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MADMAX is a Haskell-embedded DSL for multi-attribute, multi-layered decision making. An important feature of this DSL is the ability to generate explanations of why a computed optimal solution is better than its alternatives. The functional approach and Haskell's type system support a high-level formulation of decision-making problems, which facilitates a number of innovations, including the gradual evolution and adaptation of problem representations, a more user-friendly form of sensitivity analysis based on problem domain data, and fine-grained control over explanations. 

Circularly polarized light interacts preferentially with the biomolecules to generate spectral fingerprints reflecting their primary and secondary structure in the ultraviolet region of the electromagnetic spectrum. The spectral features can be transferred to the visible and near‐infrared regions by coupling the biomolecules with plasmonic assemblies made of noble metals. Nanoscale gold tetrahelices were used to detect the presence of chiral objects that are 40 times smaller in size by using plane‐polarized light of 550 nm wavelength. The emergence of chiral hotspots in the gaps between 80 nm long tetrahelices differentiate between weakly scattering S‐ vs R‐molecules with optical constants similar to that of organic solvents. Simulations map the spatial distribution of the scattered field to reveal enantiomeric discrimination with selectivity up to 0.54.

 

Abstract In this paper, we present a method for explaining the results produced by dynamic programming (DP) algorithms. Our approach is based on retaining a granular representation of values that are aggregated during program execution. The explanations that are created from the granular representations can answer questions of why one result was obtained instead of another and therefore can increase the confidence in the correctness of program results. Our focus on dynamic programming is motivated by the fact that dynamic programming offers a systematic approach to implementing a large class of optimization algorithms which produce decisions based on aggregated value comparisons. It is those decisions that the granular representation can help explain. Moreover, the fact that dynamic programming can be formalized using semirings supports the creation of a Haskell library for dynamic programming that has two important features. First, it allows programmers to specify programs by recurrence relationships from which efficient implementations are derived automatically. Second, the dynamic programs can be formulated generically (as type classes), which supports the smooth transition from programs that only produce result to programs that can run with granular representation and also produce explanations. Finally, we also demonstrate how to anticipate user questions about program results and how to produce corresponding explanations automatically in advance. 

The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability.

 

Pivotal to functional van der Waals stacked flexible electronic/excitonic/spintronic/thermoelectric chips is the synergy amongst constituent layers. However; the current techniques viz. sequential chemical vapor deposition, micromechanical/wet‐chemical transfer are mostly limited due to diffused interfaces, and metallic remnants/bubbles at the interface. Inter‐layer‐coupled 2+δ‐dimensional materials, as a new class of materials can be significantly suitable for out‐of‐plane carrier transport and hence prompt response in prospective devices. Here, the discovery of the use of exotic electric field ≈10⁶ V cm⁻¹(at microwave hot‐spot) and 2 thermomechanical conditions i.e. pressure ≈1 MPa, T ≈ 200 °C (during solvothermal reaction) to realize 2+δ‐dimensional materials is reported. It is found that P_zP_zchemical bonds form between the component layers, e.g., CB and CN in G‐BN, MoN and MoB in MoS₂‐BN hybrid systems as revealed by X‐ray photoelectron spectroscopy. New vibrational peaks in Raman spectra (BC ≈1320 cm^–1for the G‐BN system and MoB ≈365 cm^–1for the MoS₂‐BN system) are recorded. Tunable mid‐gap formation, along with diodic behavior (knee voltage ≈0.7 V, breakdown voltage ≈1.8 V) in the reduced graphene oxide‐reduced BN oxide (RGO‐RBNO) hybrid system is also observed. Band‐gap tuning in MoS₂‐BN system is observed. Simulations reveal stacking‐dependent interfacial charge/potential drops, hinting at the feasibility of next‐generation functional devices/sensors.