Development and function explain the modular evolution of phalanges in gecko lizards

Selective regimes favouring the evolution of functional specialization probably affect covariation among phenotypic traits. Phalanges of most tetrapods develop from a conserved module that constrains their relative proportions. In geckos, however, biomechanical specializations associated with adhesive toepads involve morphological variation in the autopodium and might reorganize such modular structures. We tested two hypotheses to explain the modular architecture of hand bones in geckos, one based on developmental interactions and another incorporating functional associations related to locomotion, and compared the empirical support for each hypothetical module between padded and padless lineages. We found strong evidence for developmental modules in most species, which probably reflects embryological constraints during phalangeal formation. Although padded geckos exhibit a functional specialization involving the hyperextension of the distal phalanges that is absent in padless species, the padless species are the ones that show a distal functional module with high integration. Some ancestrally padless geckos apparently deviate from developmental predictions and present a relatively weak developmental module of phalanges and a strongly integrated distal module, which may reflect selective regimes involving incipient frictional adhesion in digit morphology. Modularity of digit elements seems dynamic along the evolutionary history of geckos, being associated with the presence/absence of adhesive toepads.

Evolution of two-component quorum sensing systems

Quorum sensing (QS) is a cell-to-cell communication system that enables bacteria to coordinate their gene expression depending on their population density, via the detection of small molecules called autoinducers. In this way bacteria can act collectively to initiate processes like bioluminescence, virulence and biofilm formation. Autoinducers are detected by receptors, some of which are part of two-component signal transduction systems (TCS), which comprise of a (usually membrane-bound) sensor histidine kinase (HK) and a cognate response regulator (RR). Different QS systems are used by different bacterial taxa, and their relative evolutionary relationships have not been extensively studied. To address this, we used the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to identify all the QS HKs and RRs that are part of TCSs and examined their conservation across microbial taxa. We compared the combinations of the highly conserved domains in the different families of receptors and response regulators using the Simple Modular Architecture Research Tool (SMART) and KEGG databases, and we also carried out phylogenetic analyses for each family, and all families together. The distribution of the different QS systems across taxa, indicates flexibility in HK–RR pairing and highlights the need for further study of the most abundant systems. For both the QS receptors and the response regulators, our results indicate close evolutionary relationships between certain families, highlighting a common evolutionary history which can inform future applications, such as the design of novel inhibitors for pathogenic QS systems.

Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network

Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have been successful in solving the Gait Emotion Recognition (GER) problem. However, a vast majority of such methods rely on Deep Neural Networks (DNNs) with a significant number of model parameters, which lead to model overfitting as well as increased inference time. This paper contributes to the domain of knowledge by proposing a new lightweight bi-modular architecture with handcrafted features that is trained using a RMSprop optimizer and stratified data shuffling. The method is highly effective in correctly inferring human emotions from gait, achieving a micro-mean average precision of 0.97 on the Edinburgh Locomotive Mocap Dataset. It outperforms all recent deep-learning methods, while having the lowest inference time of 16.3 milliseconds per gait sample. This research study is beneficial to applications spanning various fields, such as emotionally aware assistive robotics, adaptive therapy and rehabilitation, and surveillance.

Towards a Modular Elbow Exoskeleton: Concepts for Design and System Control

In industrial workplaces, strenuous, repetitive, and long-term tasks at head level or above as well as carrying heavy loads may lead to musculoskeletal disorders of different task dependent body parts. With an increasing trend towards wearable support systems, there is already a large quantity of exoskeletons that may support the user during movements, or stabilize postures, in order to reduce strain on various parts of the body. However, most commercially available exoskeletons mainly focus on the back and shoulder support. Only a few of them address the elbow joint, despite it being prone to injury. Therefore, this paper discusses different possible design and control concepts of modular elbow exoskeletons. The modular architecture potentially enables coupling to existing commercial- and research-associated systems, through appropriate interfaces. Different morphological structures and control mechanisms are assessed in respect to their ability to extend common exoskeletons for back and shoulder support. Based on these considerations, a first functional passive prototype is presented, which supports the flexion of the elbow joint and can be coupled to an existing exoskeleton. In future work, the prototype may be used for further elaboration and practical investigations in laboratory settings to evaluate its technical functionality and biomechanical effects on the user.

An Integrated Method for Modular Design Based on Auto-Generated Multi-Attribute DSM and Improved Genetic Algorithm

Modular architecture is very conducive to the development, maintenance, and upgrading of electromechanical products. In the initial stage of module division, the design structure matrix (DSM) is a crucial measure to concisely express the component relationship of electromechanical products through the visual symmetrical structure. However, product structure modeling, as a very important activity, was mostly carried out manually by engineers relying on experience in previous studies, which was inefficient and difficult to ensure the consistency of the model. To overcome these problems, an integrated method for modular design based on auto-generated multi-attribute DSM and improved genetic algorithm (GA) is presented. First, the product information extraction algorithm is designed based on the automatic programming structure provided by commercial CAD software, to obtain the assembly, degrees of freedom, and material information needed for modeling. Secondly, based on the evaluation criteria of product component correlation strength, the structural correlation DSM and material correlation DSM of components are established, respectively, and the comprehensive correlation DSM of products is obtained through weighting processing. Finally, the improved GA and the modularity evaluation index Q are used to complete the product module division and obtain the optimal modular granularity. Based on a model in published literature and a bicycle model, comparative studies are carried out to verify the effectiveness and practicality of the proposed method.

Mapping the Deformability of Natural and Designed Cellulosomes in solution

Background : Natural cellulosome multi-enzyme complexes, their components, and engineered ‘designer cellulosomes’ (DCs) promise an efficient means of breaking down cellulosic substrates into valuable biofuel products. Their broad uptake in biotechnology relies on boosting proximity-based synergy among the resident enzymes but the modular architecture challenges structure determination and rational design.Results: We used small angle X-ray scattering combined with molecular modeling to study the solution structure of cellulosomal components. These include three dockerin-bearing cellulases with distinct substrate specificities, original scaffoldins from the human gut bacterium Ruminococcus champanellensis (ScaA, ScaH and ScaK) and a trivalent cohesin-bearing designer scaffoldin (Scaf20L), followed by cellulosomal complexes comprising these components, and the nonavalent fully loaded Clostridium thermocellum CipA in complex with Cel8A from the same bacterium. The size analysis of Rg and Dmax values deduced from the scattering curves and corresponding molecular models highlight their variable aspects, depending on composition, size and spatial organization of the objects in solution.Conclusion: Our data quantifies variability of form and compactness of cellulosomal components in water and confirms that this native plasticity may well be related to speciation with respect to the substrate that is targeted. By showing that scaffoldins or components display enhanced compactness compared to the free objects, we provide new routes to rationally enhance their stability and performance in their environment of action.

A Modular Digital Twinning Framework for Safety Assurance of Collaborative Robotics

Digital twins offer a unique opportunity to design, test, deploy, monitor, and control real-world robotic processes. In this paper we present a novel, modular digital twinning framework developed for the investigation of safety within collaborative robotic manufacturing processes. The modular architecture supports scalable representations of user-defined cyber-physical environments, and tools for safety analysis and control. This versatile research tool facilitates the creation of mixed environments of Digital Models, Digital Shadows, and Digital Twins, whilst standardising communication and physical system representation across different hardware platforms. The framework is demonstrated as applied to an industrial case-study focused on the safety assurance of a collaborative robotic manufacturing process. We describe the creation of a digital twin scenario, consisting of individual digital twins of entities in the manufacturing case study, and the application of a synthesised safety controller from our wider work. We show how the framework is able to provide adequate evidence to virtually assess safety claims made against the safety controller using a supporting validation module and testing strategy. The implementation, evidence and safety investigation is presented and discussed, raising exciting possibilities for the use of digital twins in robotic safety assurance.

Linked Data: A Framework for Publishing FiveStar Open Government Data

With the increased adoption of open government initiatives around the world, a huge amount of governmental raw datasets was released. However, the data was published in heterogeneous formats and vocabularies and in many cases in bad quality due to inconsistency, messy, and maybe incorrectness as it has been collected by practicalities within the source organization, which makes it inefficient for reusing and integrating it for serving citizens and third-party apps. This research introduces the LDOG (Linked Data for Open Government) experimental framework, which aims to provide a modular architecture that can be integrated into the open government hierarchy, allowing huge amounts of data to be gathered in a fine-grained manner from source and directly publishing them as linked data based on Tim Berners lee’s five-star deployment scheme with a validation layer using SHACL, which results in high quality data. The general idea is to model the hierarchy of government and classify government organizations into two types, the modeling organizations at higher levels and data source organizations at lower levels. Modeling organization’s experts in linked data have the responsibility to design data templates, ontologies, SHACL shapes, and linkage specifications. whereas non-experts can be incorporated in data source organizations to utilize their knowledge in data to do mapping, reconciliation, and correcting data. This approach lowers the needed experts that represent a problem of linked data adoption. To test the functionality of our framework in action, we developed the LDOG platform which utilizes the different modules of the framework to power a set of user interfaces that can be used to publish government datasets. we used this platform to convert some of UAE's government datasets into linked data. Finally, on top of the converted data, we built a proof-of-concept app to show the power of five-star linked data for integrating datasets from disparate organizations and to promote the governments' adoption. Our work has defined a clear path to integrate the linked data into open governments and solid steps to publishing and enhancing it in a fine-grained and practical manner with a lower number of experts in linked data, It extends SHACL to define data shapes and convert CSV to RDF.

Support for Computing in Distributed Environments Based on Continuous Integration

Nowadays, tools for designing scientific applications often do not implement the required continuous integration capabilities of the applied software. Therefore, such overheads as the application development time and experiment execution makespan are substantially increased. In this regard, we propose a new approach to developing scientific applications and carrying out experiments with them. It is based on applying continuous integration to both the applied and system software in developing distributed applied software packages with a modular architecture using the Orlando Tools framework. Within the proposed approach, we provide integrating the Orlando Tools subsystems with the GitLab system and automating the development of package modules. At the same time, Orlando Tools fully support constructing and testing problem-solving schemes (workflows) that combine package modules located on environment resources with different computational characteristics. To this end, Orlando Tools provides the necessary configuring and setting up of computational resources. The practical significance of our study is substantial reduction overheads needed to experiment fulfillments and increase of the resource use efficiency.

Modularity Design Rules for Architecture Development: Theory, Implementation, and Evidence from the Development of the Renault–Nissan Alliance “Common Module Family” Architecture

In this paper, we propose a set of rules for developing modular architectures. We first consider the well-known concept of “Design Rules” advanced by Baldwin and Clark. We then propose a broader conceptualization called “Modularity Design Rules” that is derived from later studies of the strategic, managerial, and organizational processes that must also be undertaken to implement successful modular development projects. We elaborate the critical role that the proposed Modularity Design Rules play in strategically grounding, organizing, and managing modular architecture development processes. We also identify key roles that top management must fulfill in supporting implementation of the proposed rules. We then provide evidence in support of the proposed Modularity Design Rules through a case study of the Renault–Nissan Alliance’s successful development and use of a modular “Common Module Family” architecture between 2009 and 2014. We then suggest some important implications of the Modularity Design Rules for open innovation processes in new product development.