A ‘locking-bolt’ demountable shear connector (LBDSC) is proposed to facilitate the deconstruction and reuse of steel-concrete composite structures, in line with achieving a more sustainable construction design paradigm. The LBDSC is comprised of a grout-filled steel tube and a geometrically compatible partially threaded bolt. The latter has a geometry that ‘locks’ the bolt in compatible holes predrilled on the steel flange and eliminates initial slip and construction tolerance issues. The structural behaviour of the LBDSC is evaluated through nine pushout tests using a horizontal test setup. The effects of the tube thickness, strength of concrete slab, and strength of infilled grout on the shear resistance, initial stiffness, and du

In this work, the structure properties of nano Lead sulfide PbS thin films are studied. Thin samples were prepared by pulse laser deposition and deposited on glass substrates at wavelength 1064nm wavelength with a various laser energies (200,300,400,500)nm. The study of atomic force microscope (AFM) and X-ray diffraction as well as the effect of changing the laser energy on the structural properties has been studied. It has been observed that the membrane formed is of the polycrystalline type and the predominant phase is the plane (111) and (200). The minimum grain size obtained was 16.5 nm at a laser energy about 200 mJ. The results showed that thin films of average granular sizes (75 nm) could be prepared.As for the optical properties,

The disruptions in supply chains have put small‐ and medium‐sized enterprises (SMEs) in dire need of resilient supply chains through which they can improve their performance. Based on the resource dependence theory, this study proposes a mediation model to improve the environmental performance (EP) of SMEs. The purpose of this study is to investigate the effect of supply chain resilience (SCR) on EP mediated by ambidextrous green innovation (AMGI). We proved a structural equation model based on questionnaire data from 261 companies in Iraq to test our hypotheses. The results show that SCR has a positive effect on AMGI for proactive and exploitative green innovation dimensions and positive impact on SMEs’ EP. AMGI plays a media

In Baghdad city, Iraq, the traffic volumes have rapidly grown during the last 15 years. Road networks need to reevaluate and decide if they are operating properly or not regarding the increase in the number of vehicles. Al-Jadriyah intersection (a four-leg signalized intersection) and Kamal Junblat Square (a multi-lane roundabout), which are two important intersections in Baghdad city with high traffic volumes, were selected to be reevaluated by the SIDRA package in this research. Traffic volume and vehicle movement data were abstracted from videotapes by the Smart Traffic Analyzer (STA) Software. The performance measures include delay and LOS. The analysis results by SIDRA Intersection 8.0.1 show that the performance of the roundab

The goal of this experimental study is to determine the effects of different parameters (Flow rate, cuttings density, cuttings size, and hole inclination degree) on hole cleaning efficiency. Freshwater was used as a drilling fluid in this experiment. The experiments were conducted by using flow loop consist of approximately 14 m (46 ft) long with transparent glass test section of 3m (9.84 ft.) long with 4 inches (101.6 mm) ID, the inner metal drill pipe with 2 inches (50.8 mm) OD settled with eccentric position positive 0.5. The results obtained from this study show that the hole cleanings efficiency become better with high flow rate (21 m3/hr) and it increase as the hole inclination angles increased from 60 to 90 degree due to dominated

Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr