Novel Approach For Integration Research Articles

Towards the Possibility of Additive Manufacturing of XNA-Based Devices Using Molecular Engineering Principles

This paper considers a novel approach for integration between molecular engineering of XNA-based structures and additive manufacturing of XNA-based devices based on multiparametric characterization of XNAs by different functional descriptors (such as physical properties of XNA-based materials and precursors of XNA-based molecular devices) and the possibility of thermal or electron-beam processing as a prerequisite of the industrial technical process development for such device implementation. This can be performed in the framework of additive manufacturing by connecting the output of the XNA synthesizer or nucleic acid synthesizer with 3D-printer nozzles in such a way that oligos / AGCTX products are supported into the nozzles separately.

Integration of CMOS Image Sensor and Microwell Array Using 3-D WLCSP Technology for Biodetector Application

Integration of microfluidics with integrated circuit chip is a promising method to achieve lab-on-a-chip for its high-level miniaturization and portability in application. In this paper, a novel approach for integration of complementary metal–oxide–semiconductor image sensor and microwell array using the 3-D wafer-level chip scale package (3-D WLCSP) technology for biodetector is presented. The key process including silicon microwell array fabricating, wafer-level bonding, backside grinding, through-silicon via etching, redistribution layer, and ball grid array formation are developed. A high-density serpentine electrical circuit is integrated on the backside of the package to work as a temperature controller. After the fabrication process optimization, a yield rate of 94.6% is obtained. The reliability of the packages is characterized by thermal cycling and highly accelerated stress test temperature storage tests, and the results show a good package level reliability. The proposed 3-D WLCSP provides a low-cost and reliable solution for the fluidic biodetector integration.

Identifying strategic maintenance capacity for accidental damage occurrence in aircraft operations

Airline operators face accidental damages on their fleet of aircraft as part of operational practice. Individual occurrences are hard to predict; consequently, the approach towards repairing accidental damage is reactive in aircraft maintenance practice. However, by aggregating occurrence data and predicting future occurrence rates, it is possible to predict future long-term (strategic) demand for maintenance capacity. In this paper, a novel approach for integration of reliability modelling and inventory control is presented. Here, the concept of a base stock policy has been translated to the maintenance slot capacity problem to determine long-term cost-optimal capacity. Demand has been modelled using a superposed Non-homogeneous Poisson Process (NHPP). A case study has been performed on damage data from a fleet of Boeing 777 aircraft. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future damage occurrence and associated maintenance slot requirements.

Game Mastering in Collaborative Serious Games

In collaborative learning scenarios, the role of the instructor is vital. This aspect directly carries over to the concept of collaborative multiplayer Serious Games, where a group of players is learning together using a Serious Game. In this paper, the authors propose a novel approach for integration and support of instructors in collaborative multiplayer Serious Game scenarios. Their approach considers instructor tasks and responsibilities. It defines an interface for 3D action adventure-like games, defining relevant game information and adaptation access. It further includes a Game Mastering framework for orchestrating and adapting such games at runtime via an instructor. The concept was implemented on top of an existing Serious Game (Escape from Wilson Island) and evaluated in a user-centric study with N=40 participants (age m=23.39; SD=3.05). Results show that an instructor using our framework in a collaborative learning scenario can positively influence players' game experience (p<.05) and success towards the intended goals, as well as recognize and counteract problems at runtime.

Positional integratomic approach in identification of genomic candidate regions for Parkinson's disease

Recent abundance of data from studies employing high-throughput technologies to reveal alterations in human disease on genomic, transcriptomic, proteomic and other levels, offer the possibility to integrate this information into a comprehensive picture of molecular events occurring in human disease. Diversity of data originating from these studies presents a methodological obstacle in the integration process, also due to difficulties in choosing the optimal unified denominator that would allow inclusion of variables from various types of studies. We present a novel approach for integration of such multi-origin data based on positions of genetic alterations occurring in human diseases. Parkinson's disease (PD) was chosen as a model for evaluation of our methodology. Datasets from various types of studies in PD (linkage, genome-wide association, transcriptomic and proteomic studies) were obtained from online repositories or were extracted from available research papers. Subsequently, human genome assembly was subdivided into 10 kb regions, and significant signals from aforementioned studies were arranged into their corresponding regions according to their genomic position. For each region, rank product values were calculated and significance values were estimated by permuting the original dataset. Altogether, 179 regions (representing 33 contiguous genomic regions) had significant accumulation of signals when P-value cut-off was set at 0.0001. Identified regions with significant accumulation of signals contained 29 plausible candidate genes for PD. In conclusion, we present a novel approach for identification of candidate regions and genes for various human disorders, based on the positional integration of data across various types of omic studies.