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This research aims to present a standardized evaluation system to review and further enhance users’ levels of satisfaction with technologies, facilities, and services of a modern smart city at a time when the smart city paradigm has shifted from the focus of its infrastructural features to citizens. The study also seeks to verify the standardized system, so as to explore the possibility of its future application. For the goals, this research established the Structural Equation Model (SEM) based upon the basic structure of the Customer Satisfaction Index, which is a widely used ex-post assessment model, and upon implications of related studies. To verify the SEM, this study chose two cities, which are located far away from one another and employ different business methods, and conducted a survey of 212 and 197 residents, respectively, with the results being applied to the model for analysis to ascertain if the SEM is reliable and adequate. The analysis results showed that the model secures explanatory power in statistical terms, partially proving that it can be developed into a post-evaluation system for a citizens-centric smart city down the road. However, as meaningful differences were spotted in accordance with characteristics of each urban project, this study tried to come up with the background information of and reasons for such variations, to present implications for urban planning.

<p>This paper presents cuckoo search algorithm (CSA) for solving non-convex economic load dispatch (ELD) problems of fossil fuel fired generators considering transmission losses and valve point loading effect. CSA is a new meta-heuristic optimisation technique inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species. The strength of the proposed meta-heuristic optimization technique CSA has been tested and validated on the standard IEEE 14-bus, 26-bus and 30-bus system with several heuristic load patterns. The results have indicated that the proposed approach is able to obtain significant economic load dispatch solutions than those of Firefly Algorithm (FFA) and other soft computing techniques reported in the literature.</p>

United Nation’s sustainable goals place emphasis on renewable energy resources to transform the world into a sustainable one and enable it to have affordable energy. Of late alternative green fuels are becoming promising sources of fuel to address the energy crisis. Waste cooking oil (WCO) is the raw material used for biodiesel preparation, which can be employed as a fuel in compression ignition (CI) engines. This article investigates the dual impact of biodiesel, a green fuel and a renewable green additive (turpentine oil) on the performance and emissions of a CI engine. The studies were performed using a B20 (20% biodiesel + 80% diesel) WCO biodiesel fuel blend with 2.5% and 5% vol. of turpentine oil as an additive. The outcome of this research showed an improvement in brake thermal efficiency for the B20 biodiesel blend with a 5% turpentine oil additive. Turpentine oil additive promoted more complete combustion and produced lower carbon monoxide (CO), hydrocarbon (HC) and smoke emissions. However, the oxides of nitrogen (NOx) emissions increased as the turpentine oil composition increased from 2.5% to 5%, which is 4.5% more than B20. The outcome of this study can be extended to accomplish the objectives of sustainable development goal 7 (affordable and clean energy) and goal 13 (climate action) which are planned to be achieved by 2030.

Abstract Recently, many studies have exploited the potential of deep learning to forecast energy demand, but they cannot explain the results. They only analyze the simple correlations between the input and output to discover the most important input features, or they depend on the manual investigation of the latent space embedded with power demand patterns. In this paper, to overcome these shortcomings, we propose a deep autoencoder that can explain the prediction results by manipulating the latent space. It consists of 1) a power encoder that embeds power information, 2) an auxiliary encoder that embeds auxiliary information for an interpretable latent space in two dimensions, 3) a predictor that predicts power demand by using concatenated values of the latent variables extracted from the two encoders, and 4) an explainer that provides the most important input features in predicting the future demand by utilizing the interpretable latent variables. Several experiments on a dataset of household electric energy demand show that the proposed model not only performs better than conventional models, with a mean squared error of 0.376 in predicting electricity demand for 60 min, but also provides the capacity to explain the results by analyzing the correlation of inputs, latent variables, and energy demand predicted.

AbstractThe Green Revolution is often seen as epitomising the dawn of scientific and technological advancement and modernity in the agricultural sector across developing countries, a process that unfolded from the 1940s through to the 1980s. Despite the time that has elapsed, this episode of the past continues to resonate today, and still shapes the institutions and practices of agricultural science and technology. In Brazil, China, and India, narratives of science-led agricultural transformations portray that period in glorifying terms—entailing pressing national imperatives, unprecedented achievements, and heroic individuals or organizations. These “epic narratives” draw on the past to produce meaning and empower the actors that deploy them. Epic narratives are reproduced over time and perpetuate a conviction about the heroic power of science and technology in agricultural development. By crafting history and cultivating a sense of scientific nationalism, exceptionalism, and heritage, these epic narratives sustain power-knowledge relations in agricultural science and technology, which are underpinned by a hegemonic modernization paradigm. Unravelling the processes of assemblage and reproduction of epic narratives helps us make sense of how science and technology actors draw on their subjective representations of the past to assert their position in the field at present. This includes making claims about their credentials to envision and deliver sustainable solutions for agriculture into the future.

New powertrain technologies, such as those used in Hybrid Electric Vehicles, have a price premium which is often offset by lower running costs. Total Cost of Ownership (TCO) combines these purchase and operating expenses to identify the most economical vehicles overall. This is a valuable assessment for both private and fleet purchasers. To date, no studies have compared Total Cost of Ownership across more than two vehicle markets or analysed historic costs. To address this gap, this research provides a more extensive assessment of TCO in three industrialized countries — the U.S.A. (California specifically), the UK, and Republic of Korea — for the time period from 2015 ∼ 2019. As an early analysis of the regional characteristics of TCO, this study aims to examine how TCO varies across different geographic regions and develop an analysis methodology for future analysis of environmental vehicles. To analyze the TCO under identical conditions for each region, the TCO result is calculated using data from conventional vehicles by utilizing sufficient statistical data. The TCO model includes registration and road taxes as well as insurance, fuel, financial interest, depreciation, and maintenance costs. To represent the local features, the research considers four vehicles from different manufacturers. Using the proposed TCO model, the regional characteristic is analyzed and a sensitivity analysis is performed with the parameters constituting the TCO model. This research has implications for both fleet purchasers and private owners considering new vehicles. The findings are also relevant for carmakers aiming to develop vehicle sales strategies optimized for specific regions.

Energy is considered as a key factor which determines the economic development in the entire sector of any region. Biomass is one of the primary energy sources in rural areas. The study was carried out to examine the utilization pattern of biomass energy and socioeconomic dimensions associated with rural areas of Yelandur, Karnataka, India. Field studies in these villages covering 645 households were made to collect the data and assess the socioeconomic conditions that govern the bio- mass utilization pattern for meeting energy requirements. Firewood is the primary energy source (94.78 %) for cooking and heating among these rural folk. Most of them are illiterates (60 %) with 28.96 % of them having a meagre income. Traditional biomass stoves are used pre- dominantly. The study shows that there is a positive cor- relation (R 2 = 0.98) between the households size and volume of firewood consumption. The study has revealed that the firewood fuels are the dominant source of energy for cooking and heating purposes.

India's steadily expanding populace has made it important to create innovative methods of transportation with electric vehicles (EV's) being the most favoured alternative. The major impediment is the negative impact caused by the improper setup of charging stations on the utility distribution system. This paper takes up the issue of optimal planning (siting and sizing) of charging station (CS) infrastructure in the city of Allahabad, India. It has been labelled as a future smart city under a pilot transportation project started by Government of India. A hybrid algorithm comprising of Genetic Algorithm and improved version of conventional Particle Swarm Optimization (GAIPSO) is employed in this study. The aim is to find optimal location of proposed charging stations in the Allahabad distribution system. PSO further re-optimizes the received sub-optimal solution (site and size of station) which leads to an overall improvement in its functionality and solution. Simulation studies on a real time system of Allahabad city clearly exhibits the superior performance of the aforementioned technique with respect to GA and PSO in terms of improvement in voltage profile and quality of solution obtained for the objective function.

Lithium, a vital element in lithium-ion batteries, is pivotal in the global shift towards cleaner energy and electric mobility. The relentless demand for lithium-ion batteries necessitates an in-depth exploration of lithium extraction methods. This literature review delves into the historical evolution, contemporary practices, and emerging technologies of lithium extraction. It scrutinizes environmental and economic impacts, identifies research gaps, and underscores sustainable extraction’s imperative. It examines conventional methods like spodumene mining and brine extraction, highlighting their advantages and challenges. Emerging technologies, particularly Direct Lithium Extraction (DLE) and geothermal brine recovery, are evaluated for their potential to revolutionize the industry. Environmental considerations, including water usage, chemical disposal, and habitat disruption, are assessed alongside economic implications. The review also identifies critical research gaps, beckoning the scientific community to develop solutions that meet lithium’s surging demand while safeguarding the environment. In conclusion, this literature review emphasizes the need for sustainable lithium extraction to facilitate a future powered by cleaner energy sources and sustainable transportation.