Building Blocks Of Architecture Research Articles

Biointerface Fiber Technology from Electrospinning to Inflight Printing.

Building two-dimensional (2D) and three-dimensional (3D) micro- and nanofibril structures with designable patterns and functionalities will offer exciting prospects for numerous applications spanning from permeable bioelectronics to tissue engineering scaffolds. This Spotlight on Applications highlights recent technological advances in fiber printing and patterning with functional materials for biointerfacing applications. We first introduce the current state of development of micro- and nanofibers with applications in biology and medical wearables. We then describe our contributions in developing a series of fiber printing techniques that enable the patterning of functional fiber architectures in three dimensions. These fiber printing techniques expand the material library and device designs, which underpin technological capabilities from enabling fundamental studies in cell migration to customizable and ecofriendly fabrication of sensors. Finally, we provide an outlook on the strategic pathways for developing the next-generation bioelectronics and "Fiber-of-Things" (FoT) using nano/micro-fibers as architectural building blocks.

A System for the Efficient Charging of EV Fleets

Smart charging is a means of monitoring and actively controlling EV chargers to optimize the distribution and consumption of energy with a focus on peak-load avoidance. This paper describes the most important requirements that have influenced the design and implementation of the “Smart Charging System” (SCS). It presents the architecture and main functional building blocks of the SCS, which have been realized in an iterative development process as an extension component of the already existing open-source solution “Open e-Mobility”. We also provide details on the functionality of the core smart charging algorithm within SCS and show how various data sources can be utilized by the system to increase the safety and efficiency of EV charging processes. Furthermore, we describe our iterative approach to developing the system, introduce the real-world testbed at SAP Labs France in Mougins/France, and share evaluation results and experiences gathered over a three-year period.

Neural oscillations and speech processing at birth

Are neural oscillations biologically endowed building blocks of the neural architecture for speech processing from birth, or do they require experience to emerge? In adults, delta, theta, and low-gamma oscillations support the simultaneous processing of phrasal, syllabic, and phonemic units in the speech signal, respectively. Using electroencephalography to investigate neural oscillations in the newborn brain we reveal that delta and theta oscillations differ for rhythmically different languages, suggesting that these bands underlie newborns' universal ability to discriminate languages on the basis of rhythm. Additionally, higher theta activity during post-stimulus as compared to pre-stimulus rest suggests that stimulation after-effects are present from birth.

A Review of Graphene‐Based Memristive Neuromorphic Devices and Circuits

As data processing volume increases, the limitations of traditional computers and the need for more efficient computing methods become evident. Neuromorphic computing mimics the brain's low‐power and high‐speed computations, making it crucial in the era of big data and artificial intelligence. One significant development in this field is the memristor, a device that exhibits neuromorphic tendencies. The performance of memristive devices and circuits relies on the materials used, with graphene being a promising candidate due to its unique properties. Researchers are investigating graphene‐based memristors for large‐scale, sustainable fabrication. Herein, progress in the development of graphene‐based memristive neuromorphic devices and circuits is highlighted. Graphene and its common fabrication methods are discussed. The fabrication and production of graphene‐based memristive devices are reviewed and comparisons are provided among graphene‐ and nongraphene‐based memristive devices. Next, a detailed synthesis of the devices utilizing graphene‐based memristors is provided to implement the basic building blocks of neuromorphic architectures, that is, synapses, and neurons. This is followed by reviewing studies building graphene memristive spiking neural networks (SNNs). Finally, insights on the prospects of graphene‐based neuromorphic memristive systems including their device‐ and network‐level challenges and opportunities are given.

Recent Progress of Graphene Fiber/Fabric Supercapacitors: From Building Block Architecture, Fiber Assembly, and Fabric Construction to Wearable Applications

Recent Progress of Graphene Fiber/Fabric Supercapacitors: From Building Block Architecture, Fiber Assembly, and Fabric Construction to Wearable Applications

Chromatin structure meets cryo-EM: Dynamic building blocks of the functional architecture

Chromatin is a dynamic molecular complex composed of DNA and proteins that package the DNA in the nucleus of eukaryotic cells. The basic structural unit of chromatin is the nucleosome core particle, composed of ~150 base pairs of genomic DNA wrapped around a histone octamer containing two copies each of four histones, H2A, H2B, H3, and H4. Individual nucleosome core particles are connected by short linker DNAs, forming a nucleosome array known as a beads-on-a-string fiber. Higher-order structures of chromatin are closely linked to nuclear events such as replication, transcription, recombination, and repair. Recently, a variety of chromatin structures have been determined by single-particle cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET), and their structural details have provided clues about the chromatin architecture functions in the cell. In this review, we highlight recent cryo-EM structural studies of a fundamental chromatin unit to clarify the functions of chromatin.

Two-Dimensional Hybrid Nanosheet-Based Supercapacitors: From Building Block Architecture, Fiber Assembly, and Fabric Construction to Wearable Applications.

Fiber-based supercapacitors (F-SCs) have inspired widespread interest in the fields of wearable technology, energy, and carbon neutralization due to their highly deformable flexibility, fast charging/discharging capability, long-term stability, and energy conservation ability. In this review, we summarize the latest developments for fabricating fibrous electrodes of F-SCs where advanced micro two-dimensional (2D) building blocks (e.g., MXene and graphene) are chemically assembled and constructed into ordered mesofibers and multifunctional macrofabrics. Diverse fundamental principles of 2D hybrid nanosheets with respect to surface controls, pseudocapacitive modifications, and microstructural manipulations, promoting rapid electron transfer and charge conduction, are introduced. Additionally, various spinning methods for assembling and fabricating sophisticated fibers with advanced nano/microstructures, including hierarchical skeletons, anisotropic backbones, surface/entire porous frameworks, and vertical-aligned networks, for boosting ionic kinetic transport/storage are presented. Likewise, the structure-activity relationships between the porous structure and electrochemical performance are clarified. Moreover, multifunctional fabrics in terms of high flexibilities/strengths, superior electrical conductivities, and stabilized operations, which realize large energy density, deformable capability, and robust stability under harsh conditions, are emphasized. In particular, the potential power-supply applications, including flexible electronic devices, self-powered functions, and energy-sensor systems, are highlighted. Finally, a short conclusion and outlook, along with the current challenges and future opportunities of next-generation F-SCs, are proposed.

Synapse Formation and Function Across Species: Ancient Roles for CCP, CUB, and TSP-1 Structural Domains.

The appearance of synapses was a crucial step in the creation of the variety of nervous systems that are found in the animal kingdom. With increased complexity of the organisms came a greater number of synaptic proteins. In this review we describe synaptic proteins that contain the structural domains CUB, CCP, or TSP-1. These domains are found in invertebrates and vertebrates, and CUB and CCP domains were initially described in proteins belonging to the complement system of innate immunity. Interestingly, they are found in synapses of the nematode C. elegans, which does not have a complement system, suggesting an ancient function. Comparison of the roles of CUB-, CCP-, and TSP-1 containing synaptic proteins in various species shows that in more complex nervous systems, these structural domains are combined with other domains and that there is partial conservation of their function. These three domains are thus basic building blocks of the synaptic architecture. Further studies of structural domains characteristic of synaptic proteins in invertebrates such as C. elegans and comparison of their role in mammals will help identify other conserved synaptic molecular building blocks. Furthermore, this type of functional comparison across species will also identify structural domains added during evolution in correlation with increased complexity, shedding light on mechanisms underlying cognition and brain diseases.

Prediction and Analysis of Artwork Price Based on Deep Neural Network

The use of deep learning methods to solve problems in the field of artwork prices has attracted widespread attention, especially the superiority of long short-term memory network (LSTM) in dealing with time series problems. However, the potential for deep learning in the prediction of artwork price has not been fully explored. This paper proposes a deep prediction network structure that considers the correlation between time series data and the combination of two-way LSTM as well as one-way LSTM networks to predict the price of artworks. This paper proposes a deep-level two-way and one-way LSTM to predict the price of artworks in the art market. Taking into account the potential reverse dependence of the time series, the bidirectional LSTM layer is used to obtain bidirectional time correlation from historical data. This research uses a matrix to represent the artwork price data and fully considers the spatial correlation characteristics of the artwork price. Simultaneously, this paper uses the two-way LSTM network to correlate the potential contextual information of the historical data of the artwork price stream and fully perform feature learning. This study applies the two-way LSTM network layer to the building blocks of the deep architecture to measure the inverse dependence of the price fluctuation data. The comparison with other prediction models shows that the LSTM neural network fused with one-way and two-way proposed in this paper is superior to other neural networks for predicting price of artworks in terms of prediction accuracy.

Double–Double laminates for aerospace applications — Finding best laminates for given load sets

The Double–Double (DD) family of laminates is characterized by the parametric laminate description [±φ,±ψ]rT. DD promises considerable advantages for aerospace laminates, as the unique combination of the laminates’ building-block architecture simplifies manufacturing and in particular laminate optimization. DD eliminates complicated permutation issues know from conventional laminates and allows for easy stiffness tailoring, by locally varying repeats r. The present paper proposes a DD-laminate optimization scheme for laminate-strength, which focusses on robustness in the identification of safe laminates. The scheme is presented for a multi-load scenario, each consisting of five individual loads. The combination of the Nettles circle failure enveloped in principle strain with the DD features lead to a rather simple optimization scheme, accompanied by meaningful illustrations, which are presented in the present paper.

Dual-Path Morph-UNet for Road and Building Segmentation From Satellite Images

Building footprints and road network detection have gained significant attention for map preparation, humanitarian aid dissemination, disaster management, to name a few. Traditionally, morphological filters excel at extracting shape features from remotely sensed images and have been widely used in the literature. However, the structural element (SE) dimension selection impedes these classical and learning-based methods utilizing any morphological operators. To overcome this aspect, we propose a novel framework to extract road and building from remote sensing (RS) images by exploiting morphological networks. The method predominantly aims at learning an optimized SE to capture variably-sized building and road footprints. We substitute convolutions with 2-D morphological operations in the basic building blocks of the network architecture (Dual-path Morph-UNet) to manage the intricate task of optimizing the SE in addition to the actual segmentation task. The dual-path framework incorporates parallel residual and dense paths in an encoder-decoder architecture, which permits learning of higher-level feature representations with fewer parameters. Finally, we implement the proposed framework on the benchmarked Massachusetts roads and buildings dataset and demonstrate superior results than the state-of-the-art (SOTA). In addition, the proposed network consists of 10x less learnable parameters than the SOTA methods.

Excited-state dynamics of dipyrrolyldiketone difluoroboron complexes.

Anion-responsive photofunctional materials have been extensively studied because anions are important for biotic activity and constitute the building blocks of elegant supramolecular architectures. A number of fluorescent anion receptors that can probe anions in their environments have been reported, but the excited states of many of these molecules remain elusive. Studies on excited-state dynamics provide fruitful information for optimizing the emission properties, minimizing the photodegradation and photorelease of anions, and exploring novel photofunctions. In this study, we investigated the excited-state dynamics of an aryl-substituted dipyrrolyldiketone difluoroboron complex, a π-conjugated anion receptor, by time-resolved visible and infrared absorption spectroscopy and emission decay measurements combined with quantum chemical calculations. Anion binding was found to alter the radiative and nonradiative rate constants and the excited-state absorption of the anion receptor. In contrast, the molecular structures and binding abilities were similar in the S0 and S1 states.

TAD-like single-cell domain structures exist on both active and inactive X chromosomes and persist under epigenetic perturbations

BackgroundTopologically associating domains (TADs) are important building blocks of three-dimensional genome architectures. The formation of TADs has been shown to depend on cohesin in a loop-extrusion mechanism. Recently, advances in an image-based spatial genomics technique known as chromatin tracing lead to the discovery of cohesin-independent TAD-like structures, also known as single-cell domains, which are highly variant self-interacting chromatin domains with boundaries that occasionally overlap with TAD boundaries but tend to differ among single cells and among single chromosome copies. Recent computational modeling studies suggest that epigenetic interactions may underlie the formation of the single-cell domains.ResultsHere we use chromatin tracing to visualize in female human cells the fine-scale chromatin folding of inactive and active X chromosomes, which are known to have distinct global epigenetic landscapes and distinct population-averaged TAD profiles, with inactive X chromosomes largely devoid of TADs and cohesin. We show that both inactive and active X chromosomes possess highly variant single-cell domains across the same genomic region despite the fact that only active X chromosomes show clear TAD structures at the population level. These X chromosome single-cell domains exist in distinct cell lines. Perturbations of major epigenetic components and transcription mostly do not affect the frequency or strength of the single-cell domains. Increased chromatin compaction of inactive X chromosomes occurs at a length scale above that of the single-cell domains.ConclusionsIn sum, this study suggests that single-cell domains are genome architecture building blocks independent of the tested major epigenetic components.

A reference framework for the implementation of data governance systems for industry 4.0

The fourth industrial revolution, or Industry 4.0, represents a new stage of evolution in the organization, management and control of the value chain throughout the product or service life cycle. This is mainly based on the digitalization of the industrial environment by means of the convergence of Information Technologies (IT) and operational Technologies (OT) through cyber-physical systems and the Industrial IoT (IIoT) and the use of data generated in real time for gaining insights and making decisions. Therefore data becomes a critical asset for Industry 4.0 and must be managed and governed like a strategic asset. We rely on Data Governance (DG) as a key instrument for carrying out this transformation. This paper presents the design of a specific governance framework for Industry 4.0. First, this contextualizes data governance for Industry 4.0 environments and identifies the requirements that this framework must address, which are conditioned by the specific features of Industry 4.0, among others, the intensive use of big data, the cloud and edge computing, the artificial intelligence and the current regulations. Next, we formally define a reference framework for the implementation of Data Governance Systems for Industry 4.0 using international standards and providing several examples of architecture building blocks.

Design of Hierarchical NiCo2O4 Nanocages with Excellent Electrocatalytic Dynamic for Enhanced Methanol Oxidation.

Although sheet-like materials have good electrochemical properties, they still suffer from agglomeration problems during the electrocatalytic process. Integrating two-dimensional building blocks into a hollow cage-like structure is considered as an effective way to prevent agglomeration. In this work, the hierarchical NiCo2O4 nanocages were successfully synthesized via coordinated etching and precipitation method combined with a post-annealing process. The nanocages are constructed through the interaction of two-dimensional NiCo2O4 nanosheets, forming a three-dimensional hollow hierarchical architecture. The three-dimensional supporting cavity effectively prevents the aggregation of NiCo2O4 nanosheets and the hollow porous feature provides amounts of channels for mass transport and electron transfer. As an electrocatalytic electrode for methanol, the NiCo2O4 nanocages-modified glassy carbon electrode exhibits a lower overpotential of 0.29 V than those of NiO nanocages (0.38 V) and Co3O4 nanocages (0.34 V) modified glassy carbon electrodes. The low overpotential is attributed to the prominent electrocatalytic dynamic issued from the three-dimensional hollow porous architecture and two-dimensional hierarchical feature of NiCo2O4 building blocks. Furthermore, the hollow porous structure provides sufficient interspace for accommodation of structural strain and volume change, leading to improved cycling stability. The NiCo2O4 nanocages-modified glassy carbon electrode still maintains 80% of its original value after 1000 consecutive cycles. The results demonstrate that the NiCo2O4 nanocages could have potential applications in the field of direct methanol fuel cells due to the synergy between two-dimensional hierarchical feature and three-dimensional hollow structure.

Influence of Poly(ethylene glycol) Molecular Architecture on Particle Assembly and Ex Vivo Particle-Immune Cell Interactions in Human Blood.

Poly(ethylene glycol) (PEG) is widely used in particle assembly to impart biocompatibility and stealth-like properties in vivo for diverse biomedical applications. Previous studies have examined the effect of PEG molecular weight and PEG coating density on the biological fate of various particles; however, there are few studies that detail the fundamental role of PEG molecular architecture in particle engineering and bio-nano interactions. Herein, we engineered PEG particles using a mesoporous silica (MS) templating method and investigated how the PEG building block architecture impacted the physicochemical properties (e.g., surface chemistry and mechanical characteristics) of the PEG particles and subsequently modulated particle-immune cell interactions in human blood. Varying the PEG architecture from 3-arm to 4-arm, 6-arm, and 8-arm generated PEG particles with a denser, stiffer structure, with increasing elastic modulus from 1.5 to 14.9 kPa, inducing an increasing level of immune cell association (from 15% for 3-arm to 45% for 8-arm) with monocytes. In contrast, the precursor PEG particles with the template intact (MS@PEG) were stiffer and generally displayed higher levels of immune cell association but showed the opposite trend-immune cell association decreased with increasing PEG arm numbers. Proteomics analysis demonstrated that the biomolecular corona that formed on the PEG particles minimally influenced particle-immune cell interactions, whereas the MS@PEG particle-cell interactions correlated with the composition of the corona that was abundant in histidine-rich glycoproteins. Our work highlights the role of PEG architecture in the design of stealth PEG-based particles, thus providing a link between the synthetic nature of particles and their biological behavior in blood.

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

Neural Architecture Search (NAS) is an open and challenging problem in machine learning. While NAS offers great promise, the prohibitive computational demand of most of the existing NAS methods makes it difficult to directly search the architectures on large-scale tasks. The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset. Despite a number of recent results that show the promise of transfer from proxy datasets, a comprehensive evaluation of different NAS methods studying the impact of different source datasets has not yet been addressed. In this work, we propose to analyze the architecture transferability of different NAS methods by performing a series of experiments on large scale benchmarks such as ImageNet1K and ImageNet22K. We find that: (i) The size and domain of the proxy set does not seem to influence architecture performance on the target dataset. On average, transfer performance of architectures searched using completely different small datasets (e.g., CIFAR10) perform similarly to the architectures searched directly on proxy target datasets. However, design of proxy sets has considerable impact on rankings of different NAS methods. (ii) While different NAS methods show similar performance on a source dataset (e.g., CIFAR10), they significantly differ on the transfer performance to a large dataset (e.g., ImageNet1K). (iii) Even on large datasets, random sampling baseline is very competitive, but the choice of the appropriate combination of proxy set and search strategy can provide significant improvement over it. We believe that our extensive empirical analysis will prove useful for future design of NAS algorithms.

DORR: A DOR-Based Non-Blocking Optical Router for 3D Photonic Network-on-Chips

Recently, researchers paid more attention on designing optical routers, since they are essential building blocks of all photonic interconnection architectures. Thus, improving them could lead to a spontaneous improvement in the overall performance of the network. Optical routers suffer from the dilemma of increased insertion loss and crosstalk, which upraises the power consumed as the network scales. In this paper, we propose a new 7×7 non-blocking optical router based on the Dimension Order Routing (DOR) algorithm. Moreover, we develop a method that can ensure the least number of MicroRing Resonators (MRRs) in an optical router. Therefore, by reducing these optical devices, the optical router proposed can decrease the crosstalk and insertion loss of the network. This optical router is evaluated and compared to Ye's router and the optimized crossbar for 3D Mesh network that uses XYZ routing algorithm. Unlike many other proposed routers, this paper evaluates optical routers not only from router level prospective yet also consider the overall network level condition. The appraisals show that our optical router can reduce the worst-case network insertion loss by almost 8.7%, 46.39%, 39.3%, and 41.4% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively. Moreover, it decreases the Optical Signal-to-Noise Ratio (OSNR) worst-case by almost 27.92%, 88%, 77%, and 69.6% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively. It also reduces the power consumption by 3.22%, 23.99%, 19.12%, and 20.18% compared to Ye's router, optimized crossbar, optimized universal OR, and Optimized VOTEX, respectively.

HIDRA 1.0: deep-learning-based ensemble sea level forecasting in the northern Adriatic

Abstract. Interactions between atmospheric forcing, topographic constraints to air and water flow, and resonant character of the basin make sea level modelling in the Adriatic a challenging problem. In this study we present an ensemble deep-neural-network-based sea level forecasting method HIDRA, which outperforms our set-up of the general ocean circulation model ensemble (NEMO v3.6) for all forecast lead times and at a minuscule fraction of the numerical cost (order of 2×10-6). HIDRA exhibits larger bias but lower RMSE than our set-up of NEMO over most of the residual sea level bins. It introduces a trainable atmospheric spatial encoder and employs fusion of atmospheric and sea level features into a self-contained network which enables discriminative feature learning. HIDRA architecture building blocks are experimentally analysed in detail and compared to alternative approaches. Results show the importance of sea level input for forecast lead times below 24 h and the importance of atmospheric input for longer lead times. The best performance is achieved by considering the input as the total sea level, split into disjoint sets of tidal and residual signals. This enables HIDRA to optimize the prediction fidelity with respect to atmospheric forcing while compensating for the errors in the tidal model. HIDRA is trained and analysed on a 10-year (2006–2016) time series of atmospheric surface fields from a single member of ECMWF atmospheric ensemble. In the testing phase, both HIDRA and NEMO ensemble systems are forced by the ECMWF atmospheric ensemble. Their performance is evaluated on a 1-year (2019) hourly time series from a tide gauge in Koper (Slovenia). Spectral and continuous wavelet analysis of the forecasts at the semi-diurnal frequency (12 h)−1 and at the ground-state basin seiche frequency (21.5 h)−1 is performed. The energy at the basin seiche in the HIDRA forecast is close to that observed, while our set-up of NEMO underestimates it. Analyses of the January 2015 and November 2019 storm surges indicate that HIDRA has learned to mimic the timing and amplitude of basin seiches.

A unified framework for inferring the multi-scale organization of chromatin domains from Hi-C.

Chromosomes are giant chain molecules organized into an ensemble of three-dimensional structures characterized with its genomic state and the corresponding biological functions. Despite the strong cell-to-cell heterogeneity, the cell-type specific pattern demonstrated in high-throughput chromosome conformation capture (Hi-C) data hints at a valuable link between structure and function, which makes inference of chromatin domains (CDs) from the pattern of Hi-C a central problem in genome research. Here we present a unified method for analyzing Hi-C data to determine spatial organization of CDs over multiple genomic scales. By applying statistical physics-based clustering analysis to a polymer physics model of the chromosome, our method identifies the CDs that best represent the global pattern of correlation manifested in Hi-C. The multi-scale intra-chromosomal structures compared across different cell types uncover the principles underlying the multi-scale organization of chromatin chain: (i) Sub-TADs, TADs, and meta-TADs constitute a robust hierarchical structure. (ii) The assemblies of compartments and TAD-based domains are governed by different organizational principles. (iii) Sub-TADs are the common building blocks of chromosome architecture. Our physically principled interpretation and analysis of Hi-C not only offer an accurate and quantitative view of multi-scale chromatin organization but also help decipher its connections with genome function.