Current trends and innovations affecting the potential for a widespread adoption of electric buses - A comparative case study of 22 cities in the Americas, Asia-Pacific and Europe

Electric buses have environmental, economic, and health benefits, which many cities want to achieve by transitioning their fleets. However, the actual worldwide electric bus adoption is geographically uneven and limited in scale, and few studies analyzed what factors can potentially shape a wider adoption. The paper is based on real world experiences, and applies a comparative multi-case study to 22 cities in 14 countries. A common framework is used for analysis, which includes non-reimbursable funds, investment capital, and legal arrangements. Results show that four key factors are shaping the widespread adoption of electric buses. Firstly, public and private grants, which, when dedicated to cleaning the fleet, appears as a strong factor underpinning existing clean bus systems. Secondly, less costly sources of financing can reduce financial risks and enable more adoption, and it is where innovation can happen. Also, innovative ways of structuring contractual implementation effectively connect stakeholders and involve third-party players, which leads to shared and mitigated risks, increased efficiency and improved performance. In addition, some other elements outside of the business model framework also prove to be enabling the adoption of electric buses

Influence of fixed orthodontic appliances on the change in oral Candida strains among adolescents

AbstractBackground/purposeThe aim of this study was to explore the presence and variability of oral Candida in adolescents before and during treatment with fixed orthodontic appliances.Materials and methodsA total of 50 patients aged 10–18 years old were randomly selected for this study. Microorganism samples were obtained prior to and after orthodontic treatment and identified by culture methods. Molecular biology techniques were used to investigate the samples further and the effect of the orthodontic appliance on oral pathogenic yeasts was studied longitudinally.ResultsThe percentage of patients with candidiasis and the total number of colony-forming units significantly increased 2 months after orthodontic treatment. Changes in the type of oral candidiasis prior to and after treatment were significant.ConclusionFixed orthodontic appliances can influence the growth of oral pathogenic yeasts among adolescents

Mangroves in Ecuador: An application and comparison of ecosystem service models

Mangroves provide an abundant supply of ecosystem services such as coastal protection, fish nursery, recreation, and carbon sequestration. After a severe loss of mangroves predominately due to shrimp farming from 1969 to 2000, Ecuador realized the importance of mangroves and their related ecosystem services. In response, the government’s interest grew to understand ecosystem services valuation (ESV) models that provide robust valuation for the ecosystem services(ES). Working with the Conservation Strategy Fund (CSF), this report identified and evaluated applicable ESV models, valued and mapped the ecosystem services values of Ecuadorian mangrove with ESV models. This report aims to calculate the value of ecosystem services of mangroves with the existing modeling tools. The following models were initially considered: InVEST, AIRES, MIMES, Co$ting Nature, EcoServ, LUCI, and SolVES. Each model is different, and therefore likely to generate a different valuation of ecosystem services for the same area. In addition, the report compared the variance within models for four different scenarios: status-quo, lose-all, reforestation, full-recovery. Results include both numerical information and highlight the usefulness of each different modeling tool. Based on results and analyses, suggestions are made on suitable ESV models for mangrove ecosystems, and decision support information are provided to Socio Manglar program of Ministry of Environment of Ecuador

Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback

Deep learning has revolutionized the design of the channel state information (CSI) feedback module in wireless communications. However, designing the optimal neural network (NN) architecture for CSI feedback can be a laborious and time-consuming process. Manual design can be prohibitively expensive for customizing NNs to different scenarios. This paper proposes using neural architecture search (NAS) to automate the generation of scenario-customized CSI feedback NN architectures, thereby maximizing the potential of deep learning in exclusive environments. By employing automated machine learning and gradient-descent-based NAS, an efficient and cost-effective architecture design process is achieved. The proposed approach leverages implicit scene knowledge, integrating it into the scenario customization process in a data-driven manner, and fully exploits the potential of deep learning for each specific scenario. To address the issue of excessive search, early stopping and elastic selection mechanisms are employed, enhancing the efficiency of the proposed scheme. The experimental results demonstrate that the automatically generated architecture, known as Auto-CsiNet, outperforms manually-designed models in both reconstruction performance (achieving approximately a 14% improvement) and complexity (reducing it by approximately 50%). Furthermore, the paper analyzes the impact of the scenario on the NN architecture and its capacity.Comment: 16 pages, 10 figures, 6 table

A Study on the Right to Use Rural Homestead: Taking Changchun City and the Surrounding Area as the Example

Based on the analysis and comparison of the data collected from a field survey on the status quo of the right to use rural homestead in Changchun City and the surrounding area, this paper not only reflects on the history and theory of the right to use rural homestead in China, but also proposes solutions and suggestions in accordance with the reality towards the development of rural areas in China

Multi-task Learning-based CSI Feedback Design in Multiple Scenarios

For frequency division duplex systems, the essential downlink channel state information (CSI) feedback includes the links of compression, feedback, decompression and reconstruction to reduce the feedback overhead. One efficient CSI feedback method is the Auto-Encoder (AE) structure based on deep learning, yet facing problems in actual deployments, such as selecting the deployment mode when deploying in a cell with multiple complex scenarios. Rather than designing an AE network with huge complexity to deal with CSI of all scenarios, a more realistic mode is to divide the CSI dataset by region/scenario and use multiple relatively simple AE networks to handle subregions' CSI. However, both require high memory capacity for user equipment (UE) and are not suitable for low-level devices. In this paper, we propose a new user-friendly-designed framework based on the latter multi-tasking mode. Via Multi-Task Learning, our framework, Single-encoder-to-Multiple-decoders (S-to-M), designs the multiple independent AEs into a joint architecture: a shared encoder corresponds to multiple task-specific decoders. We also complete our framework with GateNet as a classifier to enable the base station autonomously select the right task-specific decoder corresponding to the subregion. Experiments on the simulating multi-scenario CSI dataset demonstrate our proposed S-to-M's advantages over the other benchmark modes, i.e., significantly reducing the model complexity and the UE's memory consumptionComment: 31 pages, 13 figures, 10 Table