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in Architecture & Planning
Periodicals

Reconfigurable manufacturing systems: the state of the art

In this paper, general requirements of next generation manufacturing systems are discussed, and the strategies to meet these requirements are considered. The production paradigms which apply these strategies are also classified. Particular emphasis is put on the paradigm of Reconfigurable Manufacturing System (RMS). Some key issues of the RMS design are discussed, and a critical review is presented concerning the developments of RMSs. Finally, suggestions of the RMS research are made and future research directions are identified.

Architecture & Planning• 2006• Taylor & Francis
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Skilful nowcasting of extreme precipitation with NowcastNet

Abstract Extreme precipitation is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through skilful nowcasting that has high resolution, long lead times and local details 1–3 . Current methods are subject to blur, dissipation, intensity or location errors, with physics-based numerical methods struggling to capture pivotal chaotic dynamics such as convective initiation 4 and data-driven learning methods failing to obey intrinsic physical laws such as advective conservation 5 . We present NowcastNet, a nonlinear nowcasting model for extreme precipitation that unifies physical-evolution schemes and conditional-learning methods into a neural-network framework with end-to-end forecast error optimization. On the basis of radar observations from the USA and China, our model produces physically plausible precipitation nowcasts with sharp multiscale patterns over regions of 2,048 km × 2,048 km and with lead times of up to 3 h. In a systematic evaluation by 62 professional meteorologists from across China, our model ranks first in 71% of cases against the leading methods. NowcastNet provides skilful forecasts at light-to-heavy rain rates, particularly for extreme-precipitation events accompanied by advective or convective processes that were previously considered intractable.

Architecture & Planning• 2023• Nature Portfolio
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Interaction Between Macro‐ and Micro-Nutrients in Plants

Nitrogen (N), phosphorus (P), sulfur (S), zinc (Zn), and iron (Fe) are some of the vital nutrients required for optimum growth, development, and productivity of plants. The deficiency of any of these nutrients may lead to defects in plant growth and decreased productivity. Plant responses to the deficiency of N, P, S, Fe, or Zn have been studied mainly as a separate event, and only a few reports discuss the molecular basis of biological interaction among the nutrients. Macro-nutrients like N, P, and/or S not only show the interacting pathways for each other but also affect micro-nutrient pathways. Limited reports are available on the investigation of two-by-two or multi-level nutrient interactions in plants. Such studies on the nutrient interaction pathways suggest that an MYB-like transcription factor, phosphate starvation response 1 (PHR1), acts as a master regulator of N, P, S, Fe, and Zn homeostasis. Similarly, light-responsive transcription factors were identified to be involved in modulating nutrient responses in Arabidopsis . This review focuses on the recent advances in our understanding of how plants coordinate the acquisition, transport, signaling, and interacting pathways for N, P, S, Fe, and Zn nutrition at the molecular level. Identification of the important candidate genes for interactions between N, P, S, Fe, and/or Zn metabolic pathways might be useful for the breeders to improve nutrient use efficiency and yield/quality of crop plants. Integrated studies on pathways interactions/cross-talks between macro‐ and micro-nutrients in the agronomically important crop plants would be essential for sustainable agriculture around the globe, particularly under the changing climatic conditions.

Architecture & Planning• 2021• Frontiers Media
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Security Issues in Healthcare Applications Using Wireless Medical Sensor Networks: A Survey

Healthcare applications are considered as promising fields for wireless sensor networks, where patients can be monitored using wireless medical sensor networks (WMSNs). Current WMSN healthcare research trends focus on patient reliable communication, patient mobility, and energy-efficient routing, as a few examples. However, deploying new technologies in healthcare applications without considering security makes patient privacy vulnerable. Moreover, the physiological data of an individual are highly sensitive. Therefore, security is a paramount requirement of healthcare applications, especially in the case of patient privacy, if the patient has an embarrassing disease. This paper discusses the security and privacy issues in healthcare application using WMSNs. We highlight some popular healthcare projects using wireless medical sensor networks, and discuss their security. Our aim is to instigate discussion on these critical issues since the success of healthcare application depends directly on patient security and privacy, for ethic as well as legal reasons. In addition, we discuss the issues with existing security mechanisms, and sketch out the important security requirements for such applications. In addition, the paper reviews existing schemes that have been recently proposed to provide security solutions in wireless healthcare scenarios. Finally, the paper ends up with a summary of open security research issues that need to be explored for future healthcare applications using WMSNs.

Architecture & Planning• 2011• Multidisciplinary Digital Publishing Institute
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Blockchain: case studies in food supply chain visibility

Purpose This paper aims to investigate how blockchain has moved beyond cryptocurrencies and is being deployed to enhance visibility and trust in supply chains, their limitations and potential impact. Design/methodology/approach Qualitative analysis are undertaken via case studies drawn from food companies using semi-structured interviews. Findings Blockchain is demonstrated as an enabler of visibility in supply chains. Applications at scale are most likely for products where the end consumer is prepared to pay the premium currently required to fund the technology, e.g. baby food. Challenges remain in four areas: trust of the technology, human error and fraud at the boundaries, governance, consumer data access and willingness to pay. Research limitations/implications The paper shows that blockchain can be utilised as part of a system generating visibility and trust in supply chains. Research directs academic attention to issues that remain to be addressed. The challenges pertaining to the technology itself we believe to be generalisable; those specific to the food industry may not hold elsewhere. Practical implications From live case studies, we provide empirical evidence that blockchain provides visibility of exchanges and reliable data in fully digitised supply chains. This provides provenance and guards against counterfeit goods. However, firms will need to work to gain consumer buy-in for the technology following repeated past claims of trustworthiness. Originality/value This paper provides primary evidence from blockchain use cases “in the wild”. The exploratory case studies examine application of blockchain for supply chain visibility.

Architecture & Planning• 2020• Emerald Publishing Limited
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Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

Architecture & Planning• 2011• Public Library of Science
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Distinct Pathogenesis and Host Responses during Infection of C. elegans by P. aeruginosa and S. aureus

The genetically tractable model host Caenorhabditis elegans provides a valuable tool to dissect host-microbe interactions in vivo. Pseudomonas aeruginosa and Staphylococcus aureus utilize virulence factors involved in human disease to infect and kill C. elegans. Despite much progress, virtually nothing is known regarding the cytopathology of infection and the proximate causes of nematode death. Using light and electron microscopy, we found that P. aeruginosa infection entails intestinal distention, accumulation of an unidentified extracellular matrix and P. aeruginosa-synthesized outer membrane vesicles in the gut lumen and on the apical surface of intestinal cells, the appearance of abnormal autophagosomes inside intestinal cells, and P. aeruginosa intracellular invasion of C. elegans. Importantly, heat-killed P. aeruginosa fails to elicit a significant host response, suggesting that the C. elegans response to P. aeruginosa is activated either by heat-labile signals or pathogen-induced damage. In contrast, S. aureus infection causes enterocyte effacement, intestinal epithelium destruction, and complete degradation of internal organs. S. aureus activates a strong transcriptional response in C. elegans intestinal epithelial cells, which aids host survival during infection and shares elements with human innate responses. The C. elegans genes induced in response to S. aureus are mostly distinct from those induced by P. aeruginosa. In contrast to P. aeruginosa, heat-killed S. aureus activates a similar response as live S. aureus, which appears to be independent of the single C. elegans Toll-Like Receptor (TLR) protein. These data suggest that the host response to S. aureus is possibly mediated by pathogen-associated molecular patterns (PAMPs). Because our data suggest that neither the P. aeruginosa nor the S. aureus-triggered response requires canonical TLR signaling, they imply the existence of unidentified mechanisms for pathogen detection in C. elegans, with potentially conserved roles also in mammals.

Architecture & Planning• 2010• Public Library of Science
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Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer's physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient's functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices.

Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
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Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI's applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI's transformative impact on the pharmaceutical industry and its broader implications for healthcare.

Architecture & Planning• 2024• Multidisciplinary Digital Publishing Institute
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Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.

Architecture & Planning• 2021• Multidisciplinary Digital Publishing Institute
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A theoretical investigation of reference frames for the planning of speech movements.

Does the speech motor control system use invariant vocal tract shape targets when producing vowels and semivowels? A 4-part theoretical treatment favoring models whose only invariant targets are regions in auditory perceptual space over models that posit invariant constriction targets is presented. Auditory target regions are hypothesized to arise during development as an emergent property of neural map formation in the auditory system. Furthermore, speech movements are planned as trajectories in auditory perceptual space. These trajectories are then mapped into articulator movements through a neural mapping that allows motor equivalent variability in constriction locations and degrees when needed. These hypotheses are illustrated using computer simulations of the DIVA model of speech acquisition and production. Finally, several difficult challenges to proponents of constriction theories based on this theoretical treatment are posed.

Architecture & Planning• 1998• American Psychological Association
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Bone morphogenetic protein 4 regulates the budding site and elongation of the mouse ureter

In the normal mouse embryo, Bmp4 is expressed in mesenchymal cells surrounding the Wolffian duct (WD) and ureter stalk, whereas bone morphogenetic protein (BMP) type I receptor genes are transcribed either ubiquitously (Alk3) or exclusively in the WD and ureter epithelium (Alk6). Bmp4 heterozygous null mutant mice display, with high penetrance, abnormalities that mimic human congenital anomalies of the kidney and urinary tract (CAKUT), including hypo/dysplastic kidneys, hydroureter, ectopic ureterovesical (UV) junction, and double collecting system. Analysis of mutant embryos suggests that the kidney hypo/dysplasia results from reduced branching of the ureter, whereas the ectopic UV junction and double collecting system are due to ectopic ureteral budding from the WD and accessory budding from the main ureter, respectively. In the cultured metanephros deprived of sulfated glycosaminoglycans (S-GAGs), BMP4-loaded beads partially rescue growth and elongation of the ureter. By contrast, when S-GAGs synthesis is not inhibited, BMP4 beads inhibit ureter branching and expression of Wnt 11, a target of glial cell-derived neurotrophic factor signaling. Thus, Bmp4 has 2 functions in the early morphogenesis of the kidney and urinary tract. One is to inhibit ectopic budding from the WD or the ureter stalk by antagonizing inductive signals from the metanephric mesenchyme to the illegitimate sites on the WD. The other is to promote the elongation of the branching ureter within the metanephros, thereby promoting kidney morphogenesis.

Architecture & Planning• 2000• American Society for Clinical Investigation
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The Cognitive Atlas: Toward a Knowledge Foundation for Cognitive Neuroscience

Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what "mental processes" exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at http://www.cognitiveatlas.org), and outline how this project has the potential to drive novel discoveries about both mind and brain.

Architecture & Planning• 2011• Frontiers Media
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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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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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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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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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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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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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