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In the present work, a novel hybrid solar-based smart building energy system is introduced and studied. The system comprises innovative photovoltaic-thermal-cooling (PVTC) panels integrated with hot and cold storages with two-way interaction with electricity, heat, and cooling networks (if any). The proposed system is compared with PV-based systems integrated with battery and heat pump for a case study complex building in Aarhus, Denmark. The comparison is conducted by evaluating the performance and economic indicators and investigating the effect of significant parameters on each scenario via a parametric study. Furthermore, the optimal operating conditions and sizing of the proposed system are determined using the genetic algorithm method considering initial cost and traded energy with local energy networks as the objective functions. The comparison results show that the proposed solution is the most cost-effective scenario with the lowest initial cost of about 457,000 $ and a payback period of 6.6 years. This is mainly due to the simultaneous interaction with electricity/heat/cooling networks as well as the elimination of the battery and the heat pump, which are offered by the proposed scenario. It is shown that, in comparison to PV panels, the PVTC can produce 328.7 MWh and 125.6 MWh extra heat and cooling annually. The scatter distribution of significant parameters shows that the panel area and heat storage capacity are not sensitive parameters, and keeping the cold storage capacity at the lower bound is a techno-economically better option.

A fixed Filter-Thyristor Controlled Reactor (FF-TCR) type of compensator is proposed to improve the power factor of a single phase thyristor-controlled inductive load and it is shown that this compensator gives better power factor improvement than the more generally used FC-TCR type of compensator. Analysis is carried out for the exact equivalent circuit of an FF-TCR compensator containing two filter branches tuned to the third and the fifth harmonics respectively. It is shown that with an FF-TCR compensator the power factor improvement is much better when the source impedance is large. However, even with a small source impedance, a reasonable improvement in power factor is realised. It is further shown that even when the source impedance is large the distortion in the terminal voltage waveform is kept within reasonable limits. The analytical results are verified experimentally.

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.