Showing papers by "Polytechnic University of Milan published in 2020"
TL;DR: In this paper, the authors proposed a new model that predicts the course of the SARS-CoV-2 pandemic to help plan an effective control strategy, including social distancing, testing and contact tracing.
Abstract: In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.
1,432 citations
TL;DR: This work addresses the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab, and identifies information spreading from questionable sources, finding different volumes of misinformation in each platform.
Abstract: We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.
1,008 citations
TL;DR: Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, it is concluded that verifiable evidence exists to support the planning of emergency measures.
Abstract: The spread of coronavirus disease 2019 (COVID-19) in Italy prompted drastic measures for transmission containment. We examine the effects of these interventions, based on modeling of the unfolding epidemic. We test modeling options of the spatially explicit type, suggested by the wave of infections spreading from the initial foci to the rest of Italy. We estimate parameters of a metacommunity Susceptible-Exposed-Infected-Recovered (SEIR)-like transmission model that includes a network of 107 provinces connected by mobility at high resolution, and the critical contribution of presymptomatic and asymptomatic transmission. We estimate a generalized reproduction number ([Formula: see text] = 3.60 [3.49 to 3.84]), the spectral radius of a suitable next-generation matrix that measures the potential spread in the absence of containment interventions. The model includes the implementation of progressive restrictions after the first case confirmed in Italy (February 21, 2020) and runs until March 25, 2020. We account for uncertainty in epidemiological reporting, and time dependence of human mobility matrices and awareness-dependent exposure probabilities. We draw scenarios of different containment measures and their impact. Results suggest that the sequence of restrictions posed to mobility and human-to-human interactions have reduced transmission by 45% (42 to 49%). Averted hospitalizations are measured by running scenarios obtained by selectively relaxing the imposed restrictions and total about 200,000 individuals (as of March 25, 2020). Although a number of assumptions need to be reexamined, like age structure in social mixing patterns and in the distribution of mobility, hospitalization, and fatality, we conclude that verifiable evidence exists to support the planning of emergency measures.
948 citations
Queensland University of Technology1, University of Leicester2, Pennsylvania State University3, Delft University of Technology4, University of Cassino5, Chinese Academy of Sciences6, Edinburgh Napier University7, University of Cambridge8, ICM Partners9, Lund University10, Cooperative Institute for Research in Environmental Sciences11, Tallinn University of Technology12, University of Hong Kong13, Eindhoven University of Technology14, University of New South Wales15, Virginia Tech16, Polytechnic University of Milan17, Technical University of Denmark18, University of Colorado Boulder19, University of Maryland, College Park20, University of California, Berkeley21, Aalborg University22, University of Leeds23, Yale University24, Spanish National Research Council25, National University of Singapore26, Aalto University27, McGill University28, Peking University29
TL;DR: It is argued that existing evidence is sufficiently strong to warrant engineering controls targeting airborne transmission as part of an overall strategy to limit infection risk indoors, and that the use of engineering controls in public buildings would be an additional important measure globally to reduce the likelihood of transmission.
924 citations
TL;DR: A massive analysis of the impact of lockdown measures introduced in response to the spread of novel coronavirus disease 2019 (COVID-19) on socioeconomic conditions of Italian citizens is presented and evidence of a segregation effect is found, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita.
Abstract: In response to the coronavirus disease 2019 (COVID-19) pandemic, several national governments have applied lockdown restrictions to reduce the infection rate. Here we perform a massive analysis on near-real-time Italian mobility data provided by Facebook to investigate how lockdown strategies affect economic conditions of individuals and local governments. We model the change in mobility as an exogenous shock similar to a natural disaster. We identify two ways through which mobility restrictions affect Italian citizens. First, we find that the impact of lockdown is stronger in municipalities with higher fiscal capacity. Second, we find evidence of a segregation effect, since mobility contraction is stronger in municipalities in which inequality is higher and for those where individuals have lower income per capita. Our results highlight both the social costs of lockdown and a challenge of unprecedented intensity: On the one hand, the crisis is inducing a sharp reduction of fiscal revenues for both national and local governments; on the other hand, a significant fiscal effort is needed to sustain the most fragile individuals and to mitigate the increase in poverty and inequality induced by the lockdown.
708 citations
TL;DR: Generic chips can accelerate the development of future photonic circuits by providing a higher-level platform for prototyping novel optical functionalities without the need for custom chip fabrication.
Abstract: The growing maturity of integrated photonic technology makes it possible to build increasingly large and complex photonic circuits on the surface of a chip. Today, most of these circuits are designed for a specific application, but the increase in complexity has introduced a generation of photonic circuits that can be programmed using software for a wide variety of functions through a mesh of on-chip waveguides, tunable beam couplers and optical phase shifters. Here we discuss the state of this emerging technology, including recent developments in photonic building blocks and circuit architectures, as well as electronic control and programming strategies. We cover possible applications in linear matrix operations, quantum information processing and microwave photonics, and examine how these generic chips can accelerate the development of future photonic circuits by providing a higher-level platform for prototyping novel optical functionalities without the need for custom chip fabrication. The current state of programmable photonic integrated circuits is discussed, including recent developments in their building blocks, circuit architectures, electronic control and programming strategies, as well as different application spaces.
521 citations
TL;DR: An innovative framework both highlighting the links between I4.0 and CE and unveiling future research fields has been developed, and results show as it is possible to enhance a set of different relations.
Abstract: Industry 4.0 (I4.0) and Circular Economy (CE) are undoubtedly two of the most debated topics of the last decades. Progressively, they gained the interest of policymakers, practitioners and scholars...
322 citations
TL;DR: In this paper, the theoretical and practical relationship between business model innovation (BMI) and Lean Startup Approaches (LSAs) in dynamic digital environments has been investigated, with the aim of developing a research agenda directed towards integrating BMI, LSAs and AD processes and methods.
320 citations
TL;DR: It is argued that a strengthened multi-interdisciplinary approach, involving urban planning, publicmental health, environmental health, epidemiology, and sociology, is needed to investigate the effects of the built environment on mental health, so as to inform welfare and housing policies centered on population well-being.
Abstract: Since the World Health Organization (WHO) declared the coronavirus infectious disease 2019 (COVID-19) outbreak a pandemic on 11 March, severe lockdown measures have been adopted by the Italian Government. For over two months of stay-at-home orders, houses became the only place where people slept, ate, worked, practiced sports, and socialized. As consolidated evidence exists on housing as a determinant of health, it is of great interest to explore the impact that COVID-19 response-related lockdown measures have had on mental health and well-being. We conducted a large web-based survey on 8177 students from a university institute in Milan, Northern Italy, one of the regions most heavily hit by the pandemic in Europe. As emerged from our analysis, poor housing is associated with increased risk of depressive symptoms during lockdown. In particular, living in apartments <60 m2 with poor views and scarce indoor quality is associated with, respectively, 1.31 (95% CI: 1046-1637), 1.368 (95% CI: 1166-1605), and 2.253 (95% CI: 1918-2647) times the risk of moderate-severe and severe depressive symptoms. Subjects reporting worsened working performance from home were over four times more likely to also report depression (OR = 4.28, 95% CI: 3713-4924). Housing design strategies should focus on larger and more livable living spaces facing green areas. We argue that a strengthened multi-interdisciplinary approach, involving urban planning, public mental health, environmental health, epidemiology, and sociology, is needed to investigate the effects of the built environment on mental health, so as to inform welfare and housing policies centered on population well-being.
320 citations
TL;DR: Monitoring of SARS-CoV-2 in sewage, although no evidence of COVID-19 transmission has been found via this route, could be advantageously exploited as an early warning of outbreaks.
274 citations
TL;DR: A monthly agrohydrological analysis is developed to map agricultural regions affected by agricultural economic water scarcity, finding these regions account for up to 25% of the global croplands, mostly across Sub-Saharan Africa, Eastern Europe, and Central Asia.
Abstract: Water scarcity raises major concerns on the sustainable future of humanity and the conservation of important ecosystem functions. To meet the increasing food demand without expanding cultivated areas, agriculture will likely need to introduce irrigation in croplands that are currently rain-fed but where enough water would be available for irrigation. “Agricultural economic water scarcity” is, here, defined as lack of irrigation due to limited institutional and economic capacity instead of hydrologic constraints. To date, the location and productivity potential of economically water scarce croplands remain unknown. We develop a monthly agrohydrological analysis to map agricultural regions affected by agricultural economic water scarcity. We find these regions account for up to 25% of the global croplands, mostly across Sub-Saharan Africa, Eastern Europe, and Central Asia. Sustainable irrigation of economically water scarce croplands could feed an additional 840 million people while preventing further aggravation of blue water scarcity.
TL;DR: The state-of-the-art of biological activities and applications of conductive PANI-based nanocomposites in the biomedical fields, such as antimicrobial therapy, drug delivery, biosensors, nerve regeneration and tissue engineering are described.
Abstract: Inherently conducting polymers (ICPs) are a specific category of synthetic polymers with distinctive electro-optic properties, which involve conjugated chains with alternating single and double bonds. Polyaniline (PANI), as one of the most well-known ICPs, has outstanding potential applications in biomedicine because of its high electrical conductivity and biocompatibility caused by its hydrophilic nature, low-toxicity, good environmental stability, and nanostructured morphology. Some of the limitations in the use of PANI, such as its low processability and degradability, can be overcome by the preparation of its blends and nanocomposites with various (bio)polymers and nanomaterials, respectively. This review describes the state-of-the-art of biological activities and applications of conductive PANI-based nanocomposites in the biomedical fields, such as antimicrobial therapy, drug delivery, biosensors, nerve regeneration, and tissue engineering. The latest progresses in the biomedical applications of PANI-based nanocomposites are reviewed to provide a background for future research.
TL;DR: In this article, a systematic review of the literature on the design of business models in the context of circular economy is presented, aiming to offer an overview of the state of research and outline a promising research agenda.
Abstract: The concept of circular economy is increasingly receiving attention in different domains, including strategic management, operations management, and technology management. It requires companies to design their business model (i.e., the value network, the relationships with the supply chain partners, and the value propositions towards customers) around a new concept of sustainable development that reduces consumption of natural resources and preserves the environment. However, extant research falls short in terms of explaining how companies design their business model according to the circular economy principles. Starting from this premise, the present paper provides a systematic review of the literature on the design of business models in the context of circular economy, aiming to offer an overview of the state of research and outline a promising research agenda.
University of Lyon1, Polytechnic University of Milan2, Swansea University3, University of Padua4, Technical University of Denmark5, Polish Academy of Sciences6, Leibniz Association7, University of Oviedo8, Oregon State University9, University of Southampton10, Durham University11, Marine Institute of Memorial University of Newfoundland12, University College Cork13, University of Kent14, University of Murcia15, Technical University of Madrid16, The Nature Conservancy17, François Rabelais University18, University of Strasbourg19, Princeton University20
TL;DR: There are at least 1.2 million instream barriers in 36 European countries, 68 per cent of which are structures less than two metres in height that are often overlooked, and the main predictors of barrier density are agricultural pressure, density of river-road crossings, extent of surface water and elevation.
Abstract: Rivers support some of Earth's richest biodiversity1 and provide essential ecosystem services to society2, but they are often fragmented by barriers to free flow3. In Europe, attempts to quantify river connectivity have been hampered by the absence of a harmonized barrier database. Here we show that there are at least 1.2 million instream barriers in 36 European countries (with a mean density of 0.74 barriers per kilometre), 68 per cent of which are structures less than two metres in height that are often overlooked. Standardized walkover surveys along 2,715 kilometres of stream length for 147 rivers indicate that existing records underestimate barrier numbers by about 61 per cent. The highest barrier densities occur in the heavily modified rivers of central Europe and the lowest barrier densities occur in the most remote, sparsely populated alpine areas. Across Europe, the main predictors of barrier density are agricultural pressure, density of river-road crossings, extent of surface water and elevation. Relatively unfragmented rivers are still found in the Balkans, the Baltic states and parts of Scandinavia and southern Europe, but these require urgent protection from proposed dam developments. Our findings could inform the implementation of the EU Biodiversity Strategy, which aims to reconnect 25,000 kilometres of Europe's rivers by 2030, but achieving this will require a paradigm shift in river restoration that recognizes the widespread impacts caused by small barriers.
Woolcock Institute of Medical Research1, University of Vermont2, New York University3, University of Liverpool4, Rio de Janeiro State University5, Polytechnic University of Milan6, University of Barcelona7, Telethon Institute for Child Health Research8, Boston Children's Hospital9, University of Lorraine10, University of Iowa11, Tohoku University12, Dalhousie University13, University Medical Center Groningen14, University of Antwerp15
TL;DR: An update to the 2003 European Respiratory Society technical standards document was developed by an ERS task force of international experts to provide technical recommendations regarding oscillometry measurement including hardware, software, testing protocols and quality control.
Abstract: Oscillometry (also known as the forced oscillation technique) measures the mechanical properties of the respiratory system (upper and intrathoracic airways, lung tissue and chest wall) during quiet tidal breathing, by the application of an oscillating pressure signal (input or forcing signal), most commonly at the mouth. With increased clinical and research use, it is critical that all technical details of the hardware design, signal processing and analyses, and testing protocols are transparent and clearly reported to allow standardisation, comparison and replication of clinical and research studies. Because of this need, an update of the 2003 European Respiratory Society (ERS) technical standards document was produced by an ERS task force of experts who are active in clinical oscillometry research.The aim of the task force was to provide technical recommendations regarding oscillometry measurement including hardware, software, testing protocols and quality control.The main changes in this update, compared with the 2003 ERS task force document are 1) new quality control procedures which reflect use of "within-breath" analysis, and methods of handling artefacts; 2) recommendation to disclose signal processing, quality control, artefact handling and breathing protocols (e.g. number and duration of acquisitions) in reports and publications to allow comparability and replication between devices and laboratories; 3) a summary review of new data to support threshold values for bronchodilator and bronchial challenge tests; and 4) updated list of predicted impedance values in adults and children.
TL;DR: A comprehensive review of the existing modeling strategies for masonry structures, as well as a novel classification of these strategies are presented, which attempts to make some order on the wide scientific production on this field.
Abstract: Masonry structures, although classically suitable to withstand gravitational loads, are sensibly vulnerable if subjected to extraordinary actions such as earthquakes, exhibiting cracks even for events of moderate intensity compared to other structural typologies like as reinforced concrete or steel buildings. In the last half-century, the scientific community devoted a consistent effort to the computational analysis of masonry structures in order to develop tools for the prediction (and the assessment) of their structural behavior. Given the complexity of the mechanics of masonry, different approaches and scales of representation of the mechanical behavior of masonry, as well as different strategies of analysis, have been proposed. In this paper, a comprehensive review of the existing modeling strategies for masonry structures, as well as a novel classification of these strategies are presented. Although a fully coherent collocation of all the modeling approaches is substantially impossible due to the peculiar features of each solution proposed, this classification attempts to make some order on the wide scientific production on this field. The modeling strategies are herein classified into four main categories: block-based models, continuum models, geometry-based models, and macroelement models. Each category is comprehensively reviewed. The future challenges of computational analysis of masonry structures are also discussed.
TL;DR: The current alternatives for the recycling of Lithium-ion batteries are analyzed, specifically focusing on available procedures for batteries securing and discharging, mechanical pre-treatments and materials recovery processes (i.e. pyro- and hydrometallurgical), and the pros and cons of treatments in terms of energy consumption, recovery efficiency and safety issues.
TL;DR: In this article, a wide-range investigation of the oxidation mechanism of ammonia was performed in a jet-stirred reactor and a flow reactor under lean conditions (0.01 ≤ Φ ≤ 0.375).
Abstract: A complete understanding of the mechanism of ammonia pyrolysis and oxidation in the full range of operating conditions displayed by industrial applications is one of the challenges of modern combustion kinetics. In this work, a wide-range investigation of the oxidation mechanism of ammonia was performed. Experimental campaigns were carried out in a jet-stirred reactor and a flow reactor under lean conditions (0.01 ≤ Φ ≤ 0.375), such to cover the full range of operating temperatures (500 K ≤ T ≤ 2000 K). Ammonia conversion and the formation of products and intermediates were analyzed. At the same time, the ammonia decomposition reaction, H-abstractions and the decomposition of the HNO intermediate were evaluated ab initio, and the related rates were included in a comprehensive kinetic model, developed according to a first-principles approach. Low-temperature reactor experiments highlighted a delayed reactivity of ammonia, in spite of the high amount of oxygen. A very slow increase in NH3 consumption rate with temperature was observed, and a full reactant consumption was possible only ∼150–200 K after the reactivity onset. The use of flux analysis and sensitivity analysis allowed explaining this effect with the terminating effect of the H-abstraction on NH3 by O2, acting in the reverse direction because of the high amounts of HO2. The central role of H2NO was observed at low temperatures (T < 1200 K), and H-abstractions from it by HO2, NO2 and NH2 were found to control reactivity, especially at higher pressures. On the other side, the formation of HNO intermediate via NH2 + O = HNO + H and its decomposition were found to be crucial at higher temperatures, affecting both NO/N2 ratio and flame propagation.
TL;DR: Through the analysis of three case studies of firms that digitally transformed their business—namely ABB, CNH Industrial, and Vodafone—this article presents a framework than can help companies implement their digital transformation strategy and thereby renovate their business model.
Abstract: The rapid growth of digital technologies and the extraordinary amount of data that devices and applications collect each day are increasingly driving companies to radically transform the business a
TL;DR: In this paper, the existence and properties of ground states for the nonlinear Schrodinger equation with combined power nonlinearities were studied under different assumptions on q p, a > 0, and μ ∈ R. The authors proved several existence and stability/instability results.
TL;DR: An improved recurrent neural network (RNN) scheme is proposed to perform the trajectory control of redundant robot manipulators using remote center of motion (RCM) constraints to facilitate accurate task tracking based on the general quadratic performance index.
TL;DR: In this paper, the authors provide fundamental insights into the reaction mechanisms, electrochemical challenges and modification strategies of lithium-rich oxides, including lattice oxygen oxidation, oxygen vacancy formation, transition-metal migration, layered to spinel transitions, two-phase mechanism, and lattice evolution.
Abstract: Due to their high specific capacities beyond 250 mA h g−1, lithium-rich oxides have been considered as promising cathodes for the next generation power batteries, bridging the capacity gap between traditional layered-oxide based lithium-ion batteries and future lithium metal batteries such as lithium sulfur and lithium air batteries. However, the practical application of Li-rich oxides has been hindered by formidable challenges. To address these challenges, the understanding of their electrochemical behaviors becomes critical and is expected to offer effective guidance for both materials and cell development. This review aims to provide fundamental insights into the reaction mechanisms, electrochemical challenges and modification strategies of lithium-rich oxides. We first summarize the research history, the pristine structures, and the classification of lithium-rich oxides. Then we review the critical reaction mechanisms that are closely related to their electrochemical features and performances, such as lattice oxygen oxidation, oxygen vacancy formation, transition-metal migration, layered to spinel transitions, ‘two-phase mechanism’, and lattice evolution. These discussions are coupled with state-of-the-art characterization techniques. As a comparison, the anionic redox reactions of layered sodium transition metal oxides are also discussed. Finally, after a brief overview of the correlation among the aforementioned mechanisms, we provide perspectives on the rational design of lithium-rich oxides with high energy densities and long-term cycling stability.
TL;DR: In this paper, the authors identify the managerial actions at organizational and process level that companies perform to implement digital technologies in their open innovation processes, and investigate how and why these managerial actions required for and enabled by digital technologies help firms to develop and nurture open innovation.
Abstract: Digital transformation has undoubtedly become a key enabler of innovation as evidenced by the numerous firms that use digital technologies to manage their innovation processes. This issue is even more relevant today when innovation processes have become more open and require greater resources in the different implementation phases to capture and transfer knowledge within and outside the firm's boundaries. This implies additional challenges in managing the increasing amount of knowledge and information flows. Accordingly, digital technologies can be used and implemented to manage open innovation processes through easier access and sharing the knowledge created and transferred. Nevertheless, literature in these fields does not provide a structured view of how and why digital technologies are used to manage innovation processes in an open perspective. This paper aims to bridge this gap by adopting the theoretical lenses of change management to identify the managerial actions at organizational and process level that companies perform to implement digital technologies in their open innovation processes. Accordingly, the paper investigates how and why these managerial actions required for and enabled by digital technologies help firms to develop and nurture open innovation. From an empirical point of view, the exploratory multiple case study analyzes nine firms operating in different industries and varying in size, market share, and organizational structure.
TL;DR: A fuzzy sliding-mode controller is developed to realize reachability of a predefined switching surface and desirable sliding motion and sufficient conditions for stochastic stability of the obtained sliding mode dynamics is developed in the sense of generally uncertain transition rates.
Abstract: This paper is focused on the event-triggered fuzzy sliding-mode control of networked control systems regulated by semi-Markov process. First, through movement-decomposition method, the networked control system is transformed into two lower-order subsystems. Then, an event-triggered scheme based on a delay system model approach is proposed in designing the switching surface and obtaining the sliding mode dynamics. Furthermore, a fuzzy sliding-mode controller is developed to realize reachability of a predefined switching surface and desirable sliding motion. Moreover, in terms of linear matrix inequality method, sufficient conditions for stochastic stability of the obtained sliding mode dynamics is developed in the sense of generally uncertain transition rates. Finally, the applicability of the proposed results are verified numerically on the single-link robot arm system.
TL;DR: This article addresses the quantized nonstationary filtering problem for networked Markov switching repeated scalar nonlinear systems (MSRSNSs), in which the correlation among modes of systems, quantizer, and controller are presented in terms of non stationary Markov process.
Abstract: This article addresses the quantized nonstationary filtering problem for networked Markov switching repeated scalar nonlinear systems (MSRSNSs). A more general issue is explored for MSRSNSs, where the measurement outputs are characterized by packet losses, nonstationary quantized output, and randomly occurred sensor nonlinearities (ROSNs) simultaneously. Note that both packet losses and ROSNSs are depicted by Bernoulli distributed sequences. By utilizing a multiple hierarchical structure strategy, the nonstationary filters are designed for MSRSNSs, in which the correlation among modes of systems, quantizer, and controller are presented in terms of nonstationary Markov process. A practical example is provided to verify the proposed theoretical results.
TL;DR: An OAM-tunable vortex microlaser is demonstrated, providing chiral light states of variable topological charges at a single telecommunication wavelength, providing a route for the development of the next generation of multidimensional O AM-spin-wavelength division multiplexing technology.
Abstract: The orbital angular momentum (OAM) intrinsically carried by vortex light beams holds a promise for multidimensional high-capacity data multiplexing, meeting the ever-increasing demands for information. Development of a dynamically tunable OAM light source is a critical step in the realization of OAM modulation and multiplexing. By harnessing the properties of total momentum conservation, spin-orbit interaction, and optical non-Hermitian symmetry breaking, we demonstrate an OAM-tunable vortex microlaser, providing chiral light states of variable topological charges at a single telecommunication wavelength. The scheme of the non–Hermitian-controlled chiral light emission at room temperature can be further scaled up for simultaneous multivortex emissions in a flexible manner. Our work provides a route for the development of the next generation of multidimensional OAM-spin-wavelength division multiplexing technology.
TL;DR: An adaptive event-triggering protocol is constructed for consensus control by using relative information between agents by incorporating both adaptive control and event-triggered control.
TL;DR: In this paper, the authors provide an overview of the memory device technologies which have been proposed for synapse and neuron circuits in neuromorphic systems and describe the implementation of synaptic learning in the two main types of neural networks, namely the deep neural network and the spiking neural network (SNN).
Abstract: Artificial intelligence (AI) has the ability of revolutionizing our lives and society in a radical way, by enabling machine learning in the industry, business, health, transportation, and many other fields. The ability to recognize objects, faces, and speech, requires, however, exceptional computational power and time, which is conflicting with the current difficulties in transistor scaling due to physical and architectural limitations. As a result, to accelerate the progress of AI, it is necessary to develop materials, devices, and systems that closely mimic the human brain. In this work, we review the current status and challenges on the emerging neuromorphic devices for brain-inspired computing. First, we provide an overview of the memory device technologies which have been proposed for synapse and neuron circuits in neuromorphic systems. Then, we describe the implementation of synaptic learning in the two main types of neural networks, namely the deep neural network and the spiking neural network (SNN). Bio-inspired learning, such as the spike-timing dependent plasticity scheme, is shown to enable unsupervised learning processes which are typical of the human brain. Hardware implementations of SNNs for the recognition of spatial and spatio-temporal patterns are also shown to support the cognitive computation in silico. Finally, we explore the recent advances in reproducing bio-neural processes via device physics, such as insulating-metal transitions, nanoionics drift/diffusion, and magnetization flipping in spintronic devices. By harnessing the device physics in emerging materials, neuromorphic engineering with advanced functionality, higher density and better energy efficiency can be developed.
TL;DR: This paper first reviews the progress in ultrafast optics, which has enabled the generation of broadly tunable light pulses with duration down to a few optical cycles, and discusses the pump-probe technique, showing examples of its capability to combine very high time resolution, down to the attosecond regime, with broad spectral coverage.
Abstract: Ultrafast spectroscopy techniques use sequences of ultrashort light pulses (with femto- to attosecond durations) to study photoinduced dynamical processes in atoms, molecules, nanostructures, and s...
TL;DR: This work confirms the effectiveness at the regional level of the national lockdown strategy and proposes coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs.
Abstract: The COVID-19 epidemic hit Italy particularly hard, yielding the implementation of strict national lockdown rules. Previous modelling studies at the national level overlooked the fact that Italy is divided into administrative regions which can independently oversee their own share of the Italian National Health Service. Here, we show that heterogeneity between regions is essential to understand the spread of the epidemic and to design effective strategies to control the disease. We model Italy as a network of regions and parameterize the model of each region on real data spanning over two months from the initial outbreak. We confirm the effectiveness at the regional level of the national lockdown strategy and propose coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs. Our study and methodology can be easily extended to other levels of granularity to support policy- and decision-makers.