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AbstractThis work reports the synthesis, characterization, photophysical, and photovoltaic properties of five new thieno[3,2‐b][1]benzothiophene isoindigo (TBTI)‐containing low bandgap donor–acceptor conjugated polymers with a series of comonomers and different side chains. When TBTI is combined with different electron‐rich moieties, even small structural variations can have significant impact on thin film morphology of the polymer:phenyl C70 butyric acid methyl ester (PCBM) blends. More importantly, high‐resolution electron energy loss spectroscopy is used to investigate the phase‐separated bulk heterojunction domains, which can be accurately and precisely resolved, enabling an enhanced correlation between polymer chemical structure, photovoltaic device performance, and morphology.

Abstract In this paper, we examine the time-varying causal relationship between green bonds and other assets including US conventional bonds, WilderHill clean energy (equity) index, and CO2 emission allowances price during the period spanning from 30 July 2014 to 10 February 2020. We apply the novel time-varying Granger causality test (Shi et al. 2018) based on the recursive evolving algorithm introduced by Phillips et al. (2015a, 2015b) for controlling financial bubbles to detect real–time causality, detecting possible changes in the causal direction and dating financial turbulences, The study based on this algorithm reveals a significant causality running from the US 10-year Treasury bond index to green bonds starting from the end of the year 2016 until the end of the sample period. Besides, we find that the link CO2 emission allowances price causing green bonds is significant from the beginning of the sample period to the end of the year 2015. Furthermore, by using the recursive-evolving causality algorithm of the Shi et al. (2018) test, we find that the causality running from the clean energy index to green bonds is very limited to the year 2019. On the other hand, there is no significant causality running from green bonds to all considered assets, indicating no predictive power for this asset in its proper domain, which is not yet examined in the literature.

The availability of food in a country and the capacity of its citizens to access, acquire, and receive enough food are both referred to as having food security. A crucial component of food security is ensuring and maintaining safe and high-quality goods, which the supply chain process should take into due deliberation. To enhance the food supply chain, organic and wholesome food items should be encouraged. Although packaged goods are evaluated and approved by legal authorities, there is no mechanism in place for testing and assessing the market’s available supply on a regular basis. As a result, food manufacturers are compelled to provide nutritious and healthy products. In this research, we propose an explainable artificial intelligence-based faster regions with convolutional neural networks (XAI-based Faster RCNN) model to evaluate the contents of the food items through user-friendly web-based front-end design and QR code. To validate each communication token in the network, an elliptic curve integrated encrypted scheme (ECIES) based on blockchain technology is utilized. Additionally, artificial rabbit optimization (ARO) is used to register each user and assign him a key. The user will gain a deeper understanding of machine learning (ML) and AI applications using the XAI technique. An EAI-based Faster RCNN model is proposed to help digitize information about food products, rapidly retrieve the information, and discover any hidden information in the quick response (QR) code that could have impacted the safety and quality of the food. The results of the experiments indicated that the proposed method requires less response time than other existing methods with the increase of payload and users. The Shapley additive explanation is used to obtain a legal plea for the laboratory test based on the nutritional information present in the QR code. The benefits provided by ECIES-based blockchain technology assist policymakers, manufacturers, and merchants in efficient decision-making, minimizing public health hazards, and improving welfare. This paper also shows that the accuracy achieved by the proposed method reached 99.53%, with the lowest processing time.

The study reported in this paper investigates the feasibility of utilizing the fluid energy,which is causing the vibrations, to suppress the flow induced vibration of a mechanical structure. A cantilever beam with uniform thickness and variable tip mass subjected to harmonic excitation is considered to simulate the oscillating structure (the primary system). Two hinged plates, as a secondary system, are attached at its free end to provide the flow induced dynamic suppression by tuning their characteristic vibration to the oscillation of the primary system. A computer program was developed to evaluate the optimum design parameters of the secondary system (absorber) at any flow speed for a given structure. Experimental verification of the response with the calculated optimum absorber at each flow speed was undertaken. The results show a good correlation between the predicted and the experimentally measured suppressed amplitudes of the primary system.

AbstractFor mobile clients, sufficient resources with the assurance of efficient performance and energy efficiency are the core concerns. This article mainly considers this need and proposes a resourceful architecture, called mRARSA that addresses the critical need in a mobile cloud environment. This architecture consists of cloud resources, mobile devices, and a set of functional components. The performance efficiency evaluates implementing the proposed context-aware multi-criteria decision offloading algorithm. This algorithm considers both device context (network parameters) and application content (task size) at run time when offloading an executable code to allocate the cloud resources. The appropriate resources select based on offloading decisions and via the wireless communication channels. The architecture’s remarkable component is the signal strength analyzer that determines the signal quality (e.g.-60 dBm) and contributes to performance efficiency. The proposed prototype model has implemented several times to monitor the performance efficiency, mobility, performance at communication barriers, and the outcomes of resource-demanding application’s execution. Results indicate performance improvement, such as the algorithm appropriately decides the cloud resources based on device network context, application content, mobility, and the signal strength quality and range. Moreover, the results also show significant improvement in achieving performance and energy efficiency. Sufficient resources and performance efficiency are the most significant features that distinguish this framework from the other existing frameworks.

This paper describes the flow dynamics inside a supersonic ejector using CFD modelling. Suitable ejector geometry is proposed for the high compression ratios encountered in real-world applications. Post-processing and physical analysis of the CFD results are presented to better understand the entrainment mechanism and the mixing between the primary and the secondary fluids. The effect of the primary pressure on the flow dynamics and the entrainment ratio is discussed for the two compression ratios of 1.67 and 3.4. It is found that the proposed ejector can operate at compression ratio as high as 3.4. It is also found that the strength of the shock waves, the supersonic jet expansion and the mixing between the primary and secondary fluid flows inside the ejector are strongly affected by the primary pressure. A significant increase in both the shear forces amplitude and the wall friction losses is observed when the boundary layer separates from the ejector wall.

Energy transition has brought widespread attentions to the concept of coupled utilization of the geothermal and solar energy. This paper provides an integrated assessment on developing a nanofluid geothermal-photovoltaic hybrid system that addresses the multi-objective optimization and multi-criteria evaluation difficulties. The coupling system design and dispatch are optimized by considering the multiple objectives from the microscopic particles to the system. The life cycle cost, levelized cost of energy, levelized cost of heat, and the irreversibility are introduced in the optimization stage. The optimization parameters include the pipe arrangement, type of nanoparticles, and the concentration of the nanoparticles in nanofluids. A combined analysis including the energy, exergy, economy, and the environment is proposed to evaluate the various objectives and cases. The results show that the combination of 2% Al2O3 nanofluid and spiral pipe has the optimum performance. The monocrystalline solar panels with the nanofluids-aided heat pump create the least CO2 emissions (550 kg/year), the least LCOE (198.18 $), and the highest exergy efficiency. However, the LCOH (211.78 $/MWh) is still much high. Only when the electricity cost is higher than 0.11$/kWh, the proposed coupling system would show competitiveness. In summary, these results effectively prove the robustness and superiority of the hybrid system.

Sustainable business in the hospitality sector should be designed and restructured to offer services meant to satisfy the customers aware of the ethical behavior toward environment. Green environmental marketing strategies (GES) are based on the desire of the customers to access hospitality services with zero or a less negative impact with the adaptation of greening activities. Therefore, this research aims to explore the direct effects of green environmental strategies on ethical consumption and the indirect influence of green marketing and its mediating effect between green environmental strategies and ethical consumption links. Additionally, this research also reveals that psychological aspects play the moderating role in the relationship between GES and ethical consumption. The data were collected from a sample of 545 respondents with the support of Saudi tourism companies, and a structural equation model was used to process them. The findings confirm the positive relationship between green environmental strategy and ethical consumption. The outcomes also corroborate that green marketing is interplaying between green environmental strategy and ethical consumption. In addition, this study validates that psychological aspects strengthen the link between GES and ethical consumption. This study adds to the knowledge in the literature through emphasizing the crucial role of psychological factors in improving green environmental strategies and developing ethical consumption habits among members to increase ethical consumption among tourism companies. The findings support companies from the hospitality sector implementing green sustainable services, to stimulate ethical consumption and to use their competitive advantage in green marketing strategies.