Latent ties identification in inter-firms social networks

Social networks are usually analyzed through manifest variables. However there are social latent aspects that strongly qualify the networks. This paper aims to propose a statistical methodology to identify latent variable in inter-firm social networks. A multidimensional scaling technique is proposed to measure this latent variable as a combination of an appropriate set of two or more manifest relational aspects. This method, tested on an inter-firm social network in theMarche region, is a new way to grasp social aspect with quantitative tools that could be implemented under several different conditions, using also other variable

Performance Results of a Prototype Board Designed for Copper Data Transmission in KM3NeT

International audienceThe experience gained in designing submarine neutrino telescopes suggested to explore new ways of realizing the data transmission backbone at the detection unit level. In order to decrease the difficulties of integration and handling of the backbones, some effort has been spent in developing a backbone based on copper links with simple tracts of cable connecting contiguous storeys. This work is aimed at the presentation of the general architecture of the system, at the description of an electronic board prototype designed to test the project feasibility with the first results obtained. The main goal of the experimental setup was measuring the recovered clock jitter under various conditions, with and without cables. The jitter measured on the cleaned clock amounts to hundreds of picoseconds, well below the sub-nanosecond time resolution required by this kind of experiments

A neural-network approach to radon short-range forecasting from concentration time series

The relevance of particulate radon progeny measurements for an estimation of the mixing height was recently established. Here, an attempt at a shortrange forecast of radon concentration is presented using a neural-network model applied at a 2-hour based time series. This forecasting activity leads to useful predictions of the mixing height during stability conditions

Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system

Abstract A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the last 140 years. In particular, our neural network model shows the ability to catch non-linear relationships among these variables and to reconstruct temperature records with a high degree of accuracy. In this framework, we clearly show the need of including anthropogenic inputs for explaining the temperature behaviour at global scale and recognise the role of El Nino southern oscillation for catching the inter-annual variability of temperature data. Furthermore, we analyse the relative influence of global forcing and a regional circulation pattern in determining the winter temperatures in Central England, showing that the North Atlantic oscillation represents the driven element in this case study. Our modelling activity and results can be very useful for simple assessments of relationships in the complex climate system and for identifying the fundamental elements leading to a successful downscaling of atmosphere–ocean general circulation models

Finding the right partners? Examining inequalities in the global investment landscape of hydropower

Clean and affordable energy is crucial to achieve a sustainable future. Despite being controversial, hydropower remains the predominant low-cost and reliable source of energy at global level, as it stabilizes the provision of electricity and it bears the power peaks without losing efficiency. However, hydropower requires huge upfront investments and patient functional capital. Under the Paris Agreement, countries committed to direct financial capital flows towards a low-emission pathway in order to enable the transition. Furthermore, private capital strongly engaged with a transition towards a climate-smart economy. The aim of this work is to study the investment system behind hydropower, investors’ behaviour and the optimal allocation of finance to favour the deployment of capital flows. We use Bloomberg Energy Finance database to track public–private investments over the past century (1903–2020). We use network models to represent the hydropower project financing landscape as a network of co-investments. We find that investors are highly localized, with continental players mostly interacting with counterparts in the same area of the world. Powerful exceptions are international organisations and multilateral banks which coinvest across the globe. They also tend to support low-income and fragile countries, meeting their mandate of sustainable development champions. Multilateral banks and international organisations are the most critical actors in enabling public–private co-investments; they activate partnerships with a wider diversity of investors within the network creating more opportunities for blended finance tools. Our results offer a novel perspective on finance for the energy transition: it challenges the idea that more capital invested is better and calls for a more efficient allocation of the available resources

Investment suitability and path dependency perpetuate inequity in international mitigation finance toward developing countries

Developed country pledges to provide finance to developing countries for their mitigation actions sit at the heart of international climate cooperation. Currently, climate finance largely flows to big and fast-growing developing countries while low-income and vulnerable countries are underserved. Here, using wind and solar project data, we highlight inequities in the distribution of international investments in mitigation across developing countries and explore the factors that influence public and private investment flows. Results show that public actors are influenced by domestic climate policies since the Paris Agreement, while private finance flows are shaped by investment suitability conditions, which restricts access to both types of finance in the poorest countries. Further, public and private flows are strongly shaped by path dependency, generating an “investment lock-in” that perpetuates distributional inequities. Future international commitments to direct climate finance should address distributional issues to meet countries’ needs and the goals of the Paris Agreement

Bidirectional and Stretchable Piezoresistive Sensors Enabled by Multimaterial 3D Printing of Carbon Nanotube/Thermoplastic Polyurethane Nanocomposites

Fabricating complex sensor platforms is still a challenge because conventional sensors are discrete, directional, and often not integrated within the system at the material level. Here, we report a facile method to fabricate bidirectional strain sensors through the integration of multiwalled carbon nanotubes (MWCNT) and multimaterial additive manufacturing. Thermoplastic polyurethane (TPU)/MWCNT filaments were first made using a two-step extrusion process. TPU as the platform and TPU/MWCNT as the conducting traces were then 3D printed in tandem using multimaterial fused filament fabrication to generate uniaxial and biaxial sensors with several conductive pattern designs. The sensors were subjected to a series of cyclic strain loads. The results revealed excellent piezoresistive responses with cyclic repeatability in both the axial and transverse directions and in response to strains as high as 50%. It was shown that the directional sensitivity could be tailored by the type of pattern design. A wearable glove, with built-in sensors, capable of measuring finger flexure was also successfully demonstrated where the sensors are an integral part of the system. These sensors have potential applications in wearable electronics, soft robotics, and prosthetics, where complex design, multi-directionality, embedding, and customizability are demanded

The role of inhalational anesthetic drugs in patients with hepatic dysfunction: A review article

Context: Anesthetic drugs including halogenated anesthetics have been common for many years. Consequent hepatic injury has been reported in the literature. The mechanism of injury is immunoallergic. The first generation drug was halothane; it had the most toxicity when compared to other drugs. The issue becomes more important when the patient has an underlying hepatic dysfunction. Evidence Acquisition: In this paper, reputable internet databases from 1957�2014 were analyzed and 43 original articles, 3 case reports, and 3 books were studied. A search was performed based on the following keywords: inhalational anesthesia, hepatic dysfunction, halogenated anesthetics, general anesthesia in patients with hepatic diseases, and side effects of halogenated anesthetics from reliable databases. Reputable websites like PubMed and Cochrane were used for the searches. Results: In patients with hepatic dysfunction in addition to hepatic system and dramatic hemostatic dysfunction, dysfunction of cardiovascular, renal, respiratory, gastrointestinal, and central nervous systems may occur. On the other hand, exposure to inhalational halogenated anesthetics may have a negative impact (similar to hepatitis) on all aforementioned systems in addition to direct effects on liver function as well as the effects are more pronounced in halothane. Conclusions: Despite the adverse effects of inhalational halogenated anesthetics (especially halothane) on hepatic patients when necessary. The effects on all systems must be considered and the necessary preparations must be provided. These drugs are still used, if necessary, due to the presence of positive effects and advantages mentioned in other studies as well as the adverse effects of other drugs. © 2015, Iranian Society of Regional Anesthesia and Pain Medicine (ISRAPM)

NaNet: a Low-Latency, Real-Time, Multi-Standard Network Interface Card with GPUDirect Features

While the GPGPU paradigm is widely recognized as an effective approach to high performance computing, its adoption in low-latency, real-time systems is still in its early stages. Although GPUs typically show deterministic behaviour in terms of latency in executing computational kernels as soon as data is available in their internal memories, assessment of real-time features of a standard GPGPU system needs careful characterization of all subsystems along data stream path. The networking subsystem results in being the most critical one in terms of absolute value and fluctuations of its response latency. Our envisioned solution to this issue is NaNet, a FPGA-based PCIe Network Interface Card (NIC) design featuring a configurable and extensible set of network channels with direct access through GPUDirect to NVIDIA Fermi/Kepler GPU memories. NaNet design currently supports both standard - GbE (1000BASE-T) and 10GbE (10Base-R) - and custom - 34~Gbps APElink and 2.5~Gbps deterministic latency KM3link - channels, but its modularity allows for a straightforward inclusion of other link technologies. To avoid host OS intervention on data stream and remove a possible source of jitter, the design includes a network/transport layer offload module with cycle-accurate, upper-bound latency, supporting UDP, KM3link Time Division Multiplexing and APElink protocols. After NaNet architecture description and its latency/bandwidth characterization for all supported links, two real world use cases will be presented: the GPU-based low level trigger for the RICH detector in the NA62 experiment at CERN and the on-/off-shore data link for KM3 underwater neutrino telescope