Abstract: This paper discusses the efficiency of Hybrid Primal-Dual (HPD) type algorithms to approximately solve discrete Optimal Transport (OT) and Wasserstein Barycenter (WB) problems, with and without entropic regularization. Our first contribution is an analysis showing that these methods yield state-of-the-art convergence rates, both theoretically and practically. Next, we extend the HPD algorithm with the linesearch proposed by Malitsky and Pock in 2018 to the setting where the dual space has a Bregman divergence, and the dual function is relatively strongly convex to the Bregman's kernel. This extension yields a new method for OT and WB problems based on smoothing of the objective that also achieves state-of-the-art convergence rates. Finally, we introduce a new Bregman divergence based on a scaled entropy function that makes the algorithm numerically stable and reduces the smoothing, leading to sparse solutions of OT and WB problems. We complement our findings with numerical experiments and comparisons.

Abstract: Diversifying the energy components of a country's transport sector is essential to guarantee the fuel supply to consumers and increase the market dynamics and competitiveness. Among the known alternative fuels, biogas is a renewable source and after upgrading to biomethane, it presents a similar composition to natural gas (>90% of CH4; 35-40 MJ m(-3)). In addition, it can be produced from a wide variety of biological resources and at different scales In this study, two scenarios have been developed that evaluate the use of liquefied biomethane (LBM) as a diesel replacement option in the freight sector of an area of 248,223 km(2) (equivalent to the area of the UK). Sugarcane vinasse (SVC) and Municipal Solid Waste (MSW) were the sole feedstocks for biogas production. The first scenario, non-restricted scenario (NRS), covered the entire territory while, the second scenario, restricted scenario (RS), includes only the area where gas pipelines are available. An economic assessment of the entire biogas value chain including, biogas production units, purification, transport and end-use was performed. The minimum selling price (MSP) of biomethane throughout the biogas chain was then estimated. LBM is estimated to be a cost-effective and affordable fuel choice compared to diesel. The technical potential of biogas production by the sugarcane mills and landfills of Sao Paulo state can replace up to half of the diesel consumed in the territory. The minimum distances and optimal locations methodology indicated the need for 120 liquefaction plants in the NRS, 35 injection points in the RS, and 7 refuelling stations to supply LBM throughout the state of Sao Paulo. The units for CO2 removal had the greatest influence on capital costs (similar to 60%) in both scenarios. Expenditure associated with the gas injection operation and its transport comprised more than 90% of the operating costs of the RS. Electricity purchasing represented the highest share of the operating costs at biogas purification (20%-30%) and biomethane liquefaction (65%-91%) units. Personnel costs are observed along the entire biogas chain, especially, in the biomethane transport step (40%), indicating an opportunity to generate wealth, jobs, and income. Despite our projections for the cost-effective and competitive supplies of LBM as a diesel replacement fuel, policy support measures such as a feed-in tariff, are likely to be necessary in order to overcome non-technical barriers and gain wider acceptability.

Abstract: Carbon emissions from the Chilean public transport sector have doubled between 2000 and 2013. Thus, the need to find alternative clean-air solutions is becoming increasingly critical. With this in mind, this paper addresses a solution to the aforementioned problem by studying the conversion of both taxis and “colectivos” (public taxis with defined routes) into electric vehicles (EV) to be used in the Chilean public transport sector. In Chile, 80% of taxis are Nissan V16s, meaning that all of these vehicles will be replaced and end up in landfills within the next 5-8 years. This study presents an option to give a second life to these vehicles, addressing environmental and financial issues. It compares emissions from the Nissan V16's conventional internal combustion engine (ICE) with an equivalent electric conversion prototype. Furthermore, it analyses the CO2 emissions from the EV that has been fully charged by the Chilean grid as well as one that has been charged by a solar photovoltaic system. A solar recharging station for EV taxis is designed, taking advantage of the high levels of solar radiation in Chile. The results show that EV conversion does not lead to real environmental benefits, in comparison to its ICE equivalent, when it is charged with the grid. However, the results also show a considerable decrease in cost and CO2 emissions per km travelled when using solar energy to charge the batteries. Future challenges include identifying solutions to cope with solar intermittence and minimizing CO2 emissions during periods of low radiation. (C) 2018 Elsevier Ltd. All rights reserved.

Abstract: A novel carbonate-based nanocomposite synthesized by hydrothermal technique for planar perovskite solar cells (PPSCs) applications. According to this study, SnO2@CdCO3and SnO2/SnO2@CdCO3 performs as dual electron transporting layers for CsPbBr3 based perovskite solar cell. The fabricated PSCs with architecture of FTO/SnO2/ SnO2@CdCO3/CsPbBr3/C showed a Voc of 0.84 V, Jsc of 12.30 mA cmi 2, PCE of 6.67 % and FF of 0.64 are obtained at ambient condition.

Abstract: Enhancing the understanding of passenger satisfaction in public transportation is crucial for operators to refine transit services and to establish and elevate quality standards. While many researchers have tackled this issue using diverse tools and methods, the prevalent approach involves surveys with discrete choice models or structural equations. However, a common limitation of these models lies in their inherent assumptions and predefined relationships between dependent and independent variables. To address these limitations, we introduce a novel perspective by harnessing machine learning (ML) models to gauge and predict passenger satisfaction. ML models are advantageous when dealing with complex, non-linear relationships and massive datasets, and do not rely on predefined assumptions. Thus, in this paper, we evaluate four ML models for the prediction of ratings of the quality of transit service. These models were calibrated using data from the Transantiago bus system in Chile. Among the ML models, the Random Forest model emerges as the most effective, showcasing its ability to analyze and predict passengers' satisfaction levels. We delve deeper into its capabilities by examining the impact of three pivotal variables on passengers' score ratings: waiting time, bus occupation, and bus speed. The Random Forest model is able to capture threshold values for these variables that significantly influence or have no effect on passenger preferences.

Abstract: In many countries, bus operators are private companies whose service has been leased by government agencies. These agencies develop service compliance indices or measures to keep track of factors such as passenger satisfaction, frequency, and regularity but do not necessarily include the objectives of the operators in the assessment. In this paper, we used slack-based measure data envelopment analysis (SBM) to investigate whether it is possible for a bus operator to be efficient (from a private perspective) and match required standards of frequency and regularity. In doing so, data collected from two major bus operators in Santiago, Chile has been used comprising 99 services. The results show that when private objectives, namely revenues, are included in the analysis, bus operators do not necessarily seek to improve the regularity of their service. Moreover, it was found that some bus services are on the efficient frontier while keeping low performance measure standards. Using the shadow prices of the models, it was also found that improving the performance measures will be hard for many bus services unless there is a significant change in factors that are not under control of the operators (i.e., number of stops, length of the route, etc.). This shows the difficulty of correctly aligning the private objectives of operators with agencies' objectives.