Innovative Architecture Research Articles

Early modernism in Zagreb. Novakova street.

Architectural modernism appeared in cities of the Kingdom of Yugoslavia during the end of the twenties and beginning of the thirties of the XXth century with different manifestations but within a quite coherent cultural scenario. In those years, most of the Yugoslavian cities were transforming themselves and they were all searching for a new identity, where a new architectural “style” had a substantial importance. The cultural life in these cities was extremely rich and open to external influences, also due to the presence of young architects and academics that had studied abroad and were eager to bring in their own towns their personal experiences. Zagreb was certainly one of the most active cities from an economical, social and cultural point, a city where urban transformations and architectural innovation were reinforcing each other. The unique example of the residential development along Novakova street must be seen as a very early attempt of creating a modern identity, also in relationship to other important European experiences, first with the Weissenhof in Stuttgart.

An 8 bit-ENOB Sampling-rate Reconfigurable Asynchronous SAR ADC with Metastability Watchdog Circuit for Activity-driven Multi-Channel CMOS Readout ASICs for Space Applications

This article presents an innovative architecture and design of an 8-bits Effective-Number-of-Bits (ENOB) Asynchronous Successive-Approximation-Register (ASAR) Analog-to-Digital-Converter (ADC), aiming at activity-driven multi-channel Complementary-Metal-Oxide-Semiconductor (CMOS) readout Application-Specific-Integrated-Circuits (ASICs) for imaging and spectroscopic applications in space field. The proposed ADC needs only a single low duty-cycle clock signal ‘CLKSampling’ to initiate the conversion process and generates all other required clock signals internally. Unlike conventional ASAR ADCs, this ADC makes use of a unique rising edge only variable delay-cell to introduce desired delay values to the internally generated clock signal. In addition, the ADC features sampling-rate (fs)-reconfigurability, from typical 40 kHz to 167 kHz, with a proportional power consumption. Also, the proposed architecture is flexible with respect to the time-period and duty-cycle of the ‘CLKSampling’ signal, that can be tailored keeping in view the primary requirements of the desired applications. Moreover, the proposed design is equipped with a simple metastability watchdog circuitry. Fabricated in a 0.35 µm CMOS process at 3.3 V supply, the ADC secures a measured ENOB, and Signal-to-Noise-and-Distortion-Ratio (SNDR) of 7.76-bits, and 48.47 dB, respectively at the typical fs of 40 kHz. Measured Integral-Non-Linearity (INL), and Differential-Non-Linearity (DNL) are within one LSB. The ADC normalized figure-of-merit (FoMWN) - proposed to fairly compare high voltage-based ADCs with low voltage-based ADCs - is 476 fJ/C-step at its maximum fs with all the sections of the ADC operating at 3.3 V. The FoMWN improves to 261.7 fJ/c-step and 243 fJ/c-step with the digital section and delay-cell operated at 1.2 V with and without level-shifters power contribution, respectively without any performance deterioration.

The influence of multisensory indoor environment on the perception of orange juice

Drinking (as well as eating) represents a fundamental multisensory experience of our lives. Recent studies have proposed the implementation of multisensory contexts to improve the taste perception of food and beverages. This topic becomes relevant when considering the possibility of promoting the consumption of healthy beverages (e.g., orange juice). Only a few studies have concentrated on the multisensory features of the indoor environment (e.g., acoustic and visual context) reporting effects on the perception of healthy beverages. This study investigates how multisensory indoor environments modify the taste perception while drinking orange juice. By controlling the room microclimate and combining the correlated colour temperature of lighting (i.e. warm and cool) and the background wall colour (i.e., red, green) in the Sens i-Lab, with different audio stimuli (i.e. low and high-frequency pure tone), participants were invited to taste the low-sugar orange juice and to rate the fruit juice aroma, sweetness, sourness, bitterness, freshness, and thickness. The results showed that listening to a high-frequency tone in an environment with warm lighting and a red background increases the sweetness perception of orange juice compared to the control environment. In addition, compared to the control condition, the juice aroma was rated more intense in almost all multisensory conditions and thicker with cool lighting and a green background while listening to a low-frequency tone. Our findings suggested that the development of multisensory design can contribute to interior design strategies and design innovation in food architecture, where people can improve their taste in healthy foods and beverages.

Transpiler-Based Architecture Design Model for Back-End Layers in Software Development

The utilization of software architectures and designs is widespread in software development, offering conceptual frameworks to address recurring challenges. A transpiler is a tool that automatically converts source code from one high-level programming language to another, ensuring algorithmic equivalence. This study introduces an innovative software architecture design model that integrates transpilers into the back-end layer, enabling the automatic transformation of business logic and back-end components from a single source code (the coding artifact) into diverse equivalent versions using distinct programming languages (the automatically produced code). This work encompasses both abstract and detailed design aspects, covering the proposal, automated processes, layered design, development environment, nest implementations, and cross-cutting components. In addition, it defines the main target audiences, discusses pros and cons, examines their relationships with prevalent design paradigms, addresses considerations about compatibility and debugging, and emphasizes the pivotal role of the transpiler. An empirical experiment involving the practical application of this model was conducted by implementing a collaborative to-do list application. This paper comprehensively outlines the relevant methodological approach, strategic planning, precise execution, observed outcomes, and insightful reflections while underscoring the the model’s pragmatic viability and highlighting its relevance across various software development contexts. Our contribution aims to enrich the field of software architecture design by introducing a new way of designing multi-programming-language software.

Semantic Innovation as Design Strategy for Sustainability and Ergonomics – A Case Study

This study deploys the framework of architectural innovation (Henderson and Clark 1990), demonstrating that a design-driven strategy leverages incremental and radical innovation through a system of core design concepts. The presented case study of an Italian high-end kitchen producer, Valcucine, is particularly interesting due to its track of innovation in sustainable and ergonomic systems with high aesthetic quality. Findings reveal how meanings are encapsulated in the core design concepts and explored in the strategy of technological innovation ­­­– from architectural to modular, radical to incremental. The findings also support previous studies arguing that semantics in design-driven industries are nested into technological innovation.

An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings.

Identifying and managing osteosarcoma pose significant challenges, especially in resource-constrained developing nations. Advanced diagnostic methods involve isolating the nucleus from cancer cells for comprehensive analysis. However, two main challenges persist: mitigating image noise during the capture and transmission of cellular sections, and providing an efficient, accurate, and cost-effective solution for cell nucleus segmentation. To tackle these issues, we introduce the Twin-Self and Cross-Attention Vision Transformer (TSCA-ViT). This pioneering AI-based system employs a directed filtering algorithm for noise reduction and features an innovative transformer architecture with a twin attention mechanism for effective segmentation. The model also incorporates cross-attention-enabled skip connections to augment spatial information. We evaluated our method on a dataset of 1000 osteosarcoma pathology slide images from the Second People's Hospital of Huaihua, achieving a remarkable average precision of 97.7%. This performance surpasses traditional methodologies. Furthermore, TSCA-ViT offers enhanced computational efficiency owing to its fewer parameters, which results in reduced time and equipment costs. These findings underscore the superior efficacy and efficiency of TSCA-ViT, offering a promising approach for addressing the ongoing challenges in osteosarcoma diagnosis and treatment, particularly in settings with limited resources.

Does black-box supplier involvement help buyers' product modular and architectural innovation? The moderating role of product modularity

PurposeIn product modular design tasks, especially in the high-tech manufacturing industry, buyers and supplies play distinct roles, which may have different impacts on product architectural and modular innovation. Prior research has tended to view product innovation as a holistic concept, overlooking the importance of this differentiated influence. This study, from a modular design perspective, aims to clarify the impact of black-box supplier involvement on product architectural and modular innovation, as well as the influence of product modularity on these relationships.Design/methodology/approachBased on the theory of product modular design, this study decomposes product innovation into architectural and modular innovation from the perspective of the product internal structure to conduct in-depth theoretical analysis and model construction. A total of 276 valid questionnaires are collected from typical Chinese high-tech manufacturing firms and used to empirically test the constructed theoretical model using multiple hierarchical regression analysis.FindingsThe results show that black-box supplier involvement positively affects modular innovation and takes an inverted U-shape, as moderated by product modularity. However, the impact of black-box supplier involvement on architectural innovation shows contradictory differences at different modularity levels. Under a low level of product modular design, black-box supplier involvement has a negative impact on architectural innovation, but under a moderate level of modular design, it has a positive impact. After the degree of modular design exceeds a certain threshold, the impact gradually weakens.Practical implicationsThe results provide valuable insights for managers, highlighting the need to avoid oversimplifying the innovation impact of black-box suppliers solely based on overall product innovation. Instead, a more accurate assessment of the innovation contributions of both the buyer and supplier should be based on the degree of architectural and modular innovation. Additionally, the findings suggest that managers should consider the alignment between their company's product modular design features and innovation priorities (i.e. modular innovation or architectural innovation) when determining an appropriate supplier collaborative development strategy.Originality/valueThis study not only reveals the different impacts of black-box supplier involvement on architectural and modular innovation, but also proves the significant synergistic innovation effect of the relationship between black-box supplier involvement and product modularization. It constitutes an enriched and deepened exploration in the existing research on supplier involvement in product innovation.

Systematic Review of Architectural Innovations in Nigerian Social Housing Towards Advancing SDG-11

Systematic Review of Architectural Innovations in Nigerian Social Housing Towards Advancing SDG-11

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and involves time-consuming trial-and-error processes, along with substantial computational resources. To overcome this challenge, an automated network design method known as Modified Teaching-Learning-Based Optimization with Refined Knowledge Sharing (MTLBORKS-CNN) is introduced. It autonomously searches for optimal CNN architectures, achieving high classification performance on specific datasets without human intervention. MTLBORKS-CNN incorporates four key features. It employs an effective encoding scheme for various network hyperparameters, facilitating the search for innovative and valid network architectures. During the modified teacher phase, it leverages a social learning concept to calculate unique exemplars that effectively guide learners while preserving diversity. In the modified learner phase, self-learning and adaptive peer learning are incorporated to enhance knowledge acquisition of learners during CNN architecture optimization. Finally, MTLBORKS-CNN employs a dual-criterion selection scheme, considering both fitness and diversity, to determine the survival of learners in subsequent generations. MTLBORKS-CNN is rigorously evaluated across nine image datasets and compared with state-of-the-art methods. The results consistently demonstrate MTLBORKS-CNN’s superiority in terms of classification accuracy and network complexity, suggesting its potential for infrastructural development of smart devices.

Recent Advances in Skin-Interfaced Wearable Sweat Sensors: Opportunities for Equitable Personalized Medicine and Global Health Diagnostics.

Recent advances in skin-interfaced wearable sweat sensors enable the noninvasive, real-time monitoring of biochemical signals associated with health and wellness. These wearable platforms leverage microfluidic channels, biochemical sensors, and flexible electronics to enable the continuous analysis of sweat-based biomarkers such as electrolytes, metabolites, and hormones. As this field continues to mature, the potential of low-cost, continuous personalized health monitoring enabled by such wearable sensors holds significant promise for addressing some of the formidable obstacles to delivering comprehensive medical care in under-resourced settings. This Perspective highlights the transformative potential of wearable sweat sensing for providing equitable access to cutting-edge healthcare diagnostics, especially in remote or geographically isolated areas. It examines the current understanding of sweat composition as well as recent innovations in microfluidic device architectures and sensing strategies by showcasing emerging applications and opportunities for innovation. It concludes with a discussion on expanding the utility of wearable sweat sensors for clinically relevant health applications and opportunities for enabling equitable access to innovation to address existing health disparities.

Implementing Tensor-Organized Memory for Message Retrieval Purposes in Neuromorphic Chips

This paper introduces Tensor-Organized Memory (TOM), a novel neuromorphic architecture inspired by the human brain’s structural and functional principles. Utilizing spike-timing-dependent plasticity (STDP) and Hebbian rules, TOM exhibits cognitive behaviors similar to the human brain. Compared to conventional architectures using a simplified leaky integrate-and-fire (LIF) neuron model, TOM showcases robust performance, even in noisy conditions. TOM’s adaptability and unique organizational structure, rooted in the Columnar-Organized Memory (COM) framework, position it as a transformative digital memory processing solution. Innovative neural architecture, advanced recognition mechanisms, and integration of synaptic plasticity rules enhance TOM’s cognitive capabilities. We have compared the TOM architecture with a conventional floating-point architecture, using a simplified LIF neuron model. We also implemented tests with varying noise levels and partially erased messages to evaluate its robustness. Despite the slight degradation in performance with noisy messages beyond 30%, the TOM architecture exhibited appreciable performance under less-than-ideal conditions. This exploration into the TOM architecture reveals its potential as a framework for future neuromorphic systems. This study lays the groundwork for future applications in implementing neuromorphic chips for high-performance intelligent edge devices, thereby revolutionizing industries and enhancing user experiences within the power of artificial intelligence.

Temporal-Aware Neural Network for Dense Non-Rigid Structure from Motion

Modern neural networks addressing dense Non-Rigid Structure from Motion (NRSFM) dilemmas often grapple with intricate a priori constraints, deterring scalability, or overlook the imperative of consistent application of a priori knowledge throughout the entire input sequence. In this paper, an innovative neural network architecture is introduced. Initially, the complete 2D sequence image undergoes embedding into a low-dimensional space. Subsequently, multiple self-attention layers are employed to extract inter-frame features, with the objective of deriving a more continuous and temporally smooth low-dimensional structure closely resembling real data’s intrinsic structure. Moreover, it has been demonstrated by others that gradient descent during the training of multilayer linear networks yields minimum rank solutions, implicitly providing regularization that is equally applicable to this task. Benefiting from the excellence of the proposed network architecture, no additional a priori knowledge is mandated, barring the constraint of temporal smoothness. Extensive experimentation confirms the method’s exceptional performance in addressing dense NRSFM challenges, outperforming recent results across various dense benchmark datasets.

Towards Feasible Capsule Network for Vision Tasks

Capsule networks exhibit the potential to enhance computer vision tasks through their utilization of equivariance for capturing spatial relationships. However, the broader adoption of these networks has been impeded by the computational complexity of their routing mechanism and shallow backbone model. To address these challenges, this paper introduces an innovative hybrid architecture that seamlessly integrates a pretrained backbone model with a task-specific capsule head (CapsHead). Our methodology is extensively evaluated across a range of classification and segmentation tasks, encompassing diverse datasets. The empirical findings robustly underscore the efficacy and practical feasibility of our proposed approach in real-world vision applications. Notably, our approach yields substantial 3.45% and 6.24% enhancement in linear evaluation on the CIFAR10 dataset and segmentation on the VOC2012 dataset, respectively, compared to baselines that do not incorporate the capsule head. This research offers a noteworthy contribution by not only advancing the application of capsule networks, but also mitigating their computational complexities. The results substantiate the feasibility of our hybrid architecture, thereby paving the way for a wider integration of capsule networks into various computer vision tasks.

Tradition and Modernity: Inheritance and Innovation of Ancient Chinese Architecture

With a history of five thousand years, China has made many outstanding achievements in architecture. Even in modern times with advanced technology, there are still many advantages that can be used for reference. This article expounds the modern development of ancient Chinese architecture through the unique structure of ancient Chinese architecture and the application and evolution of ancient Chinese architectural styles in modern times. In general, the two unique structures of ancient Chinese architecture, the mortise and tenon structure and the bucket arch structure, due to the development of science and technology, no longer need them to play a load-bearing role. Now they are more used as decorations to reflect Chinese culture in architecture. middle. The architectural style of ancient China is also combined with modern architectural style to simplify the cumbersome construction process while still retaining the unique charm of ancient Chinese architecture. The main purpose of this study is to study the application and development trend of ancient Chinese architecture in modern times.

TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation

BackgroundWith the outbreak and spread of COVID-19 worldwide, limited ventilators fail to meet the surging demand for mechanical ventilation in the ICU. Clinical models based on structured data that have been proposed to rationalize ventilator allocation often suffer from poor ductility due to fixed fields and laborious normalization processes. The advent of pre-trained models and downstream fine-tuning methods allows for learning large amounts of unstructured clinical text for different tasks. But the hardware requirements of large-scale pre-trained models and purposeless networks downstream have led to a lack of promotion in the clinical domain. ObjectiveIn this study, an innovative architecture of a task-driven predictive model is proposed and a Task-driven Gated Recurrent Attention Pool model (TGRA-P) is developed based on the architecture. TGRA-P predicts early mortality risk from patients' clinical notes on mechanical ventilation in the ICU, which is used to assist clinicians in diagnosis and decision-making. MethodsSpecifically, a Task-Specific Embedding Module is proposed to fine-tune the embedding with task labels and save it as static files for downstream calls. It serves the task better and prevents GPU overload. The Gated Recurrent Attention Unit (GRA) is proposed to further enhance the dependency of the information preceding and following the text sequence with fewer parameters. In addition, we propose a Residual Max Pool (RMP) to avoid ignoring words in common text classification tasks by incorporating all word-level features of the notes for prediction. Finally, we use a fully connected decoding network as a classifier to predict the mortality risk. ResultThe proposed model shows very promising results with an AUROC of 0.8245±0.0096, an AUPRC of 0.7532±0.0115, an accuracy of 0.7422±0.0028 and F1-score of 0.6612±0.0059 for 90-day mortality prediction using clinical notes of ICU mechanically ventilated patients on the MIMIC-III dataset, all of which are better than previous studies. Moreover, the superiority of the proposed model in comparison with other baseline models is also statistically validated through the calculated Cohen's d effect sizes. ConclusionThe experimental results show that TGRA-P based on the innovative task-driven prognostic architecture obtains state-of-the-art performance. In future work, we will build upon the provided code and investigate its applicability to different datasets. The model balances performance and efficiency, not only reducing the cost of early mortality risk prediction but also assisting physicians in making timely clinical interventions and decisions. By incorporating textual records that are challenging for clinicians to utilize, the model serves as a valuable complement to physicians' judgment, enhancing their decision-making process.

Programmable Data Plane Intelligence: Advances, Opportunities, and Challenges

Recently, Programmable Data Planes (PDPs) have brought new opportunities to unprecedented innovations in network protocols and architectures. Among them, programmable data plane intelligence, including achieving networking for Machine Learning (ML) and ML for networking with PDP, has attracted great interest from researchers. Motivated by this research trend, this article presents a survey of recent advances in the use of PDP in terms of machine learning. We first introduce PDP intelligence and the benefits of assisting ML with PDP. Then, the difficulties of realizing it are shown, which are caused by PDP's limited capabilities. After that, we present recent advances to cope with the difficulties, where each work is analyzed from multiple perspectives. Finally, we show the opportunities and challenges for further research.

Exploration and Application of Cloud-Network Integration Control Architecture in Industry

Cloud-network integration(CNI) is an inevitable choice to enhance the supply capacity of industrial Internet. In traditional industrial control systems, there are problems such as insufficient depth of perception, insufficient connectivity and insufficient analytical predictability. Firstly, an industrial CNI control architecture is proposed in this paper. Then a software-defined PLC integrated development & runtime environment based on the cloud framework is developed and deployed on a private cloud platform. An efficient 5G private network is created to facilitate wireless omnidirectional connectivity of cloud PLC. The problems of complex industrial field circuits and difficult to unify protocols are solved through 5G intelligent terminal devices. Finally, the cloud control architecture and software platform are successfully applied to the remote centralized control of the tundish preparation system in a steel enterprise’s two steelmaking plants. The results shows that the innovative system architecture meet the requirements for high-performance access, transmission, computing and storage in industrial plants, which achieves the cloud control of entire process industrial automation production.

Deep learning denoising by dimension reduction: Application to the ORION-B line cubes

Context. The availability of large bandwidth receivers for millimeter radio telescopes allows for the acquisition of position-position-frequency data cubes over a wide field of view and a broad frequency coverage. These cubes contain a lot of information on the physical, chemical, and kinematical properties of the emitting gas. However, their large size coupled with an inhomogenous signal-to-noise ratio (S/N) are major challenges for consistent analysis and interpretation. Aims. We searched for a denoising method of the low S/N regions of the studied data cubes that would allow the low S/N emission to be recovered without distorting the signals with a high S/N. Methods. We performed an in-depth data analysis of the 13CO and C17O (1–0) data cubes obtained as part of the ORION-B large program performed at the IRAM 30 m telescope. We analyzed the statistical properties of the noise and the evolution of the correlation of the signal in a given frequency channel with that of the adjacent channels. This has allowed us to propose significant improvements of typical autoassociative neural networks, often used to denoise hyperspectral Earth remote sensing data. Applying this method to the 13CO (1–0) cube, we were able to compare the denoised data with those derived with the multiple Gaussian fitting algorithm ROHSA, considered as the state-of-the-art procedure for data line cubes. Results. The nature of astronomical spectral data cubes is distinct from that of the hyperspectral data usually studied in the Earth remote sensing literature because the observed intensities become statistically independent beyond a short channel separation. This lack of redundancy in data has led us to adapt the method, notably by taking into account the sparsity of the signal along the spectral axis. The application of the proposed algorithm leads to an increase in the S/N in voxels with a weak signal, while preserving the spectral shape of the data in high S/N voxels. Conclusions. The proposed algorithm that combines a detailed analysis of the noise statistics with an innovative autoencoder architecture is a promising path to denoise radio-astronomy line data cubes. In the future, exploring whether a better use of the spatial correlations of the noise may further improve the denoising performances seems to be a promising avenue. In addition, dealing with the multiplicative noise associated with the calibration uncertainty at high S/N would also be beneficial for such large data cubes.

Learning-Based Multimodal Information Fusion and Behavior Recognition of Vascular Interventionists' Operating Skills.

The operating skills of vascular interventionists have an important impact on the effect of surgery. However, current research on behavior recognition and skills learning of interventionists' operating skills is limited. In this study, an innovative deep learning-based multimodal information fusion architecture is proposed for recognizing and analyzing eight common operating behaviors of interventionists. An experimental platform integrating four modal sensors is used to collect multimodal data from interventionists. The ANOVA and Manner-Whitney tests is used for relevance analysis of the data. The analysis results demonstrate that there is almost no significant difference ( p <0.001) between the actions related to the unimodal data, which cannot be used for accurate behavior recognition. Therefore, a study of the fusion architecture based on the existing machine learning classifier and the proposed deep learning fusion architecture is carried out. The research findings indicate that the proposed deep learning-based fusion architecture achieves an impressive overall accuracy of 98.5%, surpassing both the machine learning classifier (93.51%) and the unimodal data (90.05%). The deep learning-based multimodal information fusion architecture proves the feasibility of behavior recognition and skills learning of interventionist's operating skills. Furthermore, the application of deep learning-based multimodal fusion technology of surgeon's operating skills will help to improve the autonomy and intelligence of surgical robotic systems.

Opportunities for cost-effective manufacturing of fully printed high performance displays enabled by vertical light-emitting transistor pixels

With the vertical organic light-emitting transistor (VOLET), we introduce a promising solution that could significantly benefit the manufacturing of displays, accelerating the wide adoption of flexible and printed electronics. The VOLET—like conventional, lateral channel, organic thin film transistors—is compatible with a variety of printing techniques as well as flexible substrates and low-temperature processing. In combination these devices will enable a more cost-effective approach to mass-production that can dramatically extend the market potential of active-matrix organic light-emitting diode (AMOLED) displays. In this paper we discuss the prospects that AMOLED presents for the future of the display market, with a focus on the innovative VOLET device architecture. We assess how the integration of this device into active-matrix displays can contribute to the long range sustained competitiveness of AMOLED technology. We review recent progress in mass production techniques for printed electronics, with a particular emphasis on large-scale carbon nanotube material deposition. Finally, we explore the prospects for fully printed active-matrix light-emitting displays, including a review of high-performance printed components whose integration could facilitate the mass production of low-cost, high-performance, VOLET based AMOLEDs.