COVID-19 has been a sustained and global crisis with a strong continual impact on daily life. Staying accurately informed about COVID-19 has been key to personal and communal safety, especially for essential workers---individuals whose jobs have required them to go into work throughout the pandemic---as their employment has exposed them to higher risks of contracting the virus. Through 14 semi-structured interviews, we explore how essential workers across industries navigated the COVID-19 information landscape to get up-to-date information in the early months of the pandemic. We find that essential workers living through a sustained crisis have a broad set of information needs. We summarize these needs in a framework that centers 1) fulfilling job requirements, 2) assessing personal risk, and 3) keeping up with crisis news coverage. Our findings also show that the sustained nature of COVID-19 crisis coverage led essential workers to experience breaking points and develop coping strategies. Additionally, we show how workplace communications may act as a mediating force in this process: lack of adequate information in the workplace caused workers to struggle with navigating a contested information landscape, while consistent updates and information exchanges at work could ease the stress of information overload. Our findings extend the crisis informatics field by providing contextual knowledge about the information needs of essential workers during a sustained crisis.

Retention registers are utilized in power gating design to hold design state during power down and to allow safe and fast system reactivation. Since a retention register consumes more power and costs more area than a non-retention register, it is desirable to minimize the use of retention registers. However, relaxing retention requirement to a minimal subset of registers can be computationally challenging. In this paper, we adopt satisfiability solving for scalable selection of registers whose retention is unnecessary and exploit input sequence constraints to increase the number of non-retention registers. Empirical results on industrial benchmarks show that our proposed methods are efficient and effective in identifying non-retention registers.

Reactive system synthesis from safety specifications is a promising approach to the correct-by-construction methodology. The synthesis process is often divided into two separate steps: First, check specification realizability by computing the winning region of states under a game-theoretic interpretation; second, synthesize the implementation circuit based on the computed winning region if the specification is realizable. Moreover, recent results suggest that methods based on satisfiability (SAT) solving outperform those based on Binary Decision Diagrams (BDDs) especially on large benchmark instances. In this paper, we focus on the the winning region computation and propose a SAT-based algorithm. By adopting the concepts from the state-of-the-art model checking algorithm property directed reachability (PDR, a.k.a. IC3), we aim at devising an efficient computation method for automatic controller synthesis. Experimental results on the benchmarks from the synthesis competition (SyntComp 2014) show that our proposed algorithm outperforms the existing SAT-based and QBF-based methods by some margin.

Sample preparation plays an essential role in most biochemical reactions. Raw reactants are diluted to solutions with desirable concentration values in this process. Since the reactants, like infant's blood, DNA evidence collected from crime scenes, or costly reagents, are extremely valuable, their usage should be minimized whenever possible. In this paper, we propose a two-phased reactant minimization algorithm (REMIA), for sample preparation on digital microfluidic biochips. In the former phase, REMIA builds a reactant-minimized interpolated dilution tree with specific leaf nodes for a target concentration. Two approaches are developed for tree construction; one is based on integer linear programming (ILP) and the other is heuristic. The ILP one guarantees to produce an optimal dilution tree with minimal reactant consumption, whereas the heuristic one ensures runtime efficiency. Then, REMIA constructs a forest consisting of exponential dilution trees to produce those aforementioned specific leaf nodes with minimal reactant consumption in the latter phase. Experimental results show that REMIA achieves a reduction of reactant usage by 32%-52% as compared with three existing state-of-the-art sample preparation approaches. Besides, REMIA can be easily extended to solve the sample preparation problem with multiple target concentrations, and the extended version also effectively lowers the reactant consumption further.

Sample preparation is an indispensable process to biochemical reactions. Original reactants are usually diluted to the solutions with desirable concentrations. Since the reactants, like infant's blood, DNA evidence collected from a crime scene, or costly reagents, are extremely valuable, the usage of reactant must be minimized in the sample preparation process. In this paper, we propose the first reactant minimization approach, REMIA, during sample preparation on digital microfluidic biochips (DMFBs). Given a target concentration, REMIA constructs a skewed mixing tree to guide the sample preparation process for reactant minimization. Experimental results demonstrate that REMIA can save about 31%~52% of reactant usage on average compared with three existing sample preparation methods. Besides, REMIA can be extended to tackle the sample preparation problem with multiple target concentrations, and the extended version also successfully decreases the reactant usage further.