bluuebunny/crossref_metadata_2025 huggingface dataset:
Overall Introduction
Authors: Shi Wenzhong , Goodchild Michael F. , Batty Michael , Kwan Mei-Po , Zhang Anshu
Published: April 2021

Urban informatics is an interdisciplinary approach to understanding, managing, and designing the city using systematic theories and methods based on new information technologies. Integrating urban science, geomatics, and informatics, urban informatics is a particularly timely way of fusing many interdisciplinary perspectives in studying city systems. This edited book aims to meet the urgent need for works that systematically introduce the principles and technologies of urban informatics. The book gathers over 40 world-leading research teams from a wide range of disciplines, who provide comprehensive reviews of the state of the art and the latest research achievements in their various areas of urban informatics. The book is organized into six parts, respectively covering the conceptual and theoretical basis of urban informatics, urban systems and applications, urban sensing, urban big data infrastructure, urban computing, and prospects for the future of urban informatics. This introductory chapter provides a definition of urban informatics and an outline of the book’s structure and scope.
Introduction to Urban Systems and Applications
Authors: Kwan Mei-Po
Published: April 2021

As new information technologies and large amounts of data from a wide range of sources become available to government agencies and the public, urban researchers have started to investigate how these data can be used to enhance the planning and management of various urban systems. As a result, new methods for collecting and analyzing complex space–time data about urban systems have been developed to address various urban issues. These urban systems include transportation systems, energy systems, and health systems. In recent years, considerable new work has been conducted to examine how new information technologies and data can enhance our understanding of and ability to address urban issues. The eight chapters in this section present various applications of urban informatics to specific urban systems or phenomena, including human mobility and travel, urban freight systems, urban resilience and disaster response, urban crime, urban governance, the use of remote sensing for environmental monitoring, health and wellbeing, and urban energy systems. All of them emphasize how new, big, or open data are useful for helping us to better understand and manage specific urban systems. They also highlight significant challenges in such applications of urban informatics, which would be particularly helpful to urban researchers and planners.
Laboratories for Research on Freight Systems and Planning
Authors: Alho André Romano , Sakai Takanori , Zhao Fang , You Linlin , Jing Peiyu , Cheah Lynette , Zegras Christopher , Ben-Akiva Moshe
Published: April 2021

Advancements in information and communication technologies (ICT) and the advent of novel mobility solutions have brought about drastic changes in the urban mobility environment. Pervasive ICT devices acquire new sources of data that can inform detailed transportation simulation models, and are useful in analyzing new policies and technologies. In this context, we developed software laboratories that leverage the latest technological developments and enhance freight research. Future mobility sensing (FMS) is a data-collection platform that integrates tracking devices and mobile apps, a backend with machine-learning technologies and user interfaces to deliver highly accurate and detailed mobility data. The second platform, SimMobility, is an open-source, agent-based urban simulation platform which replicates urban passenger and goods movements in a fully disaggregated manner. The two platforms have been used jointly to advance the state of the art in behavioral modeling for passenger and goods movements. In this chapter, we review recent developments in freight-transportation data-collection techniques, including contributions to transportation modeling, and state-of-the-art transportation models. We then introduce FMS and SimMobility and demonstrate a coordinated application using three examples. Lastly, we highlight potential innovations and future challenges in these research domains.
Urban Risks and Resilience
Authors: Cutter Susan L.
Published: April 2021

The resilience concept has become more significant in the past decade as a means for understanding how cities prepare and plan for, absorb, recover from, and more successfully adapt to adverse events. Definitional differences—resilience as an outcome or end-point versus resilience as a process of building capacity—dominate the literature. Lagging behind are efforts to systematically measure resilience to produce a baseline and subsequent monitoring, in order to gauge what, where, and how intervention or mitigation strategies would strengthen or weaken urban resilience. The chapter reviews research and practitioner attempts to develop urban informatics for resilience and provides selected case studies of cities as exemplars.
Urban Crime and Security
Authors: Cheng Tao , Chen Tongxin
Published: April 2021

Scientists have an enduring interest in understanding urban crime and developing security strategies for mitigating this problem. This chapter reviews the progress made in this topic from historic criminology to data-driven policing. It first reviews the broad implications of urban security and its implementation in practice. Next, it focuses on the tools to prevent urban crime and improve security, from analytical crime hotspot mapping to police resource allocation. Finally, a manifesto of data-driven policing is proposed, with its practical demand for efficient security strategies and the development of big data technologies. It emphasizes that data-driven strategies could be applied in cities due to their promising effectiveness for crime prevention and security improvement.
Urban Energy Systems: Research at Oak Ridge National Laboratory
Authors: Bhaduri Budhendra , McManamay Ryan , Omitaomu Olufemi , Sanyal Jibo , Rose Amy
Published: April 2021

In the coming decades, our planet will witness unprecedented urban population growth in both established and emerging communities. The development and maintenance of urban infrastructures are highly energy-intensive. Urban areas are dictated by complex intersections among physical, engineered, and human dimensions that have significant implications for traffic congestion, emissions, and energy usage. In this chapter, we highlight recent research and development efforts at Oak Ridge National Laboratory (ORNL), the largest multipurpose science laboratory within the U.S. Department of Energy’s (DOE) national laboratory system, that characterizes the interactions between the human dynamics and critical infrastructures in conjunction with the integration of four distinct components: data, critical infrastructure models, and scalable computation and visualization, all within the context of physical and social systems. Discussions focus on four key topical themes: population and land use, sustainable mobility, the energy-water nexus, and urban resiliency, that are mutually aligned with DOE’s mission and ORNL’s signature science and technology capabilities. Using scalable computing, data visualization, and unique datasets from a variety of sources, the institute fosters innovative interdisciplinary research that integrates ORNL expertise in critical infrastructures including energy, water, transportation, and cyber, and their interactions with the human population.
Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics
Authors: Yao Wei , Wu Jianwei
Published: April 2021

In this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.
What Urban Cameras Reveal About the City: The Work of the Senseable City Lab
Authors: Duarte Fábio , Ratti Carlo
Published: April 2021

Cameras are part of the urban landscape and a testimony to our social interactions with city. Deployed on buildings and street lights as surveillance tools, carried by billions of people daily, or as an assistive technology in vehicles, we rely on this abundance of images to interact with the city. Making sense of such large visual datasets is the key to understanding and managing contemporary cities. In this chapter, we focus on techniques such as computer vision and machine learning to understand different aspects of the city. Here, we discuss how these visual data can help us to measure legibility of space, quantify different aspects of urban life, and design responsive environments. The chapter is based on the work of the Senseable City Lab, including the use of Google Street View images to measure green canopy in urban areas, the use of thermal images to actively measure heat leaks in buildings, and the use of computer vision and machine learning techniques to analyze urban imagery in order to understand how people move in and use public spaces.
User-Generated Content and Its Applications in Urban Studies
Authors: Tu Wei , Li Qingquan , Zhang Yatao , Yue Yang
Published: April 2021

The emergence of Web 2.0 and mobile Internet produces massive user-generated content (UGC), including geo-tagged photos, social network posts, street view images, and crowdsourced GPS trajectories. UGC creates unprecedented opportunities to sense what was previously hidden in the physical surfaces of cities and to portray the interactions of infrastructures, geo-information, and people; therefore, it is not only a new lens for urban space but also leads to innovative applications. In this chapter, we will introduce several typical types of UGC, such as geo-tagged photos, social media data, crowdsourcing GPS trajectories, and videos. We showcase ways in which user-generated big data can be harvested and analyzed to generate invisible and impressionistic landscapes of urban dynamics and to stimulate innovative applications. We discuss typical UGC-driven applications to demonstrate the potential of UGC in revealing how urban spaces are perceived by the public, establishing links between tangible artifacts and physical-cyber-social spaces. This fosters alternative approaches to urban informatics that better capture the intricate nature of urban space and its dynamics.
Introduction to Urban Computing
Authors: Shi Wenzhong , Zhang Anshu
Published: April 2021

This chapter overviews Part V of this book themed urban computing. This part of the book covers the topics of visual analytics, cloud, edge, and mobile computing, data mining and knowledge discovery, AI and deep learning for urban computing, and a range of mainstream urban models and simulation methods. It provides a systematic review of computing technologies for urban governance and urban services, together with the examples of their usage, in the context of urban computing.
Street View Imaging for Automated Assessments of Urban Infrastructure and Services
Authors: Zünd Daniel , Bettencourt Luís M. A.
Published: April 2021

Many forms of ambient data in cities are starting to become available that allows tracking of short-term urban operations, such as traffic management, trash collections, inspections, or non-emergency maintenance requests. However, arguably the greatest promise of urban analytics is to set up measurable objectives and track progress toward systemic development goals connected to human development and sustainability over the longer term. The challenge for such an approach is the connection between new technological capabilities, such as sensing and machine learning and local knowledge, and operations of residents and city governments. Here, we describe an emerging project for the long-term monitoring of sustainable development in fast-growing towns in the Galapagos Islands through the convergence of these methods. We demonstrate how collaborative mapping and the capture of 360-degree street views can produce a general basis for a broad set of quantitative analytics, when such actions are coupled to mapping and deep-learning characterizations of urban environments. We map and assess the precision of urban assets via automatic object classification and characterize their abundance and spatial heterogeneity. We also discuss how these methods, as they continue to improve, can provide the means to perform an ambient census of urban assets (buildings, vehicles, services) and environmental conditions.
Cloud, Edge, and Mobile Computing for Smart Cities
Authors: Liu Qian , Gu Juan , Yang Jingchao , Li Yun , Sha Dexuan , Xu Mengchao , Shams Ishan , Yu Manzhu , Yang Chaowei
Published: April 2021

Smart cities evolve rapidly along with the technical advances in wireless and sensor networks, information science, and human–computer interactions. Urban computing provides the processing power to enable the integration of such technologies to improve the living quality of urban citizens, including health care, urban planning, energy, and other aspects. This chapter uses different computing capabilities, such as cloud computing, mobile computing, and edge computing, to support smart cities using the urban heat island of the greater Washington DC area as an example. We discuss the benefits of leveraging cloud, mobile, and edge computing to address the challenges brought by the spatiotemporal dynamics of the urban heat island, including elevated emissions of air pollutants and greenhouse gases, compromised human health and comfort, and impaired water quality. Cloud computing brings scalability and on-demand computing capacity to urban system simulations for timely prediction. Mobile computing brings portability and social interactivity for citizens to report instantaneous information for better knowledge integration. Edge computing allows data produced by in-situ devices to be processed and analyzed at the edge of the network, reducing the data traffic to the central repository and processing engine (data center or cloud). Challenges and future directions are discussed for integrating the three computing technologies to achieve an overall better computing infrastructure supporting smart cities. The integration is discussed in aspects of bandwidth issue, network access optimization, service quality and convergence, and data integrity and security.
Data Mining and Knowledge Discovery
Authors: Zhang Chao , Han Jiawei
Published: April 2021

Our physical world is being projected into online cyberspace at an unprecedented rate. People nowadays visit different places and leave behind them million-scale digital traces such as tweets, check-ins, Yelp reviews, and Uber trajectories. Such digital data are a result of social sensing: namely people act as human sensors that probe different places in the physical world and share their activities online. The availability of massive social-sensing data provides a unique opportunity for understanding urban space in a data-driven manner and improving many urban computing applications, ranging from urban planning and traffic scheduling to disaster control and trip planning. In this chapter, we present recent developments in data-mining techniques for urban activity modeling, a fundamental task for extracting useful urban knowledge from social-sensing data. We first describe traditional approaches to urban activity modeling, including pattern discovery methods and statistical models. Then, we present the latest developments in multimodal embedding techniques for this task, which learns vector representations for different modalities to model people's spatiotemporal activities. We study the empirical performance of these methods and demonstrate how data-mining techniques can be successfully applied to social-sensing data to extract actionable knowledge and facilitate downstream applications.
Transportation Modeling
Authors: Miller Eric J.
Published: April 2021

Informatics are rapidly and radically transforming urban transportation in ways not seen since the introduction of the automobile over a hundred years ago. Near-ubiquitous smartphone usage, pervasive cellular and Wi-Fi connectivity, powerful and cost-effective computing capabilities, advanced GIS software and databases, advanced platforms for managing and scheduling service operations, etc., are combining to enable the introduction of new mobility services and technologies that are increasingly disrupting conventional trip-making behavior and the “rules of the game” in terms of transportation network operations and the regulation of system performance. The implications of these major informatics-driven changes for transportation modeling are equally disruptive and major. These include changes in: travel behavior; transportation system performance; the data available for model development and application; and modeling methods. Each of these broad areas of impact are discussed in this chapter.
Urban Human Dynamics
Authors: Shaw Shih-Lung
Published: April 2021

Urban areas are places where people concentrate in a relatively high density built environment to carry out a wide range of activities. Each urban area should provide adequate infrastructure and services to support the needs of its population. Since various resources, services, and facilities are at different locations, urban areas manifest a complex system of flows of people, goods, and information to support the economic, social, cultural, and political systems in human society. These activities, flows, and systems are driven by various processes and exhibit various spatiotemporal patterns that are the outcomes of human dynamics. However, how we investigate the various dynamic processes and complex systems in urban areas has been and continues to be a challenging research topic. Urban human dynamics cover multiple aspects and can be studied from different perspectives. This chapter discusses urban dynamics and human dynamics in terms of their respective approaches and methods, along with some selected examples. It then connects urban human dynamics research with urban informatics to highlight their relationships and how together they could lead to urban areas that can better serve human needs and improve the quality of life.
Geosmartness for Personalized and Sustainable Future Urban Mobility
Authors: Raubal Martin , Bucher Dominik , Martin Henry
Published: April 2021

Urban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.
Urban Metabolism
Authors: Derrible Sybil , Cheah Lynette , Arora Mohit , Yeow Lih Wei
Published: April 2021

Urban metabolism (UM) is fundamentally an accounting framework whose goal is to quantify the inflows, outflows, and accumulation of resources (such as materials and energy) in a city. The main goal of this chapter is to offer an introduction to UM. First, a brief history of UM is provided. Three different methods to perform an UM are then introduced: the first method takes a bottom-up approach by collecting/estimating individual flows; the second method takes a top-down approach by using nation-wide input–output data; and the third method takes a hybrid approach. Subsequently, to illustrate the process of applying UM, a practical case study is offered using the city-state of Singapore as an exemplar. Finally, current and future opportunities and challenges of UM are discussed. Overall, by the early twenty-first century, the development and application of UM have been relatively slow, but this might change as more and better data sources become available and as the world strives to become more sustainable and resilient.
Conceptualizing the City of the Information Age
Authors: Couclelis Helen
Published: April 2021

Cities are among humanity’s most important and most complex creations, and they have been steadily increasing in complexity since the advent of the digital age. Informatics, the science of information, has by now advanced to a point where high expectations of improved understanding and evidence-based actionable knowledge for urban researchers, managers, and planners appear justified. But while there is more information than ever before, many kinds of theories, models, approaches, and tools that we have relied on thus far may no longer be of much use in the city of the information age. This chapter provides an overview of the state of affairs in urban science and planning, pointing out the limitations of formerly reliable methods and tools in the face of dramatic developments in the life and function of cities in the developed world. The chapter closes with suggestions for data-oriented strategies that might replace the ways we have used urban data up until recently.
More on holographic correlators: twisted and dimensionally reduced structures
Authors: Behan Connor , Ferrero Pietro , Zhou Xinan
Published: April 2021

Abstract

Recently four-point holographic correlators with arbitrary external BPS operators were constructively derived in [1, 2] at tree-level for maximally superconformal theories. In this paper, we capitalize on these theoretical data, and perform a detailed study of their analytic properties. We point out that these maximally supersymmetric holographic correlators exhibit a hidden dimensional reduction structure à la Parisi and Sourlas. This emergent structure allows the correlators to be compactly expressed in terms of only scalar exchange diagrams in a dimensionally reduced spacetime, where formally both the AdS and the sphere factors have four dimensions less. We also demonstrate the superconformal properties of holographic correlators under the chiral algebra and topological twistings. For AdS5× S5 and AdS7× S4, we obtain closed form expressions for the meromorphic twisted correlators from the maximally R-symmetry violating limit of the holographic correlators. The results are compared with independent field theory computations in 4d $$ \mathcal{N} $$
N
= 4 SYM and the 6d (2, 0) theory, finding perfect agreement. For AdS4× S7, we focus on an infinite family of near-extremal four-point correlators, and extract various protected OPE coefficients from supergravity. These OPE coefficients provide new holographic predictions to be matched by future supersymmetric localization calculations. In deriving these results, we also develop many technical tools which should have broader applicability beyond studying holographic correlators.
Erratum to: Effective field theory after a new-physics discovery
Authors: Alte Stefan , König Matthias , Neubert Matthias
Published: April 2021

The two linear relations between operators shown in eq. (3.29) were missing an integral over the momentum fraction u on the right-hand side. In the one-particle anomalous dimensions in eq. (5.7) two fractions were mistyped.
O(D, D) and the string α′ expansion: an obstruction
Authors: Hronek Stanislav , Wulff Linus
Published: April 2021

Abstract

Double Field Theory (DFT) is an attempt to make the O(d, d) T-duality symmetry of string theory manifest, already before reducing on a d-torus. It is known that supergravity can be formulated in an O(D, D) covariant way, and remarkably this remains true to the first order in α′. We set up a systematic way to analyze O(D, D) invariants, working order by order in fields, which we carry out up to order α′3. At order α′ we recover the known Riemann squared invariant, while at order α′2 we find no independent invariant. This is compatible with the α′ expansion in string theory. However, at order α′3 we show that there is again no O(D, D) invariant, in contradiction to the fact that all string theories have quartic Riemann terms with coefficient proportional to ζ (3). We conclude that DFT and similar frameworks cannot capture the full α′ expansion in string theory.
CP violation in rare lepton-number-violating W decays at the LHC
Authors: Najafi Fatemeh , Kumar Jacky , London David
Published: April 2021

Abstract

Some models of leptogenesis involve a quasi-degenerate pair of heavy neutrinos N1,2 whose masses can be small, O(GeV). Such neutrinos can contribute to the rare lepton-number-violating (LNV) decay $$ {W}^{\pm}\to {\mathrm{\ell}}_1^{\pm }{\mathrm{\ell}}_2^{\pm }{\left({q}^{\prime}\overline{q}\right)}^{\mp } $$

W
±

→

ℓ
1
±

ℓ
2
±

q
′

q
¯

∓

. If both N1 and N2 contribute, there can be a CP-violating rate difference between the LNV decay of a W− and its CP-conjugate decay. In this paper, we examine the prospects for measuring such a CP asymmetry ACP at the LHC. We assume a value for the heavy-light neutrino mixing parameter |BℓN|2 = 10−5, which is allowed by the present experimental constraints, and consider 5 GeV ≤ MN≤ 80 GeV. We consider three versions of the LHC — HL-LHC, HE-LHC, FCC-hh — and show that small values of the CP asymmetry can be measured at 3σ, in the range 1% ≲ ACP ≲ 15%.
Symmetry structure of the interactions in near-BPS corners of $$ \mathcal{N} $$ = 4 super-Yang-Mills
Authors: Baiguera Stefano , Harmark Troels , Lei Yang , Wintergerst Nico
Published: April 2021

Abstract

We consider limits of $$ \mathcal{N} $$
N
= 4 super-Yang-Mills (SYM) theory that approach BPS bounds. These limits result in non-relativistic near-BPS theories that describe the effective dynamics near the BPS bounds and upon quantization are known as Spin Matrix theories. The near-BPS theories can be obtained by reducing $$ \mathcal{N} $$
N
= 4 SYM on a three-sphere and integrating out the fields that become non-dynamical in the limits. We perform the sphere reduction for the near-BPS limit with SU(1, 2|2) symmetry, which has several new features compared to the previously considered cases with SU(1) symmetry, including a dynamical gauge field. We discover a new structure in the classical limit of the interaction term. We show that the interaction term is built from certain blocks that comprise an irreducible representation of the SU(1, 2|2) algebra. Moreover, the full interaction term can be interpreted as a norm in the linear space of this representation, explaining its features including the positive definiteness. This means one can think of the interaction term as a distance squared from saturating the BPS bound. The SU(1, 1|1) near-BPS theory, and its subcases, is seen to inherit these features. These observations point to a way to solve the strong coupling dynamics of these near-BPS theories.
Old and new vacua of 5D maximal supergravity
Authors: Dall’Agata G. , Inverso G. , Partipilo D.
Published: April 2021

Abstract

We look for critical points with U(2) residual symmetry in 5-dimensional maximally supersymmetric gauged supergravity, by varying the embedding tensor, rather than directly minimizing the scalar potential. We recover all previously known vacua and we find four new vacua, with different gauge groups and cosmological constants. We provide the first example of a maximal supergravity model in D ≥ 4 having critical points with both positive and vanishing cosmological constant. For each vacuum we also compute the full mass spectrum. All results are analytic.
CLUSTER ALGEBRAS FROM SURFACES AND EXTENDED AFFINE WEYL GROUPS
Authors: FELIKSON ANNA , LAWSON JOHN W. , SHAPIRO MICHAEL , TUMARKIN PAVEL
Published: April 2021

We characterize mutation-finite cluster algebras of rank at least 3 using positive semi-definite quadratic forms. In particular, we associate with every unpunctured bordered surface a positive semi-definite quadratic space V , and with every triangulation a basis in V , such that any mutation of a cluster (i.e., a flip of a triangulation) transforms the corresponding bases into each other by partial reflections. Furthermore, every triangulation gives rise to an extended affine Weyl group of type A, which is invariant under flips. The construction is also extended to exceptional skew-symmetric mutation-finite cluster algebras of types E.
Characterisation of hardwood fibres used for wood fibre insulation boards (WFIB)
Authors: Imken Arne A. P. , Plinke Burkhard , Mai Carsten
Published: April 2021

Wood fibre insulation boards (WFIB) are typically made from softwood fibres. However, due to the rapid decrease in softwood stands in Germany, the industry will be forced to adapt to the wood market. Therefore, alternative approaches for the substitution of softwood with hardwood will be needed in the fibre industry. The objective of this paper is to address the characterisation of hardwood fibres regarding their availability for the WFIB industry. The physico-mechanical properties of WFIB are significantly determined by the length of the fibres. Longer softwood fibres usually generate higher strength properties and a lower thermal conductivity than shorter hardwood fibres. In this paper, the potential application of hardwood fibres (up to 20,500 μm long) produced in a refiner by thermo-mechanical pulping (TMP) to WFIB production was examined. The scanner-based system FibreShape was used for the automatic optical analysis of the geodesic length distribution of fibres. The analysed hardwood fibres contained significantly more dust and were shorter than respectively produced softwood fibres, limiting their applicability for WFIB production. Thus, two analytical approaches were chosen to receive longer fibres and less dust: (1) blending hardwood fibres with supporting softwood fibres (20%, 50 and 80% proportion of softwood), and (2) mathematical fractionation of hardwood fibres based on the fibre length to remove all particles smaller than 500 μm. It was concluded that the practical fractionation seems to be economically and ecologically challenging and that blending hardwood fibres with at least 50% softwood fibres offers a promising approach, which should be further studied.
Obesity and its impact on COVID-19
Authors: de Leeuw Angélica J. M. , Oude Luttikhuis Maureen A. M. , Wellen Annemarijn C. , Müller Christine , Calkhoven Cornelis F.
Published: April 2021

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic has proven a challenge to healthcare systems since its first appearance in late 2019. The global spread and devastating effects of coronavirus disease 2019 (COVID-19) on patients have resulted in countless studies on risk factors and disease progression. Overweight and obesity emerged as one of the major risk factors for developing severe COVID-19. Here we review the biology of coronavirus infections in relation to obesity. In particular, we review literature about the impact of adiposity-related systemic inflammation on the COVID-19 disease severity, involving cytokine, chemokine, leptin, and growth hormone signaling, and we discuss the involvement of hyperactivation of the renin-angiotensin-aldosterone system (RAAS). Due to the sheer number of publications on COVID-19, we cannot be completed, and therefore, we apologize for all the publications that we do not cite.
Model-based Safety Assessment of a Triple Modular Generator with xSAP
Authors: Bozzano Marco , Cimatti Alessandro , Gario Marco , Jones David , Mattarei Cristian
Published: April 2021

The system design process needs to cope with the increasing complexity and size of systems,motivating the replacement of labor intensivemanual techniques with automated and semi-automated approaches.Recently, formal methods techniques, such as model-based verification and safety assessment, have been increasingly used to model systems under fault and to analyze them, generating artifacts such as fault trees and FMEA tables. In this paper, we show how to apply model-based techniques to a realistic case study from the avionics domain: a high integrity power distribution system, the Triple Modular Generator (TMG). The TMG is composed of a redundant and reconfigurable plant and a controller that must guarantee a high level of reliability. The case study is a significant challenge, from the modeling perspective, since it implements a complex reconfiguration policy, specified via a number of requirements in natural language, including a set of mutually dependent and potentially conflicting priority constraints. Moreover, from the verification standpoint, the controller must be able to handle an exponential number of possible faulty configurations. Our contribution is twofold. First, we formalize and validate the requirements and, using a constraint-based modeling style, we synthesize a correct by construction controller, avoiding the enumeration of all possible fault configurations, as is currently done by manual approaches. Second, we describe a comprehensive methodology and process, supported by the xSAP safety analysis platform that targets the modeling and safety assessment of faulty systems. Using xSAP, we are able to automatically extract minimal cut sets for the TMG. We demonstrate the scalability of our approach by analyzing a parametric version of the TMG case study that contains more than 700 variables and 90 faults.
Novel nucleic acid origami structures and conventional molecular beacon–based platforms: a comparison in biosensing applications
Authors: Bellassai Noemi , D’Agata Roberta , Spoto Giuseppe
Published: April 2021

Nucleic acid nanotechnology designs and develops synthetic nucleic acid strands to fabricate nanosized functional systems. Structural properties and the conformational polymorphism of nucleic acid sequences are inherent characteristics that make nucleic acid nanostructures attractive systems in biosensing. This review critically discusses recent advances in biosensing derived from molecular beacon and DNA origami structures. Molecular beacons belong to a conventional class of nucleic acid structures used in biosensing, whereas DNA origami nanostructures are fabricated by fully exploiting possibilities offered by nucleic acid nanotechnology. We present nucleic acid scaffolds divided into conventional hairpin molecular beacons and DNA origami, and discuss some relevant examples by focusing on peculiar aspects exploited in biosensing applications. We also critically evaluate analytical uses of the synthetic nucleic acid structures in biosensing to point out similarities and differences between traditional hairpin nucleic acid sequences and DNA origami.

Graphical abstract
Informatikunterricht in Deutschland – eine Übersicht
Authors: Schwarz Richard , Hellmig Lutz , Friedrich Steffen
Published: April 2021

Nunmehr 10 Jahre nach der letzten Synopse zur Situation des Informatikunterrichts in Deutschland wird mit dieser Untersuchung ein aktuelles Bild der informatischen Bildung in den 16 Bundesländern gezeichnet. Dem allgemeinbildenden Charakter der informatischen Bildung entsprechend liegt der Fokus auf Angeboten für einen verbindlichen – durch die Existenz eines curricularen Rahmens abgesicherten – Informatikunterrichts für alle Schülerinnen und Schüler in allen Schularten weiterführender Schulen. Die Grundlage für die Erhebungen bildeten sowohl umfangreiche Analysen bildungspolitischer Dokumente, Stundentafeln und curricularer Vorgaben als auch eine Verifikation der erhobenen Daten durch Expertinnen und Experten in den jeweiligen Bundesländern. Neben einer vergleichbaren Darstellung für jedes Bundesland machen die Übersichten zum Sekundarbereich I und II über alle Bundesländer deutlich, welche Defizite in der Grundlagenbildung zur Informatik noch immer vorhanden sind und das Vorankommen in der Digitalisierung in Schulen zusätzlich hemmen.
Irreversible electroporation (IRE) in renal cell carcinoma (RCC): a mid-term clinical experience
Authors: Wah Tze Min , Lenton James , Smith Jonathan , Bassett Paul , Jagdev Satinder , Ralph Christy , Vasudev Naveen , Bhattarai Selina , Kimuli Michael , Cartledge Jon
Published: April 2021

Objectives

To evaluate the safety and efficacy of CT-guided IRE of clinical T1a (cT1a) renal tumours close to vital structures and to assess factors that may influence the technical success and early oncological durability.

Methods

CT-guided IRE (2015–2020) was prospectively evaluated. Patients’ demographics, technical details/success, Clavien-Dindo (CD) classification of complications (I–V) and oncological outcome were collated. Statistical analysis was performed to determine variables associated with complications. The overall 2- and 3-year cancer-specific (CS), local recurrence-free (LRF) and metastasis-free (MF) survival rates are presented using the Kaplan-Meier curves.

Results

Thirty cT1a RCCs (biopsy-proven/known VHL disease) in 26 patients (age 32–81 years) were treated with IRE. The mean tumour size was 2.5 cm and the median follow-up was 37 months. The primary technical success rate was 73.3%, where 22 RCCs were completely IRE ablated. Seven residual diseases were successfully ablated with cryoablation, achieving an overall technical success rate of 97%. One patient did not have repeat treatment as he died from unexpected stroke at 4-month post-IRE. One patient had CD-III complication with a proximal ureteric injury. Five patients developed > 25% reduction of eGFR immediately post-IRE. All patients have preservation of renal function without the requirement for renal dialysis. The overall 2- and 3-year CS, LRF and MF survival rates are 89%, 96%, 91% and 87%.

Conclusion

CT-guided IRE in cT1a RCC is safe with acceptable complications. The primary technical success rate was suboptimal due to the early operator’s learning curve, and long-term follow-up is required to validate the IRE oncological durability.

Key Points

• Irreversible electroporation should only be considered when surgery or image-guided thermal ablation is not an option for small renal cancer.

• This non-thermal technique is safe in the treatment of small renal cancer and the primary technical success rate was 73.3%.

• This can be used when renal cancer is close to important structure.
Deep learning in forensic gunshot wound interpretation—a proof-of-concept study
Authors: Oura Petteri , Junno Alina , Junno Juho-Antti
Published: April 2021

While the applications of deep learning are considered revolutionary within several medical specialties, forensic applications have been scarce despite the visual nature of the field. For example, a forensic pathologist may benefit from deep learning-based tools in gunshot wound interpretation. This proof-of-concept study aimed to test the hypothesis that trained neural network architectures have potential to predict shooting distance class on the basis of a simple photograph of the gunshot wound. A dataset of 204 gunshot wound images (60 negative controls, 50 contact shots, 49 close-range shots, and 45 distant shots) was constructed on the basis of nineteen piglet carcasses fired with a .22 Long Rifle pistol. The dataset was used to train, validate, and test the ability of neural net architectures to correctly classify images on the basis of shooting distance. Deep learning was performed using the AIDeveloper open-source software. Of the explored neural network architectures, a trained multilayer perceptron based model (MLP_24_16_24) reached the highest testing accuracy of 98%. Of the testing set, the trained model was able to correctly classify all negative controls, contact shots, and close-range shots, whereas one distant shot was misclassified. Our study clearly demonstrated that in the future, forensic pathologists may benefit from deep learning-based tools in gunshot wound interpretation. With these data, we seek to provide an initial impetus for larger-scale research on deep learning approaches in forensic wound interpretation.
Photo-CIDNP in Solid State
Authors: Matysik Jörg , Ding Yonghong , Kim Yunmi , Kurle Patrick , Yurkovskaya Alexandra , Ivanov Konstantin , Alia A.
Published: April 2021

Photo-CIDNP (photo-chemically induced dynamic nuclear polarization) refers to nuclear polarization created by the spin-chemical evolution of spin-correlated radical pairs (SCRPs). This phenomenon occurs in gases, liquids and solids. Based on the solid-state photo-CIDNP effect observed under magic-angle spinning (MAS), photo-CIDNP MAS NMR has been developed as analytical method. Here we report the origin, the theory and the state of the art of this method.
Italy’s Political Turmoil and Mario Draghi’s European Challenges
Authors: Pianta Mario
Published: April 2021

Italy’s economy is not expected to return to pre-pandemic levels before the first half of 2023, and an early return to budgetary constraints would be disastrous.
Mechanisms and cell lineages in lymphatic vascular development
Authors: Jafree Daniyal J. , Long David A. , Scambler Peter J. , Ruhrberg Christiana
Published: April 2021

Lymphatic vessels have critical roles in both health and disease and their study is a rapidly evolving area of vascular biology. The consensus on how the first lymphatic vessels arise in the developing embryo has recently shifted. Originally, they were thought to solely derive by sprouting from veins. Since then, several studies have uncovered novel cellular mechanisms and a diversity of contributing cell lineages in the formation of organ lymphatic vasculature. Here, we review the key mechanisms and cell lineages contributing to lymphatic development, discuss the advantages and limitations of experimental techniques used for their study and highlight remaining knowledge gaps that require urgent attention. Emerging technologies should accelerate our understanding of how lymphatic vessels develop normally and how they contribute to disease.
Rely-guarantee bound analysis of parameterized concurrent shared-memory programs
Authors: Pani Thomas , Weissenbacher Georg , Zuleger Florian
Published: April 2021

We present a thread-modular proof method for complexity and resource bound analysis of concurrent, shared-memory programs. To this end, we lift Jones’ rely-guarantee reasoning to assumptions and commitments capable of expressing bounds. The compositionality (thread-modularity) of this framework allows us to reason about parameterized programs, i.e., programs that execute arbitrarily many concurrent threads. We automate reasoning in our logic by reducing bound analysis of concurrent programs to the sequential case. As an application, we automatically infer time complexity for a family of fine-grained concurrent algorithms, lock-free data structures, to our knowledge for the first time.
Estimation Method for Roof‐damaged Buildings from Aero-Photo Images During Earthquakes Using Deep Learning
Authors: Fujita Shono , Hatayama Michinori
Published: April 2021

Issuing a disaster certificate, which is used to decide the contents of a victim’s support, requires accuracy and rapidity. However, in Japan at large, issuing of damage certificates has taken a long time in past earthquake disasters. Hence, the government needs a more efficient mechanism for issuing damage certificates. This study developed an estimation system of roof-damaged buildings to obtain an overview of earthquake damage based on aero-photo images using deep learning. To provide speedy estimation, this system utilized the trimming algorithm, which automatically generates roof image data using the location information of building polygons on GIS (Geographic Information System). Consequently, the proposed system can estimate, if a house is covered with a blue sheet with 97.57 % accuracy and also detect whether a house is damaged, with 93.51 % accuracy. It would therefore be worth considering the development of an image recognition model and a method of collecting aero-photo data to operate this system during a real earthquake.