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The proliferation of enterprise social media generates an ever-growing record of digital traces that provides ample opportunities to study the social making of organisations. Subsequently, we present the social fabric framework, which comprises a structured five-step approach for eliciting, interpreting, and representing the situated social idiosyncrasies and underlying patterns of the social making of organisations. The paper focuses on the application of the social fabric framework as a research method. However, the framework also lends itself to practice as a diagnostic tool that can detect emergent changes in the social fabric of an organisation as well as support organisational development and change. Moreover, by providing a vocabulary for articulating the social making of organisations, the framework can help organisation members reify their dispositions, make sense of the social dynamics, and enable a constructive discussion at the grassroots level about any controversy or aspiration.

Social touch is essential to human development and communication. Mediated social touch is suggested as a solution for circumstances where distance prevents skin-to-skin contact. However, past research aimed at demonstrating efficacy of mediated touch in reducing stress and promoting helping have produced mixed findings. These inconsistent findings could possibly be due to insufficient control of contextual factors combined with unnatural interaction scenarios. For example, touch occurs less frequently among strangers and is often accompanied with nonverbal visual cues. We investigated how visual presentation of touch, and interpersonal relationship to the sender influence perception, affective experiences, and autonomic responses the touch evoke. Fifty couples of mixed gender were recruited. A mediated touch was repeatedly applied by either the male partner or male confederate to female participants. The latter witnessed through a webcam as the sender caressed a rubber hand or touchpad to send the touch. Following our hypotheses, touch sent by one's partner was perceived softer and more comforting than stranger touch. The partner's touch also resulted in weaker skin conductance responses, particularly when sent by touching a touchpad. In sum, how a mediated touch is experienced depends both on who is touching, and on how the touch is visually represented.

The rise of the digital society is accompanied by incalculable social risks, but very little IS research has examined the implications of the new digital society. Drawing on concepts from Beck’s critical theory of the risk society and critical discourse analysis, this study examines the public discourse on risk events during the launch of NemID, a personal digital identifier for Danish citizens. This research illustrates our difficulties and challenges in managing some of the fundamental social risks from societal digitalisation. Limited institutional capabilities for digital technologies force public officials to depend on private companies motived by profit instead of the public interest. Beliefs in digital technology as the primary determinant of social and economic progress also present many public management dilemmas. When digital risk events occur and citizens’ fears are stoked by news media and public discourse, public officials seem to have no other strategy for managing the escalating fears than systematically distorted communication. The continued rise of the digital risk society demands that IS research respond to the challenge of generating knowledge for its public management.

In this study, CO₂ conversion to acetate and CH₄ was achieved in a flow-through laboratory-scale microbial electrosynthesis (MES) cell composed of a 3D conductive polylactic acid (cPLA) lattice cathode with electrodeposited metal electrocatalyst coatings. The MES cell with a bare cPLA cathode showed the poorest performance with the lowest H₂ and CH₄ production rates and low Coulombic efficiency. This was ascribed to a poor electrocatalytic activity of cPLA towards H₂ production and high electrode resistivity. When the cPLA electrode was modified with metal coatings, the CH₄, acetate and H₂ production rate increased significantly, with the following trend: cPLA < Ni < NiFe < NiFeMn. The better performance of the metal-coated cPLA in terms of CH₄ production was attributed to the lower electrical resistance, enhanced H₂ production and enhanced electron transfer between the cathode and the biofilm. At the cell potential of 2.8 V, the best-performing NiFeMn cPLA cathode showed stable production of CH₄ (50 ± 6 mL d⁻¹), acetate (185 ± 27 mg d⁻¹), and H₂ (545 ± 175 mL d⁻¹) at close to 100% Coulombic efficiency.

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.

The aim of the study was to investigate whether jerk cost (JC) can discriminate between swimming levels. Nine elite and nine non-elite swimmers swam a 50-m front-crawl sprint wearing a 3D accelerometer on their back between the inferior angles of the scapulae. Lap times and JC were calculated from the acceleration signal and compared between groups and between swimmers within a group. The elite swimmers swam significantly faster lap times than the non-elite swimmers (p < 0.001). They did so with significantly lower levels of JC compared to the non-elite swimmers (p = 0.005). Furthermore, a stepwise multiple linear regression showed JC accounted for 32.9% of the variation in lap time of the elite swimmers. These results indicate that it is possible to discriminate elite from non-elite swimmers using JC: elite swimmers swim with lower JCs than non-elite swimmers. Additionally, swimming at higher speed is associated with more accelerations and decelerations in both elite and non-elite swimmers, which is reflected by higher JCs and lower smoothness. In sum, JC provides an index of swimming technique that is easy to use in training practice.

This paper presents a techno-economic model of two interconnected hybrid microgrids (MGs) whose electricity and thermal dispatch strategy are managed with Sequential Least Squares Programming (SLSQP) optimization technique. MGs combine multiple thermal and electric power generation, transmission, and distribution systems as a whole, to gain a tight integration of weather-dependent distributed renewable generators with multiple stochastic load profiles. Moreover MGs allow to achieve an improvement in the return of investment and better cost of energy. The first part of the work deals with a method to obtain an accurate prediction of climate variables. This method makes use of Fast Fourier Transform (FFT) and polynomial regression to manipulate climate datasets issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). The second part of the work is focused on the optimization of interconnected MGs operations through the SLSQP algorithm. The objective is to obtain the best financial performance (IRR) when clean distributed energy resources (DERs) are exchanging both thermal and electric energy. SLSQP optimizes the energy flows by balancing their contribution with their nominal Levelized Cost of Energy (LCOE). The proposed algorithm is used to simulate innovative business scenarios where revenue streams are generated via sales of energy to end users, sell backs and deliveries of demand response services to the other grids. A business case dealing with two MGs providing clean thermal and electric energies to household communities nearby the city of Bremen (Germany) is examined in the last part of the work. This business case with a payback in two years, an internal rate of return (IRR) at 65% and a LCOE at 0.14 €/kWh, demonstrates how the interconnection of multiple hybrid MGs with SLSQP optimization techniques, makes renewable and DERs outcompeting and could strand investments in fossil fuel generation, shaping the future of clean energy markets.

Co-production in climate change has arisen in response to improving climate knowledge. Climate service co-production provides climate knowledge for decision-making, benefiting vulnerable communities with useful and usable information such as flooding risk areas. A climate service co-production project, including mapping technologies, has been conducted in Jakarta, Indonesia. However, it has not been integrated into mainstream spatial planning. This paper investigates an enabling environment for the integration of climate service co-production and spatial planning using a collaborative governance framework. We conducted semi-structured interviews, photovoice, and focus group discussions. The results highlight factors to enable integration; (1) an institutional reform to put climate service co-production on the agenda, (2) top-down priorities aligned with local-level needs, (3) local government will and leadership to execute the tasks and actions towards climate resilience, (4) trust of the local government with the community, (5) integrated geospatial data to have an effective climate knowledge to spatial planning.

In this paper, a non‐negative matrix factorization feature expansion (NMFFE) approach was proposed to overcome the feature‐sparsity issue when expanding features of short‐text. First, we took the internal relationships of short texts and words into account when segmenting words from texts and constructing their relationship matrix. Second, we utilized the Dual regularization non‐negative matrix tri‐factorization (DNMTF) algorithm to obtain the words clustering indicator matrix, which was used to get the feature space by dimensionality reduction methods. Thirdly, words with close relationship were selected out from the feature space and added into the short‐text to solve the sparsity issue. The experimental results showed that the accuracy of short text classification of our NMFFE algorithm increased 25.77%, 10.89%, and 1.79% on three data sets: Web snippets, Twitter sports, and AGnews, respectively compared with the Word2Vec algorithm and Char‐CNN algorithm. It indicated that the NMFFE algorithm was better than the BOW algorithm and the Char‐CNN algorithm in terms of classification accuracy and algorithm robustness.

This paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.

International audience; COVID-19 has caused unprecedented disruption to previously settled everyday routines, prompting a period of forced experimentation as people have adjusted to rapid changes in their private and working lives. For discussions regarding consumption, this period of experimentation has been interesting, as the apparent instability has disturbed the ongoing trajectory of consumption practices, and with it has created possibilities for transition toward sustainability. In this article, we examine food practices (e.g., food shopping, preparation, and eating) in seven countries (France, Germany, Italy, Netherlands, Norway, UK, and Vietnam) to assess what we can learn to accelerate transitions toward sustainable consumption. Grounded in a practice theoretical approach, our empirical analysis shows how disruption of everyday routines has generated socio-materially bounded experimentation. We demonstrate commonalities across contexts in how lockdown measures have restricted the performance of previously taken-for-granted practices. We also show diversity in experimentation as food consumption is entangled in other everyday practices. Our study, on one hand, portrays how adaptation of food practices allows disruption to be managed, demonstrating creativity in working within and around restrictions to continue to provide services for everyday life. On the other hand, we reveal that the capacity of experimentation is not evenly distributed among people and this variation helps in identifying the wider socio-material conditions that constrain and enable opportunities for readjustment. Understanding disparities that affect experimentation (e.g., integration of food practices with work and caring practices) is informative when thinking about how to stimulate sustainability transformations in food practices and provides critical reflections on strategies to enable sustainable consumption.

A novel recycling solution for thermoplastic composites (TPCs) was recently implemented. The processing steps comprise shredding of TPC offcuts to flakes of a few centimetres, melting and blending of the flakes in a low-shear mixer, extrusion of a molten mixed dough and subsequent compression moulding in a press. This material and process are similar to the compression moulding of long-fibre thermoplastics (LFTs) that have been in the market for decades, such as glass mat thermoplastics (GMT) or direct-LFT. However, the input material in this recycling route consists of multi-layered woven flakes, which is very different from the pellets or chopped rovings of other LFTs. Process- and material-induced heterogeneities such as fibre orientation, percolation, variation of fibre fraction, or fibre attrition may be different for this new material. The development of this recycling technology and future industrial applications require more confidence in the material and process. The objective of this study is to characterise these heterogeneities for this recycling solution, and compare them to those generated in regular LFTs. It was found that the process- and material-induced heterogeneities of the recycled TPCs are similar to other LFTs, for the aspects listed here: fibre orientation, percolation, variation of fibre fraction and fibre attrition. In comparison to GMT, the effect of the mixing step is particularly noticeable on the local variation of fibre fraction within the panels. Industrial applications of this recycling route will benefit from this similarity, as it improves the confidence in the material and process combination.

Funding Information: This research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement (EnergyMatching project number 768766), and J. Gust. Richert foundation in Sweden (grant number: 2020-00586). Funding Information: This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement (EnergyMatching project number 768766 ), and J. Gust. Richert foundation in Sweden (grant number: 2020-00586 ). Publisher Copyright: © 2021 The Author(s) Peer-to-peer (P2P) energy sharing among neighboring households is a promising solution to mitigating the difficulties of renewable power (such as solar Photovoltaics (PV)) penetration on the power grid. Until now, there is still a lack of study on the impacts of future climate change on the P2P energy trading performances. The future climate change will cause variances in the renewable energy production and further lead to changes in the economic performances of households with various energy uses and affect the decision making in PV ownership and pricing strategies. Being unaware of these impacts could potentially hinder the P2P energy sharing application in practice. To bridge such knowledge gap, this paper conducts a systematic investigation of the climate change impacts on the energy sharing performance in solar PV power shared communities. The future weather data is generated using the Morphine method, and an agent-based modeling method is used for simulating the energy trading behaviors of households. Four comparative scenarios of different PV ownerships and pricing strategies are designed. The detailed energy trading performances (including the PV power self-sufficiency, cost saving, revenues, and compound annual growth rate) for the four comparative scenarios are analyzed under both the present and future climates and compared. The study results of a building community located in Sweden show that the future climate change is more beneficial to large energy use households while less beneficial to small households. High price of energy trading can improve the fairness of the economic performances in the community, especially when some of the households do not have any PV ownership. This study can help understand the future climate impacts on the energy sharing performances of building communities, which can in turn guide decision making in PV ownership and price setting for different households under the future climate change to facilitate real applications.

We consider a stochastic EOQ-type model, with demand operating in a two-state random environment. This environment alternates between exponentially distributed periods of high demand and generally distributed periods of low demand. The inventory level starts at some level q, and decreases according to different compound Poisson processes during the periods of high demand and of low demand. Refilling of the inventory level to level q is required when level 0 is hit or when an expiration date is reached, whichever comes first. If such an event occurs during a high demand period, an order is instantaneously placed; otherwise, ordering is postponed until the beginning of the next high demand period. We determine various performance measures of interest, like the distribution of the inventory level at time t and of the inventory demand up to time t, the distribution of the time until refilling is required, the expected time between two refillings, the expected amount of discarded material and the expected total amount of material held in between two refillings, and the expected values of various kinds of shortages. For a given cost/revenue structure, we can thus determine the long-run average profit.

Motivated by the ABO issue of the blood bank system, in which the portions stored have constant shelf life, we consider two subsystems of perishable inventory. The two Perishable Inventory Subsystems-PIS A and PIS B, are correlated to each other through a one-way substitution of demands. Specifically, the input streams and the demand streams applied to each subsystem are four Poisson processes, which are independent of one another. However, if the shelf of PIS A (blood of type O) is empty of items, an arriving demand of type A is unsatisfied, since demand of type A cannot be satisfied by an item of type B (blood portions of type AB), but if the shelf of PIS B is empty of items, an arriving demand of type B is applied to PIS A, since demands of type B can be satisfied by both types. This one-way substitution of the issuing policy generates for PIS A a modulated Poisson demand process operating in a two-state non-Markovian environment. The performance analysis of PIS B is known from previous work. Thus, in this study we focus on the marginal performance analysis of PIS A. Based on a fluid formulation and a Markovian approximation for the one-way substitution demand process, we develop a unified approach to efficiently and accurately approximate the performance of the PIS A. The effectiveness of the approach is investigated by extensive numerical experiments.

The seven editions of Geography and Geographers and of Political Geography form two intimidating collections creating order in their (sub)disciplines. As successive editions engage with an exponentially growing and diversifying scholarly literature they have adopted different strategies to order ‘work in progress’. This commentary also considers their ordering and bordering of the (sub) discipline, epistemologically but also linguistically, foregrounding the predicament of their readership at the non-Anglophone borderlands of Anglo-American (political) geography.

This paper presents some new criteria for partial exponential stability of a slow-fast nonlinear system with a fast scalar variable using periodic averaging methods. Unlike classical averaging techniques, we construct an averaged system by averaging over this fast scalar variable instead of the time variable. We then show that partial exponential stability of the averaged system implies partial exponential stability of the original one. As some intermediate results, we also obtain a new converse Lyapunov theorem and some perturbation theorems for partially exponentially stable systems. We then apply our established criteria to study remote synchronization of Kuramoto-Sakaguchi oscillators coupled by a star network with two peripheral nodes. We analytically show that detuning the natural frequency of the central mediating oscillator can increase the robustness of the remote synchronization against phase shifts.

We introduce and study a class of optimization problems we call replenishment problems with fixed turnover times: a very natural model that has received little attention in the literature. Clients with capacity for storing a certain commodity are located at various places; at each client the commodity depletes within a certain time, the turnover time, which is constant but can vary between locations. Clients should never run empty. The natural feature that makes this problem interesting is that we may schedule a replenishment (well) before a client becomes empty, but then the next replenishment will be due earlier also. This added workload needs to be balanced against the cost of routing vehicles to do the replenishments. In this paper, we focus on the aspect of minimizing routing costs. However, the framework of recurring tasks, in which the next job of a task must be done within a fixed amount of time after the previous one is much more general and gives an adequate model for many practical situations. Note that our problem has an infinite time horizon. However, it can be fully characterized by a compact input, containing only the location of each client and a turnover time. This makes determining its computational complexity highly challenging and indeed it remains essentially unresolved. We study the problem for two objectives: min–avg minimizes the average tour cost and min–max minimizes the maximum tour cost over all days. For min–max we derive a logarithmic factor approximation for the problem on general metrics and a 6-approximation for the problem on trees, for which we have a proof of NP-hardness. For min–avg we present a logarithmic factor approximation on general metrics, a 2-approximation for trees, and a pseudopolynomial time algorithm for the line. Many intriguing problems remain open.

It has been suggested that more globalized cities are more polarized and segregated than other cities. However, this is mainly evidenced by advanced capitalist cities and other selected developed cities, while less developed cities are clearly under-researched. This paper draws on both developed and less developed cities in Guangdong province, and asks how socio-economic segregation levels differ among cities of varying economic development levels. We find that, contrary to expectation, less developed cities are showing higher levels of segregation than well-developed cities. We suggest that the key contributing factor is that the well-developed cities in Guangdong province are characterized by manufacturing-dominant economic structures and industrialization-based economic transformation. Correspondingly, their occupational structures are predominantly characterized by employees with a recent agricultural background; the restructuring process is therefore dominated by professionalization of the lower class. The stronger and faster industrialization and professionalization processes produce lower segregation levels in those more developed cities.

Election violence is common in many developing countries and has potentially detrimental implications for democratic consolidation. Drawing on political psychology, we argue that citizens’ fear of campaign violence undermines support for democracy while increasing support for autocracy. Using individual-level survey data from 21 electoral democracies in Sub-Saharan Africa, we find robust support for our argument. Citizens fearing campaign violence are less likely to support democracy and multi-party competition, more likely to favor a return to autocracy, and less likely to turn out to vote. Our findings have important implications for democratic survival and provide further impetus for reducing electoral violence.

A novel tribologic system was developed in which volatile lubricants (carbon dioxide-CO2 or nitrogen-N2) were used as a substitute for mineral oil-based lubricants in deep drawing processes. This process allows an intermediate medium to be introduced into the tool contact surfaces under high pressure by flow-optimized, laser-drilled microholes. This eliminates the need for subsequent cost-intensive cleaning processes as volatile lubricants evaporate while expanding to ambient pressure without leaving any residue. This article gives an overview of the current findings to enable and characterize the novel tribologic system. The areas of microhole laser drilling by ultrashort pulsed laser radiation, characterization of the novel tribologic system and realization of the system using a prototype tool will be described., Deutsche Forschungsgemeinschaft, Projekt DEAL

This paper discusses the digital automation workflows and co-design methods that made possible the comprehensive robotic prefabrication of the BUGA Wood Pavilion - a large-scale production case study of robotic timber construction. Latest research in architectural robotics often focuses on the advancement of singular aspects of integrated digital fabrication and computational design techniques. Few researchers discuss how a multitude of different robotic processes can come together into seamless, collaborative robotic fabrication workflows and how a high level of interaction within larger teams of computational design and robotic fabrication experts can be achieved. It will be increasingly important to discuss suitable methods for the management of robotics and computational design in construction for the successful implementation of robotic fabrication systems in the context of the industry. We present here how a co-design approach enabled the organization of computational design decisions in reciprocal feedback with the fabrication planning, simulation and robotic code generation. We demonstrate how this approach can implement direct and curated reciprocal feedback between all planning domains - paving the way for fast-paced integrative project development. Furthermore, we discuss how the modularization of computational routines simplify the management and computational control of complex robotic construction efforts on a per-project basis and open the door for the flexible reutilization of developed digital technologies across projects and building systems., European Regional Development Fund, Deutsche Forschungsgemeinschaft, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Projekt DEAL, Bundesgartenschau Heilbronn 2019 GmbH, GETTYLAB

This paper introduces an adaptive filtering process based on shrinking wavelet coefficients from the corresponding signal wavelet representation. The filtering procedure considers a threshold method determined by an iterative algorithm inspired by the control charts application, which is a tool of the statistical process control (SPC). The proposed method, called SpcShrink, is able to discriminate wavelet coefficients that significantly represent the signal of interest. The SpcShrink is algorithmically presented and numerically evaluated according to Monte Carlo simulations. Two empirical applications to real biomedical data filtering are also included and discussed. The SpcShrink shows superior performance when compared with competing algorithms., 19 pages, 10 figures, 2 tables

The wide availability of embedded sensor platforms and lowcost cameras—together with the developments in wireless communication—make it now possible the conception of pervasive intelligent systems based on vision. Such systems may be understood as distributed and collaborative sensor networks, able to produce, aggregate and process images in order to understand the observed scene and communicate the relevant information found about it. In this paper, we investigate the peculiarities of visual sensor networks with respect to standard vision systems and we identify possible strategies to accomplish image processing and analysis tasks over them. Although the rather strong constraints in computational and transmission power of embedded platforms that may prevent the use of state of the art computer vision and pattern recognition methods, we argue that multinode processing methods may be envisaged to decompose a complex task into a hierarchy of computationally simpler problems to be solved over the nodes of the network. These ideas are illustrated by describing an application of visual sensor network to infomobility. In particular, we consider an experimental setting in which several views of a parking lot are acquired by the sensor nodes in the network. By integrating the various views, the network is capable to provide a description of the scene in terms of the available spaces in the parking lot.

Edge computing is projected to become the dominant form of cloud computing in the future because of the significant advantages it brings to both users (less latency, higher throughput) and telecom operators (less Internet traffic, more local management). However, to fully unlock its potential at scale, system designers and automated optimization systems alike will have to monitor closely the dynamics of both processing and communication facilities. Especially the latter is often neglected in current systems since network performance in cloud computing plays only a minor role. In this paper, we propose the architecture of MECPerf, which is a solution to collect network measurements in a live edge computing domain, to be collected for offline provisioning analysis and simulations, or to be provided in real-time for on-line system optimization. MECPerf has been validated in a realistic testbed funded by the European Commission (Fed4Fire+), and we describe here a summary of the results, which are fully available as open data and through a Python library to expedite their utilization. This is demonstrated via a use case involving the optimization of a system parameter for migrating clients in a federated edge computing system adopting the GSMA platform operator concept.

In classical logic, nonBoolean fluents, such as the location of an object, can be naturally described by functions. However, this is not the case in answer set programs, where the values of functions are pre-defined, and nonmonotonicity of the semantics is related to minimizing the extents of predicates but has nothing to do with functions. We extend the first-order stable model semantics by Ferraris, Lee, and Lifschitz to allow intensional functions -- functions that are specified by a logic program just like predicates are specified. We show that many known properties of the stable model semantics are naturally extended to this formalism and compare it with other related approaches to incorporating intensional functions. Furthermore, we use this extension as a basis for defining Answer Set Programming Modulo Theories (ASPMT), analogous to the way that Satisfiability Modulo Theories (SMT) is defined, allowing for SMT-like effective first-order reasoning in the context of ASP. Using SMT solving techniques involving functions, ASPMT can be applied to domains containing real numbers and alleviates the grounding problem. We show that other approaches to integrating ASP and CSP/SMT can be related to special cases of ASPMT in which functions are limited to non-intensional ones., 69 pages

Synthetic two-dimensional (2D) mono-elemental crystals, namely X-enes, have recently emerged as a new frontier for atomically thin nanomaterials with on-demand properties. Among X-enes, antimonene, the β-phase allotrope of antimony, is formed by atoms arranged in buckled hexagonal rings bearing a comparatively higher environmental stability with respect to other players of this kind. However, the exploitation of monolayer or few-layer antimonene and other 2D materials in novel opto-electronic devices is still hurdled by the lack of scalable processes. Here, we demonstrated the viability of a bottom-up process for the epitaxial growth of antimonene-like nanocrystals (ANCs), based on a Metal-Organic Chemical Vapor Deposition (MOCVD) process, assisted by gold nanoparticles (Au NPs) on commensurate (1 1 1)-terminated Ge surfaces. The growth mechanism was investigated by large- and local-area microstructural analysis, revealing that the etching of germanium, catalyzed by the Au NPs, led to the ANCs growth on the exposed Ge (1 1 1) planes. As a supportive picture, ab-initio calculations rationalized this epitaxial relationship in terms of compressively strained β-phase ANCs. Our process could pave the way to the realization of large-area antimonene layers by a deposition process compatible with the current semiconductor manufacturing technology.

Short-range wireless technologies will enable vehicles to communicate and coordinate their actions, thus improving people's safety and traffic efficiency. Whereas IEEE 802.11p (and related standards) had been the only practical solution for years, in 2016 a new option was introduced with Release of 14 long-term evolution (LTE), which includes new features to enable direct vehicle-to-vehicle (V2V) communications. LTE-V2V promises a more efficient use of the channel compared to IEEE 802.11p, thanks to an improved PHY layer and the use of orthogonal resources at the MAC layer. In LTE-V2V, a key role is played by the resource allocation algorithm and increasing efforts are being made to design new solutions to optimize the spatial reuse. In this context, an important aspect still little studied, is therefore that of identifying references that allow, first, to have a perception of the space in which the resource allocation algorithms move and, second, to verify the performance of new proposals. In this paper, we focus on a highway scenario and identify two algorithms to be used as a minimum and maximum reference in terms of the packet reception probability (PRP). The PRP is derived as a function of various parameters that describe the scenario and settings, from the application to the physical layer. Results, obtained both in a simplified Poisson point process scenario and with realistic traffic traces, show that the PRP varies considerably with different algorithms and that there is room for the improvement of current solutions.

none 12 si In this paper we present D.A.V.I.D.E. (Development for an Added Value Infrastructure Designed in Europe), an innovative and energy efficient High Performance Computing cluster designed by E4 Computer Engineering for PRACE (Partnership for Advanced Computing in Europe). D.A.V.I.D.E. is built using best-in-class components (IBM’s POWER8-NVLink CPUs, NVIDIA TESLA P100 GPUs, Mellanox InfiniBand EDR 100 Gb/s networking) plus custom hardware and an innovative system middleware software. D.A.V.I.D.E. features (i) a dedicated power monitor interface, built around the BeagleBone Black Board that allows high frequency sampling directly from the power backplane and scalable integration with the internal node telemetry and system level power management software; (ii) a custom-built chassis, based on OpenRack form factor, and liquid cooling that allows the system to be used in modern, energy efficient, datacenter; (iii) software components designed for enabling fine grain power monitoring, power management (i.e. power capping and energy aware job scheduling) and application power profiling, based on dedicated machine learning components. Software APIs are offered to developers and users to tune the computing node performance and power consumption around on the application requirements. The first pilot system that we will deploy at the beginning of 2017, will demonstrate key HPC applications from different fields ported and optimized for this innovative platform. open Abu Ahmad, Wissam; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca; Borghesi, Andrea; Cicala, Marco; Forestieri, Privato; Gianfreda, Cosimo; Gregori, Daniele; Libri, Antonio; Spiga, Filippo; Tinti, Simone Abu Ahmad, Wissam; Bartolini, Andrea; Beneventi, Francesco; Benini, Luca; Borghesi, Andrea; Cicala, Marco; Forestieri, Privato; Gianfreda, Cosimo; Gregori, Daniele; Libri, Antonio; Spiga, Filippo; Tinti, Simone

We investigated the growth and electronic properties of Co-phthalocyanine (CoPc) molecule deposited on iron film with different structures (pseudomorph-fcc and bcc) and on iron nanowires by scanning tunnelling microscopy and X-ray absorption spectroscopy (XAS). CoPc molecules self-assemble in a two-dimensional (2D) arrangement with the molecular plane parallel to the iron surfaces, and the local order is lost after the first layer. The molecule ferromagnet interaction causes the broadening of Co and N unoccupied molecular states as well as different electronic distribution of N states as a function of the atomic structure of iron surface. The ferromagnetic coupling between the molecule and the iron film is dominated by the electronic interaction between Co and the first Fe layer. CoPc 2D arrangement turns into 1D by using as a template the iron nanowire grown on a facet surface of oxidized Cu(332) surface. CoPc molecules interact weakly with the iron nanowires manifesting a substantial Co 3d(z) spectral feature in XAS spectrum and the possibility of a magnetic interaction between Co moment and iron nanowires. Both CoPc 2D and 1D arrangements can open up new interesting scenarios to tune the magnetic properties of hybrid interfaces involving metallorganic molecules.

This paper presents the results of cement-based mortars characterizations that were taken from rendered layers of military bunkers in the Rupnik Line. The Rupnik Line was conceived as a part of the fortified defence system protecting the Rapallo Border between the Kingdom of Italy and the Kingdom of Yugoslavia, established in 1920. The bunkers were built between 1938 and 1941, and the renders of various compositions were applied as a camouflage layer that merged the bunkers with the environment. Results of petrographic examination and microstructural and chemical analysis of the samples confirmed that locally available crushed dolomite or limestone sands were used as an aggregate in the mortars. As binders, pure Portland cement or a mixture of cement and ground granulated blast furnace slag were used. The selection of the aggregate and binder type in a particular mortar was based on the colour of the artificial stone produced. All samples with the dolomite aggregate show the presence of alkali-aggregate reactions, dedolomitization of the grains and secondary calcite formation in the cement binder, along with the Mg-Al, Mg-Si and Mg-Al-Si phase formations. In mortar compositions with a high Portland cement content, the presence of delayed ettringite formation was also confirmed.

For each of (i) arbitrary stochastic reset, (ii) deterministic reset with arbitrary period, (iii) reset at arbitrary constant rate, and then in the sense of either (a) first-order stochastic dominance or (b) expectation (i.e. for each of the six possible combinations of the preceding), those laws of random times are precisely characterized that are rendered no bigger [rendered no smaller; left invariant] by all possible restart laws (within the classes (i), (ii), (iii), as the case may be). Partial results in the same vein for reset with branching are obtained. In particular it is found that deterministic and arbitrary stochastic restart lead to the same characterizations, but this equivalence fails to persist for exponential (constant-rate) reset.

Screw and Lie group theory allows for user-friendly modeling of multibody systems (MBS) while at the same they give rise to computationally efficient recursive algorithms. The inherent frame invariance of such formulations allows for use of arbitrary reference frames within the kinematics modeling (rather than obeying modeling conventions such as the Denavit-Hartenberg convention) and to avoid introduction of joint frames. The computational efficiency is owed to a representation of twists, accelerations, and wrenches that minimizes the computational effort. This can be directly carried over to dynamics formulations. In this paper recursive $O\left( n\right) $ Newton-Euler algorithms are derived for the four most frequently used representations of twists, and their specific features are discussed. These formulations are related to the corresponding algorithms that were presented in the literature. The MBS motion equations are derived in closed form using the Lie group formulation. One are the so-called 'Euler-Jourdain' or 'projection' equations, of which Kane's equations are a special case, and the other are the Lagrange equations. The recursive kinematics formulations are readily extended to higher orders in order to compute derivatives of the motions equations. To this end, recursive formulations for the acceleration and jerk are derived. It is briefly discussed how this can be employed for derivation of the linearized motion equations and their time derivatives. The geometric modeling allows for direct application of Lie group integration methods, which is briefly discussed.

Exchange processes between a turbulent free flow and a porous media flow are sensitive to the flow dynamics in both flow regimes, as well as to the interface that separates them. Resolving these complex exchange processes across irregular interfaces is key in understanding many natural and engineered systems. With soil-water evaporation as the natural application of interest, the coupled behavior and exchange between flow regimes are investigated numerically, considering a turbulent free flow as well as interfacial forms and obstacles. Interfacial forms and obstacles will alter the flow conditions at the interface, creating flow structures that either enhance or reduce exchange rates based on their velocity conditions and their mixing with the main flow. To evaluate how these interfacial forms change the exchange rates, interfacial conditions are isolated and investigated numerically. First, different flow speeds are compared for a flat surface. Second, a porous obstacle of varied height is introduced at the interface, and the effects the flow structures that develop have on the interface are analyzed. The flow parameters of this obstacle are then varied and the interfacial exchange rates investigated. Next, to evaluate the interaction of flow structures between obstacles, a second obstacle is introduced, separated by a varied distance. Finally, the shape of these obstacles is modified to create different wave forms. Each of these interfacial forms and obstacles is shown to create different flow structures adjacent to the surface which alter the mass, momentum, and energy conditions at the interface. These changes will enhance the exchange rate in locations where higher velocity gradients and more mixing with the main flow develop, but will reduce the exchange rate in locations where low velocity gradients and limited mixing with the main flow occur., Deutsche Forschungsgemeinschaft, Projekt DEAL

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users’ interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area.

The widespread diffusion of connected smart devices has greatly contributed to the rapid expansion and evolution of the Internet at its edge, where personal mobile devices follow the behavior of their human users and interact with other smart objects located in the surroundings. In such a scenario, the user context is represented by a large variety of information that can rapidly change, and the ability of personal mobile devices to locally process this data is fundamental to make the system able to quickly adapt its behavior to the current situation. This ability, in practice, can be represented by a single elaboration process integrated in the final user application, or by a middleware platform aimed at implementing different context processing and reasoning to support third-party applications. However, the lack of public datasets that take into account the complexity of the user context in the mobile environment strongly limits the advance of the research in this field. In this paper, we present MyDigitalFootprint, a novel large-scale dataset composed of smartphone embedded sensors data, physical proximity information, and Online Social Networks interactions aimed at supporting multimodal context-recognition and social relationships modeling. The dataset includes two months of measurements and information collected from the personal mobile devices of 31 volunteer users, in their natural environment, without limiting their usual behavior. Existing public datasets generally consist of a limited set of context data, aimed at optimizing specific application domains (human activity recognition is the most common example). On the contrary, our dataset contains a comprehensive set of information describing the user context in the mobile environment. In order to demonstrate the efficacy of the proposed dataset, we present three context-aware applications based on different machine learning tasks: (i) a social link prediction algorithm based on physical proximity data, (ii) the recognition of daily-life activities based on smartphone-embedded sensors data, and (iii) a pervasive context-aware recommender system. To the best of our knowledge, this is the first large-scale dataset containing such heterogeneity of information, representing an invaluable source of data to validate new research in mobile and edge computing.

Modern mobile devices are able to provide context-aware and personalized services to the users, by leveraging on their sensing capabilities to infer the activity and situation in which a person is currently involved. Current solutions for context-recognition rely on annotated data and experts’ knowledge to predict the user context. In addition, their prediction ability is strongly limited to the set of situations considered during the model training or definition. However, in a mobile environment, the user context continuously evolves, and it cannot be merely restricted to a set of predefined classes. To overcome these limitations, we propose COMPASS, a novel unsupervised and online clustering algorithm aimed at identifying the user context in mobile environments based on the stream of high-dimensional data generated by smartphone sensors. COMPASScan distinguish an arbitrary number of user’s contexts from the sensors’ data, without defining a priori the collection of expected situations. This key feature makes it a general-purpose solution to provide context-aware features to mobile devices, supporting a broad set of applications. Experimental results on 18 synthetic and 2 real-world datasets show that COMPASS correctly identifies the user context from the sensors’ data stream, and outperforms the state-of-the-art solutions in terms of both clusters configuration and purity. Eventually, we evaluate its performances in terms of execution time and the results show that COMPASS can process 1000 high-dimensional samples in less than 20 seconds, while the reference solutions require about 60 minutes to evaluate the entire dataset. Keywords: Context-awareness, Unsupervised Machine Learning, Online Clustering, Mobile Computing

This paper presents an Expert-guided Mixed-Initiative Control Switcher (EMICS) for remotely operated mobile robots. The EMICS enables switching between different levels of autonomy during task execution initiated by either the human operator and/or the EMICS. The EMICS is evaluated in two disaster response inspired experiments, one with a simulated robot and test arena, and one with a real robot in a realistic environment. Analyses from the two experiments provide evidence that: a) Human-Initiative (HI) systems outperform systems with single modes of operation, such as pure teleoperation, in navigation tasks; b) in the context of the simulated robot experiment, Mixed-Initiative (MI) systems provide improved performance in navigation tasks, improved operator performance in cognitive demanding secondary tasks, and improved operator workload compared to HI. Results also reinforce previous human-robot interaction evidence regarding the importance of the operator's personality traits and their trust in the autonomous system. Lastly, our experiment on a physical robot provides empirical evidence that identify two major challenges for MI control: a) the design of context-aware MI control systems; and b) the conflict for control between the robot's MI control system and the operator. Insights regarding these challenges are discussed and ways to tackle them are proposed., Submitted for journal publication, under review

Composite electrolytes containing lithium ion conducting polymer matrix and ceramic filler are promising solid-state electrolytes for all solid-state lithium ion batteries due to their wide electrochemical stability window, high lithium ion conductivity and low electrode/electrolyte interfacial resistance. In this study, we report on the polymer infiltration of porous thin films of aluminum-doped cubic garnet fabricated via a combination of nebulized spray pyrolysis and spin coating with subsequent post annealing at 1173 K. This method offers a simple and easy route for the fabrication of a three-dimensional porous garnet network with a thickness in the range of 50 to 100 µm, which could be used as the ceramic backbone providing a continuous pathway for lithium ion transport in composite electrolytes. The porous microstructure of the fabricated thin films is confirmed via scanning electron microscopy. Ionic conductivity of the pristine films is determined via electrochemical impedance spectroscopy. We show that annealing times have a significant impact on the ionic conductivity of the films. The subsequent polymer infiltration of the porous garnet films shows a maximum ionic conductivity of 5.3 × 10-7 S cm-1 at 298 K, which is six orders of magnitude higher than the pristine porous garnet film., Deutsche Forschungsgemeinschaft

We show that a A-linear map of Hilbert A-modules is induced by a unitary Hilbert module operator if and only if it extends to an ordinary unitary on appropriately defined enveloping Hilbert spaces. Applications to the theory of multiplicative unitaries let us to compute the equivalence classes of Hilbert modules over a class of C*-algebraic quantum groups. We, thus, develop a theory that, for example, could be used to show non-existence of certain co-actions. In particular, we show that the Cuntz semigroup functor takes a co-action to a multiplicative action.

A major drawback of nonlinear dimensionality reduction (DR) techniques is their inability to preserve some authentic information from the source domain, leading to projections that are often hard to interpret when it comes to observing anything other than the topological structure of the data. In this paper, we propose a nonlinear DR approach enforcing projection constraints resulting from an a priori knowledge about the structure of the data in multivariate periodical time series. We then propose several ways of exploiting this constrained projection to extract user-relevant information, such as the nominal behavior of a periodical dynamical system or the deviant behaviors which may occur at different time scales. The techniques are demonstrated on both a synthetic dataset composed of simulated multivariate data exhibiting a periodical behavior, and a real dataset corresponding to six months of sensor data acquisitions and recordings inside experimental buildings. 1 We would like to thank the Institut National de l'Energie Solaire (INES) and the CEA, LITEN, Laboratoire Energetique du Bâtiment for providing the data.

Marine litter is one of the main sources of anthropogenic pollution in the marine ecosystem, with plastic representing a global threat. This paper aims to assess the spatial distribution of plastic macro-litter on the seafloor, identifying accumulation hotspots at a northern Mediterranean scale. Density indices (items km–2) from the MEDITS trawl surveys (years 2013-2015) were modelled by generalized additive models using a Delta-type approach and several covariates: latitude, longitude, depth, seafloor slope, surface oceanographic currents and distances from main ports. To set thresholds for the identification of accumulation areas, the percentiles (85th, 90th and 95th) of the plastic spatial density distribution were computed on the raster data. In the northern Mediterranean marine macro-litter was widespread (90.13% of the 1279 surveyed stations), with plastic by far the most recurrent category. The prediction map of the plastic density highlighted accumulation areas (85th, 90th and 95th percentiles of the distribution, respectively, corresponding to 147, 196 and 316 items km–2) in the Gulf of Lions, eastern Corsica, the eastern Adriatic Sea, the Argo-Saronic region and waters around southern Cyprus. Maximum densities were predicted in correspondence to the shallower depths and in proximity to populated areas (distance from the ports). Surface currents and local water circulation with cyclonic and anticyclonic eddies were identified as drivers likely facilitating the sinking to the bottoms of floating debris, Sí

In the National Development Plan, cooperatives and agriculture development are identified as possible solutions for addressing rural poverty and unemployment, especially among the youth. However, according to most research/literature, agricultural cooperatives fail for many reasons, including but not limited to lack of capital, incompetent management and organizational deficiencies. This study applied a qualitative analysis to the qualitative data using a case study of Zanokhanyo Food Security Cooperative (ZFSC) in Ndabakazi, Butterworth. Interviews with the project members, ex-members, extension officers and youth were conducted through a semi-structured questionnaire administered in IsiXhosa. According to the results, lack of intensive production resulting in very low incomes is one of the reasons why projects such as ZFSC fail to attract young people and provide employment for rural people. Agricultural extension advisory services play a very limited role because of their generalist approach; they lack depth of knowledge about diverse agricultural subject areas. This study recommends that agricultural extension and the farmers’ support system be improved by employing or outsourcing specialists to cater to the needs of agricultural cooperatives in order to improve the productivity and income of agricultural cooperatives.

Given a set $P$ of $n$ points in $\mathbb{R}^2$ and an input line $\gamma$ in $\mathbb{R}^2$, we present an algorithm that runs in optimal $\Theta(n\log n)$ time and $\Theta(n)$ space to solve a restricted version of the $1$-Steiner tree problem. Our algorithm returns a minimum-weight tree interconnecting $P$ using at most one Steiner point $s \in \gamma$, where edges are weighted by the Euclidean distance between their endpoints. We then extend the result to $j$ input lines. Following this, we show how the algorithm of Brazil et al. ("Generalised k-Steiner Tree Problems in Normed Planes", arXiv:1111.1464) that solves the $k$-Steiner tree problem in $\mathbb{R}^2$ in $O(n^{2k})$ time can be adapted to our setting. For $k>1$, restricting the (at most) $k$ Steiner points to lie on an input line, the runtime becomes $O(n^{k})$. Next we show how the results of Brazil et al. ("Generalised k-Steiner Tree Problems in Normed Planes", arXiv:1111.1464) allow us to maintain the same time and space bounds while extending to some non-Euclidean norms and different tree cost functions. Lastly, we extend the result to $j$ input curves., Comment: 31 pages (26 of content), 4 figures (last one as 3 subfigures). Minor corrections since publication: notably our analysis of the Brazil et al. k-steiner tree algorithm in section 4.1 (results unchanged), and a correction to some corollary statements to correct the space usage. Also added short discussion of how the algorithm still works when the MST of the input point set is not unique

Poisson Voronoi (PV) tessellations as artificial microstructures are widely used in investigations of material deformation behaviors. However, a PV structure usually describes a relative homogeneous field. This work presents a simple numerical method for generating 2D/3D artificial microstructures based on hierarchical PV tessellations. If grains/particles of a phase cover a large size span, the concept of “artificial phases” can be used to create a more realistic size distribution. From case to case, detailed microstructural features cannot be directly achieved by commercial or free softwares, but they are necessary for a deep or thorough study of the material deformation behavior. PV tessellations created in our process can fulfill individual requirements from material designs. Another reason to use PV tessellations is due to the limited experimental data. Concerning the application of PV microstructures, four examples are given. The FE models and results will be presented in consecutive works, i.e. “part II: applications”., Deutsche Forschungsgemeinschaft, Projekt DEAL

Mixing and evaporation processes play an important role in fluid injection and disintegration. Laser-induced thermal acoustics (LITA) also known as laser-induced grating spectroscopy (LIGS) is a promising four-wave mixing technique capable to acquire speed of sound and transport properties of fluids. Since the signal intensity scales with pressure, LITA is effective in high-pressure environments. By analysing the frequency of LITA signals using a direct Fourier analysis, speed of sound data can be directly determined using only geometrical parameters of the optical arrangement no equation of state or additional modelling is needed at this point. Furthermore, transport properties, like acoustic damping rate and thermal diffusivity, are acquired using an analytical expression for LITA signals with finite beam sizes. By combining both evaluations in one LITA signal, we can estimate mixing parameters, such as the mixture temperature and composition, using suitable models for speed of sound and the acquired transport properties. Finally, direct measurements of the acoustic damping rate can provide important insights on the physics of supercritical fluid behaviour., Deutsche Forschungsgemeinschaft, Projekt DEAL

In the autonomous ocean monitoring task, the sampling robot moves in the environment and accumulates data continuously. The widely adopted spatial modeling method - standard Gaussian process (GP) regression - becomes inadequate in processing the growing sensing data of a large size. To overcome the computational challenge, this paper presents an environmental modeling framework using a sparse variant of GP called streaming sparse GP (SSGP). The SSGP is able to handle streaming data in an online and incremental manner, and is therefore suitable for long-term autonomous environmental monitoring. The SSGP summarizes the collected data using a small set of pseudo data points that best represent the whole dataset, and updates the hyperparameters and pseudo point locations in a streaming fashion, leading to high-quality approximation of the underlying environmental model with significantly reduced computational cost and memory demand., Proceedings of OCEANS 2019, SEATTLE

Let $\mathcal{M}$ be a $\sigma$-finite von Neumann algebra, equipped with a normal faithful state $\varphi$, and let $\mathcal{A}$ be maximal subdiagonal algebra of $\mathcal{M}$. We prove Stein-Weiss type interpolation theorem of Haagerup noncommutative $H^{p}$-spaces associated with $\A$., Comment: 16. arXiv admin note: text overlap with arXiv:2209.08542

Recent efforts have been made on acoustic scene classification in the audio signal processing community. In contrast, few studies have been conducted on acoustic scene clustering, which is a newly emerging problem. Acoustic scene clustering aims at merging the audio recordings of the same class of acoustic scene into a single cluster without using prior information and training classifiers. In this study, we propose a method for acoustic scene clustering that jointly optimizes the procedures of feature learning and clustering iteration. In the proposed method, the learned feature is a deep embedding that is extracted from a deep convolutional neural network (CNN), while the clustering algorithm is the agglomerative hierarchical clustering (AHC). We formulate a unified loss function for integrating and optimizing these two procedures. Various features and methods are compared. The experimental results demonstrate that the proposed method outperforms other unsupervised methods in terms of the normalized mutual information and the clustering accuracy. In addition, the deep embedding outperforms many state-of-the-art features., 9 pages, 6 figures, 11 tables. Accepted for publication in IEEE TMM

As the development of autonomous vehicles rapidly advances, the use of convoying/platooning becomes a more widely explored technology option for saving fuel and increasing the efficiency of traffic. In cooperative adaptive cruise control (CACC), the vehicles in a convoy follow each other under adaptive cruise control (ACC) that is augmented by the sharing of preceding vehicle acceleration through the vehicle to vehicle communication in a feedforward control path. In general, the desired velocity optimization for vehicles in the convoy is based on fuel economy optimization, rather than driveability. This paper is a preliminary study on the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles and the controller gain impact on the driveability. A simple low-level longitudinal model of the vehicle has been used along with a PD type cruise controller and a generic spacing policy for ACC/CACC. The acceleration of the previous vehicle is available to the next vehicle as input, and the simulations are performed as Cooperative Adaptive Cruise Control of a convoy of vehicles. Individual vehicle acceleration profiles have been analyzed for driveability for two different velocity profiles that are followed in a stretch of 720 m between stop signs. The controller gains have been re-tuned based on the parameter space robust control PID approach for driveability and compared with the original gains. The US06 SFTP drive cycle has also been used for the comparison of the two different controller gain sets.