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D-optimal experimental design is a classical statistical problem in which one chooses a collection of data vectors, from some available large pool, in order to maximize a measure of predictive quality. In the classical formulation, the only constraint is on the cardinality of the collection, that is, the number of vectors chosen. We study a more general budget-constrained variant in which vectors have heterogeneous costs, and develop four new algorithms (two deterministic and two randomized) with approximation guarantees. Our methods handle heterogeneous costs using a novel exchange rule that interchanges packs of data vectors whose total costs are similar (up to some controlled amount of rounding error). The algorithms outperform the only existing method for this problem from both theoretical and empirical standpoints. Funding: The first and third authors gratefully acknowledge support from the National Science Foundation (NSF) Division of Civil, Mechanical and Manufacturing Innovation [Grant CMMI-2112828]. The second author gratefully acknowledges support from the NSF Division of Computing and Communication Foundations [Grant CCF-2246417] and Office of Naval Research [Grant N00014-24-1-2066]. 

Routing a Vehicle to Collect Data After an Earthquake In the immediate aftermath of a major earthquake, it is crucial to quickly and accurately assess structural damage throughout the region. It is especially important to identify buildings that have become unsafe in order to prioritize evacuation efforts. Only a very small number of building inspections can be feasibly performed in a narrow time frame; however, their results can then be combined with other data sources to predict damage at other locations that were not inspected. In “D-Optimal Orienteering for Postearthquake Reconnaissance Planning,” Wang, Xie, Ryzhov, Marković, and Ou present a novel nonlinear integer program that combines vehicle routing with a statistical objective, the goal being to maximize data quality. An exact method based on row and column generation is developed to solve problems with up to 200 buildings. The approach is validated in a realistic case study using real-world building data obtained from a state-of-the-art earthquake simulator. 

Abstract Soft (flexible and stretchable) biosensors have great potential in real-time and continuous health monitoring of various physiological factors, mainly due to their better conformability to soft human tissues and organs, which maximizes data fidelity and minimizes biological interference. Most of the early soft sensors focused on sensing physical signals. Recently, it is becoming a trend that novel soft sensors are developed to sense and monitor biochemical signalsin situin real biological environments, thus providing much more meaningful data for studying fundamental biology and diagnosing diverse health conditions. This is essential to decentralize the healthcare resources towards predictive medicine and better disease management. To meet the requirements of mechanical softness and complex biosensing, unconventional materials, and manufacturing process are demanded in developing biosensors. In this review, we summarize the fundamental approaches and the latest and representative design and fabrication to engineer soft electronics (flexible and stretchable) for wearable and implantable biochemical sensing. We will review the rational design and ingenious integration of stretchable materials, structures, and signal transducers in different application scenarios to fabricate high-performance soft biosensors. Focus is also given to how these novel biosensors can be integrated into diverse important physiological environments and scenariosin situ, such as sweat analysis, wound monitoring, and neurochemical sensing. We also rethink and discuss the current limitations, challenges, and prospects of soft biosensors. This review holds significant importance for researchers and engineers, as it assists in comprehending the overarching trends and pivotal issues within the realm of designing and manufacturing soft electronics for biochemical sensing.