General Framework For Design Research Articles

Bi-directional homogenization method for the design of multi-scale mechanical metamaterials

Inverse Homogenization (IH) is a classical concept for the Topology Optimization (TO) of metamaterials. Traditional IH design methods are mainly the single-scale TO within a Representative Volume Element (RVE), suffering from challenges like design inefficiency and under-utilization of the design space. To address the problems, a Bi-Directional Homogenization (BDH) method based on the multi-scale TO principle is proposed for the design of mechanical metamaterials. The general design framework includes a forward homogenization process from the microscale to the mesoscale, and an inverse design process from the macroscale to the mesoscale. Firstly, at the microscale, the Graded Microstructures (GMs) are generated via a multi-cut level set method. Then, by varying the relative densities, the microstructure instances are sampled and the mesoscopic equivalent properties are computed using the homogenization method. After that, a spectral decomposition-based interpolation model is used to predict the relationship between the relative densities and the elastic tensors. These preparations allow for the mesoscopic optimization of the GMs distribution, and the reconstruction of the graded multi-scale metamaterial structures by using a mapping transformation on the density field. Various types of auxetic metamaterials are performed to demonstrate the effectiveness and versatility of the proposed method.

Development of fluorescence lifetime biosensors for ATP, cAMP, citrate, and glucose using the mTurquoise2-based platform

Single-fluorescent protein (FP)-based FLIM (fluorescence lifetime imaging microscopy) biosensors can visualize intracellular processes quantitatively. They require a single wavelength for detection, which facilitates multi-color imaging. However, their development has been limited by the absence of a general design framework and complex screening processes. In this study, we engineered FLIM biosensors for ATP (adenosine triphosphate), cAMP (cyclic adenosine monophosphate), citrate, and glucose by inserting each sensing domain into mTurquoise2 (mTQ2) between Tyr-145 and Phe-146 using peptide linkers. Fluorescence intensity-based screening yielded FLIM biosensors with a 0.5 to 1.0ns dynamic range upon analyte binding, demonstrating that the mTQ2(1-145)-GT-X-EF-mTQ2(146-238) backbone is a versatile platform for FLIM biosensors, allowing for simple intensity-based screening while providing dual-functional biosensors for both FLIM and intensity-based imaging. As a proof of concept, we monitored cAMP and Ca2+ dynamics simultaneously in living cells by dual-color imaging. Our results complement recent studies, establishing mTQ2 as a valuable framework for developing FLIM biosensors.

A general design framework of flexible thermoelectric devices bridging power requirements for wearable electronics

Wearable thermoelectric generators (WTEGs) promise sustainable power for wearable electronics with various strategies proposed for on-body applications. However, there is still a lack of power-demand-oriented system design that directly provides WTEG configurations tailored to targeted end electronics, resulting in sufficient power only under extreme ambience. To address this, we propose a straightforward, power-demand-oriented design framework for WTEGs that bridges the power requirements of end electronics. As we input the properties of thermoelectric materials, specific operational conditions (temperature and heat transfer coefficient), expected power output and required flexibility, the design framework can directly determine the geometric features of thermoelectric pillars and fill factor. The effectiveness has been widely verified using data from the literature, showing a mean absolute deviation of 7.1 %. This framework shows high working efficiency and significantly shortens the design process, which is the very first ever-reported design tool for WTEGs. As a case, we use the framework and design a skin-conformable thermoelectric textile (TET) with optimal structure configurations and enhanced heat transfer capabilities, achieving a high normalized power density of 4.48 μW cm−2 K−2. As expected, the TET, with a maximum power density of 231 μW cm−2 successfully powered a series of on-body electronics when attached to the forearm at a breezy ambient temperature of ∼283 K. On the other hand, the TET exhibits a cooling effect of 5.73 K. Our work provides a thermal design guide for WTEGs, shedding light on the direct connection between WTEG configurations and end electronics.

An Iterative Participatory Design Approach to Develop Collaborative Augmented Reality Activities for Older Adults in Long-Term Care Facilities.

Over four million older adults living in long-term care (LTC) communities experience loneliness, adversely impacting their health. Increased contact with friends and family is an evidence-based intervention to reduce loneliness, but in-person visits are not always possible. Augmented Reality (AR)-based telepresence activities can offer viable alternatives with increased immersion and presence compared to video calls. However, its feasibility as an interaction technology for older adults is not known. In this paper, we detail the design of two dyadic collaborative AR activities that accommodate diminished physical and cognitive abilities of older adults. The findings include a general design framework based on an iterative participatory design focusing on preferred activities, modes of interaction, and overall AR experience of eight older adults, two family members, and five LTC staff. Results demonstrate the potential of collaborative AR as an effective means of interaction for older adults with their family, if designed to cater to their needs.

Broadband achromatic and wide field of view metalens-doublet by inverse design

Metalenses, composed of patterned meta-atoms in various dimensions, offer tailored modulation of phase, amplitude, and polarization for diverse imaging applications across the visible and near-infrared spectra. However, simultaneously achieving achromatic and wide field of view (WFOV) imaging remains a significant challenge. In this paper, we propose a general inverse design framework for metalens-doublets that simultaneously enables broadband achromatic and WFOV imaging. The broadband achromatic and WFOV (BA&WFOV) metalens-doublet comprises a propagation phase metalens and a geometric phase metalens positioned on opposite sides of the substrate. This framework requires only once polarization conversion and mitigates aperture size constraints imposed by the limited group delay (GD) range of meta-atoms. We present a BA&WFOV metalens-doublet with an f-number of 3.9, a full field of view (FOV) of 68°, and a wavelength range from 640nm to 820nm. This metalens-doublet exhibits diffraction-limited focusing with an average absolute focusing efficiency of 16% and an average relative focusing efficiency of 60%. This innovative framework holds significant promise for applications in fields such as phone cameras, VR/AR, and endoscopes.

Efficient learning methods for large-scale optimal inversion design

In this work, we investigate various approaches that use learning from training data to solve inverse problems, following a bi-level learning approach. We consider a general framework for optimal inversion design, where training data can be used to learn optimal regularization parameters, data fidelity terms, and regularizers, thereby resulting in superior variational regularization methods. In particular, we describe methods to learn optimal $ p $ and $ q $ norms for $ {\rm L}^p-{\rm L}^q $ regularization and methods to learn optimal parameters for regularization matrices defined by covariance kernels. We exploit efficient algorithms based on Krylov projection methods for solving the regularized problems, both at training and validation stages, making these methods well-suited for large-scale problems. Our experiments show that the learned regularization methods perform well even when there is some inexactness in the forward operator, resulting in a mixture of model and measurement error.

General deep learning framework for emissivity engineering

Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve desired target emissivity spectra, as a typical emissivity engineering, for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc. However, previous designs require prior knowledge of materials or structures for different applications and the designed WS-TEs usually vary from applications to applications in terms of materials and structures, thus lacking of a general design framework for emissivity engineering across different applications. Moreover, previous designs fail to tackle the simultaneous design of both materials and structures, as they either fix materials to design structures or fix structures to select suitable materials. Herein, we employ the deep Q-learning network algorithm, a reinforcement learning method based on deep learning framework, to design multilayer WS-TEs. To demonstrate the general validity, three WS-TEs are designed for various applications, including thermal camouflage, radiative cooling and gas sensing, which are then fabricated and measured. The merits of the deep Q-learning algorithm include that it can (1) offer a general design framework for WS-TEs beyond one-dimensional multilayer structures; (2) autonomously select suitable materials from a self-built material library and (3) autonomously optimize structural parameters for the target emissivity spectra. The present framework is demonstrated to be feasible and efficient in designing WS-TEs across different applications, and the design parameters are highly scalable in materials, structures, dimensions, and the target functions, offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems beyond thermal metamaterials.

Prediction and design of high hardness high entropy alloy through machine learning

Two data-driven machine learning (ML) models were proposed for the hardness prediction of high-entropy alloys (HEA) and the composition optimization of high hardness HEAs, respectively. The hardness prediction model combined interpretable ML methods with solid solution strengthening theory, and the R2 and RMSE values of 0.9716 and 39.2525 were respectively achieved under the leave-one-out validation method. The optimization model adopted an intelligent optimization algorithm to design the optimized elemental molar ratios of high hardness HEAs and was experimentally verified. A general design framework was summarized for prediction and composition optimization of various HEA performances.

Inverse design of all-dielectric metasurfaces with accidental bound states in the continuum.

Metasurfaces with bound states in the continuum (BICs) have proven to be a powerful platform for drastically enhancing light-matter interactions, improving biosensing, and precisely manipulating near- and far-fields. However, engineering metasurfaces to provide an on-demand spectral and angular position for a BIC remains a prime challenge. A conventional solution involves a fine adjustment of geometrical parameters, requiring multiple time-consuming calculations. In this work, to circumvent such tedious processes, we develop a physics-inspired, inverse design method on all-dielectric metasurfaces for an on-demand spectral and angular position of a BIC. Our suggested method predicts the core-shell particles that constitute the unit cell of the metasurface, while considering practical limitations on geometry and available materials. Our method is based on a smart combination of a semi-analytical solution, for predicting the required dipolar Mie coefficients of the meta-atom, and a machine learning algorithm, for finding a practical design of the meta-atom that provides these Mie coefficients. Although our approach is exemplified in designing a metasurface sustaining a BIC, it can, also, be applied to many more objective functions. With that, we pave the way toward a general framework for the inverse design of metasurfaces in specific and nanophotonic structures in general.

Continuous-Spectrum-Polarization Recombinant Optical Encryption with a Dielectric Metasurface.

The Jones matrix, with eight degrees of freedom (DoFs), provides a general mathematical framework for the multifunctional design of metasurfaces. Theoretically, the maximum eight DoFs can be further extended in the spectrum dimension to endow unique encryption capabilities. However, the topology and intrinsic spectral responses of meta-atoms constrains the continuous engineering of polarization evolution over wavelength dimension. In this work, a forward evolution strategy to quickly establish the mapping relationships between the solutions of the dispersion Jones matrix and the spectral responses of meta-atoms is reported. Based on the eigenvector transformation method, arbitrary conjugate polarization channels over the continuous-spectrum dimension are successfully reconstructed. As a proof-of-concept, a silicon metadevice is demonstrated for optical information encryption transmission. Remarkably, the arbitrary combination forms of polarization and wavelength dimension increase the information capacity (210 ), and the measured polarization contrasts of the conjugate polarization conversion are >94% in the entire wavelength range (3-4µm). It is believed that the proposed approach will benefit secure optical and quantum information technologies.

De novo design of protein structure and function with RFdiffusion

There has been considerable recent progress in designing new proteins using deep-learning methods1–9. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence–structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications.

Iterative learning control for piecewise arc path tracking with validation on a gantry robot manufacturing platform

The piecewise arc path tracking problem is a common feature of manufacturing systems operating in a repetitive mode, e.g. assembly production lines. Here, the system end-effector must follow a spatial path without any specific temporal tracking constraints, which makes the temporal profile not fixed a priori. The technique of iterative learning control (ILC) is well-suited to handle this problem, since compared to classical feedback control methods, ILC is capable of learning from previous trial information to minimize the tracking error over repeated trials. This paper extends the ILC task description to address piecewise arc path tracking tasks, and further formulates a more general design framework than existing spatial ILC approaches. A comprehensive ILC algorithm is designed to handle this class of piecewise arc path tracking problems, and practical implementation instructions are provided. Validation is conducted on a gantry robot manufacturing testbed to confirm its feasibility and efficiency in practice with a comparison to existing methods showing its higher path tracking accuracy.

Optimization of maritime support network with relays under uncertainty: A novel matheuristics method

Construction and optimization of maritime support networks with relays have received extensive attention due to their implications for maritime economy and national interests. To improve total revenues under uncertain risk scenarios, we propose a general two-stage stochastic optimization framework for maritime support networks with relays. A novel matheuristics method (i.e., the interoperation of metaheuristics and mathematical programming techniques) is proposed as a general algorithm framework for reconfiguration under the serialized network disruption. Small, medium, and large benchmark groups are generated to verify the effectiveness of the proposed general network design framework, and show strong robustness and high efficiency of the proposed novel matheuristics method, compared with an exact algorithm and three types of representative heuristic algorithms.

Urban and architectural design from the perspective of flood resilience: framework development and case study of a Chinese university campus

ABSTRACT In recent years, the results of resilience research have been applied to the adaptive strategies of urban systems facing unpredictable risks, but the exploration of resilience in the field of urban and architectural design remains rare. Through analysing and synthesising research on flood resilience, this paper discusses practical methods of urban and architectural design in the context of flood resilience and develops a design framework from the perspective of engineering design. The specific operational steps are as follows: after vulnerability analysis of the target project, the components of the project are divided into three levels: planning pattern, architectural layout, and architectural details. These levels correspond to three design strategies: multidisciplinary decision-making based on GIS analysis, quantitative evaluation based on Grasshopper, and architecture optimisation guidance based on nature-based solutions. Finally, a case study of a Chinese campus shows that design optimisation based on a multi-level strategy improves the overall ability of the campus to deal with rain and flood disasters, and provides a reference for similar engineering practice. By proposing a general framework for urban and architectural design, this paper promotes the development of flood resilience in the field of applied engineering.

A grid-side multi-modal adaptive damping control of super-/sub-synchronous oscillations in type-4 wind farms connected to weak AC grid

• A multi-modal damping control (MADC) is proposed to damp multiple oscillation modes independently. • A general design framework for damping multiple oscillation modes is given. • MADC is validated to damp sub-synchronous and super-synchronous oscillations in Type-4 wind farms. Type-4 wind farms connected to weak AC grids have the risk of sub-synchronous oscillation/interaction (SSO/SSI) with multiple oscillation modes, which can lead to unplanned outages and system destabilization. This paper first presents the magnitude and frequency characteristics of the oscillations followed by the control design requirements. Then, it gives a general design of grid-side multi-modal adaptive damping control (MADC) to stabilize multiple oscillation modes. The MADC utilizes an FFT-based inter-harmonic phasor measurement algorithm, which can detect and track multiple oscillation modes simultaneously. The MADC uses bus voltages and line currents as inputs to extract and control each oscillation mode without considering the coupling of the modes due to the frequency coupling effect. The unstable modes are stabilized by injecting phase-shifted currents with multiple frequencies such that the impedance response of the whole system around the concerned frequencies is reshaped. Notable features of the proposed damping control include automatic and independent detection, tracking and suppression of multiple oscillation modes. The proposed MADC's damping performance is validated through extensive electromagnetic transient (EMT) simulations of type-4 wind farms connected to a weak ac grid causing super-/sub-synchronous oscillations. The system model and the proposed damping control are designed and tested in commercial EMT software.

Designing structures that maximize spatially averaged surface-enhanced Raman spectra.

We present a general framework for inverse design of nanopatterned surfaces that maximize spatially averaged surface-enhanced Raman (SERS) spectra from molecules distributed randomly throughout a material or fluid, building upon a recently proposed trace formulation for optimizing incoherent emission. This leads to radically different designs than optimizing SERS emission at a single known location, as we illustrate using several 2D design problems addressing effects of hot-spot density, angular selectivity, and nonlinear damage. We obtain optimized structures that perform about 4 × better than coating with optimized spheres or bowtie structures and about 20 × better when the nonlinear damage effects are included.

Design variations and evaluation of loanable technology web pages in academic library websites

Loanable technology is an emerging specialized collection in academic libraries. An effective collection depends on several moving parts, including funding; space; a collection management strategy; and information systems, including web pages, to support access to the collection. The current study surveys websites from institutions carrying the Carnegie classification of “Doctoral University: Very High Research” to determine the existence and design variations of loanable technology websites. The paper notes tendencies and exceptions in the designs and develops a general design framework to guide future design decisions. The results offer design options and recommendations based on variations of affordances; the scope and variety of the items in collections; and the use of images, catalog records, and supporting documents. A structural analysis of design variations can thus enhance the coherence, completeness, and overall effectiveness of loanable technology websites.

Finite‐frequency output feedback MPC for Markov jump systems

AbstractThis paper investigates a general design framework of dynamic output feedback model predictive control (DOFMPC) for Markov jump systems within both time and frequency domain. Such a design with guaranteed H∞ and quadratic performance is formulated by a standard semi‐definite programming (SDP), and it is achieved by employing a special congruence transformation. The SDP condition greatly reduces the computational effort by eliminating bilinear matrix inequalities or equation constraints reported in existing references. Specifically, the H∞ norm of the transfer function is optimized within three types of frequency ranges on account of generalized Kalman–Yakubovic–Popov (GKYP) lemma. The quadratic index is optimized online via SDP. Finally, we verify the feasibility and effectiveness of the proposed method from both theoretical and practical point of view.

A Framework for Hardware Impairments-Aware Multi-Antenna Transceiver Design in IoT Systems via Majorization–Minimization

In view of the nonideality of communication links in the Internet of Things (IoT) originating from transceiver hardware impairments, in this article, we introduce a general framework for hardware impairments-aware multiantenna transceiver design, which considers different availabilities of CSI at the transmitter (CSIT) and the receiver (CSIR). The well-known Kronecker model is applied to characterize stochastic channel state information (CSI) errors. For each case, we aim to minimize the (average) total mean square error (MSE) of all data streams subject to the practical per-antenna power constraints. To address the nonconvexity of the formulated problem, we propose an efficient majorization–minimization (MM)-based iterative algorithm to transform the original problem into a series of convex subproblems with semiclosed-form optimal solutions. For low-complexity implementation, we also develop an alternative scheme for directly finding a high-quality suboptimal solution by considering both worst case hardware impairments and worst case CSI errors. In particular, since an explicit expression of the average total MSE for the perfect CSIR and imperfect CSIT case is hard to derive, we instead optimize its effective upper and lower bounds. The prospective applications of our work in the two currently popular multiple-input–multiple-output (MIMO) IoT scenarios are then discussed. Furthermore, we fundamentally reveal the MSE floor effect caused by both hardware distortion and CSI imperfection in the high-SNR regime. Numerical results illustrate the excellent average total MSE and average bit error rate (BER) performance of our proposed algorithms over the adopted benchmark schemes.

On the liner shipping network design: the Maritime Silk Route case study

The Maritime Silk Route is a crucial channel for economic exchanges between China and Europe. In the year 2018, the sea route between Asia and Europe moved more than 24 million TEU. In this context, the optimal design of liner services is of crucial importance. We present a general framework for the optimal design of these services. First, we develop a gravity model representing the spatial interaction between ports. Then we obtain a minimum spanning tree by means of Kruskal optimisation and finally we use the Louvain method to determine the communities within the network. Two scenarios are discussed. The first gives more importance to distances, while the second gives more importance to port throughputs. The paper discusses the main differences between the two cases and highlights which ports gain or lose connections depending on the market environment. The resulting graphs are a good reference for designing better services.