Construction is a resource-intensive industry where a circular economy (CE) is essential to minimize global impacts and conserve natural resources. A CE achieves long-term sustainability by enabling materials to circulate along the critical supply chains. Accordingly, recent research has proposed a paradigm shift towards CE-based sustainability. However, uncertainties caused by fluctuating raw material prices, scarce materials, increasing demand, consumers’ expectations, lack of proper waste infrastructure, and the use of wrong recycling technologies all lead to complexities in the construction industry (CI). This research paper aims to determine the enablers of a CE for sustainable development in the CI. The system dynamics (SD) approach is utilized for modeling and simulation purposes to address the associated process complexity. First, using content analysis of pertinent literature, ten enablers of a CE for sustainable development in CI were identified. Then, causality among these enablers was identified via interviews and questionnaire surveys, leading to the development of the causal loop diagram (CLD) using systems thinking. The CLD for the 10 shortlisted enablers shows five reinforcing loops and one balancing loop. Furthermore, the CLD was used to develop an SD model with two stocks: “Organizational Incentive Schemes” and “Policy Support.” An additional stock (“Sustainable Development”) was created to determine the combined effect of all stocks. The model was simulated for five years. The findings show that policy support and organizational incentive schemes, among other enablers, are critical in implementing a CE for sustainable development in CI. The outcomes of this study can help CI practitioners to implement a CE in a way that drives innovation, boosts economic growth, and improves competitiveness.

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

Renewable energy is now the world’s most reliable and sustainable solution to environmental pollution, the energy crisis, and social sustainability. In order to regulate renewable energies and ensure the sustainable development of renewable energy in China, a regulatory framework is imperative. Electricity demand and supply forecasting have become one of the most important topics for research on sustainable solutions to energy issues. This article analyzes the Chinese electric power industry structure by looking at historical electricity demand and supply data. This study shows how a divergent set of energy policies can facilitate the application of renewable energy to China’s electric power sector. The analysis shows that in 2020, China’s electricity generation and consumption increased by 0.9 and 1.3 percent annually, respectively, producing capacity of power plants increased by 5.6 GW, coal consumption of power supply decreased by 4.3 g/kWh, power generation projects investment increased by 51.6 billion RMB, power grid projects investment decreased by 1.8 billion RMB, and newly installed generation capacity increased by 769 GW year on year (YOY). During the first three quarters of 2021, the generation, consumption and coal consumption of electricity increased respectively by 10.7 and 12.9%, the generating capacity of power plants increased by 9.4 GW, the average coal consumption of power supply decreased by 0.9 g/kWh, power generation projects investment increased by 1.8 billion RMB, while power grid projects investment decreased by 0.3 billion RMB and new generation capacity added around 20.16 GW compared to the same period last year. Furthermore, domestic power consumption grew by 6.0% and 7%, respectively, in 2020–2021. The paper examines China’s current climate, developments, and renewable energy targets between 2020 and 2021. This research aims to identify the main risks associated with China’s renewable energy development and propose a few policy measures for risk management.

This paper presents three new and improved non-isolated topologies of quadratic boost converters (QBC). Reduced voltage stress across switching devices and high voltage gain with single switch operation are the main advantages of the proposed topologies. These topologies utilize voltage multiplier cells (VMC) made of switched capacitors and switched inductors to increase the converter’s voltage gain. The analysis in continuous conduction mode is discussed in detail. The proposed converter’s voltage gain is higher than the conventional quadratic boost converter, and other recently introduced boost converters. The proposed topologies utilize only a single switch and have continuous input current and low voltage stress across switch, capacitors, and diodes, which leads to the selection of low voltage rating components. The converter’s non-ideal voltage gain is also determined by considering the parasitic capacitance and ON state resistances of switch and diodes. The efficiency analysis incorporating switching and conduction losses of the switching and passive elements is done using PLECS software (Plexim, Zurich, Switzerland). The hardware prototype of the proposed converters is developed and tested for verification.

Tourism makes a significant contribution to the economy of almost every country, so accurate demand forecasting can help in better planning for the government and a range of stakeholders involved in the tourism industry and can aid economic sustainability. Machine learning models, and in particular, deep neural networks, can perform better than traditional forecasting models which depend mainly on past observations (e.g., past data) to forecast future tourist arrivals. However, search intensities indices (SII) indicators have recently been included as a forecasting model, which significantly enhances forecasting accuracy. In this study, we propose a bidirectional long short-term memory (BiLSTM) neural network to forecast the arrival of tourists along with SII indicators. The proposed BiLSTM network can remember information from left to right and right to left, which further adds more context for forecasting in memory as compared to a simple long short- term memory (LSTM) network that can remember information only from left to right. A seasonal and trend decomposition using the Loess (STL) approach is utilized to decompose time series tourist arrival data suggested by previous studies. The resultant approach, called STL-BiLSTM, decomposes time series into trend, seasonality, and residual. The trend provides the general direction of the overall data. Seasonality is a regular and predictable pattern which re-occurs at fixed time intervals, and residual is a random fluctuation that is something which cannot be forecast. The proposed BiLSTM network achieves better accuracy than the other methods considered under the current study.

The authors of the present paper sought to conduct a numerical study on the convection heat transfer, along with the radiation and entropy generation (EGE) of a nanofluids (NFs) in a two and three-dimensional square enclosure, by using the FVM. The enclosure contained a high-temperature blade in the form of a vertical elliptical quadrant in the lower corner of the enclosure. The right edge of the enclosure was kept at low temperature, while the other edges were insulated. The enclosure was subjected to a magnetic field (MGF) and could be adjusted to different angles. In this research, two laboratory relationships dependent on temperature and volume fraction were used to simulate thermal conductivity and viscosity. The variables of this problem were Ra, Ha, RAP, nanoparticle (NP) volume fraction, blade aspect ratio, enclosure angles, and MGF. Evaluating the effects of these variables on heat transfer rate (HTR), EGE, and Be revealed that increasing the Ra and reducing the Ha could increase the HTR and EGE. On the other hand, adding radiation HTR to the enclosure increased the overall HTR. Moreover, an augmentation of the volume fraction of magnesium oxide NPs led to an increased amount of HTR and EGE. Furthermore, any changes to the MGF and the enclosure angle imposed various effects on the HTR. The results indicated that an augmentation of the size of the blade increased and then decreased the HTR and the generated entropy. Finally, increasing the blade always increased the Be.