Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Determination of a full range constitutive model for high strength S690 steels

202308 bcchAccepted ManuscriptRGCOthersChinese National Engineering Research Centre for Steel Construction; Department of Civil and Environmental Engineering at the Hong Kong Polytechnic University; Innovation and Technology Commission of the Government of Hong Kong; Nanjing Iron and Steel Company Ltd. in Nanjing; Pristine Steel Fabrication Company Ltd.; Research Grant Council of the Government of Hong Kong; Hong Kong Polytechnic University; Innovation and Technology Commission - Hong KongPublishe

Structural response of high strength S690 welded sections under cyclic loading conditions

202203 bcfcAccepted ManuscriptRGCOthersInnovation and Technology Fund of the Innovation and Technology Commission of the Government of the Hong Kong SAR; Research Committee of the Hong Kong PolyUPublishe

Engineering Collectives of Self-driving Vehicles: The SOTA Approach

Future cities will be populated by myriads of autonomous self-driving vehicles. Although individual vehicles have their own goals to pursue in autonomy, they may also be part of a collective of vehicles, as in the case of a fleet of vehicles of a car sharing company. Accordingly, they may also be required to act in a coordinated way towards the achievement of specific collective goals, or to meet specific city-level objectives. This raises the issue of properly engineering the behavior of such collective of vehicles, by properly capturing their collective requirements also in consideration of their individual goals, and understanding which knowledge about the state of the collective they must be provided with. In this context, this paper shows how the SOTA model can be a very effective tool to support the engineering of self-driving vehicle collectives. SOTA, by bringing together the lessons of goal-oriented requirements engineering, context-aware systems, and dynamical systems modeling, has indeed the potential for acting as a general reference model to help tackle some key issues in the design and development of complex collective systems immersed in dynamic environments, as collectives of self-driving vehicles are