Property specification patterns at work: verification and inconsistency explanation

Innovations in Systems and Software Engineering - Property specification patterns (PSPs) have been proposed to ease the formalization of requirements, yet enable automated verification thereof. In...     

Bioactive Phytochemicals from Wild Arbutus unedo L. Berries from Different Locations in Portugal: Quantification of Lipophilic Components

The lipophilic composition of wild Arbutus unedo L. berries, collected from six locations in Penacova (center of Portugal), as well as some general chemical parameters, namely total soluble solids, pH, titratable acidity, total phenolic content and antioxidant activity was studied in detail to better understand its potential as a source of bioactive compounds. The chemical composition of the lipophilic extracts, focused on the fatty acids, triterpenoids, sterols, long chain aliphatic alcohols and tocopherols, was investigated by gas chromatography–mass spectrometry (GC–MS) analysis of the dichloromethane extracts. The lipophilic extractives of the ripe A. unedo berries ranged from 0.72% to 1.66% (w/w of dry weight), and consisted mainly of triterpenoids, fatty acids and sterols. Minor amounts of long chain aliphatic alcohols and tocopherols were also identified. Forty-one compounds were identified and among these, ursolic acid, lupeol, α-amyrin, linoleic and α-linolenic acids, and β-sitosterol were highlighted as the major components. To the best of our knowledge the current research study provides the most detailed phytochemical repository for the lipophilic composition of A. unedo, and offers valuable information for future valuation and exploitation of these berries.     

Impact of social externalities on the formation of an international environmental agreement: an exploratory analysis

We analyze the impact of social externalities (SEs) on the stability of an international environmental agreement (IEA). We consider a framework in which players are divided into two homogeneous groups, namely, developed and developing countries. We assume that members of an IEA get some additional benefits, to which we refer as SEs. One main result is that any coalition that is internally stable will expand to include all countries, which is related to the minimum participation clause considered in some papers. However, our minimum, which is endogenously defined, is not the largest stable coalition size, but the grand coalition.     

An unsupervised approach for fault diagnosis of power transformers

Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.     

Metal-Coated <100>-Cut GaAs Coupled to Tapered Parallel-Plate Waveguide for Cherenkov-Phase-Matched Terahertz Detection: Influence of Crystal Thickness

The influence of crystal thickness of metal-coated <100>-cut GaAs (M-G-M) on Cherenkov-phase-matched terahertz (THz) pulse detection was studied. The M-G-M detectors were utilized in conjunction with a metallic tapered parallel-plate waveguide (TPPWG). Polarization-sensitive measurements were carried out to exemplify the efficacy of GaAs in detecting transverse magnetic (TM)- and transverse electric (TE)-polarized THz waves. The reduction of GaAs’ thickness increased the THz amplitude spectra of the detected TM-polarized THz electro-optic (EO) signal due to enhanced electric field associated with a more tightly-focused and well-concentrated THz radiation on the thinner M-G-M. The higher-fluence THz beam coupled to the thinner M-G-M improved the integrated intensity of the detected THz amplitude spectrum. This trend was not observed for TE-polarized THz waves, wherein the integrated intensities were almost comparable. Nevertheless, good agreement of spectral line shapes of the superposed TM- and TE-polarized THz-EO signals with that of elliptically polarized THz-EO signal demonstrates excellent polarization-resolved detection capabilities of M-G-M via Cherenkov-phase-matched EO sampling technique.     

The power law relationship between citation impact and multi-authorship patterns in articles in Information Science &amp; Library Science journals

The aim of this paper is to extend the conversation about the correlation between collaboration and citation impact in articles in Information Science & Library Science journals by analyzing this correlation’s behavior using a power scaling law approach. 28,131 articles that received 215,693 citations were analyzed. The number of these articles that were published through collaboration accounts for 69%. In general, the scaling exponent of multi-authored articles, both international and domestic, increases over time while the exponent of single-authored papers decreases. The citation impact and collaboration patterns exhibit a power law correlation with a scaling exponent of 1.34 ± 0.02. Citations to multi-authored articles increased \(2^{1.34}\) or 2.53 times each time the number of multi-authored papers doubled. The Matthew Effect is stronger for multi-authored papers than for single-authored. The scaling exponent for the power law relationship of domestic multi-authored papers was 1.35 ± 0.02. The citations to domestic multi-authored articles increased \(2^{1.35}\) or 2.55 times each time the number of domestic multi-authored articles doubled. Contrary to previous studies we found that the Matthew Effect is stronger for domestic multi-authored papers than for international multi-authored ones.     

Towards a Sustainable Green Design for Next-Generation Networks

Recently distributed real-time database systems are intended to manage large volumes of dispersed data. To develop distributed real-time data processing, a reality and stay competitive well defined protocols and algorithms must be required to access and manipulate the data. An admission control policy is a major task to access real-time data which has become a challenging task due to random arrival of user requests and transaction timing constraints. This paper proposes an optimal admission control policy based on deep reinforcement algorithm and memetic algorithm which can efficiently handle the load balancing problem without affecting the Quality of Service (QoS) parameters. A Markov decision process (MDP) is formulated for admission control problem, which provides an optimized solution for dynamic resource sharing. The possible solutions for MDP problem are obtained by using reinforcement learning and linear programming with an average reward. The deep reinforcement learning algorithm reformulates the arrived requests from different users and admits only the needed request, which improves the number of sessions of the system. Then we frame the load balancing problem as a dynamic and stochastic assignment problem and obtain optimal control policies using memetic algorithm. Therefore proposed admission control problem is changed to memetic logic in such a way that session corresponds to individual elements of the initial chromosome. The performance of proposed optimal admission control policy is compared with other approaches through simulation and it depicts that the proposed system outperforms the other techniques in terms of throughput, execution time and miss ratio which leads to better QoS.