Design Steps Research Articles

Representation Learning for Sequential Volumetric Design Tasks

Abstract Volumetric design, also called massing design, is the first and critical step in professional building design which is sequential in nature. As the volumetric design process is complex, the underlying sequential design process en- codes valuable information for designers. Many efforts have been made to automatically generate reasonable volumetric designs, but the quality of the generated design solutions varies, and evaluating a design solution requires either a prohibitively comprehensive set of metrics or expensive human expertise. While previous approaches focused on learning only the final design instead of sequential design tasks, we propose to encode the design knowledge from a collection of expert or high-performing design sequences and extract useful representations using transformer-based models. Later we propose to utilize the learned representations for crucial downstream applications such as design preference evaluation and procedural design generation. We develop the preference model by estimating the density of the learned representations whereas we train an autoregressive transformer model for sequential design generation. We demonstrate our ideas by leveraging a novel dataset of thousands of sequential volumetric designs. Our preference model can compare two arbitrarily given design sequences and is almost 90% accurate in evaluation against random design sequences. Our autoregressive model is also capable of autocompleting a volumetric design sequence from a partial design sequence.

Development and Design Validation of an Inflow-Settling Chamber for Turbomachinery Test-Benches

At Leibniz University of Hannover, Germany, a new turbomachinery test facility has been built over the last few years. A major part of this facility is a new 6 MW compressor station, which is connected to a large piping system, both designed and built by AERZEN. This system provides air supply to several wind tunnel and turbomachinery test rigs, e.g., axial turbines and axial compressors. These test rigs are designed to conduct high-quality aerodynamic, aeroelastic, and aeroacoustic measurements to increase physical understanding of steady and unsteady effects in turbomachines. One primary purpose of these investigations is the validation of aerodynamic and aeroacoustic numerical methods. To provide precise boundary conditions for the validation process, extremely high homogeneity of the inflow to the investigated experimental setup is imminent. Thus, customized settling chambers have been developed using analytical and numerical design methods. The authors have chosen to follow basic aerodynamic design steps, using analytical assumptions for the inlet section, the “mixing” area of a settling chamber, and the outlet nozzle in combination with state-of-the-art numerical investigations. In early 2020, the first settling chamber was brought into operation for the acceptance tests. In order to collect high-resolution flow field data during the tests, Leibniz University and AERZEN have designed a unique measurement device for robust and fast in-line flow field measurements. For this measurement device, total pressure and total-temperature rake probes, as well as traversing multi-hole probes, have been used in combination to receive high-resolution flow field data at the outlet section of the settling chamber. The paper provides information about the design process of the settling chamber, the developed measurement device, and measurement data gained from the acceptance tests.

A mixed-methods approach exploring acceptability and feasibility of trials designed to test drugs targeting prevention of post-traumatic osteoarthritis after knee injury.

To explore key stakeholder views around feasibility and acceptability of trials seeking to prevent post-traumatic osteoarthritis (PTOA) following knee injury, and provide guidance for next steps in PTOA trial design. Healthcare professionals, clinicians, and/or researchers (HCP/Rs) were surveyed, and the data were presented at a congress workshop. A second and related survey was then developed for people with joint damage caused by knee injury and/or osteoarthritis (PJDs), who were approached by a UK Charity newsletter or Oxford involvement registry. Anonymized data were collected and analyzed in Qualtrics. Survey responses (n = 19 HCP/Rs, 39 PJDs) supported studies testing pharmacological agents preventing PTOA. All HCP/Rs and 30/31 (97%) PJDs supported the development of new treatments that improved or delayed knee symptoms and damage to knee structure. PJDs thought that improving structural knee damage was more important than knee symptoms. Both groups found studies more acceptable as expected future benefit and risk of PTOA increased. All drug delivery routes were acceptable. Workshop participants (around n = 60) reflected survey views. Discussions suggested that stratifying using molecular testing for likely drug response appeared to be more acceptable than using characteristics such as sex, age, and BMI. Our findings supported PTOA drug intervention studies, including situations where there is low risk of disease, no expected benefit of treatment, and frequent treatment administration. PJDs appeared less risk-averse than HCP/Rs. This work reinforces the benefits of consensus and involvement work in the co-creation of PTOA drug trial design. Involvement of key stakeholders, such as PJDs with different risks of OA and regulatory representatives, are critical for trial design success.

Bundle fragments into a whole: Mining more complete clusters via submodular selection of interesting webpages for web topic detection

Organizing interesting webpages into hot topics is one of key steps to understand the trends of multimodal web data. A state-of-the-art solution is firstly to organize webpages into a large volume of multi-granularity topic candidates; hot topics are further identified by estimating their interestingness. However, these topic candidates contain a large number of fragments of hot topics due to both the inefficient feature representations and the unsupervised topic generation. This paper proposes a bundling-refining approach to mine more complete hot topics from fragments. Concretely, the bundling step organizes the fragment topics into coarse topics; next, the refining step proposes a submodular-based method to refine coarse topics in a scalable approach. The propose unconventional method is simple, yet powerful by leveraging submodular optimization, our approach outperforms the traditional ranking methods which involve the careful design and complex steps. Extensive experiments demonstrate that the proposed approach surpasses the state-of-the-art method (i.e., latent Poisson deconvolution Pang et al. (2016)) 20% accuracy and 10% one on two public data sets, respectively.

Robust internal model‐based control for linear‐time‐invariant systems

AbstractThis article addresses robust output regulation for systems affected by disturbances generated by an uncertain exosystem as well as matched external disturbances. The robustness of the feedback loop is achieved by combining internal model based control with sliding mode control, resulting in a structurally simple controller. The design of the proposed controller is presented first for the full information problem, that is, the disturbances generated by the exosystem and the state‐information is available for the control algorithm. This requirement is relaxed in the second step of the controller design, where the observer‐based robust output feedback‐loop structure is developed. Results from a comparative simulation study are presented to illustrate the advantages and the effectiveness of the proposed controller concept.

Healthcare compassion interventions co-design and feasibility inquiry with clinicians and healthcare leaders in Aotearoa/New Zealand

Compassion in healthcare is valued by patients, healthcare professionals (HCPs), and leads to improved outcomes. Notwithstanding reports of systemic failings in the provision of compassionate care, research regarding ways to intervene remains limited. The aim of this study is to clarify compassion intervention needs in a diverse HCP workforce in public secondary healthcare in Aotearoa New Zealand (NZ) by utilising a co-design process. The co-design process involved a series of workshops with clinicians followed by in-depth interviews with healthcare leaders to derive input regarding feasibility and implementation. Reflexive thematic analysis was used to analyze the data. There was a great deal of interest in compassion interventions from healthcare professionals and leaders. However, for compassion interventions to be acceptable, feasible, and effective, compassion interventions design should be reimagined and reflected at each step of interventional design and implementation and span across organizational levels. Namely, the results of the study showed the preference for non-individual focused multi-level interventions to build bridges and connections. The desired compassion intervention components included practising connecting with others' humanity, improving compassion knowledge and relational and reflective skills, and cultural safety and anti-racism training. Experiential training embedded in models of cultural dialogue was the preferred interventional modality. Prioritising leadership as an intervention site was suggested to improve leadership's buy-in of compassion interventions and possibly serve as a starting point for transforming the broader culture, reviving interconnectedness in a healthcare system described as fragmented, disconnected, and alienating with compassion also acting as an equalizer of power.

A constructive approach to strategy game map generation

Designing strategy games poses unique challenges due to the complicated nature of the genre where balancing game elements is a major concern. Maps in strategy games commonly contain elements such as resources to gather and buildings with different capabilities, playing a crucial role in the game life cycle. Resource and base placements must be done carefully. It is difficult to design maps manually and even harder to design multiple variations. This study presents a method for procedurally creating strategy game maps. A strategy game was developed and used as a test-bed for the study. A constructive approach is followed in the main steps of the algorithm design and a novel procedural noise function is used together with a uniform stratified sampling method to create and distribute the resources. Then eligible base locations are detected and scored according to a fitness function that defines how favorable a location is. These scores are used to place bases appropriately. The algorithm is evaluated using simulations and a small-scale user study is conducted at the end. The proposed method is capable of creating playable, diverse, and fair maps and is fast enough to work at runtime.

Fast mobility analysis for generative design applications through a real vector space representation of mobilities

During the early stages of design, mechanisms are commonly modeled as perfect joints assembled with infinitely rigid bodies. This representation enables the prediction of the system’s mobilities through a mobility analysis. However, traditional mobility analysis tools can be computationally expensive or lack critical information, such as the type or direction of mobilities. It hinders the generation of topology and configuration through generative design schemes.In this paper, we propose an alternative approach to mobility analysis based on a real vector space representation of mobilities. Our method provides relevant information for early design steps while being computationally effective through a novel formulation of series and parallel assembly topological operations. A benchmark on four selected use cases highlights an acceleration of 3 to 4 orders of magnitude compared to traditional approaches. Additionally, design rules on the joints’ positions can be automatically generated with our approach. It enables the automation of the complete design process, including topology and configuration. As such, we provide guidelines to develop a generative design scheme dedicated to the synthesis of guiding mechanisms.

Applications of reinforcement learning, machine learning, and virtual screening in SARS-CoV-2-related proteins

Similarly, to all coronaviruses, SARS-CoV-2 uses the S glycoprotein to enter host cells, which contains two functional domains: S1 and S2 receptor binding domain (RBD). Angiotensin-converting enzyme 2 (ACE2) is recognizable by the S proteins on the surface of the SARS-CoV-2 virus. The SARS-CoV-2 virus causes SARS, but some mutations in the RBD of the S protein markedly enhance their binding affinity to ACE2. Searching for new compounds in COVID-19 is an important initial step in drug discovery and materials design. Still, the problem is that this search requires trial-and-error experiments, which are costly and time-consuming. In the automatic molecular design method based on deep reinforcement learning, it is possible to design molecules with optimized physical properties by combining a newly devised coarse-grained representation of molecules with deep reinforcement learning. Also, structured-based virtual screening uses protein 3D structure information to evaluate the binding affinity between proteins and compounds based on physicochemical interactions such as van der Waals forces, Coulomb forces, and hydrogen bonds, and select drug candidate compounds. In addition, AlphaFold can predict 3D protein structures, given the amino acid sequence, and the protein building blocks. Ensemble docking, in which multiple protein structures are generated using the molecular dynamics method and docking calculations are performed for each, is often performed independently of docking calculations. In the future, the AlphaFold algorithm can be used to predict various protein structures related to COVID-19.

Flexural behavior and design of aluminum alloy portal frame eaves joints

Aluminum alloy portal frames (AAPFs) are suitable for both long-term and temporary buildings owing to their light weight, ease of installation and disassembly, and excellent corrosion resistance. Due to the critical importance of joint mechanical behavior for structural safety, this paper conducted experimental and numerical studies on the flexural behavior of AAPF eaves joints. Initially, bending tests on four AAPF eaves joints revealed three failure patterns: connection plate, column, and hole wall failure. The failure patterns are determined by the weakest of these three components. Adding haunches increased the bending zone area, thereby increasing the ultimate bearing capacity by 110 % compared to joints without haunches. Furthermore, strain curves indicated that the load transfer zone was subjected to a bending-shear force state, consistent with the principle that the bolt group shear forms a force couple to transfer the moment. Subsequently, a finite element (FE) model was developed and validated to provide a foundation for subsequent parametric studies and theoretical analysis. The parametric analysis considers bolt diameter, connection plate thickness, aluminum alloy web thickness, and bolt end distance. The bearing capacity corresponding to three failure patterns was discussed, and a design method for the flexural bearing capacity was proposed. The design method for stiffness model was also proposed based on the component-method and parametric analysis. Finally, this paper presented and validated the design steps for the flexural behavior of the eaves joints throughout the whole process, providing an important reference for engineering design.

Rapid discovery and evolution of nanosensors containing fluorogenic amino acids

Binding-activated optical sensors are powerful tools for imaging, diagnostics, and biomolecular sensing. However, biosensor discovery is slow and requires tedious steps in rational design, screening, and characterization. Here we report on a platform that streamlines biosensor discovery and unlocks directed nanosensor evolution through genetically encodable fluorogenic amino acids (FgAAs). Building on the classical knowledge-based semisynthetic approach, we engineer ~15 kDa nanosensors that recognize specific proteins, peptides, and small molecules with up to 100-fold fluorescence increases and subsecond kinetics, allowing real-time and wash-free target sensing and live-cell bioimaging. An optimized genetic code expansion chemistry with FgAAs further enables rapid (~3 h) ribosomal nanosensor discovery via the cell-free translation of hundreds of candidates in parallel and directed nanosensor evolution with improved variant-specific sensitivities (up to ~250-fold) for SARS-CoV-2 antigens. Altogether, this platform could accelerate the discovery of fluorogenic nanosensors and pave the way to modify proteins with other non-standard functionalities for diverse applications.

AI-Based Integrated Smart Process Sensor for Emulsion Control in Industrial Application

In industry, reliable process supervision is essential to ensure efficient, safe, and high-quality production. The droplet size distribution represents a critical quality attribute for emulsification processes and should be monitored. For emulsion characterization, image-based analysis methods are well-known but are often performed offline, leading to a time-delayed and error-prone process evaluation. The use of an integrated smart process sensor to characterize the emulsification process over time enables the real-time evaluation of the entire system. The presented integrated smart process sensor consists of an optical measurement flow cell built into a camera system. The overall system is placed in a bypass system of a production plant for emulsification processes. AI-based image evaluation is used in combination with a feature extraction method (You Only Look Once version 4 (YOLOv4) and Hough circle (HC)) to characterize the process over time. The sensor system is installed in the plant and tested with different cosmetic products. Various iteration, prototyping, and test steps for the final sensor design are performed prior to this in a laboratory test setup. The results indicate robust and accurate detection and determination of the droplet size in real time to improve product control and save time. For benchmarking the integrated smart process sensor, the results are compared with common analysis methods using offline samples.

Including Sexually and Gender Diverse Populations in 3,4-Methylenedioxymethamphetamine-Assisted Psychotherapy Trial Research.

Sexually and gender diverse (SGD) populations experience an increased prevalence and severity of posttraumatic stress disorder (PTSD) compared with the general population. Minority stress theory contextualizes this increased disease burden by outlining how stigma and discrimination (e.g., homophobia and transphobia) contribute to worse mental health outcomes. The standard-of-care pharmacotherapy for PTSD is associated with significant treatment resistance. 3,4-Methylenedioxymethamphetamine (MDMA)-assisted psychotherapy (MDMA-AP) has emerged as an investigational treatment for PTSD but has lacked consideration for SGD populations. This article explores next steps in clinical trial design and implementation for the study of MDMA-AP with SGD populations who have PTSD.

Formal Methods in Industry

Formal methods encompass a wide choice of techniques and tools for the specification, development, analysis, and verification of software and hardware systems. Formal methods are widely applied in industry, in activities ranging from the elicitation of requirements and the early design phases all the way to the deployment, configuration, and runtime monitoring of actual systems. Formal methods allow one to precisely specify the environment in which a system operates, the requirements and properties that the system should satisfy, the models of the system used during the various design steps, and the code embedded in the final implementation, as well as to express conformance relations between these specifications. We present a broad scope of successful applications of formal methods in industry, not limited to the well-known success stories from the safety-critical domain, like railways and other transportation systems, but also covering other areas such as lithography manufacturing and cloud security in e-commerce, to name but a few. We also report testimonies from a number of representatives from industry who, either directly or indirectly, use or have used formal methods in their industrial project endeavours. These persons are spread geographically, including Europe, Asia, North and South America, and the involved projects witness the large coverage of applications of formal methods, not limited to the safety-critical domain. We thus make a case for the importance of formal methods, and in particular of the capacity to abstract and mathematical reasoning that are taught as part of any formal methods course. These are fundamental Computer Science skills that graduates should profit from when working as computer scientists in industry, as confirmed by industry representatives.

Slot-Loaded Vivaldi Antenna for Biomedical Microwave Imaging Applications: Influence of Design Parameters on Antenna's Dimensions and Performances.

This paper demonstrates the design steps of a slot-loaded Vivaldi antenna for biomedical microwave imaging applications, showing the influence of the design parameters on the antenna's dimensions and performances. Several antenna miniaturization techniques were taken into consideration during the design: reduction in the electromagnetic wavelength by using a high-permittivity substrate material (relative permittivity ϵr=10.2), the placement of the antenna inside a coupling medium (ϵr=23), and the elongation of the current path by etching slots on each side of the radiator to reduce the antenna's lowest resonant frequency without increasing its physical dimensions. Moreover, an analysis of different antenna slot design scenarios was performed considering different slot lengths, inclination angles, positions, and numbers. Considering the frequency range of microwave imaging (i.e., about 500 MHz-5 GHz) and the array arrangement typical of microwave imaging, the best design was chosen. Finally, the antenna was fabricated and its performances in the coupling medium were characterized. The simulation and measurement results showed good agreement between each other. In comparison with literature antennas, the one developed in this work shows wide bandwidth and compact dimensions.

Rapid and simultaneous detection of common childhood diarrhea viruses by microfluidic-FEN1-assisted isothermal amplification with ultra-high specificity and sensitivity

Rapid and accurate diagnostic methods are crucial for managing viral gastroenteritis in children, a leading cause of global childhood morbidity and mortality. This study introduces a novel microfluidic-Flap endonuclease 1 (FEN1)-assisted isothermal amplification (MFIA) method for simultaneously detecting major viral pathogens associated with childhood diarrhea—rotavirus, norovirus, and adenovirus. Leveraging the specificity-enhancing properties of FEN1 with a universal dspacer-modified flap probe and the adaptability of microfluidic technology, MFIA demonstrated an exceptional detection limit (5 copies/μL) and specificity in the simultaneous detection of common diarrhea pathogens in clinical samples. Our approach addresses the limitations of current diagnostic techniques by offering a rapid (turn around time <1 h), cost-effective, easy design steps (universal flap design), and excellent detection performance method suitable for multiple applications. The validation of MFIA against the gold-standard PCR method using 150 actual clinical samples showed no statistical difference in the detection performance of the two methods, positioning it as a potential detection tool in pediatric diagnostic virology and public health surveillance. In conclusion, the MFIA method promises to transform pediatric infectious disease diagnostics and contribute significantly to global health efforts combating viral gastroenteritis.

Triangle-toothed gear occlude-guided universal nanotechnology constructs 3D symmetric DNA polyhedra with high assembly efficiency for precision cancer therapy

Chemotherapy is commonly used to treat malignant tumors. However, conventional chemotherapeutic drugs often cannot distinguish between tumor and healthy cells, resulting in adverse effects and reduced therapeutic efficacy. Therefore, zigzag-shaped gear-occlude-guided cymbal-closing (ZGC) DNA nanotechnology was developed based on the mirror-symmetry principle to efficiently construct symmetric DNA polyhedra. This nanotechnology employed simple mixing steps for efficient sequence design and assembly. A targeting aptamer was installed at a user-defined position using an octahedron as a model structure. Chemotherapeutic drug-loaded polyhedral objects were subsequently delivered into tumor cells. Furthermore, anticancer drug-loaded DNA octahedra were intravenously injected into a HeLa tumor-bearing mouse model. Assembly efficiency was almost 100 %, with no residual building blocks identified. Moreover, this nanotechnology required a few DNA oligonucleotides, even for complex polyhedrons. Symmetric DNA polyhedrons retained their structural integrity for 24 h in complex biological environments, guaranteeing prolonged circulation without drug leakage in the bloodstream and promoting efficient accumulation in tumor tissues. In addition, DNA octahedra were cleared relatively slowly from tumor tissues. Similarly, tumor growth was significantly inhibited in vivo, and a therapeutic outcome comparable to that of conventional gene-chemo combination therapy was observed. Moreover, no systemic toxicity was detected. These findings indicate the potential application of ZGC DNA nanotechnology in precision medicine.

Dynamic flexoelectric instabilities in nematic liquid crystals.

Electrohydrodynamic phenomena in liquid crystals constitute an old but still very active research area. The reason is that these phenomena play the key role in various applications of liquid crystals and due to the general interest of the physical community in out-of-equilibrium systems. Nematic liquid crystals (NLCs) are ideally representative for such investigations. Our article aims to study theoretically the linear NLCs dynamics. We include into consideration orientation elastic energy, hydrodynamic motion, external alternating electric field, electric conductivity, and flexoelectric polarization. We analyze the linear stability of the NLC film, determining dynamics of perturbations with respect to the homogeneous initial state of the NLC. For the purpose we compute eigenvalues of the evolution matrix for a period of the external alternating electric field. These eigenvalues determine the amplification factors for the modes during the period. The instability occurs when the principal eigenvalue of the evolution matrix becomes unity by its absolute value. The condition determines the threshold (critical field) for the instability of the uniform state. It turns out that one might expect various types of the instability, only partially known and investigated in the literature. Particularly, we find that the flexoelectric instability may lead to two-dimensionally space-modulated patterns exhibiting time oscillations. This type of the structures was somehow overlooked in the previous works. We formulate conditions needed for the scenario to be realized. We hope that the results of our work will open the door to a broad range of further studies. Of especial importance would be a comprehensive understanding of the role of various material parameters and nonlinear effects which is a key step for the rational design of NLCs exhibiting the predicted in this publication multidimensional oscillating in time patterns.

Open-source, high-throughput targeted in situ transcriptomics for developmental and tissue biology.

Multiplexed spatial profiling of mRNAs has recently gained traction as a tool to explore the cellular diversity and the architecture of tissues. We propose a sensitive, open-source, simple and flexible method for the generation of in situ expression maps of hundreds of genes. We use direct ligation of padlock probes on mRNAs, coupled with rolling circle amplification and hybridization-based in situ combinatorial barcoding, to achieve high detection efficiency, high-throughput and large multiplexing. We validate the method across a number of species and show its use in combination with orthogonal methods such as antibody staining, highlighting its potential value for developmental and tissue biology studies. Finally, we provide an end-to-end computational workflow that covers the steps of probe design, image processing, data extraction, cell segmentation, clustering and annotation of cell types. By enabling easier access to high-throughput spatially resolved transcriptomics, we hope to encourage a diversity of applications and the exploration of a wide range of biological questions.

Engineering thioesterase as a driving force for novel itaconate production via its degradation scheme

Incorporation of irreversible steps in pathway design enhances the overall thermodynamic favorability and often leads to better bioconversion yield given functional enzymes. Using this concept, here we constructed the first non-natural itaconate biosynthesis pathway driven by thioester hydrolysis. Itaconate is a commercially valuable platform chemical with wide applications in the synthetic polymer industry. Production of itaconate has long relied on the decarboxylation of TCA cycle intermediate cis-aconitate as the only biosynthetic route. Inspired by nature's design of itaconate detoxification, here we engineered a novel itaconate producing pathway orthogonal to native metabolism with no requirement of auxotrophic knock-out. The reversed degradation pathway initiates with pyruvate and acetyl-CoA condensation forming (S)-citramalyl-CoA, followed by its dehydration and isomerization into itaconyl-CoA then hydrolysis into itaconate. Phenylacetyl-CoA thioesterase (PaaI) from Escherichiacoli was identified via screening to deliver the highest itaconate formation efficiency when coupled to the reversible activity of citramalate lyase and itaconyl-CoA hydratase. The preference of PaaI towards itaconyl-CoA hydrolysis over acetyl-CoA and (S)-citramalyl-CoA also minimized the inevitable precursor loss due to enzyme promiscuity. With acetate recycling, acetyl-CoA conservation, and condition optimization, we achieved a final itaconate titer of 1 g/L using the thioesterase driven pathway, which is a significant improvement compared to the original degradation pathway based on CoA transferase. This study illustrates the significance of thermodynamic favorability as a design principle in pathway engineering.