Design Space Research Articles

Estimating Power, Performance, and Area for On-Sensor Deployment of AR/VR Workloads Using an Analytical Framework

Augmented Reality and Virtual Reality have emerged as the next frontier of intelligent image sensors and computer systems. In these systems, 3D die stacking stands out as a compelling solution, enabling in situ processing capability of the sensory data for tasks such as image classification and object detection at low power, low latency, and a small form factor. These intelligent 3D CMOS Image Sensor (CIS) systems present a wide design space, encompassing multiple domains (e.g., computer vision algorithms, circuit design, system architecture, and semiconductor technology, including 3D stacking) that have not been explored in-depth so far. This article aims to fill this gap. We first present an analytical evaluation framework, STAR-3DSim, dedicated to rapid pre-RTL evaluation of 3D-CIS systems capturing the entire stack from the pixel layer to the on-sensor processor layer. With STAR-3DSim, we then propose several knobs for PPA (power, performance, area) improvement of the Deep Neural Network (DNN) accelerator that can provide up to 53%, 41%, and 63% reduction in energy, latency, and area, respectively, across a broad set of relevant AR/VR workloads. Last, we present full-system evaluation results by taking image sensing, cross-tier data transfer, and off-sensor communication into consideration.

Automatic Architecture Design for Distributed Quantum Computing

Abstract In distributed quantum computing (DQC), quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections. Meanwhile, quantum software tries best to reduce the required number of remote quantum gates between chips. However, this "hardware first, software follows" methodology may not fully exploit the potential of DQC. Inspired by classical software-hardware co-design, this paper explores the design space of application-specific DQC architectures. More specifically, we propose AutoArch, an automated quantum chip network (QCN) structure design tool. With qubits grouping followed by a customized QCN design, AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms. Experimental results show that the DQC architecture generated by AutoArch can outperform other general QCN architectures when executing target quantum algorithms.

Physics-informed neural networks for biopharmaceutical cultivation processes: Consideration of varying process parameter settings.

We present a new modeling approach for the study and prediction of important process outcomes of biotechnological cultivation processes under the influence of process parameter variations. Our model is based on physics-informed neural networks (PINNs) in combination with kinetic growth equations. Using Taylor series, multivariate external process parameter variations for important variables such as temperature, seeding cell density and feeding rates can be integrated into the corresponding kinetic rates and the governing growth equations. In addition to previous approaches, PINNs also allow continuous and differentiable functions as predictions for the process outcomes. Accordingly, our results show that PINNs in combination with Taylor-series expansions for kinetic growth equations provide a very high prediction accuracy for important process variables such as cell densities and concentrations as well as a detailed study of individual and combined parameter influences. Furthermore, the proposed approach can also be used to evaluate the outcomes of new parameter variations and combinations, which enables a saving of experiments in combination with a model-driven optimization study of the design space.

“The Devil You Know”: Barriers and Opportunities for Co-Designing Microclimate Sensors, A Case Study of Manoomi

Current environmental challenges have profound local consequences and often benefit from the collection of fine-grained microclimate data. Advances in wireless sensor networks and the Internet of Things have led to technologies nominally suited to support remote sensing; however, in practice long-running deployments of in-field environmental sensors are rare. Field conditions are often remote and culturally sensitive, with limited power, Internet, transportation, and human infrastructure; advances in device technology alone will not suffice. We ask how communities, Internet of Things researchers, government, and other interested parties can work together to co-design useful, low burden, sustainability-focused infrastructure. Toward this end, we conducted 11 semi-structured interviews with 13 experts who use or rely on environmental sensing technology. To complement our interview data, we engaged in three months of participant observation while immersed in organizations specifically working toward manoomin (wild rice) conservation. We make two primary contributions. First, we confirm and enrich a five-stage model, the microclimate sensor lifecycle, focusing on desired features and persistent challenges. Second, we outline a space for co-design of microclimate sensors with emphasis on the cost of experience, the generally unaddressed issue of technical usability in the messy field, and the opportunity for community engagement to improve technical design and outcomes. Furthermore, we discuss future design opportunities, recommendations, and challenges in the microclimate sensor design, deployment, and sustainability space.

Understanding How People Perceive and Interact with Public Space through Social Media Big Data: A Case Study of Xiamen, China

Public space is a crucial forum for public interaction and diverse activities among urban residents. Understanding how people interact with and perceive these spaces is essential for public placemaking. With billions of users engaging in social media expression and generating millions of data points every second, Social Media Big Data (SMBD) offers an invaluable lens for evaluating public spaces over time, surpassing traditional methods like surveys and questionnaires. This research introduces a comprehensive analytical framework that integrates SMBD with placemaking practices, specifically applied to the city of Xiamen, China. The result shows the social sentiment, vibrancy heatmaps, leisure activities, visitor behaviors, and preferred visual elements of Xiamen, offering urban designers valuable insights into the dynamic nature of citizen experiences. The findings underscore the potential of SMBD to inform and enhance public space design, providing a holistic approach to creating more inclusive, vibrant, and functional urban environments.

Study on temperature regulation function of green spaces at community scale in high-density urban areas and planning design strategies

In recent years, the frequency of extreme high-temperature events has gradually increased. To better understand the impact of urban green space coverage's spatial pattern on land surface temperature (LST), this study selected three sample areas along the urban-rural gradient in Shanghai. Using LST inversion and resampling methods, LST data for different grid sizes were obtained for spring, summer, and winter in the 2010s, 2015s, and 2020 s. A boosting regression tree model was employed to determine the key indicators affecting LST and effective cooling thresholds. The impact of green quality, structure, and pattern at the community scale on LST was discussed, providing feasible suggestions for green space planning and design in different urban spaces. The study found significant differences in the spatial patterns of green areas and LST among the three sample regions. The quantity and integrity of green spaces in the Huangpu, Minhang, and Songjiang sample areas have progressively improved, with overall green coverages of 19 %, 38 %, and 43 %, respectively. As grid size increases, the relative influence of fractional vegetation coverage (FVC) on LST generally decreases, whereas the relative influence of green structure and patterns gradually increases. Taking the summer of the 2020 s as an example, the influence of FVC on the three study sample areas was 80.79, 83.36, and 87.18 at a 30 m grid size, which decreased to 48.87, 40.59, and 47.64 at a 120 m grid size. The green structure's impact rose from 13.09, 15.22, and 10.6–30.01, 51.82, and 38.5; the influence of green patterns increased from 6.12, 1.42, and 2.21–21.13, 7.6, and 13.86. Key indicators affecting LST include FVC (Fractional Vegetation Coverage), AREA (Green Patch Area), PD (Patch Density), COHESION (Patch Cohesion Index), and ED (Edge Density). High temperatures in summer are one of the ecological issues that need special attention in Shanghai's green space design. Setting the green space proportion to 35 % while avoiding fragmentation and low vegetation coverage can achieve effective cooling. This study's main advancement lies in utilizing machine learning algorithms to identify the principal green spatial pattern impact factors and key thresholds influencing LST at the community scale in Shanghai. The related results and proposed strategies provide a research framework and strong basis for special regulations in urban green space system planning concerning urban thermal environments. They offer references for urban renewal and new town planning to address climate change and urban high-temperature issues, such as clear requirements for optimal green space area at the community scale, community park spatial layout and quantity regulations, and urban park planning design suggestions. This study also highlights obstacles in the practical application of planning and design strategies, which can help avoid difficulties in implementing planning policies. It attempts to set goals for community-scale vegetation planning in China from a policy perspective and provides relevant recommendations.

Design and modeling of a programmable morphing structure with variable stiffness capability

The development of structures capable of both dynamic shape morphing and stiffness modulation has significant potential in various applications. However, such structures often suffer from bulkiness and control complexity. This paper addresses these challenges by exploring a scaled structure that integrates morphing capabilities and variable stiffness within a compact configuration. For the first time, we establish a comprehensive set of design criteria and obtain the previously unexplored design space, focusing on geometric parameters including layer thickness, target shape radius, the number of scales, and the number of periods per scale. Through extensive finite element simulations, we evaluate the impact of material property and geometric parameters on the performance of the scaled structure, emphasizing the role of coefficient of friction. Our findings identify a critical threshold for the coefficient of friction above which morphing ability is hindered. Additionally, we uncover a trade-off between morphing capability and stiffness variation ability, which we overcome by modifying the surface structure of the scales. The optimal design is found to be a superellipse shape with an exponent of ∼1.9. The practical potential of this structure is demonstrated through three applications: a soft gripper, a phone stand, and a foldable box, showcasing its versatility in real-world scenarios. This research provides a foundational approach for designing morphing scaled structures, offering valuable insights into optimizing morphing capability and stiffness variation ability for broader engineering applications.

RSVP for VPSA : A Meta Design Study on Rapid Suggestive Visualization Prototyping for Visual Parameter Space Analysis.

Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenario, making it hard for domain scientists to adapt them to their problems without professional help. We present RSVP, the Rapid Suggestive Visualization Prototyping system encoding VPSA knowledge to enable domain scientists to prototype custom visualization dashboards tailored to their specific needs. The system implements a task-oriented, multi-view visualization recommendation strategy over a visualization design space optimized for VPSA to guide users in meeting their analytical demands. We derived the VPSA knowledge implemented in the system by conducting an extensive meta design study over the body of work on VPSA. We show how this process can be used to perform a data and task abstraction, extract a common visualization design space, and derive a task-oriented VisRec strategy. User studies indicate that the system is user-friendly and can uncover novel insights.

Development and Evaluation of a Real-Time Phase-Triggered Stimulation Algorithm for the CorTec Brain Interchange.

With the development and characterization of biomarkers that may reflect neural network state as well as a patient's clinical deficits, there is growing interest in more complex stimulation designs. While current implantable neuromodulation systems offer pathways to expand the design and application of adaptive stimulation paradigms, technological drawbacks of these systems limit adaptive neuromodulation exploration. In this paper, we discuss the implementation of a phase-triggered stimulation paradigm using a research platform composed of an investigational system known as the CorTec Brain Interchange (CorTec GmbH, Freiburg, Germany), and an open-source software tool known as OMNI-BIC. We then evaluate the stimulation paradigm's performance in both benchtop and in vivo human demonstrations. Our findings indicate that the Brain Interchange and OMNI-BIC platform is capable of reliable administration of phase-triggered stimulation and has the potential to help expand investigation within the adaptive neuromodulation design space.

The Restorative Effects of Unique Green Space Design: Comparing the Restorative Quality of Classical Chinese Gardens and Modern Urban Parks

The Restorative Effects of Unique Green Space Design: Comparing the Restorative Quality of Classical Chinese Gardens and Modern Urban Parks

The role of acetabular cement augmentation in 2-stage revision arthroplasty for prosthetic joint infection of the hip.

In the treatment of chronic prosthetic joint infection (PJI) of the hip, two-stage exchange arthroplasty is commonly employed. Various spacer designs, including Hemi-Spacers and Articulating Spacers, are utilized during this process. However, these spacers are associated with a high rate of mechanical complications and pose a risk of progressive bone loss. This study aims to compare these two types of spacers in terms of mechanical complications, center of rotation (COR) restoration, and preservation of acetabular bone stock. From 2019 to 2022, patients who underwent two-stage exchange arthroplasty for hip PJI across three hospitals were retrospectively reviewed. Data including demographic, clinical, and microbiological information were collected. Radiographic imaging was analyzed to measure acetabular bone erosion, COR, and periacetabular bone resected. Additionally, the average surgical time in the first and second stages, mechanical complications, and the mean duration of the inter-stage period were recorded. Forty patients were divided into two groups: Group A (Articulating Spacer, n = 23) received a preformed femur spacer with acetabular cement augmentation, while Group B (Hemi-Spacer, n = 17) received a preformed femur spacer alone. Acetabular cement augmentation slightly prolonged the first stage but facilitated a faster second stage during subsequent reimplantation. Spacer dislocation rates were 8.7% in Group A and 17.6% in Group B during the interstage period. Radiographic analysis revealed a statistically significant greater degree of acetabular erosion in Group B. A significant difference in Vertical-COR differential was observed, with a more proximalized revision cup compared to the primary cup in Group B, and Horizontal-COR values closer to the native hip in Group A. Dynamic spacers with acetabular cement augmentation help preserve peri-acetabular bone stock and prevent progression of acetabular bone erosion during the inter-stage period. Additionally, these spacers reduce the dislocation rates, making reimplantation easier and leading to better restoration of hip biomechanics during the second procedure.

What can attribution methods show us about chemical language models?

Language models trained on molecular string representations have shown strong performance in predictive and generative tasks. However, practical applications require not only making accurate predictions, but also explainability - the ability to explain the reasons and rationale behind the predictions. In this work, we explore explainability for a chemical language model by adapting a transformer-specific and a model-agnostic input attribution technique. We fine-tune a pretrained model to predict aqueous solubility, compare training and architecture variants, and evaluate visualizations of attributed relevance. The model-agnostic SHAP technique provides sensible attributions, highlighting the positive influence of individual electronegative atoms, but does not explain the model in terms of functional groups or explain how the model represents molecular strings internally to make predictions. In contrast, the adapted transformer-specific explainability technique produces sparse attributions, which cannot be directly attributed to functional groups relevant to solubility. Instead, the attributions are more characteristic of how the model maps molecular strings to its latent space, which seems to represent features relevant to molecular similarity rather than functional groups. These findings provide insight into the representations underpinning chemical language models, which we propose may be leveraged for the design of informative chemical spaces for training more accurate, advanced and explainable models.

Development and validation of a DLLME-HPLC-FLD method for determination of aflatoxins in Chrysanthemum morifolium based on quality by design principles.

Aflatoxins, potent carcinogens produced by Aspergillus species, present significant health risks and commonly contaminate herbal products such as Chrysanthemum morifolium. Detecting these toxins in C. morifolium proves challenging due to the complex nature of the herbal matrix and the fluctuating levels of toxins found in different samples. This study aimed to develop and optimize a novel method for the detection of aflatoxins in C. morifolium using dispersive liquid-liquid microextraction combined with high-performance liquid chromatography-fluorescence detection based on quality by design principles. The method involved determining critical method attributes and parameters through the Plackett-Burman design, followed by optimization using the Box-Behnken design. Monte Carlo simulation was employed to establish a design space, which was experimentally verified. Method validation was performed to confirm accuracy, precision, and stability. The developed method exhibited excellent linearity (R2 > 0.9991) for aflatoxins B1, B2, G1, and G2 across a range of concentrations, with recovery rates between 85.52% and 102.01%. The validated method effectively quantified aflatoxins in C. morifolium under different storage conditions, highlighting the impact of temperature and storage time on aflatoxin production. This study successfully established a reliable and effective method for the detection of aflatoxins in C. morifolium, highlighting the importance of strict storage conditions to reduce aflatoxin contamination. Using a quality by design framework, the method demonstrated robustness and high analytical performance, making it suitable for routine quality control of herbal products.

Quality by design for mRNA platform purification based on continuous oligo-dT chromatography

Oligo-deoxythymidine (Oligo-dT) ligand-based affinity chromatography is a robust method for purifying mRNA drug substances within the manufacturing process of mRNA-based products, including vaccines and therapeutics. However, the conventional batch mode of operation for oligo-dT chromatography has certain drawbacks that reduce this process’s productivity. Here, we report a new continuous oligo-dT chromatography process for the purification of in vitro transcribed mRNA, which reduces losses, improves the efficiency of oligo-dT resin use, and intensifies the chromatography process. Furthermore, the quality by design (QbD) framework was used to establish a design space for the newly developed method. The optimisation of process parameters, including salt type, salt concentration, load flow rate and mRNA load concentration both in batch and the continuous mode, achieved >90% yield (mRNA recovery) along with >95% mRNA integrity and >99% purity. The productivity of continuous chromatography was estimated to be 5.75-fold higher, and the operating cost was estimated 15% lower, when compared to batch chromatography. Furthermore, the QbD framework was further used to map the relationship between critical quality attributes (CQAs) and key performance indicators (KPIs) as a function of critical process parameters (CPPs) and critical material attributes (CMAs).

Mesoscopic structure graphs for interpreting uncertainty in non-linear embeddings

Probabilistic-based non-linear dimensionality reduction (PB-NL-DR) methods, such as t-SNE and UMAP, are effective in unfolding complex high-dimensional manifolds, allowing users to explore and understand the structural patterns of data. However, due to the trade-off between global and local structure preservation and the randomness during computation, these methods may introduce false neighborhood relationships, known as distortion errors and misleading visualizations. To address this issue, we first conduct a detailed survey to illustrate the design space of prior layout enrichment visualizations for interpreting DR results, and then propose a node-link visualization technique, ManiGraph. This technique rethinks the neighborhood fidelity between the high- and low-dimensional spaces by constructing dynamic mesoscopic structure graphs and measuring region-adapted trustworthiness. ManiGraph also addresses the overplotting issue in scatterplot visualization for large-scale datasets and supports examining in unsupervised scenarios. We demonstrate the effectiveness of ManiGraph in different analytical cases, including generic machine learning using 3D toy data illustrations and fashion-MNIST, a computational biology study using a single-cell RNA sequencing dataset, and a deep learning-enabled colorectal cancer study with histopathology-MNIST.

Telling Data Stories with the Hero's Journey: Design Guidance for Creating Data Videos.

Data videos increasingly becoming a popular data storytelling form represented by visual and audio integration. In recent years, more and more researchers have explored many narrative structures for effective and attractive data storytelling. Meanwhile, the Hero's Journey provides a classic narrative framework specific to the Hero's story that has been adopted by various mediums. There are continuous discussions about applying Hero's Journey to data stories. However, so far, little systematic and practical guidance on how to create a data video for a specific story type like the Hero's Journey, as well as how to manipulate its sound and visual designs simultaneously. To fulfill this gap, we first identified 48 data videos aligned with the Hero's Journey as the common storytelling from 109 high-quality data videos. Then, we examined how existing practices apply Hero's Journey for creating data videos. We coded the 48 data videos in terms of the narrative stages, sound design, and visual design according to the Hero's Journey structure. Based on our findings, we proposed a design space to provide practical guidance on the narrative, visual, and sound custom design for different narrative segments of the hero's journey (i.e., Departure, Initiation, Return) through data video creation. To validate our proposed design space, we conducted a user study where 20 participants were invited to design data videos with and without our design space guidance, which was evaluated by two experts. Results show that our design space provides useful and practical guidance for data storytellers effectively creating data videos with the Hero's Journey.

Data-Driven Radial Compressor Design Space Mapping

Abstract Estimates of turbomachinery performance trends inform system-level compromises during preliminary design. Existing empirical correlations for efficiency use limited experimental data, while analytical loss models require calibration to yield predictive results. From a set of 3708 radial compressor computations, this paper maps efficiency as a function of mean-line aerodynamics, and determines the governing loss mechanisms. An open-source turbomachinery design code creates annulus and blade geometry, then runs a Reynolds-averaged Navier–Stokes simulation for compressors sampled from the mean-line design space. Polynomial surface fits yield a continuous eight-dimensional representation of the design space for analysis, predicting efficiency with a root-mean-square error of 1.2% points. The results show a balance between surface dissipation in boundary layers and mixing loss due to casing separations sets optimum values for inlet Mach number, hub-to-tip ratio, de Haller number, and backsweep angle. Surface dissipation drives the effect of flow coefficient, with high surface areas at low values, and high velocities at high values. Compact compressor designs are achieved by increasing inlet Mach number, reducing hub-to-tip ratio, and minimizing the radial loading coefficient—all of which reduce efficiency approaching design space boundaries. An interactive web-based tool makes the results available to practising engineers, demonstrating large ensembles of automated designs and simulations as a higher-fidelity replacement for legacy empirical correlations in preliminary design.

A computational framework for aesthetic preferences in architecture using computer vision and artificial neural networks

This paper introduces a computational aesthetics framework utilizing computer vision (CV) and artificial neural networks (ANN) to predict the aesthetic preferences of groups of people for architecture. It relies on part-to-whole theories from aesthetics and cognitive psychology. A survey of a group of people on preferences of images is held to record an average hedonic response (AHR). CV algorithms MSER and SAM recognize parts in images. Birkhoff’s aesthetic measure formula is adapted by employing the number of parts and their connections. These quantities are used as input layers of an ANN, and the AHR is the target output. The ANN evaluates images to output a predicted hedonic response (PHR), which is tested as a criterion in parametric design space navigation and in mapping the latent space of GANs. We conclude that such a framework is a heuristic method for better understanding the design and latent spaces and exploring designs.

Place-Making Research: A Bibliometric, Visualization, and Thematic Analysis

Place-making is recognized for its significant role in enhancing community cohesion, preserving cultural heritage, and promoting environmental sustainability. In recent years, place-making has garnered increasing attention as a multidisciplinary research topic from scholars in various fields. However, despite the increasing amount of research, systematic reviews and analyses of existing studies remain relatively scarce. Therefore, this study aims to provide a comprehensive bibliometric overview of the field of place-making through bibliometric and visualization analyses. We used CiteSpace for data analysis and visualization, focusing on the most influential articles, journals, countries, research categories, keywords, and themes in this field. The results indicate a significant increase in publications on placemaking since 2013. Major research institutions are concentrated in the United States, the United Kingdom, and Australia. Research hotspots are centered around “city”, “politics”, and “place-making”, among others. Current research is primarily categorized into four core themes: Urban Design and Public Space, Social Participation and Cultural Identity, Economic Development and Environmental Sustainability, and Governance, Policy, and Community Development. The research trajectory in this field shows trends of diversification and integration. Future research should emphasize interdisciplinary integration, combining urban planning, digital technology, and sociology.

Omnidirectional soft pneumatic actuators: a design and optimization framework

IntroductionSoft pneumatic actuators (SPAs) play a pivotal role in soft robotics due to their unique characteristics of compliance, flexibility, and adaptability. There are plenty of approaches that examine the modeling parameters of SPAs, aiming to optimize their design and, thus, achieve the most advantageous responses. Current optimization methods applied to SPAs are usually performed individually for each design parameter without considering the simultaneous effect all parameters can have on the output performance. This modeling shortcoming is essential to be addressed since customized SPAs are used in a variety of applications, each with different output requirements.MethodsThis study provides a generalized design optimization framework for modeling the SPA performance for their motion profiles, the produced strain energy while being deformed, and their stiffness characteristics. Utilizing experimentally validated finite element methods, all geometrical and material parameters of the models are investigated in response surface methodology optimization using the central composite design approach.ResultsThe results showcase the entire design space of omnidirectional SPAs, along with their output performance, providing guidelines to the end user for design optimization.DiscussionThe offering of this modeling process for SPAs can be adapted to the demands of any potential application and ensure the best performance with respect to the required output responses.