane; any combination of stiffness and preload inside the ellipse represents a parallel spring that minimizes effort source or energy consumption with respect to an actuator without a spring. This geometric interpretation intuitively guides the stiffness and preload selection process. We analytically and experimentally prove the convex quadratic function of the spring stiffness and preload. As applications, we analyze the stiffness and preload selection of a parallel spring for a knee exoskeleton using human muscle as the effort source and a prosthetic ankle powered by electric motors. The source code associated with our framework is available as supplemental open-source software. 

Natural biological branching processes can form tree-like structures at all scales and, moreover, can perform various functions to achieve specific goals; these include receiving stimuli, performing two-way communication along their branches, and dynamically reforming (extending or retracting branches). They underlie many biological systems with considerable diversity, frequency, and geometric complexity; these include networks of neurons, organ tissue, mycorrhizal fungal networks, plant growth, foraging networks, etc. This paper presents a biomimetic DNA tile assembly model (Y-STAM) to implement dynamic branching processes. The Y-STAM is a relatively compact mathematical model providing a design space where complex, biomimetic branch-like growth and behaviour can emerge from the appropriate parametrization of the model. We also introduce a class of augmented models (Y-STAM⁺) that provide time- and space-dependent modulations of tile glue strengths, which enable further diverse behaviours that are not possible in the Y-STAM; these additional behaviours include refinement of network assemblies, obstacle avoidance, and programmable growth patterns. We perform and discuss extensive simulations of the Y-STAM and the Y-STAM⁺. We envision that these models could be applied at the mesoscale and the molecular scale to dynamically assemble branching DNA nanostructures and offer insights into complex biological self-assembly processes. 

DNA strand displacement (DSD) emerged as a prominent reaction motif for engineering nucleic acid-based computational devices with programmable behaviours. However, strand displacement circuits are susceptible to background noise, known as leaks, which disrupt their intended function. The ill effects of leaks are particularly severe in circuits with complex dynamics, as leaks in them amplify nonlinearly, resulting in rapid circuit degradation. Shadow cancellation is a dynamic leak-elimination strategy originally proposed to control the leak growth in such circuits. However, the kinetic restrictions of the method incur a significant design overhead, making it less accessible. In this work, we use domain-level DSD simulations to examine the method’s capabilities, the inner workings of its components and, most importantly, its robustness to the practical deviations in its design requirements. First, we show that the method could stabilize the dynamics of several catalytic and autocatalytic dynamical systems heavily affected by leaks. Then, through several probing experiments, we show that its design restrictions could be significantly relaxed without impacting the circuit function by simply adjusting the circuit parameters. Finally, we discuss several ideas to tackle the practical challenges in applying the method to arbitrary DSD circuits, paving the way for future experimental work. 

Studies of research software development have focused on how to promote or encourage the adoption of software engineering practices, but we do not have a good empirical understanding of strategies that researchers have already begun to take in order to integrate those practices into research work in sustainable ways. We conduct a comparative case study of two research groups in different fields, and characterize two approaches that they have taken to get research software engineering work done: practice integration and differentiating expertise. From these findings we argue that examining outcomes of change in research software development practice is critical for understanding sustainability and the ramifications of such changes for scientific work. 

This study gauges the preparedness levels of individuals (younger and older) across hazards and investigates their willingness to use emerging technology for disaster preparedness. Older adults are among the most vulnerable during disasters and more likely to be displaced. As climate change contributes to the increased frequency, intensity, and scale of disasters, the number of areas impacted by multiple hazards has also increased. In December 2023, a nationwide survey with over 1,000 respondents was launched. The results indicate a variation in the perception of preparedness across hazards, at the individual level. Additionally, most respondents would use emerging technology to help them improve their disaster preparedness, including smart speakers, phones, mobile appliances, cars, wearable devices, robots, and virtual reality devices. Findings indicate that older adults may be willing to use emerging technology that they are uncomfortable with for disaster preparedness, necessitating training, exercises, and qualitative research to understand how and why. 

One commonly used workload metric in Emergency Medical Services (EMS) is the Unit Hour Utilization (UHU). The UHU is a productivity measure that, by definition, represents the ratio of patient transport calls to the total hours that ambulances are staffed. It is often misinterpreted as a utilization measure representing the percentage of crews’ available working hours that are spent performing work. This paper investigates a surrogate model to estimate a measure of EMS crew utilization that considers not only call response, but also indirect work tasks, such as documentation and shift start activities. We explored Kriging, KPLS, RBF, and physics-based models based on EMS work dynamics. The true measure of utilization was based on Montecarlo samples of estimated work time patterns associated with a year’s worth of dispatch data augmented with the results of a work measurement study. The best performing model in terms of the root mean square error (RSME), the symmetric mean absolute percent error (sMAPE), and Pearson correlation estimates, was the physics-based model. This model requires time studies to estimate the average time spent in shift start activities and documenting calls, geographic information systems to estimate the average time driving back to the post, and dispatch data analysis to estimate the average time to respond to calls. Sensitivity analysis was used to provide recommendations for when to update these parameters and general recommendations were given to implement this approach in other EMS systems. 

This research investigates optimal pricing strategies in a service-providing queueing system where customers may renege before service completion. Prices are quoted upon customer arrivals and the incoming customers join the system if their willingness to pay exceeds the quoted price. While waiting in line or during service, customers may get impatient and leave without service, incurring an abandonment cost. There is also a per-unit time per-customer holding cost. Our objective is to maximize the long-run average profit through optimal pricing policies. We model the problem as a Markov decision process and identify the optimal pricing using policy iteration. We also study the structure of the optimal pricing policy. Furthermore, we show that under mild assumptions, the optimal price increases as the number of customers in the system increases. When those assumptions do not hold, optimal price decreases and then increases as the number of customers in the system grows. 

This research investigates optimal pricing strategies in a service-providing queueing system where customers may renege before service completion. Prices are quoted upon customer arrivals and the incoming customers join the system if their willingness to pay exceeds the quoted price. While waiting in line or during service, customers may get impatient and leave without service, incurring an abandonment cost. There is also a per-unit time per-customer holding cost. Our objective is to maximize the long-run average profit through optimal pricing policies. We model the problem as a Markov decision process and identify the optimal pricing using policy iteration. We also study the structure of the optimal pricing policy. Furthermore, we show that under mild assumptions, the optimal price increases as the number of customers in the system increases. When those assumptions do not hold, optimal price decreases and then increases as the number of customers in the system grows.