Modular Methodology Research Articles

Cross-Coupling of NHC/CAAC-Based Carbodicarbene: Synthesis of Electron-Deficient Diradicaloids.

Herein, we report nickel(0)-catalyzed cross-coupling reactions of NHC/CAAC-based carbodicarbene (NHC = N-heterocyclic carbene and CAAC = cyclic(alkyl)(amino)carbene) with different aryl chlorides, bromides, and iodides. The resulting aryl-substituted cationic carbodicarbene derivatives are prone to one-electron oxidation yielding radical-dications, which, depending on the aryl motif employed, follow different modes of radical-radical dimerization and constitute an entry point to carbon/nitrogen- and nitrogen/nitrogen-centered diradicaloids. Subsequently, this coupling strategy was strategically applied to the synthesis of p-phenylene- and p,p'-biphenylene-bridged carbon/carbon-centered electron-deficient diradicaloids. The employed π-conjugated spacer plays a crucial role in determining the triplet population at room temperature by modulation of the singlet-triplet gap: EPR inactive for p-phenylene vs EPR active for p,p'-biphenylene. Nearly two decades after the disclosure of carbodicarbenes as donor-stabilized atomic carbon equivalents by Tonner and Frenking in 2007, we demonstrate their cross-couplings with a series of aryl halides/dihalides and, based on this, developed a modular methodology for the systematic synthesis of various electron-deficient diradicaloids.

Rule learning by modularity

In this paper, we present a modular methodology that combines state-of-the-art methods in (stochastic) machine learning with well-established methods in inductive logic programming (ILP) and rule induction to provide efficient and scalable algorithms for the classification of vast data sets. By construction, these classifications are based on the synthesis of simple rules, thus providing direct explanations of the obtained classifications. Apart from evaluating our approach on the common large scale data sets MNIST, Fashion-MNIST and IMDB, we present novel results on explainable classifications of dental bills. The latter case study stems from an industrial collaboration with Allianz Private Krankenversicherung which is an insurance company offering diverse services in Germany.

DarwinGSE: Towards better image retrieval systems for intellectual property datasets.

A trademark's image is usually the first type of indirect contact between a consumer and a product or a service. Companies rely on graphical trademarks as a symbol of quality and instant recognition, seeking to protect them from copyright infringements. A popular defense mechanism is graphical searching, where an image is compared to a large database to find potential conflicts with similar trademarks. Despite not being a new subject, image retrieval state-of-the-art lacks reliable solutions in the Industrial Property (IP) sector, where datasets are practically unrestricted in content, with abstract images for which modeling human perception is a challenging task. Existing Content-based Image Retrieval (CBIR) systems still present several problems, particularly in terms of efficiency and reliability. In this paper, we propose a new CBIR system that overcomes these major limitations. It follows a modular methodology, composed of a set of individual components tasked with the retrieval, maintenance and gradual optimization of trademark image searching, working on large-scale, unlabeled datasets. Its generalization capacity is achieved using multiple feature descriptions, weighted separately, and combined to represent a single similarity score. Images are evaluated for general features, edge maps, and regions of interest, using a method based on Watershedding K-Means segments. We propose an image recovery process that relies on a new similarity measure between all feature descriptions. New trademark images are added every day to ensure up-to-date results. The proposed system showcases a timely retrieval speed, with 95% of searches having a 10 second presentation speed and a mean average precision of 93.7%, supporting its applicability to real-word IP protection scenarios.

Protocell Flow Reactors for Enzyme and Whole-Cell Mediated Biocatalysis.

The design and construction of continuous flow biochemical reactors comprising immobilized biocatalysts have generated great interest in the efficient synthesis of value-added chemicals. Living cells use compartmentalization and reaction-diffusion processes for spatiotemporal regulation of biocatalytic reactions, and implementing these strategies into continuous flow reactors can offer new opportunities in reactor design and application. Herein, the fabrication of protocell-based continuous flow reactors for enzyme and whole-cell mediated biocatalysis is demonstrated. Semipermeable membranized coacervate vesicles are employed as model protocells that spontaneously sequester enzymes or accumulate living bacteria to produce embodied microreactors capable of single- or multiple-step catalytic reactions. By packing millions of the enzyme/bacteria-containing coacervate vesicles in a glass column, a facile, cost-effective, and modular methodology capable of performing oxidoreductase, peroxidase and lipolytic reactions, enzyme-mediated L-DOPA synthesis, and whole-cell glycolysis under continuous flow conditions, is demonstrated. It is shown that the protocell-nested enzymes and bacterial cells exhibit enhanced activities and stability under deleterious operating conditions compared with their non-encapsulated counterparts. These results provide a step toward the engineering of continuous flow reactors based on cell-like microscale agents and offer opportunities in the development of green and sustainable industrial bioprocessing.

A novel machine learning method to exploit EBSD and nanoindentation for TRIP steels microstructures analysis

The recognition of phases and microstructures in TRIP-assisted bainitic-ferritic steels is challenging and requires sophisticated techniques to gain insights and reveal mechanical features with nanoscale precision. EBSD and nanoindentation have been employed to assess the surface composition and their properties within a reporting depth of 30 nm. Correlative mechanical microscopy and data science were used to overcome the shortcomings associated with the lack of an inclusive solution that combines the metadata from both techniques. A modular methodology is presented, which involves routines for exploiting structural and mechanical data via reproducible Machine Learning models (code and data are shared). The approach is structured to facilitate reuse by research community for correlating characterization mapping data, not limited to nanoindentation and EBSD. Gaussian mixture models are adopted to extract mechanical phases utilizing the nanomechanical properties. The K-means++ method is used for the first time to mine information from Inverse Polar Figure (IPF) mapping about anisotropy and to extract the knowledge from images for each grain, including grain coordinates and size. Moreover, k-nearest-neighbours regression was used to perform data imputation to fill in the values of descriptors related to missing coordinates relative to those of nanoindentation, grain boundary, EBSD phase, and EBSD anisotropy maps.

Shaping the Conscious Behaviors of Product Designers in the Early Stages of Projects: Promoting Correct Material Selection and Green Self-Identity through a New Conceptual Model

Material selection for product design is a complex task. Thus, one of the objectives of this work is to analyze and understand and to promote the importance of material selection to conceive quality products with the help of designers that promote green self-identity in the early stage of new product conception. A questionnaire was sent to professional designers and engineers. Thirty-eight responses were validated, which represented the sample for this study. The aspects that influence the complex material selection process and the final quality of the products through the design and production process are presented. Taking into consideration the responses from product designers who work in the market, as well as some engineers and students who are graduating in product design, a new approach for material selection was developed. Based on a collection of main ideas from the traditional and non-traditional material selection methods, seeking to group the maximum requirements of both methods, and inspired by the “canvas” model on the basic modular methodology, a new model for new product projects is presented. Our study focuses on material selection, since this aspect is one of the most relevant steps in the early stage of the prototyping phase of new products, with a view to reducing CO2 from the air in the atmosphere that we all breathe. The classification of materials is complex due to the diversity of available options. The novelty of this model is that all the properties of a newly designed product, such as technical, aesthetic, productive, and environmental properties, are grouped in the model, which serves as an innovative support. Thus, designers have a tool at their disposal that can help them to select the best materials for the products they design. The results of this study contribute to the field of material selection, to the quality and design of new products, and to promoting green self-identity of designers in the initial phase of product design. Consequently, all consumers in search of a sustainable planet will profit from this study.

Bis-Olefin Based Crystalline Schlenk Hydrocarbon Diradicals with a Triplet Ground State.

A modular approach for the synthesis of isolable crystalline Schlenk hydrocarbon diradicals from m-phenylene bridged electron-rich bis-triazaalkenes as synthons is reported. EPR spectroscopy confirms their diradical nature and triplet electronic structure by revealing a half-field signal. A computational analysis confirms the triplet state to be the ground state. As a proof-of-principle for the modular methodology, the 4,6-dimethyl-m-phenylene was further utilized as a coupling unit between two alkene motifs. The steric conjunction of the 4,6-dimethyl groups substantially twists the substituents at the nonbonding electron bearing centers relative to the central coupling m-phenylene motif. As a result, the spin delocalization is decreased and the exchange coupling between the two unpaired spins, hence, significantly reduced. Notably, 108 years after Schlenk's m-phenylene-bis(diphenylmethyl) synthesis as a diradical, for the first time we were able to isolate its derivative with the same spacer, i.e. m-phenylene, between two radical centers in a crystalline form.

Von bis‐Olefin‐abgeleitete kristalline, diradikalische Schlenk‐Kohlenwasserstoffe mit einem Triplett‐Grundzustand

AbstractBeschrieben wird ein modularer Ansatz für die Synthese von kristallinen, diradikalischen Schlenk‐Kohlenwasserstoffen ausgehend von m‐Phenylen‐verbrückten, elektronenreichen bis‐Triazaalkenen. Das Auftreten eines Halbfeldsignals im EPR‐Spektrum bestätigt die diradikalische Natur sowie den Triplett‐Zustand der Spezies. Dass der Triplett‐Zustand auch dem Grundzustand entspricht, wird durch eine computergestützte Analyse untermauert. Um die Modularität des Ansatzes zu belegen, wurde zudem eine 4,6‐Dimethyl‐m‐Phenylen‐Brücke als Verbindung zwischen den beiden Alken‐Motiven untersucht. Der sterische Anspruch der beiden 4,6‐Dimethyl‐Gruppen führt zu einer substantiellen Rotation der Substituenten an den radikalischen Zentren relativ zum koppelnden m‐Phenylen‐Motiv. Daraus ergibt sich eine Verringerung der Spin‐Delokalisierung, was in einer signifikant reduzierten Austauschkopplung zwischen den ungepaarten Spinzuständen resultiert. Bemerkenswerterweise, konnten wir erstmalig und dies 108 Jahre nach der Synthese des m‐Phenylen‐Bis(Diphenylmethyl)‐Diradikals durch Schlenk ein Derivat mit der gleichen m‐Phenylen‐Brücke zwischen zwei radikalischen Zentren in kristalliner Form isolieren.

Automated Discovery of Container Executables

Linux container technologies such as Docker and Singularity offer encapsulated environments for easy execution of software. In high performance computing, this is especially important for evolving and complex software stacks with conflicting dependencies that must co-exist. Singularity Registry HPC (“shpc”) was created as an effort to install containers in this environment as modules, seamlessly allowing for typically hidden executables inside containers to be presented to the user as commands, and as such significantly simplifying the user experience. A remaining challenge, however, is deriving the list of important executables in the container. In this work, we present a new modular methodology that allows for discovering new containers in large community sets, deriving container entries with relevant executables therein, and fully automating both recipe generation and updates over time. As an exemplar outcome, we have employed this methodology to add to the Registry over 8,000 containers from the BioContainers community that can be maintained and updated by the software automation. All software is publicly available on the GitHub platform, and can be beneficial to container registries and infrastructure providers for automatically generating container modules, thus lowering the usage entry barrier and improving user experience.

Dynamic energy management with thermal comfort forecasting

Reducing greenhouse gas emissions and energy cost in the building sector largely relies on effective energy management. Yet, when it comes to heating or cooling, energy savings may translate to uncomfortable conditions for the building users. To ensure thermal comfort with a minimal energy consumption, in this paper we propose a modular methodology that dynamically schedules the operating hours of the heating/cooling system of the building using thermal comfort forecasts. To decide on the time and mode of operation, our approach utilizes indoor air temperature and relative humidity forecasting models, as well as a data-driven algorithm that predicts thermal comfort level based on said forecasts and employs pre-heating/cooling when necessary. Our empirical evaluation, conducted in the Casal Mira-sol culture center of Sant Cugat, Spain, suggests that our approach can improve the satisfaction of the building users, while achieving significant energy savings.

Why a dynamic multicomponential model of decision making: some milestones and a preliminary tool

Why is it necessary today to propose a new tool to evaluate decision-making skills? The neuroscientific approach proposed in this paper constitutes a new frontier for grasping the multi-component nature of the decision-maker’s thought and action skills, using an innovative tool, through a modular methodology (5 domains that characterize the decision-making process: style, strategy, efficacy, awareness and metacognition), a multimethodological approach (through tasks, tests and self-report measures, in addition to the neurophysiological level) and a digitized format, thus restoring a 360-degree perspective on the potential of making decisions in real-life contexts. DassDec - Decisional Assessment for Decision-making- sets out to do all of this.

Asymmetric Hydrophosphinylation of Alkynes: Facile Access to Axially Chiral Styrene‐Phosphines

AbstractA Cu/CPA co‐catalytic system has been developed for achieving the direct hydrophosphinylation of alkynes with phosphine oxides in delivering novel axially chiral phosphorus‐containing alkenes in high yields and excellent enantioselectivities (up to 99 % yield and 99 % ee). DFT calculations were performed to elucidate the reaction pathway and the origin of enantiocontrol. This streamlined and modular methodology establishes a new platform for the design and application of new axially chiral styrene‐phosphine ligands.

Asymmetric Hydrophosphinylation of Alkynes: Facile Access to Axially Chiral Styrene-Phosphines.

A Cu/CPA co-catalytic system has been developed for achieving the direct hydrophosphinylation of alkynes with phosphine oxides in delivering novel axially chiral phosphorus-containing alkenes in high yields and excellent enantioselectivities (up to 99 % yield and 99 % ee). DFT calculations were performed to elucidate the reaction pathway and the origin of enantiocontrol. This streamlined and modular methodology establishes a new platform for the design and application of new axially chiral styrene-phosphine ligands.

МОДУЛЬНАЯ МЕТОДИКА ПОВЫШЕНИЯ ПРОСТРАНСТВЕННО-КООРДИНАЦИОННОЙ ПОДГОТОВКИ ИГРОКОВ БАСКЕТБОЛЬНОЙ КОМАНДЫ

Statement of the problem. There are many techniques for spatial training of basketball players, but among them there are contradictory ones that need to be analyzed, their features and basic components found. Including those that are focused on block-modular management of the readiness of qualified basketball athletes to achieve high sports results. The purpose of the article is to substantiate and develop a modular methodology for improving the spatial coordination training for basketball program players. The methodology of the research consists of a modular approach, fundamental works on the theory of sports (V. N. Platonov, L. P. Matveev, etc.). Methods were used: multimedia visualization, assessment of spatial coordination, forecasting, pedagogical experiment. Research results. It was assumed that the use of proofreading content in the process of training highly qualified athletes to strengthen spatial and coordination preparedness, will increase the effectiveness of the game. The objectives of the study were presented in the development and determination of the possibility of disseminating the modular methodology on the general set of the system for training modern basketball players. The essence of the recommended means was the inclusion of a set of special exercises for the development of the vestibular analyzer and the movement control system. The content of the technology consisted of heterogeneous means of active and passive methods of vestibular training, but of unidirectional impact, alternating after 2 sessions. Conclusion. The assimilation of the number of shots from the three-point line has been verified, which marks the tendency to increase the role of the motion control system. The results of the pedagogical formative experiment increased the level of spatial coordination of the players of the team «BC Derzhava» of Tambov, which significantly improved her result in the last 9 matches in the championship of the College Basketball Association.

Methodology for effective and efficient regional seismic retrofit using machine learning and stochastic optimization

A major challenge in specifying design requirements for policy-based seismic retrofits is ensuring that the desired performance enhancements are achieved across a portfolio of buildings with diverse structural characteristics and hazard. To address this challenge, a modular optimization methodology is developed. A prediction module uses machine learning-based surrogate models as compact statistical links between building structural characteristics and seismic performance outcomes. The optimization module uses regionally targeted objective functions to determine the retrofit enhancements that achieve the most desirable aggregated performance outcome for the portfolio of buildings. Lastly, the evaluation module benchmarks the performance of the optimized retrofit scheme against the existing inventory as well as more conventional retrofit approaches. The ability of the framework to balance the need for effective (improved performance) and efficient (minimizing cost) retrofits is demonstrated by applying it to the inventory of buildings under the purview of the Los Angeles Soft-Story Ordinance.

Progressive Improvement of the Model of an Exoskeleton for the Lower Limb by Applying the Modular Modelling Methodology

Among the variety of applications of exoskeletons, it is possible to mention motor rehabilitation, enhancement of human capabilities and providing support to different types of tasks. Despite the remarkable achievements in this field, two major issues still need to be improved in the exoskeleton design methodology, the mechanical design and the controller. Considering that the dynamic modelling approach plays a key role in these issues, this article proposes the use of modular modelling methodology for the development of exoskeletons. Initially, the conceptual design of a lower limb exoskeleton is presented, then its kinematic and dynamic models are calculated. Finally, some performed simulations demonstrate the model consistency and the actuator torques estimation.

Stochastic Timed Discrete-Event Systems: Modular Modeling and Performance Evaluation Through Markovian Jumps

We are interested in a scalable, flexible, and modular methodology, for modeling and performance analysis of stochastic discrete-event systems (SDES). In this sense, we propose a modular approach for timing non-markovian SDES expressed as a parallel composition of modules that interacts with each other through events. We show how general distribution for event lifetimes can be implemented systematically by coupling timing modules to the system model. As a result, this coupling mechanism preserves modularity, leading to a compact markovian model expressed in terms of flexible modules. Therefore the methodology allows us to write the whole SDES model as a composition of the system model and the timing one, giving flexibility and scalability in modeling design, as we can modify the modules individually according to the designer’s interests. In addition, from the whole markovian SDES model, we show how to perform the model analysis through the analytic approach, as well as through Monte Carlo computer simulation. As an application, we present a numerical example of computing the abandonment rate for a service network with general service time employing both analytical and computer-simulation models.

A Modular Multi-Hazard Assessment Methodology for Sweden Starting with Wildfire Hazard

A Modular Multi-Hazard Assessment Methodology for Sweden Starting with Wildfire Hazard

Modular development enables rapid design of media for alternative hosts

Developing media to sustain cell growth and production is an essential and ongoing activity in bioprocess development. Modifications to media can often address host or product‐specific challenges, such as low productivity or poor product quality. For other applications, systematic design of new media can facilitate the adoption of new industrially relevant alternative hosts. Despite manifold existing methods, common approaches for optimization often remain time and labor‐intensive. We present here a novel approach to conventional media blending that leverages stable, simple, concentrated stock solutions to enable rapid improvement of measurable phenotypes of interest. We applied this modular methodology to generate high‐performing media for two phenotypes of interest: biomass accumulation and heterologous protein production, using high‐throughput, milliliter‐scale batch fermentations of Pichia pastoris as a model system. In addition to these examples, we also created a flexible open‐source package for modular blending automation on a low‐cost liquid handling system to facilitate wide use of this method. Our modular blending method enables rapid, flexible media development, requiring minimal labor investment and prior knowledge of the host organism, and should enable developing improved media for other hosts and phenotypes of interest.

A modular methodology for time-domain stochastic seismic wave propagation

Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. The first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM).Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.