Workflow Design Research Articles

The “Cognitive” Architectural Design Process and Its Problem with Recent Artificial Intelligence Applications

The connection between cognitive science and the architectural design process reveals significant gaps that limit the full potential of creating effective built environments. A key issue is the insufficient integration of cognitive principles into design workflows. Architects frequently rely on traditional methods and aesthetic considerations without fully understanding how spatial configurations influence human cognition and behavior. While recent AI applications in architecture, such as Computer-Aided Drafting (CAD), Building Information Modeling (BIM), and interactive web and VR presentations, show promising advancements, AI still struggles with complex architectural functions. AI lacks the creativity and imagination inherent in human cognition. It operates based on fixed programming, producing specific outcomes and requiring human oversight to apply insights from one dataset to another. The primary challenge in using AI for architectural design is ensuring minimal design flaws, as replicating human cognitive abilities with AI and various machine learning techniques remains difficult. This research paper aims to explore the relationship between cognitive science, artificial intelligence, and the architectural design process through four main objectives: First, to investigate the integration of AI with current architectural software applications. Second, to examine potential connections between AI and major architectural design trends. Third, to define two frameworks for the Cognitive Architectural Design Process to guide the development of AI systems in architectural design by analyzing key cognitive design theories. Finally, to create a proposed "Architectural Design Process-Cognitive Pilot Map" from an architectural perspective to aid AI programmers in developing architecture design software applications.

An Automated Cell-Free Workflow for Transcription Factor Engineering.

The design and optimization of metabolic pathways, genetic systems, and engineered proteins rely on high-throughput assays to streamline design-build-test-learn cycles. However, assay development is a time-consuming and laborious process. Here, we create a generalizable approach for the tailored optimization of automated cell-free gene expression (CFE)-based workflows, which offers distinct advantages over in vivo assays in reaction flexibility, control, and time to data. Centered around designing highly accurate and precise transfers on the Echo Acoustic Liquid Handler, we introduce pilot assays and validation strategies for each stage of protocol development. We then demonstrate the efficacy of our platform by engineering transcription factor-based biosensors. As a model, we rapidly generate and assay libraries of 127 MerR and 134 CadR transcription factor variants in 3682 unique CFE reactions in less than 48 h to improve limit of detection, selectivity, and dynamic range for mercury and cadmium detection. This was achieved by assessing a panel of ligand conditions for sensitivity (to 0.1, 1, 10 μM Hg and 0, 1, 10, 100 μM Cd for MerR and CadR, respectively) and selectivity (against Ag, As, Cd, Co, Cu, Hg, Ni, Pb, and Zn). We anticipate that our Echo-based, cell-free approach can be used to accelerate multiple design workflows in synthetic biology.

An integrated systematic method for interlaced unmanned spatial systems (IUSS) design process

Unmanned Aerial Systems (UAS) have garnered significant interest in recent years. These systems, commonly consisting of one or more Unmanned Aerial Vehicles (UAV) and satellite systems, have been extensively used to enhance the effectiveness of various SOSs, such as disaster management and relief efforts. In addition, the inaugural Mars unmanned helicopter, Ingenuity (Ginny), successfully took flight in April 2021, marking the beginning of the utilization of these systems on Mars. This research aims to build an integrated approach for developing Interlaced Unmanned Spatial Systems, considering the high level of precision required for space systems. This study aims to set up and optimize all parts of the proposed architecture for the design of IUSS, using Model-Based Systems Engineering theories and Dependency Structural Matrix foundations. This research introduces a comprehensive coherence architecture that considers all design domains, including the design process, design office, products, and requirements. Additionally, a design workflow model is provided.

B-159 Fast Cleanup for 19 Steroid Analytes in Serum Using Supported Liquid Extraction (SLE)

Abstract Background Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a powerful tool that holds promise in designing the future of steroid analysis in the clinical laboratory. It is already widely used in steroid analysis in endocrinology and is being utilized increasingly in newer applications for individual steroids and panels relevant in clinical research. LC-MS/MS is gaining user base owing to greater sensitivity and specificity and gradually even replacing some of the legacy test methods. Improved sample preparation, modern chromatography methods, and sensitive, faster scanning mass spectrometers have all played a role in improving LC-MS/MS. Steroid analysis for clinical research can require very low limits of detection which demand high recoveries and very clean extracted samples. Supported Liquid Extraction (SLE) can provide very clean extracted samples with less method development and a streamlined workflow compared to solid phase extraction (SPE). Laboratories desire high-throughput methods which consolidate analytes into one panel with fast chromatographic run times. Chromatographic separation of steroid isomers with the same m/z is necessary for accurate quantitation by LCMS/MS. Meeting these criteria can be challenging in a single LC-MS/MS method. Methods Each 200 µL serum sample was pretreated with 200 µL of pH 6 Ammonium Acetate buffer (aq) before being loaded onto a Novum™ PRO SLE, MAX 96-well plate, following the standard SLE protocol. Samples were eluted using 2 × 900 µL of Dichloromethane or Ethyl Acetate. After evaporation of the elution solvent, samples were reconstituted in 100 µL of 0.5 mM Ammonium Fluoride / Methanol (60:40, v/v). A Kinetex™ 2.6 μm C18, 50 x 3.0 mm column using a gradient of 0.5 mM Ammonium Fluoride (aq) and Methanol on an Agilent® 1290 Infinity UHPLC and SCIEX® 7500 Triple Quad™ were used for quantitation. Results Here, we present an effective sample extraction method and LC-MS/MS analysis method for the quantitation of a 19-analyte steroid panel from serum. The Kinetex 2.6 μm C18 column provided a fast 8-minute chromatographic separation and good selectivity for steroid analytes in both positive ion mode and negative ion mode. Calibration curves with a linear fit and 1/x weighting, showed good linearity with R2 of >0.99 for all analytes except 17-OH-Pregnenolone and DHEAS when using Dichloromethane as the elution solvent, and R2 of >0.980 for all analytes when using Ethyl Acetate as the elution the solvent. Accuracy of samples using Dichloromethane as the elution solvent were 80-112 %, except for 17-OH-Pregnenolone and DHEAS. Accuracy of all samples using the Ethyl Acetate elution solvent were 91-118 %. Conclusions The-96 well plate extraction method was robust, reproducible, and can be automated. SLE using Novum Pro MAX combined with a fast LC method provides a simple, rapid, high-throughput workflow for identification and quantitation of 19 steroids from serum.

Evaluation of an Affinity-Enhanced Anti-SARS-CoV2 Nanobody Design Workflow Using Machine Learning and Molecular Dynamics.

In silico optimization of protein binding has received a great deal of attention in the recent years. Since in silico prefiltering of strong binders is fast and cheap compared to in vitro library screening methods, the advent of powerful hardware and advanced machine learning algorithms has made this strategy more accessible and preferred. These advances have already impacted the global response to pandemic threats. In this study, we proposed and tested a workflow for designing nanobodies targeting the SARS-CoV-2 spike protein receptor binding domain (S-RBD) using machine learning techniques complemented by molecular dynamics simulations. We evaluated the feasibility of this workflow using a test set of 3 different nanobodies and 2 different S-RBD variants, from in silico design and bacterial expression to binding assays of the designed nanobody mutants. We successfully designed nanobodies that were subsequently tested against both the wild-type (Wuhan type) and the delta variant S-RBD and found 2 of them to be stronger binders compared to the wild-type nanobody. We use this case study to describe both the strengths and weaknesses of this in silico assisted nanobody design strategy.

3D constructing: Exploring the potential of 3D concrete and clay printing with generative design for architectural innovation

The architectural and construction sectors are experiencing a transformative shift with the advent of 3D constructing. This research delves into the potential of 3D printing technologies when combined with generative design principles to reshape traditional construction methods. Through a comprehensive lens, the study evaluates the generative design workflow, decision-making algorithms, machines capacities and material considerations. Results indicate that, when synergized with generative design, these technologies offer sustainable, efficient, and tailored solutions. The use of accesible materials, such as clay and cement, demonstrates their superiority over conventional alternatives. However, the challenge lies in harmoniously integrating these innovations into architectural practice, ensuring a balance between cutting-edge advancements and practical application. Conclusively, this new approach heralds a future of sustainable and user-centric design, but its full potential can only be tapped with a holistic strategy that encompasses both design and material facets.

Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking.

Antibody-antigen complex modelling is an important step in computational workflows for therapeutic antibody design. While experimentally determined structures of both antibody and the cognate antigen are often not available, recent advances in machine learning-driven protein modelling have enabled accurate prediction of both antibody and antigen structures. Here, we analyse the ability of protein-protein docking tools to use machine learning generated input structures for information-driven docking. In an information-driven scenario, we find that HADDOCK can generate accurate models of antibody-antigen complexes using an ensemble of antibody structures generated by machine learning tools and AlphaFold2 predicted antigen structures. Targeted docking using knowledge of the complementary determining regions on the antibody and some information about the targeted epitope allows the generation of high-quality models of the complex with reduced sampling, resulting in a computationally cheap protocol that outperforms the ZDOCK baseline. The source code of HADDOCK3 is freely available at github.com/haddocking/haddock3. The code to generate and analyse the data is available at github.com/haddocking/ai-antibodies. The full runs, including docking models from all modules of a workflow have been deposited in our lab collection (data.sbgrid.org/labs/32/1139) at the SBGRID data repository.

Establishment of a workflow for high-throughput identification of anti-inflammatory peptides from sea cucumbers

Developing an effective workflow for screening anti-inflammatory peptides is crucial for discovering novel food-derived anti-inflammatory peptides and optimizing the screening and identification process of bioactive peptides. Virtual screening identified three major yolk proteins as target precursor proteins for anti-inflammatory peptides in sea cucumbers. A portfolio of 170 peptides was identified from hydrolysates after 9 h of alcalase treatment by combining antioxidant activity determination and peptidomics analysis. Among these, 12 high-confidence anti-inflammatory peptides were identified through virtual screening. Three of these peptides were shown to effectively inhibit the production of NO and the release of pro-inflammatory cytokines in RAW264.7 cells. Molecular docking demonstrated that these three peptides exerted their anti-inflammatory effects primarily by binding to the active sites of cyclooxygenase-2 and inducible nitric oxide synthase through hydrophobic interactions. This study provided a reference workflow for screening anti-inflammatory peptides, facilitating the discovery of novel anti-inflammatory peptides and the high-value utilization of sea cucumber cooking liquid.

Automated membrane characterization: In-situ monitoring of the permeate and retentate solutions using a 3D printed permeate probe device

Self-driving laboratories and automated experiments can accelerate the design workflow and decrease errors associated with experiments that characterize membrane transport properties. Within this study, we use 3D printing to design a custom stirred cell that incorporates inline conductivity probes in the retentate and permeate streams. The probes provide a complete trajectory of the salt concentrations as they evolve over the course of an experiment. Here, automated diafiltration experiments are used to characterize the performance of commercial NF90 and NF270 polyamide membranes over a predetermined range of KCl concentrations from 1 to 100 mM. The measurements obtained by the inline conductivity probes are validated using offline post-experiment analyses. Compared to traditional filtration experiments, the probes decrease the amount of time required for an experimentalist to characterize membrane materials by more than 50× and increase the amount of information generated by 100×. Device design principles to address the physical constraints associated with making conductivity measurements in confined volumes are proposed. Overall, the device developed within this study provides a foundation to establish high-throughput, automated membrane characterization techniques.

Monte Carlo multiphysics simulation on adaptive unstructured mesh geometry

Monte Carlo simulation based on Constructive Solid Geometry (CSG) brings unique challenges for multiphysics simulation, including establishing field transfers with mesh-based physics codes, the combination of stochastic and deterministic solvers, and high computational expense. In this work, an adaptive, on-the-fly mesh-based Monte Carlo geometry algorithm is implemented in Cardinal to reduce the barrier-to-entry for high-fidelity multiphysics by (i) eliminating ambiguity in defining CSG cells for temperature and density feedback, (ii) enabling simple mesh convergence studies, and (iii) more closely integrating Computer Aided Design (CAD) workflows with Monte Carlo methods. During Picard iterations, an OpenMC mesh geometry is adaptively refined or coarsened by contouring temperature and/or density fields from a thermal-fluid solver. This algorithm is applied to a full-core Molten Salt Fast Reactor (MSFR) geometry with NekRS Large Eddy Simulation (LES) coupled to OpenMC neutron transport. A performance study indicates a net speedup of 2.3× in the OpenMC solver when using an adaptive geometry for cell sizes chosen intermediate to the as-built CAD geometry versus 1:1 element tracking, which points to future algorithmic research in accelerated Monte Carlo mesh tracking.

Computer-Assisted Porcelain Laminate Veneer Preparation: A Scoping Review of Stereolithographic Template Design and Fabrication Workflows.

Computer-assisted preparation of porcelain laminate veneers (PLVs) using stereolithographic templates has been developed to enhance the accuracy of tooth preparation. However, the digital workflows involved in guided PLV preparation remain inconsistently defined across various practices. Therefore, this scoping review aimed to examine publications on computer-assisted PLV preparation to identify the key stage of digital workflows involved in designing and fabricating stereolithographic templates, as well as to highlight the limitations of various template designs. This scoping review aimed to identify publications on digital workflows for designing and fabricating stereolithographic templates in computer-assisted porcelain laminate veneer preparation. A systematic search on MEDLINE/PubMed, Web of Science and Scopus identified English-language articles published from 2014 to March 2024. Eligible articles focused on digitally designed and fabricated tooth reduction templates for porcelain laminate veneers, excluding conventional tooth preparation procedures for tooth reduction assessment. Seven clinical reports were included, demonstrating various 3D data acquisition techniques for virtual patient generation. All articles described virtual diagnostic wax-ups on digital casts, with two using a virtual articulator. Only five articles documented chair-side mock-ups with resin trial restorations to evaluate planned dental esthetics. Additionally, virtual tooth preparation prior to templates design was included in only four articles. The templates were designed using different software and ranged from simple designs with access windows to complex stacked templates with rotary instrument sleeved windows. Each template design had limitations affecting tooth reduction accuracy. All articles reported printing templates in clear acrylic resin using different technologies. In conclusion, the review highlights a lack of standardization in the digital workflow for designing stereolithographic templates for PLVs. Establishing a sound protocol for designing the tooth reduction templates is essential to ensure the accuracy and consistency of veneer preparation.

Radiomic detection of abnormal brain regions in tuberous sclerosis complex.

Radiomics refers to the extraction of quantitative information from medical images and is most commonly utilized in oncology to provide ancillary information for solid tumor diagnosis, prognosis, and treatment response. The traditional radiomic pipeline involves segmentation of volumes of interest with comparison to normal brain. In other neurologic disorders, such as epilepsy, lesion delineation may be difficult or impossible due to poor anatomic definition, small size, and multifocal or diffuse distribution. Tuberous sclerosis complex (TSC) is a rare genetic disease in which brain magnetic resonance imaging (MRI) demonstrates multifocal abnormalities with variable imaging and epileptogenic features. The purpose of this study was to develop a radiomic workflow for identification of abnormal brain regions in TSC, using a whole-brain atlas-based approach with generation of heatmaps based on signal deviation from normal controls. This was a retrospective pilot study utilizing high-resolution whole-brain 3D FLAIR MRI datasets from retrospective enrollment of tuberous sclerosis complex (TSC) patients and normal controls. Subjects underwent MRI including high-resolution 3D FLAIR sequences. Preprocessing included skull stripping, coregistration, and intensity normalization. Using the Brainnetome and Harvard-Oxford atlases, brain regions were parcellated into 318 discrete regions. Expert neuroradiologists spatially labeled all tubers in TSC patients using ITK-SNAP. The pyradiomics toolbox was used to extract 88 radiomic features based on IBSI guidelines, comparing tuber-affected and non-tuber-affected parenchyma in TSC patients, as well as normal brain tissue in control patients. For model training and validation, regions with tubers from 20 TSC patients and 30 normal control subjects were randomly divided into two training sets (80%) and two validation sets (20%). Additional model testing was performed on a separate group of 20 healthy controls. LASSO (least absolute shrinkage and selection operator) was used to perform variable selection and regularization to identify regions containing tubers. Relevant radiomic features selected by LASSO were combined to produce a radiomic score ω, defined as the sum of squared differences from average control group values. Region-specific ω scores were converted to heat maps and spatially coregistered with brain MRI to reflect overall radiomic deviation from normal. The proposed radiomic workflow allows for quantification of deviation from normal in 318 regions of the brain with the use of a summative radiomic score ω. This score can be used to generate spatially registered heatmaps to identify brain regions with radiomic abnormalities. The pilot study of TSC showed radiomic scores ω that were statistically different in regions containing tubers from regions without tubers/normal brain (p<0.0001). Our model exhibits an AUC of 0.81 (95% confidence interval: 0.78-0.84) on the testing set, and the best threshold obtained on the training set, when applied to the testing set, allows us to identify regions with tubers with a specificity of 0.91 and a sensitivity of 0.60. We describe a whole-brain atlas-based radiomic approach to identify abnormal brain regions in TSC patients. This approach may be helpful for identifying specific regions of interest based on relatively greater signal deviation, particularly in clinical scenarios with numerous or poorly defined anatomic lesions.

A functional screen for ubiquitin regulation identifies an E3 ligase secreted by Pseudomonas aeruginosa.

Ubiquitin signaling controls many aspects of eukaryotic biology, including targeted protein degradation and immune defense. Remarkably, invading bacterial pathogens have adapted secreted effector proteins that hijack host ubiquitination to gain control over host responses. These ubiquitin-targeted effectors can exhibit, for example, E3 ligase or deubiquitinase activities, often without any sequence or structural homology to eukaryotic ubiquitin regulators. Such convergence in function poses a challenge to the discovery of additional bacterial virulence factors that target ubiquitin. To overcome this, we have developed a workflow to harvest natively secreted bacterial effectors and functionally screen them for ubiquitin regulatory activities. After benchmarking this approach on diverse ligase and deubiquitinase activities from Salmonella Typhimurium, Enteropathogenic Escherichia coli, and Shigella flexneri, we applied it to the identification of a cryptic E3 ligase activity secreted by Pseudomonas aeruginosa. We identified an unreported P. aeruginosa E3 ligase, which we have termed Pseudomonas Ub ligase 1 (PUL-1), that resembles none of the other E3 ligases previously established in or outside of the eukaryotic system. Importantly, in an animal model of P. aeruginosa infection, PUL-1 ligase activity plays an important role in regulating virulence. Thus, our workflow for the functional identification of ubiquitin-targeted effector proteins carries promise for expanding our appreciation of how host ubiquitin regulation contributes to bacterial pathogenesis.

Digital improvements in the design and construction process of classical Chinese garden rockeries: a study based on material digitization

Rockeries have a complex and significant role in classical Chinese garden designs. They present distinct artistic characteristics and spatial hierarchies and are crucial to garden heritage conservation. Craftsmanship in rockery construction is a significant part of China’s intangible cultural heritage. Rockeries are primarily composed of naturally occurring rocks chosen for their uniqueness and complex shapes and textures. These rocks present challenges as nonstandard elements within the traditional Chinese garden context, as it is not easy to depict them using conventional blueprints and models. This complicates the design, adjustment, display, and construction of rockeries, which lacks tangible bases for reference. Consequently, the preservation and restoration of garden rockeries is difficult, and the perpetuation and dissemination of rockery construction skills face numerous challenges. This study introduces a method that combines laser scanning and photographic measurements to digitize precisely nonstandard elements of rockery stones. This approach presents an innovative design and construction workflow for rockeries by refining design processes, showcasing real effects, and resolving assembly issues. The results demonstrate that the combination of three-dimensional laser scanning and close-range photogrammetry can accurately replicate the complex forms and textures of these nonstandard elements. The stone coding and digital management system devised based on the logic of construction effectively satisfies the design and building requirements of rockeries. Correspondingly, the proposed digital construction workflow enhances the accuracy of rockery design, presentation, and evaluation, thereby contributing to the protection and restoration of rockery heritage sites and the transmission of rockery construction techniques.

A novel barcoded nanopore sequencing workflow of high-quality, full-length bacterial 16S amplicons for taxonomic annotation of bacterial isolates and complex microbial communities.

Due to recent improvements, Nanopore sequencing has become a promising method for experiments relying on amplicon sequencing. We describe a flexible workflow to generate and annotate high-quality, full-length 16S rDNA amplicons. We evaluated it for two applications, namely, (i) identification of bacterial isolates and (ii) species-level profiling of microbial communities. We assessed the identification of single bacterial isolates by sequencing, using a set of barcoded full-length 16S rRNA gene primer pairs (pair A), on 47 isolates encompassing multiple genera and compared those results with matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS)-based identification. Species-level community profiling was tested with two sets of barcoded full-length 16S primer pairs (A and B) and compared to the results obtained with shotgun Illumina sequencing using 27 stool samples. We developed a Nextflow pipeline to retain high-quality reads and taxonomically annotate them. We found high agreement between our workflow and MALDI-TOF data for isolate identification (positive predictive value = 0.90, Cramér's V = 0.857, and Theil's U = 0.316). For species-level community profiling, we found strong correlations (rs > 0.6) of alpha diversity indices between the two primer sets and Illumina sequencing. At the community level, we found significant but small differences when comparing sequencing techniques. Finally, we found a moderate to strong correlation when comparing the relative abundances of individual species (average rs = 0.6 and 0.533 for primers A and B). Despite identified shortcomings, the proposed workflow enabled accurate identification of single bacterial isolates and prominent features in microbial communities, making it a worthwhile alternative to MALDI-TOF MS and Illumina sequencing.IMPORTANCEA quick, robust, simple, and cost-effective method to identify bacterial isolates and communities in each sample is indispensable in the fields of microbiology and infection biology. Recent technological advances in Oxford Nanopore Technologies sequencing make this technique an attractive option considering the adaptability, portability, and cost-effectiveness of the platform, even with small sequencing batches. Here, we validated a flexible workflow to identify bacterial isolates and characterize bacterial communities using the Oxford Nanopore Technologies sequencing platform combined with the most recent v14 chemistry kits. For bacterial isolates, we compared our nanopore-based approach to matrix-assisted laser desorption ionization-time of flight mass spectrometry-based identification. For species-level profiling of complex bacterial communities, we compared our nanopore-based approach to Illumina shotgun sequencing. For reproducibility purposes, we wrapped the code used to process the sequencing data into a ready-to-use and self-contained Nextflow pipeline.

Eyeing DNA barcoding for species identification of fish larvae.

Identification of fish larvae based on morphology is typically limited to higher taxonomic ranks (e.g., family or order), as larvae possess few morphological diagnostic characters for precise discrimination to species. When many samples are presented at any one time, the use of morphology to identify such specimens can be laborious and time-consuming. Using a reverse workflow for specimen sorting and identification leveraging high-throughput DNA sequencing, thousands of fish larvae can be DNA barcoded and sorted into molecular operational taxonomic units (mOTUs) in a single sequencing run with the nanopore sequencing technology (e.g., MinION). This process reduces the time and financial costs of morphology-based sorting and instead deploys experienced taxonomists for species taxonomic work where they are needed most. In this study, a total of 3022 fish larval specimens from plankton tows across four sites in Singapore were collected and sorted based on this workflow. Eye tissue from individual samples was used for DNA extraction and sequencing of cytochrome c oxidase subunit I. We generated a total of 2746 barcodes after quality filtering (90.9% barcoding success), identified 2067 DNA barcodes (75.3% identification success), and delimited 256 mOTUs (146 genera, 52 families). Our analyses identified specific challenges to species assignment, such as the potential misidentification of publicly available sequences used as reference barcodes. We highlighted how the conservative application and comparison of a local sequence database can help resolve identification conflicts. Overall, this proposed approach enables and expedites taxonomic identification of fish larvae, contributing to the enhancement of reference barcode databases and potentially better understanding of fish connectivity.

Optimization of BIPV Utilization with Parametric Approach: Case Study on Nearly Zero Carbon Building Studio Design in Medan, Indonesia

Buildings significantly consume fossil energy, leading to substantial carbon emissions contributing to global warming and rising sea levels. These environmental impacts pose serious challenges, necessitating a shift towards sustainable building designs. By integrating clean energy solutions into building architecture, we can reduce the environmental burden of emissions. This approach mitigates adverse effects on living organisms and ecosystems and promotes long-term sustainability and resilience in urban development. Building-Integrated Photovoltaic (BIPV) systems represent one such approach for substituting fossil energy. However, implementing BIPV effectively necessitates comprehensive analysis considering numerous parameters and variables. This paper introduces building design strategies employing the BIPV approach. The decision-making process in designing alternatives utilizes parametric methods, recognized for their ability to yield optimization results for various variables through the development of design algorithms. The study focuses on the design of a studio building at the University of North Sumatra. Comparative analysis of the results obtained through parametric design aims to identify the most favorable design outcomes. The research includes developing a parameterized design algorithm, workflow, and assessment results for selecting optimal design alternatives. The results indicate that the placement of PV panels on the side area (PVOS) has more potential than placement on the top area (PVOT). Despite the PVOT area being observed to be 0.57% smaller than PVOS, PVOS shows a significant increase in total Incident Radiation (IR), average IR, and total Energy Generated by 137.14%, 128.40%, and 132.97%, respectively.

Nudging Towards Sleep-Friendly Hospitalizations: A Multifaceted Approach on Reducing Unnecessary Overnight Interventions.

Choice architecture refers to the design of decision environments, which can influence healthcare decision-making. Nudges are subtle adjustments in these environments that guide decisions toward desired outcomes. For example, Computerized Provider Order Entry (CPOE) within Electronic Health Records (EHR) recommends frequencies for interventions such as nursing assessments and medication administrations, but these can link to around-the-clock schedules without clinical necessity. This study aimed to evaluate an intervention to promote sleep-friendly practices by optimizing choice architecture and employing targeted nudges on inpatient order frequencies. We employed a quasi-experimental interrupted time series analysis of a multifaceted, multiphase intervention to reduce overnight interventions in a hospital system. Our intervention featured EHR modifications to optimize the scheduling of vital sign checks, neurological checks, and medication administrations. Additionally, we used targeted secure messaging reminders and education on an inpatient neurology unit (INU) to supplement the initiative. Significant increases in sleep-friendly medication orders were observed at the academic medical center (AMC) and community hospital affiliate (CHA), particularly for acetaminophen and heparin at the AMC. This led to a reduction in overnight medication administrations, with the most substantial decrease observed with heparin at all locations (CHA: 18%, AMC: 10%, INU: 10%, p<0.05). Sleep-friendly vital sign orders increased significantly at all sites (AMC: 6.7%, CHA 4.3%, INU: 14%, p<0.05), and sleep-friendly neuro check orders increased significantly at the AMC (8.1%, p<0.05). There was also a significant reduction in overnight neurological checks at the AMC. Tailoring EHR modifications and employing multifaceted nudging strategies emerged as promising approaches for reducing unnecessary overnight interventions. The observed shifts in sleep-friendly ordering translated into decreases in overnight interventions. Multifaceted nudges can effectively influence clinician decision-making and patient care. The varied impacts across nudge types and settings emphasize the importance of thoughtful nudge design and understanding local workflows.

Accelerating imaging research at large-scale scientific facilities through scientific computing.

To date, computed tomography experiments, carried-out at synchrotron radiation facilities worldwide, pose a tremendous challenge in terms of the breadth and complexity of the experimental datasets produced. Furthermore, near real-time three-dimensional reconstruction capabilities are becoming a crucial requirement in order to perform high-quality and result-informed synchrotron imaging experiments, where a large amount of data is collected and processed within a short time window. To address these challenges, we have developed and deployed a synchrotron computed tomography framework designed to automatically process online the experimental data from the synchrotron imaging beamlines, while leveraging the high-performance computing cluster capabilities to accelerate the real-time feedback to the users on their experimental results. We have, further, integrated it within a modern unified national authentication and data management framework, which we have developed and deployed, spanning the entire data lifecycle of a large-scale scientific facility. In this study, the overall architecture, functional modules and workflow design of our synchrotron computed tomography framework are presented in detail. Moreover, the successful integration of the imaging beamlines at the Shanghai Synchrotron Radiation Facility into our scientific computing framework is also detailed, which, ultimately, resulted in accelerating and fully automating their entire data processing pipelines. In fact, when compared with the original three-dimensional tomography reconstruction approaches, the implementation of our synchrotron computed tomography framework led to an acceleration in the experimental data processing capabilities, while maintaining a high level of integration with all the beamline processing software and systems.

Machine Learning for Real-Time Building Outdoor Wind Environment Prediction Framework in Preliminary Design: Taking Xinjiekou Area of Nanjing, China as the Case

The incorporation of physical environmental performance as a primary consideration in building design can facilitate the harmonization of the built environment with the surrounding site and climate, enhance the building’s environmental adaptability and environmental friendliness, and contribute to the achievement of energy-saving and emission-reduction objectives through the integration of natural lighting and ventilation. General computational fluid dynamics (CFD) can help architects make accurate predictions and effectively control the building’s wind environment. However, CFD integration into the design workflow in the preliminary stages is frequently challenging due to program uncertainty, intricate parameter settings, and substantial computational expenses. This study offers a methodology and framework based on machine learning to overcome the complexity and computational cost barriers in simulating outdoor wind environments of buildings. In this framework, the machine learning model is trained using an automated CFD simulation system based on Butterfly and implemented within the Rhino and Grasshopper environment. This framework provides real-time simulation feedback within the design software and exhibits promising accuracy, with a Structural Similarity Index Measure (SSIM) ranging from 90–97% on a training dataset of 1200 unique urban geometries in Xinjiekou Area of Nanjing, China. Furthermore, we programmatically integrate various parts of the simulation and computation process to automate multiscenario CFD simulations and computations. This automation saves a significant amount of time in producing machine-learning training sets. Finally, we demonstrate the effectiveness and accuracy of the proposed working framework in the design process through a case study. Although our approach cannot replace CFD simulation computation in the later design stages, it can support architects in making design decisions in the preliminary stages with minimal effort and immediate performance feedback.