Durum Wheat (Triticum durum Desf.) Evaluation under Semi Arid Conditions in Eastern Algeria by Path Analysis

This study was aimed to characterize yield components and plant traits related to grain yield. Correlation and path analysis were carried out in durum wheat genotypes grown under irrigated and non-irrigated field conditions during two cropping seasons (2010/2011 and 2011/2012). In the path coefficient analysis, grain yield represented the dependent variable and the number of spikes m-2, number of grains spike-1, kernel weight and number of grains m-2 were the independent ones. Grain yield showed positive phenotypic correlation with number of spikes m-2 and number of grains per m-2under both conditions and during two cropping seasons.Path analysis revealed positive direct effect of 1000- kernels weight, number of spike m-2 and number of grains per spike on grain yield. These results indicated that the 1000- kernels weight and number of spikes m-2 followed by the number of grains per spike and number of grains per m-2 were the traits related to higher grain yield, under irrigated and late season water stress conditions.

Engineering Multi-Agent Systems: State of Affairs and the Road Ahead

The continuous integration of software-intensive systems together with the ever-increasing computing power offer a breeding ground for intelligent agents and multi-agent systems (MAS) more than ever before. Over the past two decades, a wide variety of languages, models, techniques and methodologies have been proposed to engineer agents and MAS. Despite this substantial body of knowledge and expertise, the systematic engineering of large-scale and open MAS still poses many challenges. Researchers and engineers still face fundamental questions regarding theories, architectures, languages, processes, and platforms for designing, implementing, running, maintaining, and evolving MAS. This paper reports on the results of the 6th International Workshop on Engineering Multi-Agent Systems (EMAS 2018, 14th-15th of July, 2018, Stockholm, Sweden), where participants discussed the issues above focusing on the state of affairs and the road ahead for researchers and engineers in this area

The GRANDMA network in preparation for the fourth gravitational-wave observing run

GRANDMA is a world-wide collaboration with the primary scientific goal ofstudying gravitational-wave sources, discovering their electromagneticcounterparts and characterizing their emission. GRANDMA involves astronomers,astrophysicists, gravitational-wave physicists, and theorists. GRANDMA is now atruly global network of telescopes, with (so far) 30 telescopes in bothhemispheres. It incorporates a citizen science programme (Kilonova-Catcher)which constitutes an opportunity to spread the interest in time-domainastronomy. The telescope network is an heterogeneous set of already-existingobserving facilities that operate coordinated as a single observatory. Withinthe network there are wide-field imagers that can observe large areas of thesky to search for optical counterparts, narrow-field instruments that dotargeted searches within a predefined list of host-galaxy candidates, andlarger telescopes that are devoted to characterization and follow-up of theidentified counterparts. Here we present an overview of GRANDMA after the thirdobserving run of the LIGO/VIRGO gravitational-wave observatories in $2019-2020$and its ongoing preparation for the forthcoming fourth observational campaign(O4). Additionally, we review the potential of GRANDMA for the discovery andfollow-up of other types of astronomical transients.<br

Ready for O4 II: GRANDMA Observations of Swift GRBs during eight-weeks of Spring 2022

We present a campaign designed to train the GRANDMA network and its infrastructure to follow up on transient alerts and detect their early afterglows. In preparation for O4 II campaign, we focused on GRB alerts as they are expected to be an electromagnetic counterpart of gravitational-wave events. Our goal was to improve our response to the alerts and start prompt observations as soon as possible to better prepare the GRANDMA network for the fourth observational run of LIGO-Virgo-Kagra (which started at the end of May 2023), and future missions such as SM. To receive, manage and send out observational plans to our partner telescopes we set up dedicated infrastructure and a rota of follow-up adcates were organized to guarantee round-the-clock assistance to our telescope teams. To ensure a great number of observations, we focused on Swift GRBs whose localization errors were generally smaller than the GRANDMA telescopes' field of view. This allowed us to bypass the transient identification process and focus on the reaction time and efficiency of the network. During 'Ready for O4 II', 11 Swift/INTEGRAL GRB triggers were selected, nine fields had been observed, and three afterglows were detected (GRB 220403B, GRB 220427A, GRB 220514A), with 17 GRANDMA telescopes and 17 amateur astronomers from the citizen science project Kilonova-Catcher. Here we highlight the GRB 220427A analysis where our long-term follow-up of the host galaxy allowed us to obtain a photometric redshift of $z=0.82\pm0.09$, its lightcurve elution, fit the decay slope of the afterglows, and study the properties of the host galaxy

Multi-band analyses of the bright GRB~230812B and the associated SN2023pel

GRB~230812B is a bright and relatively nearby ($z =0.36$) long gamma-ray burst that has generated significant interest in the community and therefore has been subsequently observed over the entire electromagnetic spectrum. We report over 80 observations in X-ray, ultraviolet, optical, infrared, and sub-millimeter bands from the GRANDMA (Global Rapid Advanced Network for Multi-messenger Addicts) network of observatories and from observational partners. Adding complementary data from the literature, we then derive essential physical parameters associated with the ejecta and external properties (i.e. the geometry and environment) and compare with other analyses of this event (e.g. Srinivasaragavan et al. 2023). We spectroscopically confirm the presence of an associated supernova, SN2023pel, and we derive a photospheric expansion velocity of v $\sim$ 17$\times10^3$ km $s^{-1}$. We analyze the photometric data first using empirical fits of the flux and then with full Bayesian Inference. We again strongly establish the presence of a supernova in the data, with an absolute peak r-band magnitude $M_r = - 19.41 \pm 0.10$. We find a flux-stretching factor or relative brightness $k_{\rm SN}=1.04 \pm 0.09$ and a time-stretching factor $s_{\rm SN}=0.68 \pm 0.05$, both compared to SN1998bw. Therefore, GRB 230812B appears to have a clear long GRB-supernova association, as expected in the standard collapsar model. However, as sometimes found in the afterglow modelling of such long GRBs, our best fit model favours a very low density environment ($\log_{10}({n_{\rm ISM}/{\rm cm}^{-3}}) = -2.16^{+1.21}_{-1.30}$). We also find small values for the jet's core angle $\theta_{\rm core}={1.70^{+1.00}_{-0.71}} \ \rm{deg}$ and viewing angle. GRB 230812B/SN2023pel is one of the best characterized afterglows with a distinctive supernova bump

ABSTRACT

To make large-scale multi-agent systems reliable, we propose an adaptive application of replication strategies. Critical agents are replicated to avoid failures. As criticality of agents may evolve during the course of computation and problem solving, we need to dynamically and automatically adapt the number of replicas of agents, in order to maximize their reliability and availability based on available resources. We are studying an approach and mechanisms for evaluating the criticality of a given agent and for deciding what strategy to apply (e.g., active replication, passive) and how to parameterize it (e.g., number of replicas). 1

A Real-Time Agent Model in an Asynchronous-Object Environment

To build intelligent control systems for real-life applications, we need to design software agents which combine cognitive abilities to reason about complex situations, and reactive abilities to meet hard deadlines. We propose an operational agent model which mixes AI techniques and real-time performances. Our model is based on an ATN (Augmented Transition Network) to dynamically adapt the agent&apos;s behaviour to changes in the environment. Each agent uses a production system and is provided with a synchronization mechanism to avoid the possible inconsistencies of the asynchronous execution of several rule-bases. Our agents communicate by message-passing and are implemented in an asynchronous-object environment. We report on the use of our agent model in intensive care patient monitoring