3D Printing Manufacturing of Lithium Batteries: Prospects and Challenges toward Practical Applications.

The manufacturing and assembly of components within cells have a direct impact on the sample performance. Conventional processes restrict the shapes, dimensions, and structures of the commercially available batteries. 3D printing, a novel manufacturing process for precision and practicality, is expected to revolutionize the lithium battery industry owing to its advantages of customization, mechanization, and intelligence. This technique can be used to effectively construct intricate 3D structures that enhance the designability, integrity, and electrochemical performance of both liquid- and solid-state lithium batteries. In this study, an overview of the development of 3D printing technologies is provided and their suitability for comparison with conventional printing processes is assessed. Various 3D printing technologies applicable to lithium-ion batteries have been systematically introduced, especially more practical composite printing technologies. The practicality, limitations, and optimization of 3D printing are discussed dialectically for various battery modules, including electrodes, electrolytes, and functional architectures. In addition, all-printed batteries are emphatically introduced. Finally, the prospects and challenges of 3D printing in the battery industry are evaluated.

Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design.

Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions in 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts have been made to design customized architectures specifically for this purpose. This paper presents a comprehensive evaluation on transfer learning based solutions and automatically designed networks, analyzing the accuracy and robustness of different recognition models in three folds. First, we develop six different DCNN models (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) based on transfer learning. Second, we adapt the Bayesian optimization method to optimize a CNN network (BONet) for classifying breast lesions. A retrospective dataset of 3034 US images collected from various hospitals is then used for evaluation. Extensive tests show that the BONet outperforms other models, exhibiting higher accuracy (83.33%), lower generalization gap (1.85%), shorter training time (66 min), and less model complexity (approximately 0.5 million weight parameters). We also compare the diagnostic performance of all models against that by three experienced radiologists. Finally, we explore the use of saliency maps to explain the classification decisions made by different models. Our investigation shows that saliency maps can assist in comprehending the classification decisions.

A Dynamically Stable Sulfide Electrolyte Architecture for High-Performance All-Solid-State Lithium Metal Batteries.

All-solid-state batteries employing sulfide solid electrolyte and Li metal anode are promising because of their high safety and energy densities. However, the interface between Li metal and sulfides suffers from catastrophic instability which stems the practical use. Here, a dynamically stable sulfide electrolyte architecture to construct the hierarchy of interface stability is reported. By rationally designing the multilayer structures of sulfide electrolytes, the dynamic decomposing-alloying process from MS4 (M = Ge or Sn) unit in sulfide interlayer can significantly prohibit Li dendrite penetration is revealed. The abundance of highly electronic insulating decompositions, such as Li2 S, at the sulfide interlayer interface helps to well constrain the dynamic decomposition process and preserve the long-term polarization stability is also highlighted. By using Li6 PS5 Cl||Li10 SnP2 S12 ||Li6 PS5 Cl electrolyte architecture, Li metal anode shows an unprecedented critical current density over 3 mA cm-2 and achieves the steady over-potential for ≈900 hours. Based upon the merits, the Li||LiNi0.8 Co0.1 Mn0.1 O2 battery delivers a remarkable 75.3% retention even after 600 cycles at 1 C (1C-0.95 mA cm-2 ) under a low stack pressure of 15 MPa.

A novel architecture design for artificial intelligence-assisted culture conservation management system.

Native culture construction has been a prevalent issue in many countries, and its integration with intelligent technologies seems promising. In this work, we take the Chinese opera as the primary research object and propose a novel architecture design for an artificial intelligence-assisted culture conservation management system. This aims to address simple process flow and monotonous management functions provided by Java Business Process Management (JBPM). This aims to address simple process flow and monotonous management functions. On this basis, the dynamic nature of process design, management, and operation is also explored. We offer process solutions that align with cloud resource management through automated process map generation and dynamic audit management mechanisms. Several software performance testing works are conducted to evaluate the performance of the proposed culture management system. The testing results show that the design of such an artificial intelligence-based management system can work well for multiple scenarios of culture conservation affairs. This design has a robust system architecture for the protection and management platform building of non-heritage local operas, which has specific theoretical significance and practical reference value for promoting the protection and management platform building of non-heritage local operas and promoting the transmission and dissemination of traditional culture profoundly and effectively.

Advanced Three-Dimensional Microelectrode Architecture Design for High-Performance On-Chip Micro-Supercapacitors.

The rapid development of miniaturized electronic devices has greatly stimulated the endless pursuit of high-performance on-chip micro-supercapacitors (MSCs) delivering both high energy and power densities. To this end, an advanced three-dimensional (3D) microelectrode architecture design offers enormous opportunities due to high mass loading of active materials, large specific surface areas, fast ion diffusion kinetics, and short electron transport pathways. In this review, we summarize the recent advances in the rational design of 3D architectured microelectrodes including 3D dense microelectrodes, 3D nanoporous microelectrodes, and 3D macroporous microelectrodes. Furthermore, the emergent microfabrication strategies are discussed in detail in terms of charge storage mechanisms and structure-performance correlation for on-chip MSCs. Finally, we conclude with a perspective on future opportunities and challenges in this thriving field.

Presenting a conceptual model for designing hospital architecture with a patient-centered approach based on the patient's lived experience of sense of place in the therapeutic space.

BACKGROUND: In recent years, among managers and designers of health-care spaces, there has been a growing tendency to move toward hospital design by combining patient perceptions and expectations of the physical environment of the care area. The main idea of this study was to present a conceptual model of hospital architecture in our country with a patient-centered approach based on some factors that were affecting the sense of place. This model determined the architectural features of treatment spaces from a patient's lived experience that could have a positive mental effect on patients as well. The main question of the research was how to adapt the objective perception to the patient's mental perception to create a sense of place in the hospital space? MATERIALS AND METHODS: This research was qualitative with a phenomenological approach, conducted between July and December 2020. Purposeful sampling consisted of 23 patients, 13 males in the male surgery unit and 10 females in the gynecology unit, who were interviewed in-depth. They were hospitalized for at least 3 days in two hospitals (Dr. Pirooz in Lahijan and Ghaem in Rasht). The data were analyzed by the Colaizzi method. RESULTS: The results consisted of 530 primary codes, 57 subthemes, and 7 main themes. The main themes were hospital location, access to hospital, hospital identity, hospital dependency, hospital attachment, human interactions in the hospital, and hospital evaluation. CONCLUSION: The hospital form guided the patient, and the hospital function directed and obviated the patient's needs. The healing environment and human interactions with it caused the patient to be satisfied with the hospital environment.

Joint design and compression of convolutional neural networks as a Bi-level optimization problem.

Over the last decade, deep neural networks have shown great success in the fields of machine learning and computer vision. Currently, the CNN (convolutional neural network) is one of the most successful networks, having been applied in a wide variety of application domains, including pattern recognition, medical diagnosis and signal processing. Despite CNNs' impressive performance, their architectural design remains a significant challenge for researchers and practitioners. The problem of selecting hyperparameters is extremely important for these networks. The reason for this is that the search space grows exponentially in size as the number of layers increases. In fact, all existing classical and evolutionary pruning methods take as input an already pre-trained or designed architecture. None of them take pruning into account during the design process. However, to evaluate the quality and possible compactness of any generated architecture, filter pruning should be applied before the communication with the data set to compute the classification error. For instance, a medium-quality architecture in terms of classification could become a very light and accurate architecture after pruning, and vice versa. Many cases are possible, and the number of possibilities is huge. This motivated us to frame the whole process as a bi-level optimization problem where: (1) architecture generation is done at the upper level (with minimum NB and NNB) while (2) its filter pruning optimization is done at the lower level. Motivated by evolutionary algorithms' (EAs) success in bi-level optimization, we use the newly suggested co-evolutionary migration-based algorithm (CEMBA) as a search engine in this research to address our bi-level architectural optimization problem. The performance of our suggested technique, called Bi-CNN-D-C (Bi-level convolution neural network design and compression), is evaluated using the widely used benchmark data sets for image classification, called CIFAR-10, CIFAR-100 and ImageNet. Our proposed approach is validated by means of a set of comparative experiments with respect to relevant state-of-the-art architectures.

Design Strategies for Large Current Density Hydrogen Evolution Reaction.

Hydrogen energy is considered one of the cleanest and most promising alternatives to fossil fuel because the only combustion product is water. The development of water splitting electrocatalysts with Earth abundance, cost-efficiency, and high performance for large current density industrial applications is vital for H2 production. However, most of the reported catalysts are usually tested within relatively small current densities (< 100 mA cm-2), which is far from satisfactory for industrial applications. In this minireview, we summarize the latest progress of effective non-noble electrocatalysts for large current density hydrogen evolution reaction (HER), whose performance is comparable to that of noble metal-based catalysts. Then the design strategy of intrinsic activities and architecture design are discussed, including self-supporting electrodes to avoid the detachment of active materials, the superaerophobicity and superhydrophilicity to release H2 bubble in time, and the mechanical properties to resist destructive stress. Finally, some views on the further development of high current density HER electrocatalysts are proposed, such as scale up of the synthesis process, in situ characterization to reveal the micro mechanism, and the implementation of catalysts into practical electrolyzers for the commercial application of as-developed catalysts. This review aimed to guide HER catalyst design and make large-scale hydrogen production one step further.

Architecture Design and Interface Engineering of Self-assembly VS4/rGO Heterostructures for Ultrathin Absorbent.

The employment of microwave absorbents is highly desirable to address the increasing threats of electromagnetic pollution. Importantly, developing ultrathin absorbent is acknowledged as a linchpin in the design of lightweight and flexible electronic devices, but there are remaining unprecedented challenges. Herein, the self-assembly VS4/rGO heterostructure is constructed to be engineered as ultrathin microwave absorbent through the strategies of architecture design and interface engineering. The microarchitecture and heterointerface of VS4/rGO heterostructure can be regulated by the generation of VS4 nanorods anchored on rGO, which can effectively modulate the impedance matching and attenuation constant. The maximum reflection loss of 2VS4/rGO40 heterostructure can reach - 43.5 dB at 14 GHz with the impedance matching and attenuation constant approaching 0.98 and 187, respectively. The effective absorption bandwidth of 4.8 GHz can be achieved with an ultrathin thickness of 1.4 mm. The far-reaching comprehension of the heterointerface on microwave absorption performance is explicitly unveiled by experimental results and theoretical calculations. Microarchitecture and heterointerface synergistically inspire multi-dimensional advantages to enhance dipole polarization, interfacial polarization, and multiple reflections and scatterings of microwaves. Overall, the strategies of architecture design and interface engineering pave the way for achieving ultrathin and enhanced microwave absorption materials.

On Architecture Selection for Linear Inverse Problems with Untrained Neural Networks.

In recent years, neural network based image priors have been shown to be highly effective for linear inverse problems, often significantly outperforming conventional methods that are based on sparsity and related notions. While pre-trained generative models are perhaps the most common, it has additionally been shown that even untrained neural networks can serve as excellent priors in various imaging applications. In this paper, we seek to broaden the applicability and understanding of untrained neural network priors by investigating the interaction between architecture selection, measurement models (e.g., inpainting vs. denoising vs. compressive sensing), and signal types (e.g., smooth vs. erratic). We motivate the problem via statistical learning theory, and provide two practical algorithms for tuning architectural hyperparameters. Using experimental evaluations, we demonstrate that the optimal hyperparameters may vary significantly between tasks and can exhibit large performance gaps when tuned for the wrong task. In addition, we investigate which hyperparameters tend to be more important, and which are robust to deviations from the optimum.

An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions.

The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS.

Architectural Design for Enhanced C2 Product Selectivity in Electrochemical CO2 Reduction Using Cu-Based Catalysts: A Review.

Electrochemical CO2 reduction to value-added chemicals and fuels is a promising approach to mitigate the greenhouse effect arising from anthropogenic CO2 emission and energy shortage caused by the depletion of nonrenewable fossil fuels. The generation of multicarbon (C2+) products, especially hydrocarbons and oxygenates, is of great interest for industrial applications. To date, Cu is the only metal known to catalyze the C-C coupling in the electrochemical CO2 reduction reaction (eCO2RR) with appreciable efficiency and kinetic viability to produce a wide range of C2 products in aqueous solutions. Nonetheless, poor product selectivity associated with Cu is the main technical problem for the application of the eCO2RR technology on a global scale. Based on extensive research efforts, a delicate and rational design of electrocatalyst architecture using the principles of nanotechnology is likely to significantly affect the adsorption energetics of some key intermediates and hence the inherent reaction pathways. In this review, we summarize recent progress that has been achieved by tailoring the electrocatalyst architecture for efficient electrochemical CO2 conversion to the target C2 products. By considering the experimental and computational results, we further analyze the underlying correlations between the architecture of a catalyst and its selectivity toward C2 products. Finally, the major challenges are outlined, and directions for future development are suggested.

Systems Architecture Design Pattern Catalogfor Developing Digital Twins.

A digital twin is a digital replica of a physical entity to which it is remotely connected. A digital twin can provide a rich representation of the corresponding physical entity and enables sophisticated control for various purposes. Although the concept of the digital twin is largely known, designing digital twins based systems has not yet been fully explored. In practice, digital twins can be applied in different ways leading to different architectural designs. To guide the architecture design process, we provide a pattern-oriented approach for architecting digital twin-based Internet of Things (IoT) systems. To this end, we propose a catalog of digital twin architecture design patterns that can be reused in the broad context of systems engineering. The patterns are described using the well-known documentation template and support the various phases in the systems engineering life cycle process. For illustrating the application of digital twin patterns, we adopt a case study in the agriculture and food domain.

[Key technologies and applications of industrial big data in manufacturing of Chinese medicine].

Along with the striding of the Chinese medicine(CM) manufacturing toward the Industry 4.0, some digital factories have accumulated lightweight industrial big data, which become part of the enterprise assets. These digital assets possess the possibility of solving the problems within the CM production system, like the Sigma gap and the poverty of manufacturing knowledge. From the holistic perspective, a three-tiered architecture of CM industrial big data is put forward, and it consists of the data integration layer, the data analysis layer and the application scenarios layer. In data integration layer, sensing of CM critical quality attributes is the key technology for big data collection. In data analysis and mining layer, the self-developed iTCM algorithm library and model library are introduced to facilitate the implementation of the model lifecycle methodologies, including process model development, model validation, model configuration and model maintenance. The CM quality transfer structure is closely related with the connection mode of multiple production units. The system modeling technologies, such as the partition-integration modeling method, the expanding modeling method and path modeling method, are key to mapping the structure of real manufacturing system. It is pointed out that advance modeling approaches that combine the first-principles driven and data driven technologies are promising in the future. At last, real-world applications of CM industrial big data in manufacturing of injections, oral solid dosages, and formula particles are presented. It is shown that the industrial big data can help process diagnosis, quality forming mechanism interpretations, real time release testing method development and intelligent product formulation design. As renewable resources, the CM industrial big data enable the manufacturing knowledge accumulation and product quality improvement, laying the foundation of intelligent manufacturing.

BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework.

Human intelligence is constituted by a multitude of cognitive functions activated either directly or indirectly by external stimuli of various kinds. Computational approaches to the cognitive sciences and to neuroscience are partly premised on the idea that computational simulations of such cognitive functions and brain operations suspected to correspond to them can help to further uncover knowledge about those functions and operations, specifically, how they might work together. These approaches are also partly premised on the idea that empirical neuroscience research, whether following on from such a simulation (as indeed simulation and empirical research are complementary) or otherwise, could help us build better artificially intelligent systems. This is based on the assumption that principles by which the brain seemingly operate, to the extent that it can be understood as computational, should at least be tested as principles for the operation of artificial systems. This paper explores some of the principles of the brain that seem to be responsible for its autonomous, problem-adaptive nature. The brain operating system (BrainOS) explicated here is an introduction to ongoing work aiming to create a robust, integrated model, combining the connectionist paradigm underlying neural networks and the symbolic paradigm underlying much else of AI. BrainOS is an automatic approach that selects the most appropriate model based on the (a) input at hand, (b) prior experience (a history of results of prior problem solving attempts), and (c) world knowledge (represented in the symbolic way and used as a means to explain its approach). It is able to accept diverse and mixed input data types, process histories and objectives, extract knowledge and infer a situational context. BrainOS is designed to be efficient through its ability to not only choose the most suitable learning model but to effectively calibrate it based on the task at hand.

Key technologies and applications of industrial big data in manufacturing of Chinese medicine / 中国中药杂志

Along with the striding of the Chinese medicine(CM) manufacturing toward the Industry 4.0, some digital factories have accumulated lightweight industrial big data, which become part of the enterprise assets. These digital assets possess the possibility of solving the problems within the CM production system, like the Sigma gap and the poverty of manufacturing knowledge. From the holistic perspective, a three-tiered architecture of CM industrial big data is put forward, and it consists of the data integration layer, the data analysis layer and the application scenarios layer. In data integration layer, sensing of CM critical quality attributes is the key technology for big data collection. In data analysis and mining layer, the self-developed iTCM algorithm library and model library are introduced to facilitate the implementation of the model lifecycle methodologies, including process model development, model validation, model configuration and model maintenance. The CM quality transfer structure is closely related with the connection mode of multiple production units. The system modeling technologies, such as the partition-integration modeling method, the expanding modeling method and path modeling method, are key to mapping the structure of real manufacturing system. It is pointed out that advance modeling approaches that combine the first-principles driven and data driven technologies are promising in the future. At last, real-world applications of CM industrial big data in manufacturing of injections, oral solid dosages, and formula particles are presented. It is shown that the industrial big data can help process diagnosis, quality forming mechanism interpretations, real time release testing method development and intelligent product formulation design. As renewable resources, the CM industrial big data enable the manufacturing knowledge accumulation and product quality improvement, laying the foundation of intelligent manufacturing.

An empirical study to investigate the efficacy of collaborative immersive virtual reality systems for designing information architecture of software systems.

The ability of Immersive Virtual Reality (IVR) systems to mimic the real world has made it possible to use this technology to create environments for remote collaborative work. This study aimed to understand the feasibility of immersive virtual reality when conducting a collaborative Information Architecture (IA) design task-card sorting, with geographically dispersed participants. Using a between-subjects experimental design, thirty groups of two individuals each completed a card sorting activity using conventional in-person, video screen-sharing method or immersive virtual reality methods. The dependent measures included total time, percentage match with master card set, usability, presence and perceived workload. Overall usability was found to be significantly higher for the immersive virtual reality condition when compared to conventional in-person card sorting. In addition, the new immersive virtual reality technology performed as well as the other two conditions for other dependent variables. Qualitative data from the participants also indicated a positive reaction to the use of immersive virtual reality for this task. Overall, the participants felt they were productive and enjoyed the IVR condition, indicating the potential of IVR-based approaches as an alternative to conventional approaches for IA design.

Bioinspired Architecture Selection for Multitask Learning.

Faced with a new concept to learn, our brain does not work in isolation. It uses all previously learned knowledge. In addition, the brain is able to isolate the knowledge that does not benefit us, and to use what is actually useful. In machine learning, we do not usually benefit from the knowledge of other learned tasks. However, there is a methodology called Multitask Learning (MTL), which is based on the idea that learning a task along with other related tasks produces a transfer of information between them, what can be advantageous for learning the first one. This paper presents a new method to completely design MTL architectures, by including the selection of the most helpful subtasks for the learning of the main task, and the optimal network connections. In this sense, the proposed method realizes a complete design of the MTL schemes. The method is simple and uses the advantages of the Extreme Learning Machine to automatically design a MTL machine, eliminating those factors that hinder, or do not benefit, the learning process of the main task. This architecture is unique and it is obtained without testing/error methodologies that increase the computational complexity. The results obtained over several real problems show the good performances of the designed networks with this method.

Component-Based Modelling for Scalable Smart City Systems Interoperability: A Case Study on Integrating Energy Demand Response Systems.

Smart city systems embrace major challenges associated with climate change, energy efficiency, mobility and future services by embedding the virtual space into a complex cyber-physical system. Those systems are constantly evolving and scaling up, involving a wide range of integration among users, devices, utilities, public services and also policies. Modelling such complex dynamic systems' architectures has always been essential for the development and application of techniques/tools to support design and deployment of integration of new components, as well as for the analysis, verification, simulation and testing to ensure trustworthiness. This article reports on the definition and implementation of a scalable component-based architecture that supports a cooperative energy demand response (DR) system coordinating energy usage between neighbouring households. The proposed architecture, called refinement of Cyber-Physical Component Systems (rCPCS), which extends the refinement calculus for component and object system (rCOS) modelling method, is implemented using Eclipse Extensible Coordination Tools (ECT), i.e., Reo coordination language. With rCPCS implementation in Reo, we specify the communication, synchronisation and co-operation amongst the heterogeneous components of the system assuring, by design scalability and the interoperability, correctness of component cooperation.

Application of Chemical Doping and Architectural Design Principles To Fabricate Nanowire Co2Ni3ZnO8 Arrays for Aqueous Asymmetric Supercapacitors.

Electrode materials derived from transition metal oxides have a serious problem of low electron transfer rate, which restricts their practical application. However, chemically doped graphene transforms the chemical bonding configuration to enhance electron transfer rate and, therefore, facilitates the successful fabrication of Co2Ni3ZnO8 nanowire arrays. In addition, the Co2Ni3ZnO8 electrode materials, considered as Ni and Zn ions doped into Co3O4, have a high electron transfer rate and electrochemical response capability, because the doping increases the degree of crystal defect and reaction of Co/Ni ions with the electrolyte. Hence, the Co2Ni3ZnO8 electrode exhibits a high rate property and excellent electrochemical cycle stability, as determined by electrochemical analysis of the relationship between specific capacitance, IR drop, Coulomb efficiency, and different current densities. From the results of a three-electrode system of electrochemical measurement, the Co2Ni3ZnO8 electrode demonstrates a specific capacitance of 1115 F g(-1) and retains 89.9% capacitance after 2000 cycles at a current density of 4 A g(-1). The energy density of the asymmetric supercapacitor (AC//Co2Ni3ZnO8) is 54.04 W h kg(-1) at the power density of 3200 W kg(-1).