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Periodicals

Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence

Artificial intelligence (AI) has been altering industries as evidenced by Airbnb, Uber and other companies that have embraced its use to implement innovative new business models. Yet we may not fully understand how this emerging and rapidly advancing technology influences business model innovation. While many companies are being made vulnerable to new competitors equipped with AI technology, this study attempts to focus on the proactive side of the use of AI technology to drive business model innovation. Describing AI technology as the catalyst of business model innovation, this study sheds light on contingent factors shaping business model innovation initiated by the emerging technology. This study first provides a brief overview of AI, current issues being tackled in developing AI and explains how it transforms business models. Our case study of two companies that innovated their business models using AI shows its potential impact. We also discuss how executives can create an innovative AI-based culture, which rephrases the process of AI-based business model innovation. Companies that successfully capitalize on AI can create disruptive innovation through their new business models and processes, enabling them to potentially transform the global competitive landscape.

Architecture & Planning• 2019• Springer Science+Business Media
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Bet Hedging in Yeast by Heterogeneous, Age-Correlated Expression of a Stress Protectant

Genetically identical cells grown in the same culture display striking cell-to-cell heterogeneity in gene expression and other traits. A crucial challenge is to understand how much of this heterogeneity reflects the noise tolerance of a robust system and how much serves a biological function. In bacteria, stochastic gene expression results in cell-to-cell heterogeneity that might serve as a bet-hedging mechanism, allowing a few cells to survive through an antimicrobial treatment while others perish. Despite its clinical importance, the molecular mechanisms underlying bet hedging remain unclear. Here, we investigate the mechanisms of bet hedging in Saccharomyces cerevisiae using a new high-throughput microscopy assay that monitors variable protein expression, morphology, growth rate, and survival outcomes of tens of thousands of yeast microcolonies simultaneously. We find that clonal populations display broad distributions of growth rates and that slow growth predicts resistance to heat killing in a probabalistic manner. We identify several gene products that are likely to play a role in bet hedging and confirm that Tsl1, a trehalose-synthesis regulator, is an important component of this resistance. Tsl1 abundance correlates with growth rate and replicative age and predicts survival. Our results suggest that yeast bet hedging results from multiple epigenetic growth states determined by a combination of stochastic and deterministic factors.

Architecture & Planning• 2012• Public Library of Science
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Periodicals

JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems

Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave, Python and other environments. We present the principles behind the code design, and provide several examples to guide users.

Architecture & Planning• 2014• Frontiers Media
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Periodicals

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.

Architecture & Planning• 2021• Public Library of Science
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Periodicals

The Roadmap to 6G Security and Privacy

Although the fifth generation (5G) wireless networks are yet to be fully investigated, the visionaries of the 6th generation (6G) echo systems have already come into the discussion. Therefore, in order to consolidate and solidify the security and privacy in 6G networks, we survey how security may impact the envisioned 6G wireless systems, possible challenges with different 6G technologies, and the potential solutions. We provide our vision on 6G security and security key performance indicators (KPIs) with the tentative threat landscape based on the foreseen 6G network architecture. Moreover, we discuss the security and privacy challenges that may encounter with the available 6G requirements and potential 6G applications. We also give the reader some insights into the standardization efforts and research-level projects relevant to 6G security. In particular, we discuss the security considerations with 6G enabling technologies such as distributed ledger technology (DLT), physical layer security, distributed AI/ML, visible light communication (VLC), THz, and quantum computing. All in all, this work intends to provide enlightening guidance for the subsequent research of 6G security and privacy at this initial phase of vision towards reality.

Architecture & Planning• 2021• IEEE Communications Society
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Periodicals

Revisiting 30 years of biofunctionalization and surface chemistry of inorganic nanoparticles for nanomedicine

In the last 30 years we have assisted to a massive advance of nanomaterials in material science. Nanomaterials and structures, in addition to their small size, have properties that differ from those of larger bulk materials, making them ideal for a host of novel applications. The spread of nanotechnology in the last years has been due to the improvement of synthesis and characterization methods on the nanoscale, a field rich in new physical phenomena and synthetic opportunities. In fact, the development of functional nanoparticles has progressed exponentially over the past two decades. This work aims to extensively review 30 years of different strategies of surface modification and functionalization of noble metal (gold) nanoparticles, magnetic nanocrystals and semiconductor nanoparticles, such as quantum dots. The aim of this review is not only to provide in-depth insights into the different biofunctionalization and characterization methods, but also to give an overview of possibilities and limitations of the available nanoparticles.

Architecture & Planning• 2014• Frontiers Media
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Periodicals

The Physiology of Protein<i>S-</i>acylation

Protein S-acylation, the only fully reversible posttranslational lipid modification of proteins, is emerging as a ubiquitous mechanism to control the properties and function of a diverse array of proteins and consequently physiological processes. S-acylation results from the enzymatic addition of long-chain lipids, most typically palmitate, onto intracellular cysteine residues of soluble and transmembrane proteins via a labile thioester linkage. Addition of lipid results in increases in protein hydrophobicity that can impact on protein structure, assembly, maturation, trafficking, and function. The recent explosion in global S-acylation (palmitoyl) proteomic profiling as a result of improved biochemical tools to assay S-acylation, in conjunction with the recent identification of enzymes that control protein S-acylation and de-acylation, has opened a new vista into the physiological function of S-acylation. This review introduces key features of S-acylation and tools to interrogate this process, and highlights the eclectic array of proteins regulated including membrane receptors, ion channels and transporters, enzymes and kinases, signaling adapters and chaperones, cell adhesion, and structural proteins. We highlight recent findings correlating disruption of S-acylation to pathophysiology and disease and discuss some of the major challenges and opportunities in this rapidly expanding field.

Architecture & Planning• 2015• American Physiological Society
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Periodicals

Addressing Function Approximation Error in Actor-Critic Methods

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.

Architecture & Planning• 2018• University of Amsterdam
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Periodicals

Agricultural Value Chains in Developing Countries A Framework for Analysis

The paper presents a framework for developing country value chain analysis made up of three components. The first consists of identifying major constraints for value chain upgrading: market access restrictions, weak infrastructures, lacking resources and institutional voids. In the second component three elements of a value chain are defined: value addition, horizontal and vertical chain-network structure and value chain governance mechanisms. Finally, upgrading options are defined in the area of value addition, including the search for markets, the value chain- network structure and the governance form of the chain. Part of this component is the identification of the most suitable partnerships for upgrading the value chain. The three components of the framework are derived from major theoretical streams on inter-company relationships and from the literature on developing country value chains. The framework is applied in a case example of a developing country value chain.

Architecture & Planning• 2011• University of Minnesota Rochester
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Periodicals

A Survey on OFDM-Based Elastic Core Optical Networking

Orthogonal frequency-division multiplexing (OFDM) is a modulation technology that has been widely adopted in many new and emerging broadband wireless and wireline communication systems. Due to its capability to transmit a high-speed data stream using multiple spectral-overlapped lower-speed subcarriers, OFDM technology offers superior advantages of high spectrum efficiency, robustness against inter-carrier and inter-symbol interference, adaptability to server channel conditions, etc. In recent years, there have been intensive studies on optical OFDM (O-OFDM) transmission technologies, and it is considered a promising technology for future ultra-high-speed optical transmission. Based on O-OFDM technology, a novel elastic optical network architecture with immense flexibility and scalability in spectrum allocation and data rate accommodation could be built to support diverse services and the rapid growth of Internet traffic in the future. In this paper, we present a comprehensive survey on OFDM-based elastic optical network technologies, including basic principles of OFDM, O-OFDM technologies, the architectures of OFDM-based elastic core optical networks, and related key enabling technologies. The main advantages and issues of OFDM-based elastic core optical networks that are under research are also discussed.

Architecture & Planning• 2012• Institute of Electrical and Electronics Engineers
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Periodicals

Crip Technoscience Manifesto

As disabled people engaged in disability community, activism, and scholarship, our collective experiences and histories have taught us that we are effective agents of world building and dismantling toward more socially just relations.The grounds for social justice and world remaking, however, are frictioned;technologies, architectures, and infrastructures are often designed and implemented without committing to disability as a difference that matters. This manifesto calls attention to the powerful, messy, non-innocent, contradictory, and nevertheless crucial work of what we name as “crip technoscience,” practices of critique, alteration, and reinvention of our material-discursive world. Disabled people are experts and designers of everyday life. But we also harness technoscience for political action, refusing to comply with demands to cure, fix, or eliminate disability. Attentive to the intersectional workings of power and privilege, we agitate against independence and productivity as requirements for existence. Instead, we center technoscientific activism and critical design practices that foster disability justice.

Architecture & Planning• 2019
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Periodicals

Bioprinting three-dimensional cell-laden tissue constructs with controllable degradation

Alginate hydrogel is a popular biologically inert material that is widely used in 3D bioprinting, especially in extrusion-based printing. However, the printed cells in this hydrogel could not degrade the surrounding alginate gel matrix, causing them to remain in a poorly proliferating and non-differentiating state. Here, we report a novel study of the 3D printing of human corneal epithelial cells (HCECs)/collagen/gelatin/alginate hydrogel incubated with a medium containing sodium citrate to obtain degradation-controllable cell-laden tissue constructs. The 3D-printed hydrogel network with interconnected channels and a macroporous structure was stable and achieved high cell viability (over 90%). By altering the mole ratio of sodium citrate/sodium alginate, the degradation time of the bioprinting constructs can be controlled. Cell proliferation and specific marker protein expression results also revealed that with the help of sodium citrate degradation, the printed HCECs showed a higher proliferation rate and greater cytokeratin 3(CK3) expression, indicating that this newly developed method may help to improve the alginate bioink system for the application of 3D bioprinting in tissue engineering.

Architecture & Planning• 2016• Nature Portfolio
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Periodicals

Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types. All our models and datasets in this study are open sourced and can be downloaded from https://mekhub.cn/as/fault_diagnosis_with_few-shot_learning/.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
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Sustainable Business Models: A Review

During the past two decades of e-commerce growth, the concept of a business model has become increasingly popular. More recently, the research on this realm has grown rapidly, with diverse research activity covering a wide range of application areas. Considering the sustainable development goals, the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way. The process of sustainable business model construction forms an innovative part of a business strategy. Different industries and businesses have utilized sustainable business models’ concept to satisfy their economic, environmental, and social goals simultaneously. However, the success, popularity, and progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress -either failure or success- has been reviewed, and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is that it provides an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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Periodicals

What Are You Feeling? Using Functional Magnetic Resonance Imaging to Assess the Modulation of Sensory and Affective Responses during Empathy for Pain

BACKGROUND: Recent neuroscientific evidence suggests that empathy for pain activates similar neural representations as the first-hand experience of pain. However, empathy is not an all-or-none phenomenon but it is strongly malleable by interpersonal, intrapersonal and situational factors. This study investigated how two different top-down mechanisms - attention and cognitive appraisal - affect the perception of pain in others and its neural underpinnings. METHODOLOGY/PRINCIPAL FINDINGS: We performed one behavioral (N = 23) and two functional magnetic resonance imaging (fMRI) experiments (N = 18). In the first fMRI experiment, participants watched photographs displaying painful needle injections, and were asked to evaluate either the sensory or the affective consequences of these injections. The role of cognitive appraisal was examined in a second fMRI experiment in which participants watched injections that only appeared to be painful as they were performed on an anesthetized hand. Perceiving pain in others activated the affective-motivational and sensory-discriminative aspects of the pain matrix. Activity in the somatosensory areas was specifically enhanced when participants evaluated the sensory consequences of pain. Perceiving non-painful injections into the anesthetized hand also led to signal increase in large parts of the pain matrix, suggesting an automatic affective response to the putatively harmful stimulus. This automatic response was modulated by areas involved in self/other distinction and valence attribution - including the temporo-parietal junction and medial orbitofrontal cortex. CONCLUSIONS/SIGNIFICANCE: Our findings elucidate how top-down control mechanisms and automatic bottom-up processes interact to generate and modulate other-oriented responses. They stress the role of cognitive processing in empathy, and shed light on how emotional and bodily awareness enable us to evaluate the sensory and affective states of others.

Architecture & Planning• 2007• Public Library of Science
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Competitive Coevolution through Evolutionary Complexification

Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of strategies, and because coevolution benefits from an escalating arms race, it serves as a suitable testbed for studying complexification. When compared to the evolution of networks with fixed structure, complexifying evolution discovers significantly more sophisticated strategies. The results suggest that in order to discover and improve complex solutions, evolution, and search in general, should be allowed to complexify as well as optimize.

Architecture & Planning• 2004• AI Access Foundation
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Intersubjectivity: Towards a Dialogical Analysis

Intersubjectivity refers to the variety of possible relations between perspectives. It is indispensable for understanding human social behaviour. While theoretical work on intersubjectivity is relatively sophisticated, methodological approaches to studying intersubjectivity lag behind. Most methodologies assume that individuals are the unit of analysis. In order to research intersubjectivity, however, methodologies are needed that take relationships as the unit of analysis. The first aim of this article is to review existing methodologies for studying intersubjectivity. Four methodological approaches are reviewed: comparative self‐report, observing behaviour, analysing talk and ethnographic engagement. The second aim of the article is to introduce and contribute to the development of a dialogical method of analysis. The dialogical approach enables the study of intersubjectivity at different levels, as both implicit and explicit, and both within and between individuals and groups. The article concludes with suggestions for using the proposed method for researching intersubjectivity both within individuals and between individuals and groups.

Architecture & Planning• 2009• Wiley
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Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis

Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which are frequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of change detection DL studies. Then, we focus on deep learning-based change detection methodologies for remote sensing images by giving a general overview of the existing methods. Specifically, these deep learning-based methods were classified into three groups; fully supervised learning-based methods, fully unsupervised learning-based methods and transfer learning-based techniques. As a result of these investigations, promising new directions were identified for future research. This study will contribute in several ways to our understanding of deep learning for change detection and will provide a basis for further research. Some source codes of the methods discussed in this paper are available from: https://github.com/lazharkhelifi/deeplearning_changedetection_remotesensing_review.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions

Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be a key enabler and a leading infrastructure provider in the information and communication technology industry by supporting a variety of forthcoming services with diverse requirements. Considering the ever-increasing complexity of the network, and the emergence of novel use cases such as autonomous cars, industrial automation, virtual reality, e-health, and several intelligent applications, machine learning (ML) is expected to be essential to assist in making the 5G vision conceivable. This paper focuses on the potential solutions for 5G from an ML-perspective. First, we establish the fundamental concepts of supervised, unsupervised, and reinforcement learning, taking a look at what has been done so far in the adoption of ML in the context of mobile and wireless communication, organizing the literature in terms of the types of learning. We then discuss the promising approaches for how ML can contribute to supporting each target 5G network requirement, emphasizing its specific use cases and evaluating the impact and limitations they have on the operation of the network. Lastly, this paper investigates the potential features of Beyond 5G (B5G), providing future research directions for how ML can contribute to realizing B5G. This article is intended to stimulate discussion on the role that ML can play to overcome the limitations for a wide deployment of autonomous 5G/B5G mobile and wireless communications.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
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The Lid Domain in Lipases: Structural and Functional Determinant of Enzymatic Properties

Lipases are important industrial enzymes. Most of the lipases operate at lipid-water interfaces enabled by a mobile lid domain located over the active site. Lid protects the active site and hence responsible for catalytic activity. In pure aqueous media, the lid is predominantly closed, whereas in the presence of a hydrophobic layer, it is partially opened. Hence, the lid controls the enzyme activity. In the present review, we have classified lipases into different groups based on the structure of lid domains. It has been observed that thermostable lipases contain larger lid domains with two or more helices, whereas mesophilic lipases tend to have smaller lids in the form of a loop or a helix. Recent developments in lipase engineering addressing the lid regions are critically reviewed here. After on, the dramatic changes in substrate selectivity, activity, and thermostability have been reported. Furthermore, improved computational models can now rationalize these observations by relating it to the mobility of the lid domain. In this contribution, we summarized and critically evaluated the most recent developments in experimental and computational research on lipase lids.

Architecture & Planning• 2017• Frontiers Media
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Immune System Dysregulation During Spaceflight: Potential Countermeasures for Deep Space Exploration Missions

Recent studies have established that dysregulation of the human immune system and the reactivation of latent herpesviruses persists for the duration of a 6-month orbital spaceflight. It appears certain aspects of adaptive immunity are dysregulated during flight, yet some aspects of innate immunity are heightened. Interaction between adaptive and innate immunity also seems to be altered. Some crews experience persistent hypersensitivity reactions during flight. This phenomenon may, in synergy with extended duration and galactic radiation exposure, increase specific crew clinical risks during deep space exploration missions. The clinical challenge is based upon both the frequency of these phenomena in multiple crewmembers during low earth orbit missions and the inability to predict which specific individual crewmembers will experience these changes. Thus, a general countermeasure approach that offers the broadest possible coverage is needed. The vehicles, architecture, and mission profiles to enable such voyages are now under development. These include deployment and use of a cis-Lunar station (mid 2020s) with possible Moon surface operations, to be followed by multiple Mars flyby missions, and eventual human Mars surface exploration. Current ISS studies will continue to characterize physiological dysregulation associated with prolonged orbital spaceflight. However, sufficient information exists to begin consideration of both the need for, and nature of, specific immune countermeasures to ensure astronaut health. This article will review relevant in-place operational countermeasures onboard ISS and discuss a myriad of potential immune countermeasures for exploration missions. Discussion points include nutritional supplementation and functional foods, exercise and immunity, pharmacological options, the relationship between bone and immune countermeasures, and vaccination to mitigate herpes (and possibly other) virus risks. As the immune system has sentinel connectivity within every other physiological system, translational effects must be considered for all potential immune countermeasures. Finally, we shall discuss immune countermeasures in the context of their individualized implementation or precision medicine, based on crewmember specific immunological biases.

Architecture & Planning• 2018• Frontiers Media
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