3D Layout Research Articles

Transforming Urban Façade Condition Assessments with Semantic Data Visualizations and 3D Spatial Layouts from BIMs

Safety inspection of building façades in urban settings is critical for public safety, as many incidents/accidents frequently occur due to falls from façades. This inspection is required for condition assessment of current states and their comparison to assessments conducted in a previous inspection cycle. The current practice of façade condition assessment relies on static, scattered, and textual depictions, which prevents inspectors from having a comprehensive view of façade condition and comparing it to the previous inspection cycle’s findings. Integrated visualization of spatial and semantic data and providing this data based on the preferences of the decision makers are proven to be effective in various decision domains. This study builds on previous research efforts on integrated visualization techniques to identify façade inspectors’ preferences in comprehending inspection findings. Through the design of low-fidelity visualization prototypes, this study first identifies highly frequently preferred visualization techniques by inspectors. Based on these, this study then quantifies the impact of these identified visualization preferences on accuracy and efficiency of decisions in relation to condition assessment of building façades though a set of high-fidelity prototypes and user studies. The results show that integrated visualization of façade conditions has the potential to bring the efficiency of capturing a holistic view of the façade conditions up to 65% and increase the accuracy of decisions up to 41%. The findings of this study directly benefit inspection companies and city agencies who can deploy visualization techniques that are impactful for façade condition assessment in order to holistically assess building façades.

Space Optimization for Raw Material Transportation: Reducing Environmental Impact

In today’s industrial landscape, adhering to lean manufacturing principles and achieving operational excellence is increasingly vital due to rising competition. Eliminating waste, or Muda is essential for attaining a high level of lean manufacturing. One significant area for improvement is space optimization, which can be applied to production areas and logistic warehouses on a macroeconomic scale, or to trucks and pallets on a microeconomic scale. This study focuses on optimising truck space for the transportation of raw materials. An in-depth analysis of heuristics and metaheuristics methods was conducted to select the most suitable approach for this context. The chosen metaheuristic was then tailored in terms of parameters and design for this specific application. Simulation results indicate the optimal number of trucks required to meet demand and provide a 3D layout for storing raw material-filled boxes on pallets and pallets in trucks. The proposed solution is versatile and can be adapted for various storage optimization challenges within a given space.

3D Heterogeneous Sensing System for Multimode Parrallel Signal No-Spatiotemporal Misalignment Recognition.

The spatiotemporal error caused by planar tiled structure design and the waste of communication resources brought on by the transmission of a single channel are two challenges facing the development of multifunctional intelligent sensors with high-density integration. A homo-spatiotemporal multisensory parallel transmission system (HMPTs) is expanded to realize multisignal no-spatiotemporal misalignment recognition and efficient parallel transmission. First, this system optimizes the distribution of multifunctional sensors, completes the 3D vertical heterogeneous layout of four sensors, and achieves material multi-information detection at a single place with no-spatiotemporal deviation. Additionally, the system couples and transmittes multiple sensory signals, delivering a fourfold increase in transmission efficiency and one-third of the power consumption compared to a single-channel transmission system. Finally, this system is used for the recognition of mixed materials, and human-computer interaction to realize the assignment of materials in VR, demonstrating the great accuracy and transmission efficiency of HMPTs as well as its feasibility in practical application. This is an a priori effort to enhance machine perception accuracy, improve signal transmission effectiveness, and advance human-machine-object triadic integration.

Optimized thermoelectric generation for efficient low-medium temperature geothermal energy harvesting

This paper aims to design and evaluate a thermoelectric generation system optimized for low-to-medium temperature geothermal energy, focusing on maximizing power output and system efficiency. A one-dimensional mathematical model based on energy balance is employed for a single thermoelectric element and subsequently scaled to a 600 thermoelectric generators (TEGs) module. The Peltier, Fourier, and Joule heating effects are included in the analysis, whereas the radiation and convection effects are neglected. The impact of various 2D and 3D layouts of thermoelectric generators (TEGs) and temperature variations on power generation and performance is examined. An experiment is performed to observe the power capacity of a module consisting of five thermoelectric generators. These experimental observations validate the analytical model, and reference properties are adopted to predict more complex modules. This work aims to simulate the optimal design of a TEG module, utilizing the temperature difference to generate 1 kW of power. For this purpose, a module comprising of 600 TEGs is analyzed in various layouts. Based on experimental results for the temperature difference ΔT=96.4Co, it is proposed that a compact module consisting of 600 TEGs with a 3D design of 10 × 12 × 5 will be suitable and cost-effective to generate 1 kW power.

Metamaterial-Enabled Hybrid Receive Coil for Enhanced Magnetic Resonance Imaging Capabilities.

Magnetic resonance imaging (MRI) relies on high-performance receive coils to achieve optimal signal-to-noise ratio (SNR), but conventional designs are often bulky and complex. Recent advancements in metamaterial technology have led to the development of metamaterial-inspired receive coils that enhance imaging capabilities and offer design flexibility. However, these configurations typically face challenges related to reduced adaptability and increased physical footprint. This study introduces a hybrid receive coil design that integrates an array of capacitively-loaded ring resonators directly onto the same plane as the coil, preserving its 2D layout without increasing its size. Both the coil and metamaterial are individually non-resonant at the targeted Larmor frequency, but their mutual coupling induces a resonance shift, achieving a frequency match and forming a hybrid structure with enhanced SNR. Experimental validation on a 3.0 T MRI platform shows that this design allows for adjustable trade-offs between peak SNR and penetration depth, making it adaptable for various clinical imaging scenarios.

Large-scale-integration and collective oscillations of 2D artificial cells

The on-chip large-scale-integration of genetically programmed artificial cells capable of exhibiting collective expression patterns is important for fundamental research and biotechnology. Here, we report a 3D biochip with a 2D layout of 1024 DNA compartments as artificial cells on a 5 × 5 mm2 area. Homeostatic cell-free protein synthesis reactions driven by genetic circuits occur inside the compartments. We create a reaction-diffusion system with a 30 × 30 square lattice of artificial cells interconnected by thin capillaries for diffusion of products. We program the connected lattice with a synthetic genetic oscillator and observe collective oscillations. The microscopic dimensions of the unit cell and capillaries set the effective diffusion and coupling strength in the lattice, which in turn affects the macroscopic synchronization dynamics. Strongly coupled oscillators exhibit fast and continuous 2D fronts emanating from the boundaries, which generate smooth and large-scale correlated spatial variations of the oscillator phases. This opens a class of 2D genetically programmed nonequilibrium synthetic multicellular systems, where chemical energy dissipated in protein synthesis leads to large-scale spatiotemporal patterns.

Biobased coatings for architectural timber applied using the robotic 3D printing technique

Materials applied for coating architectural timber are often environmentally harmful. This study examines a sustainable alternative – a coating from microfibrillated cellulose hydrogel applied for the first time on architectural timber using robotic 3D printing. The proposed solution is evaluated through architectural design and robotic 3D printing experiments, with patterned coating designs deposed onto eight architectural mockups. Through qualitative and quantitative analyses of coating features preproduction and postproduction, fundamental aspects affecting coating design are identified, namely, the 3D printing path geometry and layout, substrate type, and precoating material. These aspects are correlated with the final architectural coating qualities at the mesoscale and macroscale by characterizing dimensional stability, geometric features, and color appearance. The best coating effects are observed for pine substrates precoated with hydroxyethyl cellulose. By delivering new knowledge on biobased architectural coatings, this study contributes to the global effort of phasing out fossil-based materials in the built environment.

Spectroscopic, quantum computational, molecular dynamic simulations, and molecular docking investigation of the biologically important compound-2,6-diaminopyridine

To establish the role in future drug development, 2,6-diamino pyridine (2,6-DAP) was investigated experimentally using computational tools. NMR (1H,13C), FTIR, and UV–Vis spectra were analyzed for the titled compound. The molecular structure was optimized via DFT with the “B3LYP method with 6-311++G(d,p)” The optimized parameters of the structure were assessed with the observed bond characteristics. The vibrational frequencies were studied, PED was carried out, and the calculated vibrational frequency obtained was compared with the experimentally obtained FTIR. NMR (1H,13C) was calculated and compared with the experimentally obtained spectra. UV–vis spectra are calculated in the gaseous phase and different mediums of solvents (methanol, DMSO) by employing the PCM solvent model and TD-DFT method, and the estimated value was compared with experimentally measured data. NBO analysis was done to study the donor-acceptor interaction. The extent of electron charge transfer inside the molecular structure was examined using HOMO–LUMO energy values. The study of excitation analysis led to the creation of E.D.D and H.D.D maps in methanol and DMSO. The AIM and ELF analysis was employed to compute the iso-surface projections, ellipticity, and binding energies. The MEP and Fukui functions evaluate the molecule’s reactive zones. Hirshfeld research was done to analyze interactions among the molecules in the structure of the crystal, and 3D Hirshfeld tops and 2D fingerprint layouts were developed. The molecular docking procedure was used, followed by a 100 ns MD simulation and computation of binding free energy found to delineate the binding affinity of the molecule toward human carbonic anhydrase II (HCA II). Molecular docking studies with several receptor proteins were also conducted to determine the most effective protein-ligand interactions. Biological assessments like drug-likeness also act upon the compound to verify the molecule’s drug-like nature.

Pairpot: a database with real-time lasso-based analysis tailored for paired single-cell and spatial transcriptomics.

Paired single-cell and spatially resolved transcriptomics (SRT) data supplement each other, providing in-depth insights into biological processes and disease mechanisms. Previous SRT databases have limitations in curating sufficient single-cell and SRT pairs (SC-SP pairs) and providing real-time heuristic analysis, which hinder the effort to uncover potential biological insights. Here, we developed Pairpot (http://pairpot.bioxai.cn), a database tailored for paired single-cell and SRT data with real-time heuristic analysis. Pairpot curates 99 high-quality pairs including 1,425,656 spots from 299 datasets, and creates the association networks. It constructs the curated pairs by integrating multiple slices and establishing potential associations between single-cell and SRT data. On this basis, Pairpot adopts semi-supervised learning that enables real-time heuristic analysis for SC-SP pairs where Lasso-View refines the user-selected SRT domains within milliseconds, Pair-View infers cell proportions of spots based on user-selected cell types in real-time and Layer-View displays SRT slices using a 3D hierarchical layout. Experiments demonstrated Pairpot's efficiency in identifying heterogeneous domains and cell proportions.

Line-of-Sight Probability Models for 5G/6G Millimeter-Wave Networks: A Review

In the fifth-generation (5G) and future wireless networks, coverage planning for millimeter-wave (mm-wave) networks in a three-dimensional (3D) urban environment depends on the communication channel and line-of-sight (LoS) probability models. This is especially true when there are big stationary obstructions. The current literature lacks a complete review of the existing LoS probability models. To bridge this gap, this paper reviews modeling approaches and mathematical models of the LoS probability. We discovered that, due to the complexity of modeling a large number of 3D layouts, most existing models have focused on a few urban layouts. We developed a 3D synthetic urban layout generator that is based on game engine technology to simulate any ITU-R P.1410 urban layouts and collect the ray-tracing. We provided a proof-of-concept to show that the game technology could handle the complexity of simulating ray-tracing in 3D urban environments. Moreover, we addressed some of the biggest challenges and gaps in the current LoS probability literature and suggested some valid opportunities.

Printed Interconnects for Heterogeneous Systems Integration on Flexible Substrates

AbstractHeterogeneous integration on flexible substrates has been explored in recent years to meet the high‐performance and flexible form factors requirements of several emerging applications. Reliable interconnects, in both 2D and 3D layouts, are critical for the effective use of these systems. But it is challenging to employ conventional bonding and interconnect technology due to considerable mismatches in mechanical and thermal properties. For example, the ultra‐thin chips (UTCs) are too fragile to withstand the static or oscillating forces applied during conventional bonding. In this regard, printing of metal tracks on flexible substrates is a promising alternative to access the contact pads on integrated circuits (ICs), as the interconnects on diverse substrates can be realized at room temperature processing using as much materials as needed. Further, it is easier to attain step coverage. Considering the above attractive features, and the challenges associated with conventional bonding techniques, this article focuses on the development of high‐resolution printing interconnects in both 2D and 3D layouts and explores various printing routes available for high density integration. The article also discusses major conventional interposers/interconnects forming methods and their limitations for flexible hybrid electronics along with few examples of printed interconnects to highlight the opportunities for future.

AI-Driven BIM Integration for Optimizing Healthcare Facility Design

Efficient healthcare facility design is crucial for providing high-quality healthcare services. This study introduces an innovative approach that integrates artificial intelligence (AI) algorithms, specifically particle swarm optimization (PSO), with building information modeling (BIM) and digital twin technologies to enhance facility layout optimization. The methodology seamlessly integrates AI-driven layout optimization with the robust visualization, analysis, and real-time capabilities of BIM and digital twins. Through the convergence of AI algorithms, BIM, and digital twins, this framework empowers stakeholders to establish a virtual environment for the streamlined exploration and evaluation of diverse design options, significantly reducing the time and manual effort required for layout design. The PSO algorithm generates optimized 2D layouts, which are seamlessly transformed into 3D BIM models through visual programming in Dynamo. This transition enables stakeholders to visualize, analyze, and monitor designs comprehensively, facilitating well-informed decision-making and collaborative discussions. The study presents a comprehensive methodology that underscores the potential of AI, BIM, and digital twin integration, offering a path toward more efficient and effective facility design.

BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation

We present BlockFusion, a diffusion-based model that generates 3D scenes as unit blocks and seamlessly incorporates new blocks to extend the scene. BlockFusion is trained using datasets of 3D blocks that are randomly cropped from complete 3D scene meshes. Through per-block fitting, all training blocks are converted into the hybrid neural fields: with a tri-plane containing the geometry features, followed by a Multi-layer Perceptron (MLP) for decoding the signed distance values. A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed. Diffusion applied to the latent representations allows for high-quality and diverse 3D scene generation.To expand a scene during generation, one needs only to append empty blocks to overlap with the current scene and extrapolate existing latent tri-planes to populate new blocks. The extrapolation is done by conditioning the generation process with the feature samples from the overlapping tri-planes during the denoising iterations. Latent tri-plane extrapolation produces semantically and geometrically meaningful transitions that harmoniously blend with the existing scene. A 2D layout conditioning mechanism is used to control the placement and arrangement of scene elements. Experimental results indicate that BlockFusion is capable of generating diverse, geometrically consistent and unbounded large 3D scenes with unprecedented high-quality shapes in both indoor and outdoor scenarios.

Designer cellular spheroids with DNA origami for drug screening.

Current in vitro models struggle to balance the complexity of human diseases with suitability for large-scale drug tests. While 3D cultures simulate human tissues, they lack cellular intricacy, and integrating these models with high-throughput drug screening remains a challenge. Here, we introduce a method that uses self-assembling nucleic acid nanostructures decorated living cells, termed NACs, to create spheroids with a customizable 3D layout. To demonstrate its uniqueness, our method effectively creates designer 3D spheroids by combining parenchymal cells, stromal cells, and immune cells, leading to heightened physiological relevance and detailed modeling of complex chronic diseases and immune-stromal interactions. Our approach achieves a high level of biological fidelity while being standardized and straightforward to construct with the potential for large-scale drug discovery applications. By merging the precision of DNA nanotechnology with advanced cell culture techniques, we are streamlining human-centric models, striking a balance between complexity and standardization, to boost drug screening efficiency.

A Modular 512-Channel Neural Signal Acquisition ASIC for High-Density 4096 Channel Electrophysiology.

The complexity of information processing in the brain requires the development of technologies that can provide spatial and temporal resolution by means of dense electrode arrays paired with high-channel-count signal acquisition electronics. In this work, we present an ultra-low noise modular 512-channel neural recording circuit that is scalable to up to 4096 simultaneously recording channels. The neural readout application-specific integrated circuit (ASIC) uses a dense 8.2 mm × 6.8 mm 2D layout to enable high-channel count, creating an ultra-light 350 mg flexible module. The module can be deployed on headstages for small animals like rodents and songbirds, and it can be integrated with a variety of electrode arrays. The chip was fabricated in a TSMC 0.18 µm 1.8 V CMOS technology and dissipates a total of 125 mW. Each DC-coupled channel features a gain and bandwidth programmable analog front-end along with 14 b analog-to-digital conversion at speeds up to 30 kS/s. Additionally, each front-end includes programmable electrode plating and electrode impedance measurement capability. We present both standalone and in vivo measurements results, demonstrating the readout of spikes and field potentials that are modulated by a sensory input.

A three-dimensionally architected electronic skin mimicking human mechanosensation.

Human skin sensing of mechanical stimuli originates from transduction of mechanoreceptors that converts external forces into electrical signals. Although imitating the spatial distribution of those mechanoreceptors can enable developments of electronic skins capable of decoupled sensing of normal/shear forces and strains, it remains elusive. We report a three-dimensionally (3D) architected electronic skin (denoted as 3DAE-Skin) with force and strain sensing components arranged in a 3D layout that mimics that of Merkel cells and Ruffini endings in human skin. This 3DAE-Skin shows excellent decoupled sensing performances of normal force, shear force, and strain and enables development of a tactile system for simultaneous modulus/curvature measurements of an object through touch. Demonstrations include rapid modulus measurements of fruits, bread, and cake with various shapes and degrees of freshness.

Application Cluster Analysis as a Support form Modelling and Digitalizing the Logistics Processes in Warehousing

The article deals with the application of cluster analysis in modeling in-house processes, specifically supply processes. An algorithm is designed on the theoretical basis of cluster analysis and according to the analysis of the supply processes in selected industrial companies. Specifically, the algorithm is based on the hierarchical methods of cluster analysis, and the selected hierarchical clustering method is applicable in modeling storage systems under various production conditions in industrial companies. The methodology of clustering regarding the supply processes is subsequently experimentally verified. Based on the results of the cluster analysis, a system of organization was proposed for the analyzed warehouse in the form of 2D and 3D layout models of the warehouse.

A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management

Our study introduces a new approach, leveraging robotics technology and remote sensing for multifaceted applications in forest and wildfire management. Presented in this paper is PULSAR, an innovative UAV with reconfigurable capabilities, able of operating as a quadcopter, a co-axial quadcopter, and a standalone octocopter. Tailored to diverse operational requirements, PULSAR accommodates multiple payloads, showcasing its adaptability and versatility. This paper meticulously details material selection and design methods, encompassing both initial and detailed design, while the electronics design section seamlessly integrates essential avionic components. The 3D drone layout design, accomplished using SOLIDWORKS, enhances understanding by showcasing all three different configurations of PULSAR’s structure. Serving a dual purpose, this study highlights UAV applications in forest and wildfire management, particularly in detailed forest mapping, edge computing, and cartographic product generation, as well as detection and tracking of elements, illustrating how a UAV can be a valuable tool. Following the analysis of applications, this paper presents the selection and integration of payloads onto the UAV. Simultaneously, each of the three distinct UAV configurations is matched with a specific forest application, ensuring optimal performance and efficiency. Lastly, computational validation of the UAV’s main components’ structural integrity is achieved through finite element analysis (FEA), affirming the absence of issues regarding stress and displacement. In conclusion, this research underscores the efficacy of PULSAR, marking a significant leap forward in applying robotics technology for wildfire science.

PaCh (Packed Chemicals): Computationally Effective Binary Format for Chemical Structure Encoding.

In this work, we propose a versatile molecule and reaction encoding binary data format that aims to bridge the gap between the advantages of SMILES, like local stereo- and implicit hydrogen encoding, and block-structured MDL MOL with a 2D layout and explicit bond encoding, while addressing their respective limitations. Our new format introduces a balance between size efficiency, processing speed, and comprehensive representation, making it well-suited for various applications in cheminformatics, including deep learning, data storage, and searching. By offering an explicit approach to store atom connectivity (including implicit hydrogens), electronic state, stereochemistry, and other crucial molecular attributes, our proposal seeks to enhance data storage efficiency and promote interoperability among different software tools.

Palladium(II) complexes of thiobenzamide derivative ligands: Synthesis, crystal structure, supramolecular architecture, Hirshfeld surface analysis, and in vitro antibacterial and antifungal activities

Three thiobenzamide derivative ligands and their palladium complexes were synthesized. The synthesized compounds were characterized by several instrumental methods, such as 1H NMR, 13C NMR, and FT–IR. Additionally, the appropriate crystal of the [PdIICl2(L3H)2] complex was analyzed to determine the molecular structure. The single–crystal X–ray diffraction (SC–XRD) revealed that [PdIICl2(L3H)2] complex has a monoclinic crystal system, C2/c space group, a = 12.2969(11) Å, b = 18.7618(16) Å, c = 16.899(2) Å, β = 97.875(4)°. The intramolecular and intermolecular interactions in the crystal structure led to the formation of two synthon systems. In addition, a quantitative investigation of the noncovalent interactions in the structure was carried out through the utilization of Hirshfeld surface and 2D–fingerprint plot analyzes. Analysis of [PdIICl2(L3H)2] using 2D fingerprint analysis revealed a range of intermolecular interactions responsible for its stabilization. Among these interactions, intermolecular H···H (51.90%), Br···H/H···Br (16.00%), and Cl⋯H/H···Cl (10.40%) were found to be the most prominent types of interaction. The 3D layout of the crystal packing was evaluated during the energy framework computations, demonstrating that the electrostatic energy framework had a significant impact over the dispersion energy framework. Antibacterial and antifungal activities of the thiobenzamide derivative palladium complexes were also tested against Gram–positive (E. faecalis, S. aureus, B. subtilis, and S. pneumonia) and Gram–negative (P. aeruginosa and E. coli) bacterises and against fungi (C. glabrata and C. albicans). Palladium complexes exhibited to be more effective against fungi than against bacteria according to the MIC values. Particularly, [PdIICl2(L2H)]2 complex showed an excellent antifungal effect among the other palladium complexes, with low MIC values of 3.90 and 7.81 μg/mL against C. glabrata and C. albicans fungi, respectively.