Search results for: vehicle emissions

Authors: Siliang Luan, Zhongtai Jiang

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The risk of highway incidents is commonly recognized as a major concern for transportation authorities due to the hazardous consequences and negative influence. It is crucial to respond to these unpredictable events as soon as possible faced by emergency management decision makers. In this paper, we focus on path planning for emergency vehicles, one of the most significant processes to avoid congestion and reduce rescue time. A Mixed-Integer Linear Programming with Semi-Soft Time Windows Model (MIPSSTWM) is conducted to plan an optimal routing respectively considering the time consumption of arcs and nodes of the urban road network and the highway network, especially in developing countries with an enormous population. Here, the arcs indicate the road segments and the nodes include the intersections of the urban road network and the on-ramp and off-ramp of the highway networks. An attempt in this research has been made to develop a comprehensive and executive strategy for emergency vehicle routing in heavy traffic conditions. The proposed Cuckoo Search (CS) algorithm is designed by imitating obligate brood parasitic behaviors of cuckoos and Lévy Flights (LF) to solve this hard and combinatorial problem. Using a Chinese city as our case study, the numerical results demonstrate the approach we applied in this paper outperforms the previous method without considering the nodes of the road network for a real-world situation. Meanwhile, the accuracy and validity of the CS algorithm also show better performances than the traditional algorithm.

Keywords: emergency vehicle, path planning, cs algorithm, urban traffic management and urban planning

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Authors: Bifeng Zhu, Zhekai Wang, Chaoyang Sun, Bart Dewancker

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Under the tide of promoting sustainable development in the world, American universities that have been committed to sustainable practice and innovation, not only have its sustainable campus construction been in the forefront of the world, but also have developed STARS (The Sustainability Tracking, Assessment & Rating System), which is widely used in the world and highly recognized. At the same time, in the process of global sustainable campus construction, energy problem is often regarded as one of the most important sustainable aspects, even equivalent to the sustainability of campus. Therefore, the relationship between campus energy and sustainability is worth discussing. In this study, four American universities with the highest level evaluated by STARS are selected as examples to compare and analyze the campus energy consumption and the use of new energy, GHG emissions and the overall sustainability of the campus, in order to explore the relationship between campus energy and sustainable construction. It is found that the advantages of sustainable campus construction in the United States are mainly focused on the "software" of management, education, activities, etc. Although different energy-saving measures have been taken in campus energy, the construction results are quite different. Moreover, as an important aspect of sustainable campus, energy can not fully represent the sustainability of campus, but because of the various measures it takes, it can greatly promote the sustainable construction of the whole campus. These measures and construction experiences are worthy of summary and promotion, and have positive reference significance for other universities even communities around the world.

Keywords: sustainable campus, energy consumption, STARS assessment, GHG emissions

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Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: Roberto Pili, Alessandro Romagnoli, Hartmut Spliethoff, Christoph Wieland

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Internal combustion engines (ICE) are today the most common energy system to drive vehicles and transportation systems. Numerous studies state that 50-60% of the fuel energy content is lost to the ambient as sensible heat. ORC offers a valuable alternative to recover such waste heat from ICE, leading to fuel energy savings and reduced emissions. In contrast, the additional weight of the ORC affects the net energy balance of the overall system and the ORC occupies additional volume that competes with vehicle transportation capacity. Consequently, a lower income from delivered freight or passenger tickets can be achieved. The economic feasibility of integrating an ORC into an ICE and the resulting economic impact of weight and volume have not been analyzed in open literature yet. This work intends to define such a benchmark for ORC applications in the transportation sector and investigates the current situation on the market. The applied methodology refers to the freight market, but it can be extended to passenger transportation as well. The economic parameter X is defined as the ratio between the variation of the freight revenues and the variation of fuel costs when an ORC is installed as a bottoming cycle for an ICE with respect to a reference case without ORC. A good economic situation is obtained when the reduction in fuel costs is higher than the reduction of revenues for the delivered freight, i.e. X<1. Through this constraint, a maximum allowable change of transport capacity for a given relative reduction in fuel consumption is determined. The specific fuel consumption is influenced by the ORC in two ways. Firstly because the transportable freight is reduced and secondly because the total weight of the vehicle is increased. Note, that the generated electricity of the ORC influences the size of the ICE and the fuel consumption as well. Taking the above dependencies into account, the limiting condition X = 1 results in a second order equation for the relative change in transported cargo. The described procedure is carried out for a typical city bus, a truck of 24-40 t of payload capacity, a middle-size freight train (1000 t), an inland water vessel (Va RoRo, 2500 t) and handysize-like vessel (25000 t). The maximum allowable mass and volume of the ORC are calculated in dependence of its efficiency in order to satisfy X < 1. Subsequently, these values are compared with weight and volume of commercial ORC products. For ships of any size, the situation appears already highly favorable. A different result is obtained for road and rail vehicles. For trains, the mass and the volume of common ORC products have to be reduced at least by 50%. For trucks and buses, the situation looks even worse. The findings of the present study show a theoretical and practical approach for the economic application of ORC in the transportation sector. In future works, the potential for volume and mass reduction of the ORC will be addressed, together with the integration of an economic assessment for the ORC.

Keywords: ORC, transportation, volume, weight

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Authors: Salahaeddine Sbai

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Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

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Authors: Bahamin Bazooyar, Xinyan Wang, Hua Zhao

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As a zero-carbon fuel, hydrogen can be used in combustion engines to avoid carbon emissions. This paper numerically investigates the engine performance of a two-stroke opposed piston hydrogen engine by using three-dimensional (3D) Computational Fluid Dynamics (CFD) simulations. The engine displacement is 12.2 cm, and the compression ratio of 39. RANS simulations with the k-ε turbulence model and coupled chemistry combustion models are performed at an engine speed of 4500 rpm and hydrogen flow rate of up to 100 gr/s. In order to model the hydrogen injection process, the hydrogen nozzle was meshed with refined mesh, and injection pressure varied between 100 and 200 bars. In order to optimize the hydrogen combustion process, the injection timing was optimized between 15 before the top dead center and 10. The results showed that the combustion efficiency was mostly influenced by the injection pressures due to its impact on the fuel/air mixing and charge inhomogeneity. Nitrogen oxide (NOₓ) emissions are well correlated with engine peak temperatures, demonstrating that the thermal NO mechanism is dominant under engine conditions. Through the optimization of hydrogen injection timing and pressure, the peak thermal efficiency of 45 and NOx emission of 15 ppm/kWh can be achieved at an injection timing of 350 CA and pressure of 160 bars.

Keywords: engine, hydrogen, diesel, two-stroke, opposed-piston, decarbonisation

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Authors: Dina H. Gabr, Mona A. El Bannan

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With growing worldwide concern for global warming, green finance has become the fuel that pushes the world to act in combating and mitigating climate change. Coupled with adopting the Paris Agreement and the United Nations Sustainable Development Goals, Green finance became a vital tool in creating a pathway to sustainable development, as it connects the financial world with environmental and societal benefits. This paper provides a comprehensive review of the concepts and definitions of green finance and the importance of 'green' impact investments today. The core challenge in combating climate change is reducing and controlling Greenhouse gas emissions; therefore, this study explores the solutions green finance provides putting emphasis on the use of renewable energy, which is necessary for enhancing the transition to the green economy. With increasing attention to the concept of green finance, multiple forms of green investments and financial tools have come to fruition; the most prominent are green bonds. The rise of green bonds, a debt market to finance climate solutions, provide a promising mechanism for sustainable finance. Following the review, this paper compiles a comprehensive green bond dataset, presenting a statistical study of the evolution of the green bonds market from its first appearance in 2006 until 2021.

Keywords: climate change, GHG emissions, green bonds, green finance, sustainable finance

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Authors: Nafisa Mahbub, Hajo Ribberink

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Electrification of long-haul trucks has been in discussion as a potential strategy to decarbonization. These trucks will require large batteries because of their weight and long daily driving distances. Around 245 million battery electric vehicles are predicted to be on the road by the year 2035. This huge increase in the number of electric vehicles (EVs) will require intensive mining operations for metals and other materials to manufacture millions of batteries for the EVs. These operations will add significant environmental burdens and there is a significant risk that the mining sector will not be able to meet the demand for battery materials, leading to higher prices. Since the battery is the most expensive component in the EVs, technologies that can enable electrification with smaller batteries sizes have substantial potential to reduce the material usage and associated environmental and cost burdens. One of these technologies is an ‘electrified road’ (eroad), where vehicles receive power while they are driving, for instance through an overhead catenary (OC) wire (like trolleybuses and electric trains), through wireless (inductive) chargers embedded in the road, or by connecting to an electrified rail in or on the road surface. This study assessed the total material use and associated life cycle GHG emissions of two types of eroads (overhead catenary and in-road wireless charging) for long-haul trucks in Canada and compared them to electrification using stationary plug-in fast charging. As different electrification technologies require different amounts of materials for charging infrastructure and for the truck batteries, the study included the contributions of both for the total material use. The study developed a bottom-up approach model comparing the three different charging scenarios – plug in fast chargers, overhead catenary and in-road wireless charging. The investigated materials for charging technology and batteries were copper (Cu), steel (Fe), aluminium (Al), and lithium (Li). For the plug-in fast charging technology, different charging scenarios ranging from overnight charging (350 kW) to megawatt (MW) charging (2 MW) were investigated. A 500 km of highway (1 lane of in-road charging per direction) was considered to estimate the material use for the overhead catenary and inductive charging technologies. The study considered trucks needing an 800 kWh battery under the plug-in charger scenario but only a 200 kWh battery for the OC and inductive charging scenarios. Results showed that overall the inductive charging scenario has the lowest material use followed by OC and plug-in charger scenarios respectively. The materials use for the OC and plug-in charger scenarios were 50-70% higher than for the inductive charging scenarios for the overall system including the charging infrastructure and battery. The life cycle GHG emissions from the construction and installation of the charging technology material were also investigated.

Keywords: charging technology, eroad, GHG emissions, material use, overhead catenary, plug in charger

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Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

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Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: lidar, segmentation, clustering, tracking

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Authors: Juan Francisco Morales Arteaga, Simon Gluhar, Anela Kaurin, Domen Lestan

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Contaminated soils are recognized as one of the most pressing global environmental problems. As is one of the most hazardous elements: chronic exposure to arsenic has devastating effects on health, cardiovascular diseases, cancer, and eventually death. Pb, Zn and Cd are very highly toxic metals that affect almost every organ in the body. With this in mind, new technologies for soil remediation processes are urgently needed. Calcareous artificially contaminated soil containing 231 mg kg-1 As and historically contaminated with Pb, Zn and Cd was washed with a 1:1.5 solid-liquid ratio of 90 mM EDTA, 100 mM oxalic acid, and 50 mM sodium dithionite to remove 59, 75, 29, and 53% of As, Pb, Zn, and Cd, respectively. To reduce emissions of residual EDTA and chelated metals from the remediated soil, zero valent iron (ZVI) was added (1% w/w) to the slurry of the washed soil immediately prior to rinsing. Experimental controls were conducted without the addition of ZVI after remediation. The use of ZVI reduced metal leachability and minimized toxic emissions 21 days after remediation. After this time, NH4NO3 extraction was performed to determine the mobility of toxic elements in the soil. In addition, Unified Human BioaccessibilityMethod (UBM) was performed to quantify the bioaccessibility levels of metals in stimulated human gastric and gastrointestinal phases.

Keywords: soil remediation, soil science, soil washing, toxic metals removal

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Authors: Rahman Ali, Muhammad Sajjad, Farkhund Iqbal, Muhammad Sadiq Hassan Zada, Mohammed Hussain

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Emission of Carbon Dioxide (CO₂) has adversely affected the environment. One of the major sources of CO₂ emission is transportation. In the last few decades, the increase in mobility of people using vehicles has enormously increased the emission of CO₂ in the environment. To reduce CO₂ emission, sustainable transportation system is required in which smart parking is one of the important measures that need to be established. To contribute to the issue of reducing the amount of CO₂ emission, this research proposes a smart parking system. A cloud-based solution is provided to the drivers which automatically searches and recommends the most preferred parking slots. To determine preferences of the parking areas, this methodology exploits a number of unique parking features which ultimately results in the selection of a parking that leads to minimum level of CO₂ emission from the current position of the vehicle. To realize the methodology, a scenario-based implementation is considered. During the implementation, a mobile application with GPS signals, vehicles with a number of vehicle features and a list of parking areas with parking features are used by sorting, multi-level filtering, exploratory data analysis (EDA, Analytical Hierarchy Process (AHP)) and weighted sum model (WSM) to rank the parking areas and recommend the drivers with top-k most preferred parking areas. In the EDA process, “2020testcar-2020-03-03”, a freely available dataset is used to estimate CO₂ emission of a particular vehicle. To evaluate the system, results of the proposed system are compared with the conventional approach, which reveal that the proposed methodology supersedes the conventional one in reducing the emission of CO₂ into the atmosphere.

Keywords: car parking, Co2, Co2 reduction, IoT, merge sort, number plate recognition, smart car parking

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Authors: Ahmet Karagoz, Irfan Karagoz

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Synthetic Aperture Radar (SAR) is a radar mechanism that can be integrated into manned and unmanned aerial vehicles to create high-resolution images in all weather conditions, regardless of day and night. In this study, SAR images of military vehicles with different azimuth and descent angles are pre-processed at the first stage. The main purpose here is to reduce the high speckle noise found in SAR images. For this, the Wiener adaptive filter, the mean filter, and the median filters are used to reduce the amount of speckle noise in the images without causing loss of data. During the image segmentation phase, pixel values are ordered so that the target vehicle region is separated from other regions containing unnecessary information. The target image is parsed with the brightest 20% pixel value of 255 and the other pixel values of 0. In addition, by using appropriate parameters of statistical region merging algorithm, segmentation comparison is performed. In the step of feature extraction, the feature vectors belonging to the vehicles are obtained by using Gabor filters with different orientation, frequency and angle values. A number of Gabor filters are created by changing the orientation, frequency and angle parameters of the Gabor filters to extract important features of the images that form the distinctive parts. Finally, images are classified by sparse representation method. In the study, l₁ norm analysis of sparse representation is used. A joint database of the feature vectors generated by the target images of military vehicle types is obtained side by side and this database is transformed into the matrix form. In order to classify the vehicles in a similar way, the test images of each vehicle is converted to the vector form and l₁ norm analysis of the sparse representation method is applied through the existing database matrix form. As a result, correct recognition has been performed by matching the target images of military vehicles with the test images by means of the sparse representation method. 97% classification success of SAR images of different military vehicle types is obtained.

Keywords: automatic target recognition, sparse representation, image classification, SAR images

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Authors: Yann Jean Tan, Hui Meng Er, Choy Sin Lee, Shew Fung Wong, Wen Huei Lim

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Griseofulvin is a long standing fungistatic agent against dermatophytosis. Nevertheless, it has several drawbacks such as poor and highly variable bio availability, long duration of treatment, systemic side effects and drug interactions. Targeted treatment for the superficial skin infection, dermatophytosis via topical route could be beneficial. Nevertheless, griseofulvin is only available in the form of oral preparation. Hence, it generates interest in developing a topical formulation for griseofulvin, by using lipid nano particle as the vehicle. Lipid nanoparticle is a submicron colloidal carrier with a core that is solid in nature (lipid). It has combined advantages of various traditional carriers and is a promising vehicle for topical delivery. The griseofulvin loaded lipid nano particles produced using high pressure homogenization method were characterized and investigated for its skin targeting effect in vitro. It has a mean particle size of 179.8±4.9 nm with polydispersity index of 0.306±0.011. Besides, it showed higher skin permeation and better skin targeting effect compared to the griseofulvin suspension.

Keywords: lipid nanoparticles, griseofulvin, topical, dermatophytosis

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Authors: Guido Lorenzi, Massimo Santarelli, Carlos Augusto Santos Silva

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The growth of non-dispatchable renewable energy sources in the European electricity generation mix is promoting the research of technically feasible and cost-effective solutions to make use of the excess energy, produced when the demand is low. The increasing intermittent renewable capacity is becoming a challenge to face especially in Europe, where some countries have shares of wind and solar on the total electricity produced in 2015 higher than 20%, with Denmark around 40%. However, other consumption sectors (mainly transportation) are still considerably relying on fossil fuels, with a slow transition to other forms of energy. Among the opportunities for different mobility concepts, electric (EV) and biofuel-powered vehicles (BPV) are the options that currently appear more promising. The EVs are targeting mainly the light duty users because of their zero (Full electric) or reduced (Hybrid) local emissions, while the BPVs encourage the use of alternative resources with the same technologies (thermal engines) used so far. The batteries which are applied to EVs are based on ions of Lithium because of their overall good performance in energy density, safety, cost and temperature performance. Biofuels, instead, can be various and the major difference is in their physical state (liquid or gaseous). In this study gaseous biofuels are considered and, more specifically, Synthetic Natural Gas (SNG) produced through a process of Power-to-Gas consisting in an electrochemical upgrade (with Solid Oxide Electrolyzers) of biogas with CO2 recycling. The latter process combines a first stage of electrolysis, where syngas is produced, and a second stage of methanation in which the product gas is turned into methane and then made available for consumption. A techno-economic comparison between the two alternatives is possible, but it does not capture all the different aspects involved in the two routes for the promotion of a more sustainable mobility. For this reason, a more comprehensive methodology, i.e. Life Cycle Assessment, is adopted to describe the environmental implications of using excess electricity (directly or indirectly) for new vehicle fleets. The functional unit of the study is 1 km and the two options are compared in terms of overall CO2 emissions, both considering Cradle to Gate and Cradle to Grave boundaries. Showing how production and disposal of materials affect the environmental performance of the analyzed routes is useful to broaden the perspective on the impacts that different technologies produce, in addition to what is emitted during the operational life. In particular, this applies to batteries for which the decommissioning phase has a larger impact on the environmental balance compared to electrolyzers. The lower (more than one order of magnitude) energy density of Li-ion batteries compared to SNG implies that for the same amount of energy used, more material resources are needed to obtain the same effect. The comparison is performed in an energy system that simulates the Western European one, in order to assess which of the two solutions is more suitable to lead the de-fossilization of the transport sector with the least resource depletion and the mildest consequences for the ecosystem.

Keywords: electrical energy storage, electric vehicles, power-to-gas, life cycle assessment

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Authors: Mostafa Makrahy, Nouby Ghazaly, Ahmad Moaaz

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Brakes are one of the most important safety and performance components in automobiles and airplanes. Development of brakes has mainly focused on increasing braking power and stability. Nowadays, brake noise, vibration and harshness (NVH) together with brake dust emission and pad life are very important to vehicle drivers. The main objective of this research is to define the relationship between compressibility of friction materials and their tendency to generate vibration. An experimental study of the friction-induced vibration obtained by the disc brake system of a passenger car is conducted. Three commercial brake pad materials from different manufacturers are tested and evaluated under various brake conditions against cast iron disc brake. First of all, compressibility test for the brake friction material are measured for each pad. Then, brake dynamometer is used to simulate and reproduce actual vehicle braking conditions. Finally, a comparison between the three pad specimens is conducted. The results showed that compressibility have a very significant effect on reduction the vibration occurrence.

Keywords: automotive brake, friction material, brake dynamometer, compressibility test

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Authors: Miroslaw Wendeker, Piotr Kacejko, Marcin Szlachetka, Mariusz Duk

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This paper discusses a model of fuel economy in a city bus driving in a dynamic urban environment. Rapid changes in speed result in a constantly changing kinetic energy accumulated in a bus mass and an increased fuel consumption due to hardly recuperated kinetic energy. The model is based on the bench test results achieved from chassis dynamometer, airport and city street researches. The verified model was applied to simulate the behavior of a bus during the Japanese JE05 Emission Test Cycle. The fuel consumption was calculated for three separate research stages, i.e. urban, downtown and motorway. The simulations were performed for several values of vehicle mass and electrical load applied to on-board devices. The research results show fuel consumption is impacted by driving dynamics.

Keywords: city bus, heavy duty vehicle, Japanese JE05 test cycle, kinetic energy

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Authors: Monojit Manna, Arpan Adhikary

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In Mobile cloud sensing across the globe, an emerging paradigm is selected by the user to compute sensing tasks. In urban cities current days, Mobile vehicles are adapted to perform the task of data sensing and data collection for universality and mobility. In this work, we focused on the optimality and mobile nodes that can be selected in order to collect the maximum amount of data from urban areas and fulfill the required data in the future period within a couple of minutes. We map out the requirement of the vehicle to configure the maximum data optimization problem and budget. The Application implementation is basically set up to generalize a realistic online platform in which real-time vehicles are moving apparently in a continuous manner. The data center has the authority to select a set of vehicles immediately. A deep learning-based scheme with the help of mobile vehicles (DLMV) will be proposed to collect sensing data from the urban environment. From the future time perspective, this work proposed a deep learning-based offline algorithm to predict mobility. Therefore, we proposed a greedy approach applying an online algorithm step into a subset of vehicles for an NP-complete problem with a limited budget. Real dataset experimental extensive evaluations are conducted for the real mobility dataset in Rome. The result of the experiment not only fulfills the efficiency of our proposed solution but also proves the validity of DLMV and improves the quantity of collecting the sensing data compared with other algorithms.

Keywords: mobile crowdsensing, deep learning, vehicle recruitment, sensing coverage, data collection

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Authors: Vaishali D. Khairnar

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The main aim is to provide lower-latency and highly reliable communication facilities for vehicles in the automobile industry; vehicle-to-everything (V2X) communication basically intends to increase expressway road security and its effectiveness. The Ultra-Reliable Low-Latency Communications (URLLC) algorithm and cellular networks are applied in combination with Mobile Broadband (MBB). This is particularly used in express way safety-based driving applications. Expressway vehicle drivers (humans) will communicate in V2X systems using the sixth-generation (6G) communication systems which have very high-speed mobility features. As a result, we need to determine how to ensure reliable and consistent wireless communication links and improve the quality to increase channel gain, which is becoming a challenge that needs to be addressed. To overcome this challenge, we proposed a unique multi-user scheduling algorithm for ultra-massive multiple-input multiple-output (MIMO) systems using 6G. In wideband wireless network access in case of high traffic and also in medium traffic conditions, moreover offering quality-of-service (QoS) to distinct service groups with synchronized contemporaneous traffic on the highway like the Mumbai-Pune expressway becomes a critical problem. Opportunist MAC (OMAC) is a way of proposing communication across a wireless communication link that can change in space and time and might overcome the above-mentioned challenge. Therefore, a multi-user scheduling algorithm is proposed for MIMO systems using a cross-layered MAC protocol to achieve URLLC and high reliability in V2X communication.

Keywords: ultra-reliable low latency communications, vehicle-to-everything communication, multiple-input multiple-output systems, multi-user scheduling algorithm

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Authors: Lai Peng, Cristina Pintucci, Dries Seuntjens, José Carvajal-Arroyo, Siegfried Vlaeminck

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Economic incentives drive the implementation of short-cut nitrogen removal processes such as nitritation/denitritation (Nit/DNit) to manage nitrogen in waste streams devoid of biodegradable organic carbon. However, as any biological nitrogen removal process, the potent greenhouse gas nitrous oxide (N2O) could be emitted from Nit/DNit. Challenges remain in understanding the fundamental mechanisms and development of engineered mitigation strategies for N2O production. To provide answers, this work focuses on manure as a model, the biggest wasted nitrogen mass flow through our economies. A sequencing batch reactor (SBR; 4.5 L) was used treating the centrate (centrifuge supernatant; 2.0 ± 0.11 g N/L of ammonium) from an anaerobic digester processing mainly pig manure, supplemented with a co-substrate. Glycerin was used as external carbon source, a by-product of vegetable oil. Out-selection of nitrite oxidizing bacteria (NOB) was targeted using a combination of low dissolved oxygen (DO) levels (down to 0.5 mg O2/L), high temperature (35ºC) and relatively high free ammonia (FA) (initially 10 mg NH3-N/L). After reaching steady state, the process was able to remove 100% of ammonium with minimum nitrite and nitrate in the effluent, at a reasonably high nitrogen loading rate (0.4 g N/L/d). Substantial N2O emissions (over 15% of the nitrogen loading) were observed at the baseline operational condition, which were even increased under nitrite accumulation and a low organic carbon to nitrogen ratio. Yet, higher DO (~2.2 mg O2/L) lowered aerobic N2O emissions and weakened the dependency of N2O on nitrite concentration, suggesting a shift of N2O production pathway at elevated DO levels. Limiting the greenhouse gas emissions (environmental protection) from such a system could be substantially minimized by increasing the external carbon dosage (a cost factor), but also through the implementation of an intermittent aeration and feeding strategy. Promising steps forward have been presented in this abstract, yet at the conference the insights of ongoing experiments will also be shared.

Keywords: mitigation, nitrous oxide, nitritation/denitritation, pig manure

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Authors: Z. A. Ahmad Noor Syukri, A. J. Nawal Aswan, S. V. Wong

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The motorcycle has become one of the most common type of vehicles used on the road, particularly in the Asia region, including Malaysia, due to its size-convenience and affordable price. This study focuses only on crashes involving motorcycles with passenger cars consisting 43 real world crashes obtained from in-depth crash investigation process from June 2016 till July 2017. The study collected and analyzed vehicle and site parameters obtained during crash investigation and injury information acquired from the patient-treating hospital. The investigation team, consisting of two personnel, is stationed at the Emergency Department of the treatment facility, and was dispatched to the crash scene once receiving notification of the related crashes. The injury information retrieved was coded according to the level of severity using the Abbreviated Injury Scale (AIS) and classified into different body regions. The data revealed that weekend crashes were significantly higher for the night time period and the crash occurrence was the highest during morning hours (commuting to work period) for weekdays. Bad weather conditions play a minimal effect towards the occurrence of motorcycle – passenger vehicle crashes and nearly 90% involved motorcycles with single riders. Riders up to 25 years old are heavily involved in crashes with passenger vehicles (60%), followed by 26-55 year age group with 35%. Male riders were dominant in each of the age segments. The majority of the crashes involved side impacts, followed by rear impacts and cars outnumbered the rest of the passenger vehicle types in terms of crash involvement with motorcycles. The investigation data also revealed that passenger vehicles were the most at-fault counterpart (62%) when involved in crashes with motorcycles and most of the crashes involved situations whereby both of the vehicles are travelling in the same direction and one of the vehicles is in a turning maneuver. More than 80% of the involved motorcycle riders had sustained yellow severity level during triage process. The study also found that nearly 30% of the riders sustained injuries to the lower extremities, while MAIS level 3 injuries were recorded for all body regions except for thorax region. The result showed that crashes in which the motorcycles were found to be at fault were more likely to occur during night and raining conditions. These types of crashes were also found to be more likely to involve other types of passenger vehicles rather than cars and possess higher likelihood in resulting higher ISS (>6) value to the involved rider. To reduce motorcycle fatalities, it first has to understand the characteristics concerned and focus may be given on crashes involving passenger vehicles as the most dominant crash partner on Malaysian roads.

Keywords: motorcycle crash, passenger vehicle, in-depth crash investigation, injury mechanism

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Authors: Akber Oumer Abdurezak

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Road traffic accident is a major problem in Ethiopia, resulting in the deaths of many people and potential injuries and crash every year and loss of properties. According to the Federal Transport Authority, one of the main causes of traffic accident and crash in Ethiopia is over speeding. Implementation of different technologies is used to monitor the speed of vehicles in order to minimize accidents and crashes. This research aimed at designing a speed monitoring system to monitor the speed of travelling vehicles and movements, reporting illegal speeds or overspeeding vehicles to the concerned bodies. The implementation of the system is through a wireless sensor network. The proposed system can sense and detect the movement of vehicles, process, and analysis the data obtained from the sensor and the cloud system. The data is sent to the central controlling server. The system contains accelerometer and gyroscope sensors to sense and collect the data of the vehicle. Arduino to process the data and Global System for Mobile Communication (GSM) module for communication purposes to send the data to the concerned body. When the speed of the vehicle exceeds the allowable speed limit, the system sends a message to database as “over speeding”. Both accelerometer and gyroscope sensors are used to collect acceleration data. The acceleration data then convert to speed, and the corresponding speed is checked with the speed limit, and those above the speed limit are reported to the concerned authorities to avoid frequent accidents. The proposed system decreases the occurrence of accidents and crashes due to overspeeding and can be used as an eye opener for the implementation of other intelligent transport system technologies. This system can also integrate with other technologies like GPS and Google Maps to obtain better output.

Keywords: accelerometer, IOT, GSM, gyroscope

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Authors: Asser S. Abdelwahab, Nabil H. Abbasy, Ragi A. Hamdy

Abstract:

Marine shipping has now become one of the major worldwide contributors to pollution and greenhouse gas emissions. Hybrid ships technology based on multiple energy sources has taken a great scope of research to get rid of ship emissions and cut down fuel expenses. Insufficiency between power generated and the demand load to withstand the transient behavior on ships during severe climate conditions will lead to a blackout. Thus, an efficient energy management system (EMS) is a mandatory scope for achieving higher system efficiency while enhancing the lifetime of the onboard storage systems is another salient EMS scope. Considering energy storage system conditions, both the battery state of charge (SOC) and temperature represent important parameters to prevent any malfunction of the storage system that eventually degrades the whole system. In this paper, a two battery packs ratio fuzzy logic control model is proposed. The overall aim is to control the charging/discharging current while including both the battery SOC and temperature in the energy management system. The full designs of the proposed controllers are described and simulated using Matlab. The results prove the successfulness of the proposed controller in stabilizing the system voltage during both loading and unloading while keeping the energy storage system in a healthy condition.

Keywords: energy storage system, power shipboard, hybrid ship, thermal runaway

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Authors: Olubisi Friday Oluduro

Abstract:

The Paris Agreement 2015, the result of negotiations under the United Nations Framework Convention on Climate Change (UNFCCC), after Kyoto Protocol expiration, sets a long-term goal of limiting the increase in the global average temperature to well below 2 degrees Celsius above pre-industrial levels, and of pursuing efforts to limiting this temperature increase to 1.5 degrees Celsius. An advancement on the erstwhile Kyoto Protocol which sets commitments to only a limited number of Parties to reduce their greenhouse gas (GHGs) emissions, it includes the goal to increase the ability to adapt to the adverse impacts of climate change and to make finance flows consistent with a pathway towards low GHGs emissions. For it achieve these goals, the Agreement requires all Parties to undertake efforts towards reaching global peaking of GHG emissions as soon as possible and towards achieving a balance between anthropogenic emissions by sources and removals by sinks in the second half of the twenty-first century. In addition to climate change mitigation, the Agreement aims at enhancing adaptive capacity, strengthening resilience and reducing the vulnerability to climate change in different parts of the world. It acknowledges the importance of addressing loss and damage associated with the adverse of climate change. The Agreement also contains comprehensive provisions on support to be provided to developing countries, which includes finance, technology transfer and capacity building. To ensure that such supports and actions are transparent, the Agreement contains a number reporting provisions, requiring parties to choose the efforts and measures that mostly suit them (Nationally Determined Contributions), providing for a mechanism of assessing progress and increasing global ambition over time by a regular global stocktake. Despite the somewhat global look of the Agreement, it has been fraught with manifold limitations threatening its very existential capability to produce any meaningful result. Considering these obvious limitations some of which were the very cause of the failure of its predecessor—the Kyoto Protocol—such as the non-participation of the United States, non-payment of funds into the various coffers for appropriate strategic purposes, among others. These have left the developing countries largely threatened eve the more, being more vulnerable than the developed countries, which are really responsible for the climate change scourge. The paper seeks to examine the mitigation and adaptation measures under the Paris Agreement 2015, appraise the present situation since the Agreement was concluded and ascertain whether the developing countries have been better or worse off since the Agreement was concluded, and examine why and how, while projecting a way forward in the present circumstance. It would conclude with recommendations towards ameliorating the situation.

Keywords: mitigation, adaptation, climate change, Paris agreement 2015, framework

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Authors: Emma Serwaa Obobisa, Winifred Essaah

Abstract:

Significant worldwide trends including urbanization, industrialization, globalization, and the impending digitization have all contributed to human prosperity. However, the majority of these advancements fail to take sustainability into account, which caused the earth to manifest its retaliation in many forms. Since the world is already well-advanced, mankind needs a mature development that instills sustainability in its acts. As a result, the United Nations established the Sustainable Development Goals (SDGs), which set forth guidelines for human behavior to ensure that the ecosystem and mankind coexist as a unified, autonomous system. The study investigates the role of eco-innovation, renewable energy consumption, human capital, environmental tax, and natural resources in achieving European Union countries' sustainable development goals. The results show that eco-innovation, renewable energy consumption, human capital, and environmental tax have a negative relationship with consumption-based CO₂ emissions but a positive relationship with natural resources. These findings suggest that governments in European Union countries commit to encouraging environmentally friendly technology advances and green investment. It also stresses the need to enforce regulations that regulate the activities of polluting firms in the region with strictness.

Keywords: sustainable development, Eco-innovation, renewable energy, CO₂ emissions

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Authors: Zain Ul Hassan, Muhammad Zain Ul Abadin, Muhammad Zubair Khan

Abstract:

Unmanned aerial vehicles (UAVs) have now become of predominant importance for various operations, and an immense amount of work is going on in this specific category. The structural stability and life of these UAVs is key factor that should be considered while deploying them to different intelligent operations as their failure leads to loss of sensitive real-time data and cost. This paper presents an applied research on the development of a structural health monitoring system for a UAV designed and fabricated by deploying modular approach. Firstly, a modular UAV has been designed which allows to dismantle and to reassemble the components of the UAV without effecting the whole assembly of UAV. This novel approach makes the vehicle very sustainable and decreases its maintenance cost to a significant value by making possible to replace only the part leading to failure. Then the SHM for the designed architecture of the UAV had been specified as a combination of wings integrated with strain gauges, on-board data logger, bridge circuitry and the ground station. For the research purpose sensors have only been attached to the wings being the most load bearing part and as per analysis was done on ANSYS. On the basis of analysis of the load time spectrum obtained by the data logger during flight, fatigue life of the respective component has been predicted using fracture mechanics techniques of Rain Flow Method and Miner’s Rule. Thus allowing us to monitor the health of a specified component time to time aiding to avoid any failure.

Keywords: fracture mechanics, rain flow method, structural health monitoring system, unmanned aerial vehicle

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Authors: Youn-Hwan Kim, Jae-Won Moon, Hae-Joong Kim

Abstract:

This paper introduces the study on the method of accelerated life test to electrical rotating system. In recent years, as well as efficiency for motors and generators, there is a growing need for research on the life expectancy. It is considered impossible to calculate the acceleration coefficient by increasing the rotational load or temperature load as the acceleration stress in the motor system because the temperature of the copper exceeds the wire thermal class rating. In this paper, the accelerated life test methods of the electrical rotating system are classified according to the application. This paper describes the development of the test procedure for the highly accelerated life test (HALT) of the 100kW permanent magnet synchronous motor (PMSM) of electric vehicle. Finally, it explains how to select acceleration load for vibration, temperature, bearing load, etc. for accelerated life test.

Keywords: acceleration coefficient, electric vehicle motor, HALT, life expectancy, vibration

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Authors: F. Clara Fang, Hernan Castaneda

Abstract:

The Connecticut Department of Transportation (ConnDOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The queue length and level of service observed in the field appear to be better than predicted by the VISSIM model. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; recommend VISSIM calibration factors; and, provide other recommendations for roundabout traffic operations modeling. Traffic data were collected at these roundabouts using Miovision Technologies. Cameras were set up to capture vehicle circulating activity and entry behavior for two weekdays. A large sample size of filed data was analyzed to achieve accurate and statistically significant results. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. It has a significant impact to queue length, particularly when the volume is higher. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance.

Keywords: driver critical gap, roundabout analysis, simulation, VISSIM modeling

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Authors: Cagri Memis, Muzaffer Kapanoglu

Abstract:

The school bus routing problem (SBRP) is a variant of the Vehicle Routing Problem (VRP) classified as a location-allocation-routing problem. In this study, the SBRP is decomposed into two sub-problems: (1) bus route generation and (2) bus stop selection to solve large instances of the SBRP in reasonable computational times. To solve the first sub-problem, we propose a genetic algorithm to generate bus routes. Once the routes have been fixed, a sub-problem remains of allocating students to stops considering the capacity of the buses and the walkability constraints of the students. While the exact method solves small-scale problems, treating large-scale problems with the exact method becomes complex due to computational problems, a deficiency that the genetic algorithm can overcome. Results obtained from the proposed approach on 150 instances up to 250 stops show that the matheuristic algorithm provides better solutions in reasonable computational times with respect to benchmark algorithms.

Keywords: genetic algorithm, matheuristic, school bus routing problem, vehicle routing problem

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Authors: Nemi Bhattarai

Abstract:

In recent years, there has been a rapid increase in the use of Unmanned Aerial Vehicle (UAVs) in search and rescue (SAR) operations, disaster management, and many more areas where information about the location of human beings are important. This research will primarily focus on the use of optical and thermal camera via UAV platform in real-time detection, localization, and visualization of human beings on GIS. This research will be beneficial in disaster management search of lost humans in wilderness or difficult terrain, detecting abnormal human behaviors in border or security tight areas, studying distribution of people at night, counting people density in crowd, manage people flow during evacuation, planning provisions in areas with high human density and many more.

Keywords: UAV, human detection, real-time, localization, visualization, haar-like, GIS, thermal sensor

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Authors: F. Taouche-Kheloui, O. Fedaoui-Akmoussi, K. Ait tahar, Li. Alex

Abstract:

Currently, the reduction of energy consumption, through the use of abundant and recyclable natural materials, for better thermal insulation represents an important area of research. To this end, the use of bio-sourced materials has been identified as one of the green sectors with a very high economic development potential for the future. Because of its role in reducing the consumption of fossil-based raw materials, it contributes significantly to the storage of atmospheric carbon, limits greenhouse gas emissions and creates new economic opportunities. This study constitutes a contribution to the elaboration and the experimental characterization of the thermal behavior of a sawdust-reduced mortar matrix. We have taken into account the influence of the size of the grain fibers of sawdust, hence the use of three different ranges and also different percentage in the different confections. The intended practical application consists of producing a light weight compound at a lower cost to ensure a better thermal and acoustic behavior compared to that existing in the field, in addition to the desired resistances. Improving energy performance, while reducing greenhouse gas emissions from the building sector, is amongst the objectives to be achieved. The results are very encouraging and highlight the value of the proposed design of organic-source mortar panels which have specific mechanical properties acceptable for their use, low densities, lower cost of manufacture and labor, and above all a positive impact on the environment.

Keywords: mortar, sawdust waste, thermal, experimental, analysis

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