Double-stranded DNA (dsDNA) has been established as an efficient medium for charge migration, bringing it to the forefront of the field of molecular electronics as well as biological research. The charge migration rate is controlled by the electronic couplings between the two nucleobases of DNA/RNA. These electronic couplings strongly depend on the intermolecular geometry and orientation. Estimating these electronic couplings for all the possible relative geometries of molecules using the computationally demanding firstprinciples calculations requires a lot of time as well as computation resources. In this article, we present a Machine Learning (ML) based model to calculate the electronic coupling between any two bases of dsDNA/dsRNA of any length and sequence and bypass the computationally expensive first-principles calculations. Using the Coulomb matrix representation which encodes the atomic identities and coordinates of the DNA base pairs to prepare the input dataset, we train a feedforward neural network model. Our NN model can predict the electronic couplings between dsDNA base pairs with any structural orientation with a MAE of less than 0.014 eV. We further use the NN predicted electronic coupling values to compute the dsDNA/dsRNA conductance.

We present the paradigm of natural and exploratory shape modeling by introducing novel 3D interactions for creating, modifying and manipulating 3D shapes using arms and hands. Though current design tools provide complex modeling functionalities, they remain non-intuitive and require significant training since they segregate 3D shapes into hierarchical 2D inputs, thus binding the user to stringent procedural steps and making modifications cumbersome. In addition the designer knows what to design when they go to CAD systems and the creative exploration in design is lost. We present a shape creation paradigm as an exploration of creative imagination and externalization of shapes, particularly in the early phases of design. We integrate the capability of humans to express 3D shapes via hand-arm motions with traditional sweep surface representation to demonstrate rapid exploration of a rich variety of fairly complex 3D shapes. We track the skeleton of users using the depth data provided by lowcost depth sensing camera (Kinect TM ). Our modeling tool is configurable to provide a variety of implicit constraints for shape symmetry and resolution based on the position, orientation and * Address all correspondence to this author. speed of the arms. Intuitive strategies for coarse and fine shape modifications are also proposed. We conclusively demonstrate the creation of a wide variety of product concepts and show an average modeling time of a only few seconds while retaining the intuitiveness of communicating the design intent.

Customer inputs in the early stages of design can potentially lead to completely new outlooks in concept generation. We propose crowd-based co-creation as a means to this end. Our main idea is to think of the customer as a source of initial design concepts rather than a means for obtaining preferences towards designer-generated concepts. For analyzing a large collection of customer-created prototypes, we develop a framework that focuses on generating hypotheses related to customer perception of design attributes. We demonstrate our approach through a web interface to gather design requirements for a computer mouse, a bicycle seat, a pen holder, and a cola bottle. This interface was used in a crowdsourcing study with 253 users who represented potential end users for these products. Results from this study show that web-based co-creation allows designers to capture a variety of form and function-related design requirements from user-created virtual prototypes. We also found that such studies can be instrumental in identifying innovative product concepts, and gaining insights about how user perception correlates with product form. Therefore, we make the case that customer creation through distributed co-creation platforms can reinforce concept exploration in future early design processes. * Address all correspondence to this author -dev@purdue.edu typing. Our goal is to gather customer input to inform product conceptualization and prototyping in the early stages of design. Traditionally, designers use established principles such as voice of customer, focus groups, or immersive design to gather user feedback and translate them to engineering specifications or designs. Our focus is to allow customers to provide direct input to the prototyping stage by creating designs containing form and function-related data. For this, we develop a web interface that allows customers to create designs and express their preferences, perceptions, or ideas. Our work seeks to advance the early design process by leveraging the capabilities of Web 2.0 technologies [1]. Web-based engagement platforms are allowing companies to interact with large user bases and better understand user demographics. From the perspective of the consumer, these web-based tools provide an avenue for shaping the design of future products. These developments have motivated us to explore the usefulness of co-creation in the product design process. Using a crowd-based study, we show that virtual co-creation has the potential to gather a variety of user-related data. We also show that co-creation can generate valuable insights for early design through analysis of user-created prototypes. Two such methods: shape-based and semantics-based clustering, are discussed in this paper.An important focus of our work is to explore methods that provide useful insights into user perception of design attributes. Previous literature has approached these questions from the point of view of preference elicitation [2,3,4,5]. Such studies discretize the design space through comparisons ...