Multiple Domain Systems Research Articles

Human-Computer Collaborative Visual Design Creation Assisted by Artificial Intelligence

With the support and promotion of big data and cloud computing, AI has penetrated into every field of people's lives more and more deeply, with its characteristics of sustainable work, extremely fast computing speed, and a certain intelligence. This is an effective way to solve the general lack of demand and productivity of visual design, and relieve the pressure off designers to deal with relatively low-quality and high-demand designs. Therefore, the combination of design and artificial intelligence technology is a necessity. Research on the application of artificial intelligence technology for visual design is also in full swing at home and abroad However, at present, teams at home and abroad are in the exploratory stage. This paper considers whether it is possible to build an intelligent visual design and creation system by using artificial intelligence technology to help graphic communication designers achieve high-quality, high-efficiency, and high-quantity design output. Additionally, this paperexplores how to combine artificial intelligence technology with designers' design workflow so as to form a complementary human-computer cooperation mode. We will explore how to integrate AI technology with designers' design workflow and then create a human-machine collaboration model with complementary advantages to achieve the high quality, high efficiency, and high quantity of design output required by the intelligent visual design creation system being built. Finally, a basic framework of a generative smart human-computer collaborative visual design creation system based on a subset of neural network expert systems in multiple domains and an aggregate of different modules supported by the system is formed, and the working principle and usage process of the system are further elaborated with the example of packaging design.

A Temporal Case-Based Reasoning Platform Relying on a Fuzzy Vector Spaces Object-Oriented Model and a Method to Design Knowledge Bases and Decision Support Systems in Multiple Domains

Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component objects and their states (attribute values) at different levels of granularity. This paper provides some significant contributions to computer science. It extends a fuzzy vector space object-oriented model and method (FVSOOMM) to present a new platform and a method guideline capable of designing objects and attributes that represent timepoint knowledge objects. It shows how temporal case-based reasoning can use distances between temporal fuzzy vector functions to compare these knowledge objects’ evolution. It describes examples of interfaces that have been implemented on this new platform. These include an expert’s interface that describes a knowledge class diagram; a practitioner’s interface that instantiates domain objects and their attribute constraints; and an end-user interface to input attribute values of the real cases stored in a domain case database. This paper illustrates resultant knowledge bases in different domains, with examples of pulmonary embolism diagnosis in medicine and decision making in French municipal territorial recomposition. The paper concludes with the current limitations of the proposed model, its future perspectives and possible platform enhancements.

An End-to-End Deep Learning Pipeline for Assigning Secondary Structure in Proteins

Protein secondary structure assignment, a subdiscipline of computational chemistry, is yet to be explored using deep learning techniques. Protein secondary structure elements are assigned to support structural analysis and prediction. Algorithms like DSSP, generally regarded as the gold standard for assigning the secondary structure of proteins, need full atom information to label protein coordinates. The PDB database has been the major repository for data on the 3D structures of proteins, nucleic acids, and other complex assemblies since 1971. However, a significant fraction of protein structures contains missing atoms. As a result, new approaches to reliably and consistently assigning secondary structures based on coarse-grained atomic coordinates are needed. While deep learning architectures have an unparalleled track record in applications such as protein structure prediction, there are only a few known deep learning solutions for structure assignment problems. While the gold standard methods are based on bonding information and other geometric characteristics, deep learning methods extract features themselves without human intervention. The benefit that standardised datasets provide to the effectiveness of deep learning systems in multiple domains motivated us to create a labeled dataset for protein structure assignment tasks. Additionally, a deep learning model, named PSSADL, solely based on [Formula: see text] coordinates was trained on the generated dataset to validate its potency. The proposed method, which combines Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)/Bidirectional LSTM networks, has been compared to the established standards and more recent techniques. The model achieved an accuracy of [Formula: see text] on the benchmark and individual test sets. The results show that deep learning techniques have a promising future in protein structure analysis, implying that the dataset developed as part of our work will be a valuable resource for further protein structure research.

PurpleDrop: A Digital Microfluidics-Based Platform for Hybrid Molecular-Electronics Applications

Molecular manipulation and analysis are the cornerstone of life sciences. With the recent advances in molecular data storage and computing, it has become an increasingly exciting and viable alternative for the post-CMOS scaling era. Widespread use of molecular manipulation/analysis and data storage/computing requires a scalable and low-cost platform for hybrid molecular-electronics systems. This enables us to build on the best of what molecular and electronics systems can offer. In this article, we present PurpleDrop, a full-stack digital microfluidic platform for hybrid molecular-electronic systems in multiple domains, and focus on DNA data storage as a use case. We describe its design principles and relevant aspects such as closed-loop operation with computer vision and capacitive sensing, on-board magnetic bead extraction, and polymerase chain reaction, among other primitives. Importantly, we emphasize the ability to express and execute protocols and computation that include molecular and computational components.

Strengthening Surgery Strengthens Health Systems: A New Paradigm and Potential Pathway for Horizontal Development in Low- and Middle-Income Countries.

Global health is transitioning toward a focus on building strong and sustainable health systems in developing countries; however, resources, funding, and agendas continue to concentrate on "vertical" (disease-based) improvements in care. Surgical care in low- and middle-income countries (LMICs) requires the development of health systems infrastructure and can be considered an indicator of overall system readiness. Improving surgical care provides a scalable gateway to strengthen health systems in multiple domains. In this position paper by the Society of University Surgeons' Committee on Global Academic Surgery, we propose that health systems development appropriately falls within the purview of the academic surgeon. Partnerships between academic surgical institutions and societies from high-income and resource-constrained settings are needed to strengthen advocacy and funding efforts and support development of training and research in LMICs.

Reference ontologies for interoperability across multiple assembly systems

The role of information and communication technologies (ICTs) is crucial for future manufacturing organisations in order to support effective collaboration and information sharing. However, the contemporary ICT-based systems lack the required ability to adequately support interoperability across multiple domain systems. The capability of such ICT-based systems to interoperate is impeded by the semantic conflicts arising from loosely defined meanings and intents of the participating system concepts. The aim of this paper is to investigate the interoperability of assembly systems at multiple levels of concept specialisations using the concept of a formal reference ontology. Formal ontologies are providing a promising way to computationally capture the domain meanings which can subsequently provide a base to support interoperability across multiple systems and in our case multiple assembly systems. This paper takes the example of manufacturing bill of materials concept and three different domain-specific interpretations to explore and demonstrate the potential of formal reference ontologies to support interoperability.

A Human–Automation Interface Model to Guide Automation Design of System Functions

AbstractA major component of the US Army's Future Combat Systems (FCS) will be a fleet of eight different manned ground vehicles (MGV). There are promises that “advanced automation” will accomplish many of the tasks formerly performed by soldiers in legacy vehicle systems. However, the current approach to automation design does not relieve the soldier operator of tasks; rather, it changes the role of the soldiers and the work they must do, often in ways unintended and unanticipated. This paper proposes a coherent, top‐down, overarching approach to the design of a human–automation interaction model. First, a qualitative model is proposed to drive the functional architecture and human–automation interface scheme for the MGV fleet. Second, the proposed model is applied to a portion of the functional flow of the common crew station on the MGV fleet. Finally, the proposed model is demonstrated quantitatively via a computational task‐network modeling program (Improved Performance Research and Integration Tool). The modeling approach offers insights into the impacts on human task‐loading, workload, and human performance. Implications for human systems integration domains are discussed, including Manpower and Personnel, Human Factors Engineering, Training, System Safety, and Soldier Survivability. The proposed model gives engineers and scientists a top‐down approach to explicitly define and design the interactions between proposed automation schemes and the human crew. Although this paper focuses on the Army's FCS MGV fleet, the model and analytical processes proposed, or similar approaches, are appropriate for many manned systems in multiple domains (aviation, space, maritime, ground transportation, manufacturing, etc.).

A NEW APPROACH FOR CLASSIFICATION OF GENERIC AUDIO DATA

The existing audio retrieval systems fall into one of two categories: single-domain systems that can accept data of only a single type (e.g. speech) or multiple-domain systems that offer content-based retrieval for multiple types of audio data. Since a single-domain system has limited applications, a multiple-domain system will be more useful. However, different types of audio data will have different properties, this will make a multiple-domain system harder to be developed. If we can classify audio information in advance, the above problems can be solved. In this paper, we will propose a real-time classification method to classify audio signals into several basic audio types such as pure speech, music, song, speech with music background, and speech with environmental noise background. In order to make the proposed method robust for a variety of audio sources, we use Bayesian decision function for multivariable Gaussian distribution instead of manually adjusting a threshold for each discriminator. The proposed approach can be applied to content-based audio/video retrieval. In the experiment, the efficiency and effectiveness of this method are shown by an accuracy rate of more than 96% for general audio data classification.

Reasoning about nonlinear system identification

System identification is the process of deducing a mathematical model of the internal dynamics of a system from observations of its outputs. The computer program Pret automates this process by building a layer of artificial intelligence (AI) techniques around a set of traditional formal engineering methods. Pret takes a generate-and-test approach, using a small, powerful meta-domain theory that tailors the space of candidate models to the problem at hand. It then tests these models against the known behavior of the target system using a large set of more-general mathematical rules. The complex interplay of heterogeneous reasoning modes that is involved in this process is orchestrated by a special first-order logic system that uses static abstraction levels, dynamic declarative meta control, and a simple form of truth maintenance in order to test models quickly and cheaply. Unlike other modeling tools—most of which use libraries to model small, well-posed problems in limited domains and rely on their users to supply detailed descriptions of the target system— Pret works with nonlinear systems in multiple domains and interacts directly with the real world via sensors and actuators. This approach has met with success in a variety of simulated and real applications, ranging from textbook systems to real-world engineering problems.