Design Toolbox Research Articles

Theoretical framework for photon subtraction with non–mode-selective resources

This work establishes a versatile theoretical framework that explicitly describes single-photon subtraction from multimode quantum light in the context of non-Gaussian state generation and manipulation. The treatment focuses on easy-to-implement configurations in which no mode-selective operation is available and evaluates features and advantages of scheme where only simple filtering stages are employed on the experiments. Such configuration, by considerably reducing the experimental overheads, makes experiments involving single photon subtraction easier to be implemented. Obtained theoretical framework allows retrieving, given a multimode input state, optimal conditions required to herald and then to detect non-Gaussian states, providing a practical and powerful toolbox for experiments' design. The application of the proposed approach to the case study of Schr\"odinger kitten preparation starting from a frequency multimode squeezed state illustrates the impact of the derived theoretical tools.

Molecular polymer bottlebrushes in nanomedicine: therapeutic and diagnostic applications.

Molecular polymer bottlebrushes are densely grafted, individual macromolecules with nanoscale proportions. The last decade has seen an increased focus on this material class, especially in nanomedicine and for biomedical applications. This Feature Article provides an overview of major developments in this area to highlight the many opportunities that these polymer architectures bring to nano-bio research. The article covers aspects of bottlebrush synthesis and summarises their use in drug and gene delivery, imaging, as theranostics and as prototype materials to correlate nanoparticle structure and composition to biological function and behaviour. Areas for future research in this area are discussed.

Brochosome-Inspired Metal-Containing Particles as Biomimetic Building Blocks for Nanoplasmonics: Conceptual Generalizations.

Recently, biological nanostructures became an important source of inspiration for plasmonics, with many described implementations and proposed applications. Among them are brochosome-inspired plasmonic microstructures—roughly spherical core-shell particles with submicrometer diameters and with indented surfaces. Our intention was to start from the nanoplasmonic point of view and to systematically classify possible alternative forms of brochosome-inspired metal-containing particles producible by the state-of-the-art nanofabrication. A wealth of novel structures arises from this systematization of bioinspired metal-containing nanocomposites. Besides various surface nanoapertures, we consider structures closely related to them in electromagnetic sense like surface nano-protrusions, shell reliefs obtained by nano-sculpting, and various combinations of these. This approach helped us build a new design toolbox for brochosome-inspired structures. Additionally, we used the finite elements method to simulate the optical properties of simple brochosome-inspired structures. We encountered a plethora of advantageous optical traits, including enhanced absorption, antireflective properties, and metamaterial behavior (effective refractive index close to zero or negative). We conclude that the presented approach offers a wealth of traits useful for practical applications. The described research represents our attempt to outline a possible roadmap for further development of bioinspired nanoplasmonic particles and to offer a source of ideas and directions for future research.

Applications of affordance and cognitive ergonomics in virtual design: A digital camera as an illustrative case

Affordance-based design (ABD) plays an important role in identifying interactions, especially effortless ones, between users and artifacts. Cognitive ergonomics extends our understanding of this effortless interaction. This study combines the two design methodologies together in order to reduce cognitive friction in using digital products. The design process of a compact digital camera is selected as a case study that includes the design of the physical shape for a camera and of its user interface. In designing a product shape, a design toolbox was developed that integrated a modified multi-objective genetic algorithm and the ABD, which was named as affordance-based interactive genetic algorithm. Using this toolbox and interactive user feedback, the camera design evolves toward a product that better satisfies the users. User interfaces (UIs) including linear and elliptic layouts were subsequently designed based on cognitive ergonomics. A predictive tool of UI, the Cog Tool, was used to evaluate the performance of skilled users on a given task by correlating the overall task completion time. Finally, this research has the potential to not only effectively address the shortcomings of the design of consumer electronics but also enrich the generation of design solutions during the preliminary design phase of such products.

Advanced Design Methods From Materials and Devices to Circuits for Brain-Inspired Oscillatory Neural Networks for Edge Computing

In this paper, we assess an innovative concept of emulating biological neurons with oscillators to implement an oscillatory neural network (ONN) with beyond-CMOS devices based on vanadium dioxide (VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). ONNs can be of interest as an ultra-low-power neuromorphic architecture capable of performing associative memory tasks, such as pattern recognition in IoT edge devices. To explore the benefits and costs of beyond-CMOS ONNs necessitates modeling, simulation, and design methods spanning from materials (e.g., atomistic methods) to devices (e.g., technology-computer-aided-design, TCAD) up to circuits (e.g., mixed-mode simulation, compact modeling). In this work, we report on the development of such an advanced design toolbox and the results on performance and features of beyond-CMOS ONNs. The proposed design toolbox allows exploring ONN scalability, accuracy, energy, and performance for pattern recognition applications.

Performance Evaluation of Digital Image Processing by Using Scilab

Scilab is an open-source, cross-platform computational environment software available for academic and research purposes as a free of charge alternative to the matured computational copyrighted software such as MATLAB. One of important library available for Scilab is image processing toolbox dedicated solely for image and video processing. There are three major toolboxes for this purpose: Scilab image processing toolbox (SIP), Scilab image and video processing toolbox (SIVP) and recently image processing design toolbox (IPD). The target discussion in this paper is SIVP due to its vast use out there and its capability to handle streaming video file as well (note that IPD also supports video processing). Highlight on the difference between SIVP and IPD will also be discussed. From testing, it is found that in term of looping test, Octave and FreeMat are faster than Scilab. However, when converting RGB image to grayscale image, Scilab outperform Octave and FreeMat.

A comprehensive framework for hard-magnetic beams: Reduced-order theory, 3D simulations, and experiments

Thin beams made of magnetorheological elastomers embedded with hard-magnetic particles (hard-MREs) are capable of large deflections under an applied magnetic field. We propose a comprehensive framework, comprising a beam model and 3D finite element modeling (FEM), to describe the behavior of hard-MRE beams under both uniform and constant gradient magnetic fields. First, based on the Helmholtz free energy of bulk (3D) hard-MREs, we perform dimensional reduction to derive a 1D description and obtain the equilibrium equation of the beam through variational methods. In parallel, we extend the existing 3D continuum theory for hard-MREs to the general case of non-uniform fields by incorporating the magnetic body force induced by the field gradient and implementing it in FEM. Then, we validate the beam model and FEM using experiments on a cantilever beam in either a uniform or a constant gradient field, and identify the dimensionless parameters governing the magneto-elastic coupling. Further, a set of comparative numerical studies for different field configurations and magnetization profiles yields additional insight into the beam response. Our study builds on previous work on hard-MRE beams, while providing a more complete framework, both in terms of the methodologies used and the broader range of configurations considered, serving as a valuable predictive toolbox for the rational design of beam-like hard-magnetic structures.

Squaraine-Based Optical Sensors: Designer Toolbox for Exploring Ionic and Molecular Recognitions

Small molecule-based chromogenic and fluorogenic probes play an indispensable role in many sensing applications. Ideal optical chemosensors should provide selectivity and sensitivity towards a variety of analytes. Synthetic accessibility and attractive photophysical properties have made squaraine dyes an enticing platform for the development of chemosensors. This review highlights the versatility of modular assemblies of squaraine-based chemosensors and chemodosimeters that take advantage of the availability of various structurally and functionally diverse recognition motifs, as well as utilizing additional recognition capabilities due to the unique structural features of the squaraine ring.

Research on Construction Cost Estimation based on Artificial Intelligence Technology

For the prediction of economic expenses involved in construction industry, cost estimation has become an important aspect of construction management for the prediction of economic expenses and successful completion of the construction work. Cost analysis is crucial and require expertise for accurate and comprehensive estimation. In order to effectively improve the accuracy of construction project cost, this paper establishes an estimation model based on gray BP neural network. It combines the MATLAB toolbox for program design, and learns and tests the input and output of training samples. This article determines the application of grey system theory to optimize the estimation model of Back Propagation (BP) neural network. The viability of the method established in this article, is tested by collecting the engineering cost data in Zhengzhou city and comparing between the standard BP neural network and the gray BP neural network methods. The results show that the average error of the gray system theory optimized BP neural network model designed in this paper is 2.33%. The gray BP neural network model studied in this paper can not only quickly estimate the project cost, but also has high accuracy rate. The outcomes obtained establishes a model with scientific and reasonable construction project cost estimation.

Development of a generalized thermal resistance model for the calculation of effective thermal conductivities in periodic open cellular structures (POCS)

Periodic open cellular structures (POCS) are a group of porous media with promising attributes for the use in heat transfer enhancement. They consist of a periodic arrangement of unit cells that offer a vast design freedom and therefore high adaptability to the needs of a specific application. In order to use POCS efficiently, a fundamental understanding of the relation between their geometry and thermal transport properties is essential.This contribution focuses on the investigation and modeling of the effective thermal conductivity. It is based on a homogenization approach that averages the heat conduction properties over an entire unit cell, while neglecting the influence of the fluid. To this end, the investigation provides detailed insight and an important component for the subsequent model development on the overall heat transfer capabilities of POCS, including convection and conjugate heat transfer. A numerical simulation setup is developed, which allows the determination of this quantity in isotropic as well as anisotropic POCS with varying unit cell type, porosity, tapering and strut cross-sectional shape. The results indicate the impact of both struts as well as nodes, on the heat transfer and form the foundation for a new generalized thermal resistance network model. In contrast to similar approaches, the impact of multidimensional heat transfer within the nodes is considered enabling an accurate prediction of the effective thermal conductivity of all POCS investigated in this work. Due to its modularity, it is easily transferable to other unknown geometries, thus contributing to the toolbox for the efficient design of POCS.

Expanding the toolbox: another auxotrophic marker for targeted gene integrations in Trichoderma reesei

BackgroundThe filamentous ascomycete Trichoderma reesei is used for the industrial production of cellulases and holds the promise for heterologous gene expression due to its outstandingly high protein secretion rates and its long-term application in industry and science. A prerequisite for successful heterologous gene expression is the ability to insert a corresponding expression cassette at suitable loci in the genome of T. reesei.ResultsIn this study, we test and demonstrate the applicability of the his1 gene [encoding for the ATP phosphoribosyltransferase (EC 2.4.2.17), part of the histidine biosynthesis pathway] and locus for targeted gene insertion. Deletion of the his1 promoter and a part of the coding region leads to histidine auxotrophy. Reestablishment of the his1 locus restores prototrophy. We designed a matching plasmid that allows integration of an expression cassette at the his1 locus. This is demonstrated by the usage of the reporter EYFP (enhanced yellow fluorescence protein). Further, we describe a minimal effort and seamless marker recycling method. Finally, we test the influence of the integration site on the gene expression by comparing three strains bearing the same EYFP expression construct at different loci.ConclusionWith the establishment of his1 as integration locus and auxotrophic marker, we could expand the toolbox for strain design in T. reesei. This facilitates future strain constructions with the aim of heterologous gene expression.

Emotionally Sustainable Design Toolbox: A Card-Based Design Tool for Designing Products with an Extended Life Based on the User’s Emotional Needs

Emotionally sustainable design helps users to develop an emotional attachment to a product and motivates them to continue using it, thus extending the product lifecycle, minimizing the need for new products and achieving product sustainability. However, the existing relevant design principles are still very scattered, and they could not effectively guide the emotionally sustainable design practice in a systematic way. We proposed an emotionally sustainable design (ESD) toolbox for product design based on the literature review and expert argumentation. The toolbox consists of seven themes and 20 principles under the three levels of emotional design. The usability of the ESD toolbox was then validated through design practice for the teapot product. The result shows that the ESD toolbox improved the efficiency of the sustainable design process and was helpful to the product’s sustainability.

Weaving Enzymes with Polymeric Shells for Biomedical Applications.

Enzyme therapeutics have received increasing attention due to their high biological specificity, outstanding catalytic efficiency, and impressive therapeutic outcomes. Protecting and delivering enzymes into target cells while retaining enzyme catalytic efficiency is a big challenge. Wrapping of enzymes with rational designed polymer shells, rather than trapping them into large nanoparticles such as liposomes, have been widely explored because they can protect the folded state of the enzyme and make post-functionalization easier. In this review, the methods for wrapping up enzymes with protective polymer shells are mainly focused on. It is aimed to provide a toolbox for the rational design of polymeric enzymes by introducing methods for the preparation of polymeric enzymes including physical adsorption and chemical conjugation with specific examples of these conjugates/hybrid applications. Finally, a conclusion is drawn and key points are emphasized.

Potential habitat connectivity for Malayan gaur (Bos gaurus) in a fragmented forest area in Peninsular Malaysia

Aim: To predict the distribution of suitable habitats for Malayan gaur (Bos gaurus) at a highly fragmented forest area in Peninsular Malaysia and to identify the potential connectivity between suitable habitat patches. Methodology: Maximum entropy (MaxEnt) approach was used to predict the distribution of suitable habitats of the Malayan gaur. Gaur presence-only data and six environmental variables were collated for the habitat suitability modeling, and area under curve (AUC) value was used to estimate the performance of the model. The resulting model was then used to derive a potential connectivity map through least-cost analysis using Corridor Designer toolbox in ArcGIS 10.4. Results: The AUC value of the habitat suitability model was 0.84. Distance from urban areas indicated the highest relative contribution to the model (26.9%), followed by distance from water body (24.2%) land use (18.0%) elevation (14.3%), slope (14.0%) and lithology (2.6%). Predicted suitable habitats for gaur were found mostly in lowland forest areas, especially in the vicinity of rivers within forest reserves. A total of five wildland blocks were derived from the habitat suitability model, and several potential corridor swaths were identified connecting the wildland blocks. Interpretation: The absence of gaur occurrence in suitable habitats suggest that fragmented habitats greatly affected gaur distribution and population. Road network and agricultural lands are the major barriers of gaur movement as they are very sensitive towards disturbances and conflict. Thus, this research proposes potential connectivity at a regional scale for Malayan gaur for use in future planning in conservation, management and development.

Fabrication of Highly Anisotropic and Interconnected Porous Scaffolds to Promote Preosteoblast Proliferation for Bone Tissue Engineering

Mimicking the complex structure of natural bone remains a challenge for bone tissue scaffolds. In this study, a novel processing strategy was developed to prepare the bone-like scaffolds that are featured by highly oriented and fully interconnected pores. This type of biomimetic scaffolds was evolved from solid phase stretching of immiscible polycaprolactone (PCL)/poly(ethylene oxide) (PEO) blends with co-continuous structure and the pore morphology was inherited from selective extraction of water soluble PEO phase. The pore anisotropy was readily tuned by varying the stretching strain without loss of interconnectivity. Significant promotion in preosteoblast proliferation, alkaline phosphatase activity and osteogenic gene expression was observed in the oriented porous scaffolds compared to the isotropic porous counterpart. The oriented architecture provided a topographical cue for aligned growth of preosteoblasts, which activated the Wnt/β-catenin signaling pathway. The proposed strategy enriches the toolbox for the scaffold design and fabrication for bone tissue engineering.

Hopes and Hypes for Artificial Intelligence in Colorectal Cancer Screening

Hopes and Hypes for Artificial Intelligence in Colorectal Cancer Screening

K-Ion Battery Cathode Design Utilizing Trigonal Prismatic Ligand Field.

The intrinsic physical and chemical properties of materials are largely governed by the bonding and electronic structures of their fundamental building units. The majority of cathode materials contain octahedral TMO6 (TM = transition metal), which dominates the redox chemistry during electrochemical operation. As a less symmetric form of TMO6 , the trigonal prismatic geometry is not a traditionally favored coordination configuration as it tends to lose the crystal-field stabilization energy and thus generate large ligand repulsion. Herein, a K-ion battery cathode design, K2 Fe(C2 O4 )2 , is shown​, where the TMO6 trigonal prism (TP) is not only electrochemically active but stable enough to allow for excellent cycling stability. Detailed synchrotron X-ray absorption spectroscopy measurements reveal the evolution of localized fine structure, evidencing the electrochemical activity, reversibility, and stability of the TP motif. The findings are expected to expand the toolbox for the rational design of electrode materials by taking advantage of TP as a structural gene.

Engineering Toolbox for Systematic Design of PolyHIPE Architecture.

Polymerization of high internal phase emulsions (polyHIPEs) is a well-established method for the production of high porosity foams. Researchers are often regulated to using a time-intensive trial and error approach to achieve target pore architectures. In this work, we performed a systematic study to identify the relative effects of common emulsion parameters on pore architecture (mixing speed, surfactant concentration, organic phase viscosity, molecular hydrophobicity). Across different macromer chemistries, the largest magnitude of change in pore size was observed across surfactant concentration (~6 fold, 5–20 wt%), whereas changing mixing speeds (~4 fold, 500–2000 RPM) displayed a reduced effect. Furthermore, it was observed that organic phase viscosity had a marked effect on pore size (~4 fold, 6–170 cP) with no clear trend observed with molecular hydrophobicity in this range (logP = 1.9–4.4). The efficacy of 1,4-butanedithiol as a reactive diluent was demonstrated and provides a means to reduce organic phase viscosity and increase pore size without affecting polymer fraction of the resulting foam. Overall, this systematic study of the microarchitectural effects of these macromers and processing variables provides a framework for the rational design of polyHIPE architectures that can be used to accelerate design and meet application needs across many sectors.

Unified CACSD Toolbox for Hybrid Simulation and Robust Controller Synthesis with Applications in DC-to-DC Power Converter Control

The current article presents the design, implementation, validation, and use of a Computer-Aided Control System Design (CACSD) toolbox for nonlinear and hybrid system uncertainty modeling, simulation, and control using μ synthesis. Remarkable features include generalization of classical system interconnection operations to nonlinear and hybrid systems, automatic computation of equilibrium points for nonlinear systems, and optimization of least conservative uncertainty bounds, with direct applicability for μ synthesis. A unified approach is presented for the step-down (buck), step-up (boost), and single-ended primary-inductor (SEPIC) converters to showcase the use and flexibility of the toolbox. Robust controllers were computed by minimization of the H∞ norm of the augmented performance systems, encompassing a wide range of uncertainty types, and have been designed using the well-known mixed-sensitivity closed loop shaping μ synthesis method.

Deep learning-enabled framework for automatic lens design starting point generation.

We present a simple, highly modular deep neural network (DNN) framework to address the problem of automatically inferring lens design starting points tailored to the desired specifications. In contrast to previous work, our model can handle various and complex lens structures suitable for real-world problems such as Cooke Triplets or Double Gauss lenses. Our successfully trained dynamic model can infer lens designs with realistic glass materials whose optical performance compares favorably to reference designs from the literature on 80 different lens structures. Using our trained model as a backbone, we make available to the community a web application that outputs a selection of varied, high-quality starting points directly from the desired specifications, which we believe will complement any lens designer's toolbox.