ALAMO is a computational methodology for leaning algebraic functions from data. Given a data set, the approach begins by building a low-complexity, linear model composed of explicit non-linear transformations of the independent variables. Linear combinations of these non-linear transformations allow a linear model to better approximate complex behavior observed in real processes. The model is refined, as additional data are obtained in an adaptive fashion through error maximization sampling using derivative-free optimization. Models built using ALAMO can enforce constraints on the response variables to incorporate first-principles knowledge. The ability of ALAMO to generate simple and accurate models for a number of reaction problems is demonstrated. The error maximization sampling is compared with Latin hypercube designs to demonstrate its sampling efficiency. ALAMO's constrained regression methodology is used to further refine concentration models, resulting in models that perform better on validation data and satisfy upper and lower bounds placed on model outputs.

Variation in inflorescence development is an important target of selection for numerous crop species, including many members of the Poaceae (grasses). In Asian rice (Oryza sativa), inflorescence (panicle) architecture is correlated with yield and grain-quality traits. However, many rice breeders continue to use composite phenotypes in selection pipelines, because measuring complex, branched panicles requires a significant investment of resources. We developed an open-source phenotyping platform, PANorama, which measures multiple architectural and branching phenotypes from images simultaneously. PANorama automatically extracts skeletons from images, allows users to subdivide axes into individual internodes, and thresholds away structures, such as awns, that normally interfere with accurate panicle phenotyping. PANorama represents an improvement in both efficiency and accuracy over existing panicle imaging platforms, and flexible implementation makes PANorama capable of measuring a range of organs from other plant species. Using high-resolution phenotypes, a mapping population of recombinant inbred lines, and a dense singlenucleotide polymorphism data set, we identify, to our knowledge, the largest number of quantitative trait loci (QTLs) for panicle traits ever reported in a single study. Several areas of the genome show pleiotropic clusters of panicle QTLs, including a region near the rice Green Revolution gene SEMIDWARF1. We also confirm that multiple panicle phenotypes are distinctly different among a small collection of diverse rice varieties. Taken together, these results suggest that clusters of small-effect QTLs may be responsible for varietal or subpopulation-specific panicle traits, representing a significant opportunity for rice breeders selecting for yield performance across different genetic backgrounds.

In this study, the electroless deposition of copper and silver was investigated on epoxy and silicon dioxide-based substrates. A cost-efficient, Sn/Ag catalyst was investigated as a replacement for the Sn/Pd catalyst currently used in board technology. The surface of the epoxy based polyhedral oligomeric silsesquioxane (POSS) films was modified by plasma and chemical etching for electroless activation without the creation of a roughened surface. The electroless copper deposited on the modified POSS surface exhibited excellent adhesion when annealed at 180 • C in nitrogen for 90 min or at room temperature for 24 hr. Electroless copper deposition was also demonstrated on oxidized silicon wafers for through silicon via sidewall deposition.

Electroless copper deposition was investigated on epoxy laminate substrates (Isola 185HR) using a silver-based catalyst, and a nonroughening surface treatment method based on sulfuric acid. The current challenges in electroless copper deposition include (i) high cost of Pd-based catalysts, (ii) deterioration of electrical performance of deposited metal at high frequency due to electron scattering at the roughened surface, and (iii) limited adhesion strength of electroless layers to substrates. We investigated an electroless copper deposition procedure composed of a H 2 SO 4 surface pretreatment, two-step Sn/Ag nano-colloidal catalyst seeding, and immersion in a traditional formaldehyde-containing electroless copper bath. The H 2 SO 4 pretreatment activated the epoxy surface for electroless deposition. Other strong acids did not lead to deposition. The H 2 SO 4 treatment cleaned the substrate and provided the adhesion of the catalyst and electroless copper without increasing the surface roughness. XPS results showed a decrease in the carbonyl groups (C=O), and acid/ester functionalities (O-C=O) at the surface. Adsorbed sulfate on the substrate from the H 2 SO 4 treatment led to Sn(II) sensitization. The tin-silver activation step resulted in Sn(IV) and Ag(0) products in the form of a Sn/Ag nano-colloidal catalyst. The Sn/Ag colloid acted as a catalyst for electroless copper deposition on the epoxy laminate substrates.

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