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in Architecture & Planning
Periodicals

On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges

In the era of the fourth industrial revolution, several concepts have arisen in parallel with this new revolution, such as predictive maintenance, which today plays a key role in sustainable manufacturing and production systems by introducing a digital version of machine maintenance. The data extracted from production processes have increased exponentially due to the proliferation of sensing technologies. Even if Maintenance 4.0 faces organizational, financial, or even data source and machine repair challenges, it remains a strong point for the companies that use it. Indeed, it allows for minimizing machine downtime and associated costs, maximizing the life cycle of the machine, and improving the quality and cadence of production. This approach is generally characterized by a very precise workflow, starting with project understanding and data collection and ending with the decision-making phase. This paper presents an exhaustive literature review of methods and applied tools for intelligent predictive maintenance models in Industry 4.0 by identifying and categorizing the life cycle of maintenance projects and the challenges encountered, and presents the models associated with this type of maintenance: condition-based maintenance (CBM), prognostics and health management (PHM), and remaining useful life (RUL). Finally, a novel applied industrial workflow of predictive maintenance is presented including the decision support phase wherein a recommendation for a predictive maintenance platform is presented. This platform ensures the management and fluid data communication between equipment throughout their life cycle in the context of smart maintenance.

Architecture & Planning• 2022• Multidisciplinary Digital Publishing Institute
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Spectra stable blue perovskite light-emitting diodes

Device performance and in particular device stability for blue perovskite light-emitting diodes (PeLEDs) remain considerable challenges for the whole community. In this manuscript, we conceive an approach by tuning the 'A-site' cation composition of perovskites to develop blue-emitters. We herein report a Rubidium-Cesium alloyed, quasi-two-dimensional perovskite and demonstrate its great potential for pure-blue PeLED applications. Composition engineering and in-situ passivation are conducted to further improve the material's emission property and stabilities. Consequently, we get a prominent film photoluminescence quantum yield of around 82% under low excitation density. Encouraged by these findings, we finally achieve a spectra-stable blue PeLED with the peak external quantum efficiency of 1.35% and a half-lifetime of 14.5 min, representing the most efficient and stable pure-blue PeLEDs reported so far. The strategy is also demonstrated to be able to generate efficient perovskite blue emitters and PeLEDs in the whole blue spectral region (from 454 to 492 nm).

Architecture & Planning• 2019• Nature Portfolio
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Next-generation applications for integrated perovskite solar cells

Abstract Organic/inorganic metal halide perovskites attract substantial attention as key materials for next-generation photovoltaic technologies due to their potential for low cost, high performance, and solution processability. The unique properties of perovskites and the rapid advances that have been made in solar cell performance have facilitated their integration into a broad range of practical applications, including tandem solar cells, building-integrated photovoltaics, space applications, integration with batteries and supercapacitors for energy storage systems, and photovoltaic-driven catalysis. In this Review, we outline notable achievements that have been made in these photovoltaic-integrated technologies. Outstanding challenges and future perspectives for the development of these fields and potential next-generation applications are discussed.

Architecture & Planning• 2023• Nature Portfolio
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A Comprehensive Survey on Millimeter Wave Communications for Fifth-Generation Wireless Networks: Feasibility and Challenges

Fifth-generation (5G) cellular networks will almost certainly operate in the high-bandwidth, underutilized millimeter-wave (mmWave) frequency spectrum, which offers the potentiality of high-capacity wireless transmission of multi-gigabit-per-second (Gbps) data rates. Despite the enormous available bandwidth potential, mmWave signal transmissions suffer from fundamental technical challenges like severe path loss, sensitivity to blockage, directivity, and narrow beamwidth, due to its short wavelengths. To effectively support system design and deployment, accurate channel modeling comprising several 5G technologies and scenarios is essential. This survey provides a comprehensive overview of several emerging technologies for 5G systems, such as massive multiple-input multiple-output (MIMO) technologies, multiple access technologies, hybrid analog-digital precoding and combining, non-orthogonal multiple access (NOMA), cell-free massive MIMO, and simultaneous wireless information and power transfer (SWIPT) technologies. These technologies induce distinct propagation characteristics and establish specific requirements on 5G channel modeling. To tackle these challenges, we first provide a survey of existing solutions and standards and discuss the radio-frequency (RF) spectrum and regulatory issues for mmWave communications. Second, we compared existing wireless communication techniques like sub-6-GHz WiFi and sub-6 GHz 4G LTE over mmWave communications which come with benefits comprising narrow beam, high signal quality, large capacity data transmission, and strong detection potential. Third, we describe the fundamental propagation characteristics of the mmWave band and survey the existing channel models for mmWave communications. Fourth, we track evolution and advancements in hybrid beamforming for massive MIMO systems in terms of system models of hybrid precoding architectures, hybrid analog and digital precoding/combining matrices, with the potential antenna configuration scenarios and mmWave channel estimation (CE) techniques. Fifth, we extend the scope of the discussion by including multiple access technologies for mmWave systems such as non-orthogonal multiple access (NOMA) and space-division multiple access (SDMA), with limited RF chains at the base station. Lastly, we explore the integration of SWIPT in mmWave massive MIMO systems, with limited RF chains, to realize spectrally and energy-efficient communications.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, there are only a few publicly available data sets, which often contain samples from subjects with too similar characteristics, and very often lack specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs. no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task, we performed a 5-fold cross-validation (i.e., including samples from all the subjects in both the training and the test dataset) and a leave-one-subject-out cross-validation (i.e., the test data include the samples of a subject only, and the training data, the samples of all the other subjects). Regarding the classification tasks, the major findings can be summarized as follows: (i) it is quite easy to distinguish between falls and ADLs, regardless of the classifier and the feature vector selected. Indeed, these classes of activities present quite different acceleration shapes that simplify the recognition task; (ii) on average, it is more difficult to distinguish between types of falls than between types of activities, regardless of the classifier and the feature vector selected. This is due to the similarity between the acceleration shapes of different kinds of falls. On the contrary, ADLs acceleration shapes present differences except for a small group. Finally, the evaluation shows that the presence of samples of the same subject both in the training and in the test datasets, increases the performance of the classifiers regardless of the feature vector used. This happens because each human subject differs from other subjects in performing activities even if she shares with them the same physical characteristics.

Architecture & Planning• 2017• Multidisciplinary Digital Publishing Institute
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Periodicals

A Drought Resistance-Promoting Microbiome Is Selected by Root System under Desert Farming

BACKGROUND: Traditional agro-systems in arid areas are a bulwark for preserving soil stability and fertility, in the sight of "reverse desertification". Nevertheless, the impact of desert farming practices on the diversity and abundance of the plant associated microbiome is poorly characterized, including its functional role in supporting plant development under drought stress. METHODOLOGY/PRINCIPAL FINDINGS: We assessed the structure of the microbiome associated to the drought-sensitive pepper plant (Capsicum annuum L.) cultivated in a traditional Egyptian farm, focusing on microbe contribution to a crucial ecosystem service, i.e. plant growth under water deficit. The root system was dissected by sampling root/soil with a different degree of association to the plant: the endosphere, the rhizosphere and the root surrounding soil that were compared to the uncultivated soil. Bacterial community structure and diversity, determined by using Denaturing Gradient Gel Electrophoresis, differed according to the microhabitat, indicating a selective pressure determined by the plant activity. Similarly, culturable bacteria genera showed different distribution in the three root system fractions. Bacillus spp. (68% of the isolates) were mainly recovered from the endosphere, while rhizosphere and the root surrounding soil fractions were dominated by Klebsiella spp. (61% and 44% respectively). Most of the isolates (95%) presented in vitro multiple plant growth promoting (PGP) activities and stress resistance capabilities, but their distribution was different among the root system fractions analyzed, with enhanced abilities for Bacillus and the rhizobacteria strains. We show that the C. annuum rhizosphere under desert farming enriched populations of PGP bacteria capable of enhancing plant photosynthetic activity and biomass synthesis (up to 40%) under drought stress. CONCLUSIONS/SIGNIFICANCE: Crop cultivation provides critical ecosystem services in arid lands with the plant root system acting as a "resource island" able to attract and select microbial communities endowed with multiple PGP traits that sustain plant development under water limiting conditions.

Architecture & Planning• 2012• Public Library of Science
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Distinct neural signatures detected for ADHD subtypes after controlling for micro-movements in resting state functional connectivity MRI data

In recent years, there has been growing enthusiasm that functional magnetic resonance imaging (MRI) could achieve clinical utility for a broad range of neuropsychiatric disorders. However, several barriers remain. For example, the acquisition of large-scale datasets capable of clarifying the marked heterogeneity that exists in psychiatric illnesses will need to be realized. In addition, there continues to be a need for the development of image processing and analysis methods capable of separating signal from artifact. As a prototypical hyperkinetic disorder, and movement-related artifact being a significant confound in functional imaging studies, ADHD offers a unique challenge. As part of the ADHD-200 Global Competition and this special edition of Frontiers, the ADHD-200 Consortium demonstrates the utility of an aggregate dataset pooled across five institutions in addressing these challenges. The work aimed to (1) examine the impact of emerging techniques for controlling for "micro-movements," and (2) provide novel insights into the neural correlates of ADHD subtypes. Using support vector machine (SVM)-based multivariate pattern analysis (MVPA) we show that functional connectivity patterns in individuals are capable of differentiating the two most prominent ADHD subtypes. The application of graph-theory revealed that the Combined (ADHD-C) and Inattentive (ADHD-I) subtypes demonstrated some overlapping (particularly sensorimotor systems), but unique patterns of atypical connectivity. For ADHD-C, atypical connectivity was prominent in midline default network components, as well as insular cortex; in contrast, the ADHD-I group exhibited atypical patterns within the dlPFC regions and cerebellum. Systematic motion-related artifact was noted, and highlighted the need for stringent motion correction. Findings reported were robust to the specific motion correction strategy employed. These data suggest that resting-state functional connectivity MRI (rs-fcMRI) data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical heterogeneity of ADHD.

Architecture & Planning• 2013• Frontiers Media
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Periodicals

The spatial coding model of visual word identification.

Visual word identification requires readers to code the identity and order of the letters in a word and match this code against previously learned codes. Current models of this lexical matching process posit context-specific letter codes in which letter representations are tied to either specific serial positions or specific local contexts (e.g., letter clusters). The spatial coding model described here adopts a different approach to letter position coding and lexical matching based on context-independent letter representations. In this model, letter position is coded dynamically, with a scheme called spatial coding. Lexical matching is achieved via a method called superposition matching, in which input codes and learned codes are matched on the basis of the relative positions of their common letters. Simulations of the model illustrate its ability to explain a broad range of results from the masked form priming literature, as well as to capture benchmark findings from the unprimed lexical decision task.

Architecture & Planning• 2010• American Psychological Association
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Periodicals

What Is eHealth (4): A Scoping Exercise to Map the Field

BACKGROUND: Lack of consensus on the meaning of eHealth has led to uncertainty among academics, policymakers, providers and consumers. This project was commissioned in light of the rising profile of eHealth on the international policy agenda and the emerging UK National Programme for Information Technology (now called Connecting for Health) and related developments in the UK National Health Service. OBJECTIVES: To map the emergence and scope of eHealth as a topic and to identify its place within the wider health informatics field, as part of a larger review of research and expert analysis pertaining to current evidence, best practice and future trends. METHODS: Multiple databases of scientific abstracts were explored in a nonsystematic fashion to assess the presence of eHealth or conceptually related terms within their taxonomies, to identify journals in which articles explicitly referring to eHealth are contained and the topics covered, and to identify published definitions of the concept. The databases were Medline (PubMed), the Cumulative Index of Nursing and Allied Health Literature (CINAHL), the Science Citation Index (SCI), the Social Science Citation Index (SSCI), the Cochrane Database (including Dare, Central, NHS Economic Evaluation Database [NHS EED], Health Technology Assessment [HTA] database, NHS EED bibliographic) and ISTP (now known as ISI proceedings). We used the search query, "Ehealth OR e-health OR e*health". The timeframe searched was 1997-2003, although some analyses contain data emerging subsequent to this period. This was supplemented by iterative searches of Web-based sources, such as commercial and policy reports, research commissioning programmes and electronic news pages. Definitions extracted from both searches were thematically analyzed and compared in order to assess conceptual heterogeneity. RESULTS: The term eHealth only came into use in the year 2000, but has since become widely prevalent. The scope of the topic was not immediately discernable from that of the wider health informatics field, for which over 320000 publications are listed in Medline alone, and it is not explicitly represented within the existing Medical Subject Headings (MeSH) taxonomy. Applying eHealth as narrative search term to multiple databases yielded 387 relevant articles, distributed across 154 different journals, most commonly related to information technology and telemedicine, but extending to such areas as law. Most eHealth articles are represented on Medline. Definitions of eHealth vary with respect to the functions, stakeholders, contexts and theoretical issues targeted. Most encompass a broad range of medical informatics applications either specified (eg, decision support, consumer health information) or presented in more general terms (eg, to manage, arrange or deliver health care). However the majority emphasize the communicative functions of eHealth and specify the use of networked digital technologies, primarily the Internet, thus differentiating eHealth from the field of medical informatics. While some definitions explicitly target health professionals or patients, most encompass applications for all stakeholder groups. The nature of the scientific and broader literature pertaining to eHealth closely reflects these conceptualizations. CONCLUSIONS: We surmise that the field -- as it stands today -- may be characterized by the global definitions suggested by Eysenbach and Eng.

Architecture & Planning• 2005• JMIR Publications
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Challenges Toward Wireless Communications for High-Speed Railway

High-speed railway (HSR) brings convenience to peoples' lives and is generally considered as one of the most sustainable developments for ground transportation. One of the important parts of HSR construction is the signaling system, which is also called the “operation control system,” where wireless communications play a key role in the transmission of train control data. We discuss in detail the main differences in scientific research for wireless communications between the HSR operation scenarios and the conventional public land mobile scenarios. The latest research progress in wireless channel modeling in viaducts, cuttings, and tunnels scenarios are discussed. The characteristics of nonstationary channel and the line-of-sight (LOS) sparse and LOS multiple-input-multiple-output channels, which are the typical channels in HSR scenarios, are analyzed. Some novel concepts such as composite transportation and key challenging techniques such as train-to-train communication, vacuum maglev train techniques, the security for HSR, and the fifth-generation wireless communications related techniques for future HSR development for safer, more comfortable, and more secure HSR operation are also discussed.

Architecture & Planning• 2014• Institute of Electrical and Electronics Engineers
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Soil and Crop Management Strategies to Ensure Higher Crop Productivity within Sustainable Environments

The rising population and reduction in the amount of land and some other resources have created tremendous pressure on current agricultural producers to meet the increasing food demands. To cope with this challenge, certain key inputs, such as fertilizers and other chemicals, are overused, which are worsening the surroundings. This intensive agricultural production without adherence to ecological sustainability has led to declining soil health, land degradation, and severe environmental problems. So, future efforts to feed the growing population should aim for greater agricultural production within sustainable environments. In this regard, innovative steps are needed, as business-as-usual policies lack the potential to cope with these challenges. The concept of agricultural sustainability and various soil and crop management strategies (SCMS) that have been designed to optimize crop yield under sustainable environmental conditions are discussed, including nutrient management, site specific nutrient management (SSNM), integrated nutrient management (INM), integrated soil fertility management (ISFM), integrated soil-crop system management (ISSM), ridge-furrow mulching systems (RFMS), sustainable water management (SWM), conservation agriculture (CA), sustainable land management (SLM), vertical/sky farming, and integrated crop management, and breeding strategies as well as other approaches combined with technological and behavioural changes. The present review suggests that a sustainable production system can be developed by combining the multifaceted efforts under SCMS practices with short- and long-term preventive measures. Reducing chemicals’ usage, such as that of fertilizers and pesticides, plus improvements in the crop input use efficiency could minimize greenhouse gases emissions while protecting the environment. Sustainable agriculture holds promise for humankind and the planet Earth, and it can be successful if all developed and developing nations stand together to seek ‘our common future’ to produce more food while generating less environmental pressure.

Architecture & Planning• 2019• Multidisciplinary Digital Publishing Institute
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Food Traceability on Blockchain: Walmart’s Pork and Mango Pilots with IBM

In response to food contamination scandals worldwide, retail giant Walmart is tackling food safety in the supply chain using blockchain technology. In 2016, it established the Walmart Food Safety Collaboration Center in Beijing and plans to invest $25 million over five years to research global food safety (Yiannas and Liu, 2017). Using IBM’s blockchain solution based on Hyperledger Fabric, Walmart has successfully completed two blockchain pilots: pork in China and mangoes in the Americas (IBM, 2017). With a farm-to-table approach, Walmart’s blockchain solution reduced time for tracking mango origins from seven days to 2.2 seconds and promoted greater transparency across Walmart’s food supply chain (Yiannas, 2017). IBM called it “complete end-to-end traceability” (McDermott, 2017). The case highlights the challenges of implementing blockchain technology in the food supply chain and the opportunities for deploying blockchain solutions throughout the global food ecosystem to increase safety and reduce waste.

Architecture & Planning• 2018
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The Hyper Suprime-Cam software pipeline

Abstract In this paper, we describe the optical imaging data processing pipeline developed for the Subaru Telescope’s Hyper Suprime-Cam (HSC) instrument. The HSC Pipeline builds on the prototype pipeline being developed by the Large Synoptic Survey Telescope’s Data Management system, adding customizations for HSC, large-scale processing capabilities, and novel algorithms that have since been reincorporated into the LSST codebase. While designed primarily to reduce HSC Subaru Strategic Program (SSP) data, it is also the recommended pipeline for reducing general-observer HSC data. The HSC pipeline includes high-level processing steps that generate coadded images and science-ready catalogs as well as low-level detrending and image characterizations.

Architecture & Planning• 2017• Oxford University Press
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Periodontal Inflammation and Systemic Diseases: An Overview

Periodontitis is a common inflammatory disease of infectious origins that often evolves into a chronic condition. Aside from its importance as a stomatologic ailment, chronic periodontitis has gained relevance since it has been shown that it can develop into a systemic condition characterized by unresolved hyper-inflammation, disruption of the innate and adaptive immune system, dysbiosis of the oral, gut and other location's microbiota and other system-wide alterations that may cause, coexist or aggravate other health issues associated to elevated morbi-mortality. The relationships between the infectious, immune, inflammatory, and systemic features of periodontitis and its many related diseases are far from being fully understood and are indeed still debated. However, to date, a large body of evidence on the different biological, clinical, and policy-enabling sources of information, is available. The aim of the present work is to summarize many of these sources of information and contextualize them under a systemic inflammation framework that may set the basis to an integral vision, useful for basic, clinical, and therapeutic goals.

Architecture & Planning• 2021• Frontiers Media
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Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities

Abstract. Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

Architecture & Planning• 2012• Copernicus Publications
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Defining and Describing What We Do: Doctrinal Legal Research

The practitioner lawyer of the past had little need to reflect on process. The doctrinal research methodology developed intuitively within the common law — a research method at the core of practice. There was no need to justify or classify it within a broader research framework. Modern academic lawyers are facing a different situation. At a time when competition for limited research funds is becoming more intense, and in which interdisciplinary work is highly valued and non-lawyers are involved in the assessment of grant applications, lawyer-applicants who engage in doctrinal research need to be able to explain their methodology more clearly. Doctrinal scholars need to be more open and articulate about their methods. These methods may be different in different contexts. This paper examines the doctrinal method used in legal research and its place in recent research dialogue. Some commentators are of the view that the doctrinal method is simply scholarship rather than a separate research methodology. Richard Posner even suggests that law is ‘not a field with a distinct methodology, but an amalgam of applied logic, rhetoric, economics and familiarity with a specialized vocabulary and a particular body of texts, practices, and institutions ...’.1 Therefore, academic lawyers are beginning to realise that the doctrinal research methodology needs clarification for those outside the legal profession and that a discussion about the standing and place of doctrinal research compared to other methodologies is required.

Architecture & Planning• 2012• Deakin University School of Law
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Epileptic Seizure Detection Based on EEG Signals and CNN

Epilepsy is a neurological disorder that affects approximately fifty million people according to the World Health Organization. While electroencephalography (EEG) plays important roles in monitoring the brain activity of patients with epilepsy and diagnosing epilepsy, an expert is needed to analyze all EEG recordings to detect epileptic activity. This method is obviously time-consuming and tedious, and a timely and accurate diagnosis of epilepsy is essential to initiate antiepileptic drug therapy and subsequently reduce the risk of future seizures and seizure-related complications. In this study, a convolutional neural network (CNN) based on raw EEG signals instead of manual feature extraction was used to distinguish ictal, preictal, and interictal segments for epileptic seizure detection. We compared the performances of time and frequency domain signals in the detection of epileptic signals based on the intracranial Freiburg and scalp CHB-MIT databases to explore the potential of these parameters. Three types of experiments involving two binary classification problems (interictal vs. preictal and interictal vs. ictal) and one three-class problem (interictal vs. preictal vs. ictal) were conducted to explore the feasibility of this method. Using frequency domain signals in the Freiburg database, average accuracies of 96.7, 95.4, and 92.3% were obtained for the three experiments, while the average accuracies for detection in the CHB-MIT database were 95.6, 97.5, and 93% in the three experiments. Using time domain signals in the Freiburg database, the average accuracies were 91.1, 83.8, and 85.1% in the three experiments, while the signal detection accuracies in the CHB-MIT database were only 59.5, 62.3, and 47.9% in the three experiments. Based on these results, the three cases are effectively detected using frequency domain signals. However, the effective identification of the three cases using time domain signals as input samples is achieved for only some patients. Overall, the classification accuracies of frequency domain signals are significantly increased compared to time domain signals. In addition, frequency domain signals have greater potential than time domain signals for CNN applications.

Architecture & Planning• 2018• Frontiers Media
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Antimicrobial Photodynamic Therapy to Control Clinically Relevant Biofilm Infections

Biofilm describes a microbially-derived sessile community in which microbial cells are firmly attached to the substratum and embedded in extracellular polymeric matrix. Microbial biofilms account for up to 80% of all bacterial and fungal infections in humans. Biofilm-associated pathogens are particularly resistant to antibiotic treatment, and thus novel antibiofilm approaches needed to be developed. Antimicrobial Photodynamic therapy (aPDT) had been recently proposed to combat clinically relevant biofilms such as dental biofilms, ventilator associated pneumonia, chronic wound infections, oral candidiasis, and chronic rhinosinusitis. aPDT uses non-toxic dyes called photosensitizers (PS), which can be excited by harmless visible light to produce reactive oxygen species (ROS). aPDT is a multi-stage process including topical PS administration, light irradiation, and interaction of the excited state with ambient oxygen. Numerous in vitro and in vivo aPDT studies have demonstrated biofilm-eradication or substantial reduction. ROS are produced upon photo-activation and attack adjacent targets, including proteins, lipids and nucleic acids present within the biofilm matrix, on the cell surface and inside the microbial cells. Damage to non-specific targets leads to the destruction of both planktonic cells and biofilms. The review aims to summarize the progress of aPDT in destroying biofilms and the mechanisms mediated by ROS. Finally, a brief section provides suggestions for future research.

Architecture & Planning• 2018• Frontiers Media
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Efficient Energy Management for the Internet of Things in Smart Cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy- efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities.

Architecture & Planning• 2017• Institute of Electrical and Electronics Engineers
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<i>Euclid</i>preparation

Euclid is a mission of the European Space Agency that is designed to constrain the properties of dark energy and gravity via weak gravitational lensing and galaxy clustering. It will carry out a wide area imaging and spectroscopy survey (the Euclid Wide Survey: EWS) in visible and near-infrared bands, covering approximately 15 000 deg 2 of extragalactic sky in six years. The wide-field telescope and instruments are optimised for pristine point spread function and reduced stray light, producing very crisp images. This paper presents the building of the Euclid reference survey: the sequence of pointings of EWS, deep fields, and calibration fields, as well as spacecraft movements followed by Euclid as it operates in a step-and-stare mode from its orbit around the Lagrange point L2. Each EWS pointing has four dithered frames; we simulated the dither pattern at the pixel level to analyse the effective coverage. We used up-to-date models for the sky background to define the Euclid region-of-interest (RoI). The building of the reference survey is highly constrained from calibration cadences, spacecraft constraints, and background levels; synergies with ground-based coverage were also considered. Via purposely built software, we first generated a schedule for the calibrations and deep fields observations. On a second stage, the RoI was tiled and scheduled with EWS observations, using an algorithm optimised to prioritise the best sky areas, produce a compact coverage, and ensure thermal stability. The result is the optimised reference survey RSD_2021A, which fulfils all constraints and is a good proxy for the final solution. The current EWS covers ≈14 500 deg 2 . The limiting AB magnitudes (5 σ point-like source) achieved in its footprint are estimated to be 26.2 (visible band I E ) and 24.5 (for near infrared bands Y E , J E , H E ); for spectroscopy, the H α line flux limit is 2 × 10 −16 erg −1 cm −2 s −1 at 1600 nm; and for diffuse emission, the surface brightness limits are 29.8 (visible band) and 28.4 (near infrared bands) mag arcsec −2 .

Architecture & Planning• 2022• EDP Sciences
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Data Security and Privacy Protection for Cloud Storage: A Survey

The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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