858 research outputs found
GreenCrowd: Toward a Holistic Algorithmic Crowd Charging Framework
Crowd charging represents an alternative peer-to-peer energy replenishment option for mobile users to align with the circular economy paradigm. Following this option, users bound by finite resource capacity utilize the energy from external to the crowd wireless or wired energy sources (such as shared chargers), and internal to the crowd energy sources (such as mobile devices, via wireless power transfer). If designed carefully, such utilization can boost the energy availability of users and provide energy ubiquitously to their devices for making them functional for longer. This article proposes the GreenCrowd framework, introducing a privacy-by-design in the digital domain crowd charging process, the architecture of which incorporates multiple crowd-* components, such as online social information exploitation, algorithmic battery aging mitigation, user reward mechanisms, and advanced decision making. The primary aim of article is to present the technological and applicative requirements and constraints of GreenCrowd, and provide practical evidence on its feasibility
On Efficiently Partitioning a Topic in Apache Kafka
Apache Kafka addresses the general problem of delivering extreme high volume
event data to diverse consumers via a publish-subscribe messaging system. It
uses partitions to scale a topic across many brokers for producers to write
data in parallel, and also to facilitate parallel reading of consumers. Even
though Apache Kafka provides some out of the box optimizations, it does not
strictly define how each topic shall be efficiently distributed into
partitions. The well-formulated fine-tuning that is needed in order to improve
an Apache Kafka cluster performance is still an open research problem. In this
paper, we first model the Apache Kafka topic partitioning process for a given
topic. Then, given the set of brokers, constraints and application requirements
on throughput, OS load, replication latency and unavailability, we formulate
the optimization problem of finding how many partitions are needed and show
that it is computationally intractable, being an integer program. Furthermore,
we propose two simple, yet efficient heuristics to solve the problem: the first
tries to minimize and the second to maximize the number of brokers used in the
cluster. Finally, we evaluate its performance via large-scale simulations,
considering as benchmarks some Apache Kafka cluster configuration
recommendations provided by Microsoft and Confluent. We demonstrate that,
unlike the recommendations, the proposed heuristics respect the hard
constraints on replication latency and perform better w.r.t. unavailability
time and OS load, using the system resources in a more prudent way.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible. This work was funded by the European Union's Horizon
2020 research and innovation programme MARVEL under grant agreement No 95733
A Dodecalogue of Basic Didactics from Applications of Abstract Differential Geometry to Quantum Gravity
We summarize the twelve most important in our view novel concepts that have
arisen, based on results that have been obtained, from various applications of
Abstract Differential Geometry (ADG) to Quantum Gravity (QG). The present
document may be used as a concise, yet informal, discursive and peripatetic
conceptual guide-cum-terminological glossary to the voluminous technical
research literature on the subject. In a bonus section at the end, we dwell on
the significance of introducing new conceptual terminology in future QG
research by means of `poetic language'Comment: 16 pages, preliminary versio
Distributed Path Reconfiguration and Data Forwarding in Industrial IoT Networks
In today's typical industrial environments, the computation of the data
distribution schedules is highly centralised. Typically, a central entity
configures the data forwarding paths so as to guarantee low delivery delays
between data producers and consumers. However, these requirements might become
impossible to meet later on, due to link or node failures, or excessive
degradation of their performance. In this paper, we focus on maintaining the
network functionality required by the applications after such events. We avoid
continuously recomputing the configuration centrally, by designing an energy
efficient local and distributed path reconfiguration method. Specifically,
given the operational parameters required by the applications, we provide
several algorithmic functions which locally reconfigure the data distribution
paths, when a communication link or a network node fails. We compare our method
through simulations to other state of the art methods and we demonstrate
performance gains in terms of energy consumption and data delivery success rate
as well as some emerging key insights which can lead to further performance
gains
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Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements
This study focuses on the assessment of surface solar radiation (SSR) based on operational neural network (NN) and multi-regression function (MRF) modelling techniques that produce instantaneous (in less than 1 min) outputs. Using real-time cloud and aerosol optical properties inputs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellite and the Copernicus Atmosphere Monitoring Service (CAMS), respectively, these models are capable of calculating SSR in high resolution (1 nm, 0.05 degree, 15 min) that can be used for spectrally integrated irradiance maps, databases and various applications related to energy exploitation. The real-time models are validated against ground-based measurements of the Baseline Surface Radiation Network (BSRN) in a temporal range varying from 15 min to monthly means, while a sensitivity analysis of the cloud and aerosol effects on SSR is performed to ensure reliability under different sky and climatological conditions. The simulated outputs, compared to their common training dataset created by the radiative transfer model (RTM) libRadtran, showed median error values in the range −15 to +15 % for the NN that produces spectral irradiances (NNS), 5–6 % underestimation for the integrated NN and close to zero errors for the MRF technique. The verification against BSRN revealed that the real-time calculation uncertainty ranges from −100 to +40 and −20 to +20 W/m^2, for the 15 min and monthly mean global horizontal irradiance (GHI) averages, respectively, while the accuracy of the input parameters, in terms of aerosol and cloud optical thickness (AOD and COT), and their impact on GHI, was of the order of 10 % as compared to the ground-based measurements. The proposed system aims to be utilized through studies and real-time applications which are related to solar energy production planning and use
Color Stability of New Composite Restorative Materials Under Accelerated Aging
The color stability of seven microfilled and conventional composites under conditions of accelerated aging was evaluated by reflection spectrophotometry. During early aging the composites generally became darker, more chromatic, and more opaque. The in vitro color stability of the microfilled composites was better and less influenced by erosion than the conventional composites.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66615/2/10.1177_00220345800590120801.pd
Experimental study of the turbulent structure of the surface marine Atmospheric Boundary Layer over the Aegean Pelagos under etesian winds
In order to study the physical processes of the turbulent transportation of mass and energy within the surface Marine Atmospheric Boundary Layer over the Aegean Pelagos, field measurements were conducted on the island of Skyros, mainly under the etesian winds, during summer 2011. Α meteorological mast was installed close to the shoreline, instrumented with fast anemometer (sonic) and hydrometer measuring the three components of the wind, the virtual temperature and water vapor at 10m height with a sampling frequency of 20Hz. At the same mast slow response sensors were measuring wind speed and direction, temperature and humidity at three levels (2, 6 and 10 m). Weak stable to near neutral flows were recorded during the experimental period. The eddy correlation analysis re-vealed the momentum and heat fluxes values which are presented and discussed. The estimated values are related both with stability and wind speed variations
ML-based Approaches for Wireless NLOS Localization: Input Representations and Uncertainty Estimation
The challenging problem of non-line-of-sight (NLOS) localization is critical
for many wireless networking applications. The lack of available datasets has
made NLOS localization difficult to tackle with ML-driven methods, but recent
developments in synthetic dataset generation have provided new opportunities
for research. This paper explores three different input representations: (i)
single wireless radio path features, (ii) wireless radio link features
(multi-path), and (iii) image-based representations. Inspired by the two latter
new representations, we design two convolutional neural networks (CNNs) and we
demonstrate that, although not significantly improving the NLOS localization
performance, they are able to support richer prediction outputs, thus allowing
deeper analysis of the predictions. In particular, the richer outputs enable
reliable identification of non-trustworthy predictions and support the
prediction of the top-K candidate locations for a given instance. We also
measure how the availability of various features (such as angles of signal
departure and arrival) affects the model's performance, providing insights
about the types of data that should be collected for enhanced NLOS
localization. Our insights motivate future work on building more efficient
neural architectures and input representations for improved NLOS localization
performance, along with additional useful application features.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible. Work partly supported by the RA Science Committee grant
No. 22rl-052 (DISTAL) and the EU under Italian National Recovery and
Resilience Plan of NextGenerationEU on "Telecommunications of the Future"
(PE00000001 - program "RESTART"
Precrystallization structures in supersaturated lysozyme solutions studied by dynamic light scattering and scanning force microscopy
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