Mitigating the health and safety influence of subcontracting in construction: The approach of main contractors

Subcontracting is noted for its adverse health and safety (H&S) influence in construction. Using interviews with five of the top 20 UK contractors and one medium-sized contractor, this study explored how main contractors manage the H&S influence of subcontracting with a focus on their in-house measures. Some in-house measures found are: restricting the layers of subcontractors on projects; working with a regular chain of subcontractors; implementing a H&S reward scheme for subcontractors; and insisting on non-working subcontractor foremen who have direct responsibility for the safety of workers in their trade. These measures appear to be influenced by clients, industry peer groups, and the moral justification for occupational H&S, and they offer inter-organisational learning opportunities for contractors in devising measures to mitigate the H&S influence of subcontracting. These findings should also allay concerns that removing some of the legislative hurdles in connection with on-going red tape debate will result in poorer H&S.     

Influence of injection timing on cerium oxide nanoparticle doped in waste cooking palm oil bio-diesel and diesel blends fuelled in diesel engine

ABSTRACT

Injection timing (IT) is a vital factor among different injection parameters which governs the emissions and performance factors of the engine. This work portrays the effect of IT on cerium oxide nanoparticle doped Waste Cooking Palm Oil biodiesel and diesel blends. The doping is made at 30, 60 and 90?ppm. The modified fuels are introduced in reducing IT of 19°, 21° and 23°bTDC. 1500?rpm engine is made use in this study. Results revealed a significant reduction in emissions (CO, NO_X, HC and Smoke) at IT?=?23°bTDC. Furthermore, performance (BSFC, BTE) is improved for fuel blends at IT?=?23°bTDC.     

Machining of NiTi-shape memory alloys-A review

The emerging trends in the development of advanced smart materials with better unique properties under different environments for a particular application fascinate the researchers and industrialists. Nickel-Titanium based shape memory alloys are exotic materials due to their unique properties such as SME, SE, high damping characteristics, high corrosion and wear resistance and biocompatibility. This article presents an overview of machining processes that can be used to machine the NiTi and its surface induced characteristics such as microhardness, surface roughness, topography, induced layer, residual stress, fatigue and phase transformation. The surface integrity characteristics are discussed for machining of NiTi-SMAs under the category of traditional, non-traditional and micro-machining with the effect of input parameters such as cutting speed, feed, depth of cut, type of lubricant and type of coating material on cutting tool. The conventional machining of NiTi alloys are quite complicated due to high toughness, severe strain hardening, fatigue hardening and distinctive property of NiTi-SMAs such as pseudoelastic and shape memory effect. From this study, non-traditional process is significantly used to machine the NiTi-SMAs due to its better results on surface integrity characteristics. Consequently, future trends are also identified for machining the NiTi-SMAs and to improve the surface integrity characteristics.     

Economical treatment of reverse osmosis reject of textile industry effluent by electrodialysis–evaporation integrated process

Membrane separation methods such as electrodialysis (ED) can reduce the volume load on evaporators by facilitating further concentration of rejects from reverse osmosis (RO) plants. ED studies were carried out on a bench-scale system using five membrane cell pairs to obtain a textile effluent concentrate containing approximately 6 times the quantity of salts present in the RO reject. The limiting current densities were determined to be in the range 2.15–3.35 amp/m² for feed flow rates varying from 18 to 108 L/h. Apart from feed rate, the influence of volume of concentrate and current on membrane performance was evaluated to optimize current utilization. An estimation of energy requirement of an integrated process constituting ED and evaporation for concentration of inorganics present in textile effluent from 4.35% to 24% was made and found to be approximately one eighth of the operating cost incurred by evaporation alone. Detailed design of a commercial ED system revealed that a membrane area of 13.1 m² was required to treat a feed rate of 1500 L/h. The payback period to recover capital investment was found to be 110 days.     

External stimulus-responsive biomaterials designed for the culture and differentiation of ES,iPS, and adult stem cells

The physical and chemical characteristics of biomaterial surface and hydrogels can be altered by external stimuli, such as light irradiation, temperature changes, pH shifts, shear stress forces, electrical forces, and the addition of small chemical molecules. Such external stimulus-responsive biomaterials represent promising candidates that have been developed for the culture and differentiation of embryonic stem (ES) cells, induced pluripotent stem (iPS) cells, and adult stem cells. Biomaterials that are designed to respond in a reversible manner to specific external signals can be formed on micropatterned or non-micropatterned surface, in hydrogels, or on microcarriers. Stem cells and the cells differentiated from them into specific tissue lineages can be cultured and/or differentiated on dishes with immobilized external stimulus-responsive polymers. Cells can be detached from these dishes without using an enzymatic digestion method or a mechanical method when the appropriate external stimulus is generated on the surface. This review discusses the polymers and polymeric designs employed to produce surface and hydrogels for stem cell culture, differentiation, and/or cell detachment using various external stimuli.     

Nanocomposites based on transition metal oxides in polyvinyl alcohol for EMI shielding application

In the present work, the nanocomposites based on different transition metal oxides like iron oxide (Fe₂O₃), zinc oxide (ZnO), silicon dioxide (SiO₂), zirconium dioxide (ZrO₂), and titanium dioxide (TiO₂) in PVA matrix have been studied for their suitability as electromagnetic interference (EMI) shielding materials in the frequency range of 4–8 GHz (C-band) and 8–12 GHz (X-band). The nanocomposites containing 0.1, 0.5, 1.0, 5.0, and 10.0 wt% of oxides in the matrix were synthesised by solvent casting method. The EMI attenuation studies in 4–12 GHz frequency range were carried out using the Vector Network Analyzer R & S: ZVA40 method by measuring the loss due to reflection. The minimum reflectivity values for the composites containing Fe₂O₃, ZnO, SiO_2, ZrO₂, and TiO₂ in PVA matrix at 10 wt% concentration level in the matrix were found to be ?38.85 dB (10.4 GHz), ?33.65 dB (10.4 GHz), ?41.90 dB (10.4 GHz), ?24.90 dB (11.0 GHz), and ?32.90 dB (9.76 GHz), respectively. Based on these results, the SiO₂- and Fe₂O₃-based composites, which also exhibit high thermal stability and mechanical strength, are found to be low-cost and efficient EMI shielding materials.     

Design and Compressive Analysis of Junctionless Multigate FinFET Towards Low Power and High Frequency Applications

Silicon - Advances in microelectronics have enabled smaller technical nodes, lower threshold voltages, and greater working frequencies. Even though VLSI circuit performance and power consumption...     

Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures

Health monitoring of civil infrastructures is a key application of Internet of things (IoT), while edge computing is an important component of IoT. In this context, swarms of autonomous inspection robots, which can replace current manual inspections, are examples of edge devices. Incorporation of pretrained deep learning algorithms into these robots for autonomous damage detection is a challenging problem since these devices are typically limited in computing and memory resources. This study introduces a solution based on network pruning using Taylor expansion to utilize pretrained deep convolutional neural networks for efficient edge computing and incorporation into inspection robots. Results from comprehensive experiments on two pretrained networks (i.e., VGG16 and ResNet18) and two types of prevalent surface defects (i.e., crack and corrosion) are presented and discussed in detail with respect to performance, memory demands, and the inference time for damage detection. It is shown that the proposed approach significantly enhances resource efficiency without decreasing damage detection performance.     

Concrete crack detection using context‐aware deep semantic segmentation network

Computer‐vision and deep‐learning techniques are being increasingly applied to inspect, monitor, and assess infrastructure conditions including detection of cracks. Traditional vision‐based methods to detect cracks lack accuracy and generalization to work on complicated infrastructural conditions. This paper presents a novel context‐aware deep convolutional semantic segmentation network to effectively detect cracks in structural infrastructure under various conditions. The proposed method applies a pixel‐wise deep semantic segmentation network to segment the cracks on images with arbitrary sizes without retraining the prediction network. Meanwhile, a context‐aware fusion algorithm that leverages local cross‐state and cross‐space constraints is proposed to fuse the predictions of image patches. This method is evaluated on three datasets: CrackForest Dataset (CFD) and Tomorrows Road Infrastructure Monitoring, Management Dataset (TRIMMD) and a Customized Field Test Dataset (CFTD) and achieves Boundary F1 (BF) score of 0.8234, 0.8252, and 0.7937 under 2‐pixel error tolerance margin in CFD, TRIMMD, and CFTD, respectively. The proposed method advances the state‐of‐the‐art performance of BF score by approximately 2.71% in CFD, 1.47% in TRIMMD, and 4.14% in CFTD. Moreover, the averaged processing time of the proposed system is 0.7 s on a typical desktop with Intel^® Quad‐Core? i7‐7700 CPU@3.6 GHz Processor, 16GB RAM and NVIDIA GeForce GTX 1060 6GB GPU for an image of size 256 × 256 pixels.