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The U.S. Environmental Protection Agency identified the use of low rolling resistance (LRR) tires as an effective method of reducing vehicle fuel consumption, especially from heavyduty vehicles (HDV). LRR tires are important to HDV operations because fuel accounts for about 25% of operating costs, and improving fuel economy also reduces emissions of both greenhouse gases and oxides of nitrogen, a precursor to the formation of ozone, which is harmful to humans, plants, and animals. However, their adoption rate has been slow primarily due to performance uncertainties under real-world operating conditions. Previous mathematical models developed to help fleet operators estimate the impact of LRR tires on their operations have suffered from poor accuracy because they do not account for variable speed profiles in realworld HDV operations. Georgia Tech researchers have developed a new tool for fleet managers that better predicts the benefits of LRR tires under real-world conditions.View the NCST Project Webpage

In response to the issue that the fusion process of infrared and visible images is easily affected by lighting factors, in this paper, we propose an adaptive illumination perception fusion mechanism, which was integrated into an infrared and visible image fusion network. Spatial attention mechanisms were applied to both infrared images and visible images for feature extraction. Deep convolutional neural networks were utilized for further feature information extraction. The adaptive illumination perception fusion mechanism is then integrated into the image reconstruction process to reduce the impact of lighting variations in the fused images. A Median Strengthening Channel and Spatial Attention Module (MSCS) was designed to be integrated into the backbone of YOLOv8. In this paper, we used the fusion network to create a dataset named ivifdata for training the target recognition network. The experimental results indicated that the improved YOLOv8 network saw further enhancements of 2.3%, 1.4%, and 8.2% in the Recall, mAP50, and mAP50-95 metrics, respectively. The experiments revealed that the improved YOLOv8 network has advantages in terms of recognition rate and completeness, while also reducing the rates of false negatives and false positives. [ABSTRACT FROM AUTHOR]

The cost of fuel represents a major portion of the costs of operating on-road heavy-duty vehicles (HDV). Over the next couple of decades, the total energy demand from the HDV sector will likely increase due to forecasted growth in freight demand in many global markets, including the United States, and much of this energy will continue to be provided by fossil fuels. Therefore, carbon dioxide emissions from the HDV sector are also expected to increase in the absence of effective mitigating measures to reduce the sectors reliance on fossil fuels. Along with other fuel-saving technologies, the United States Environmental Protection Agency identified the use of Low Rolling Resistance (LRR) tires as an effective method of reducing fuel consumption. It is estimated that LRR tires can improve fuel economy in HDV by about 10 percent. However, adoption of LRR faces many barriers and the most fundamental of these barriers relate to potential performance uncertainties under real-world operating conditions. Previous published decision support tools developed to help fleet operators and other stakeholders estimate the fuel-savings from LRR tires have been found to have limited accuracy due to inherent transient speed profiles in real-world operating cycles.In this study, we develop a tool to predict the fleet-wide fuel-saving benefits from low rolling resistance tires. Unlike previous studies, the developed tool is applicable to both stabilized speed operations and transient speed operations. The tool is based on empirical models that estimate the fuel consumption contribution from tires as a function of vehicle payload, aerodynamic drag, road grade, duration of acceleration, duration of deceleration and, and road facility type (freeway, major arterial, and minor arterial/local road). We limited the scope of the developed tool to tractor-trailers in the U.S. heavy-duty vehicle market, because the United States has the second largest HDV market in the world and tractor-trailers account for the largest share of the market. The tool was developed with data generated by simulating real-world heavy-duty vehicle operating cycles with Autonomie®, the state-of-the-art model for automotive control-system design, and simulating vehicle energy consumption and performance. Autonomie® is a preferred vehicle simulation tool of the United States Department of Energy.The primary purpose of the Tool to Predict Fleet-Wide Heavy-Duty Vehicle Fuel-Saving from Low Rolling Resistance Tires is to assist fleet operators, regulatory agencies, and policy analysts in assessing the fuel consumption savings from low rolling resistance tires. To facilitate ease-of-use by stakeholders, the statistical empirical models are embedded in a Microsoft Excel® spreadsheet. Fleet managers can customize the tool to their specific fleet and the tool is designed to inform fleet operators about the benefits and costs of making low rolling resistance tire investments. In addition to fuel consumption estimates, the spreadsheet tool further estimates related emission reductions. In the future, this tool can be extended to other vehicle segments. The spreadsheet algorithms can also be developed into a web-based computer program in the future to facilitate online use of the tool.The HDV Low Rolling Resistance Tire Fuel and Emission Reduction Calculator is available to download as a spreadsheet tool here: http://transportation.ce.gatech.edu/node/95View the NCST Project Webpage

The basic isolated and screened a novel strain HAUT-1 from activated sludge using pure culture technology, identified it by 16 S rRNA gene sequence analysis, and applied it in MFCs for power generation testing and single-factor condition optimization. The results showed that the 16S rRNA sequence of strain HAUT-1 had the highest homology of 98% with Corynebacterium vitarumen (X84680), and thus the strain was named Corynebacterium vitarumen HAUT-1. The results of the single-factor test showed that the optimal power output conditions for this strain in MFCs were sodium acetate as the carbon source, glycine as the nitrogen source, incubation pH of 7, and incubation temperature of 25 °C, under which the maximum output voltage of strain HAUT-1 in MFCs could reach 635 mV; further analysis of polarization curves and power density curves showed that the open-circuit voltage of the MFCs system was 608 mV, and the output power density reached the maximum of 53.88 mW/cm² when the current density was 24 mA/cm². The results showed that the screened strain HAUT-1 in this study has a stable and highly efficient power output capacity, which is of great significance for the development of electrogenic strain resources to further enhance the efficiency of MFCs and realize the dual effects of environmental remediation and power output. [ABSTRACT FROM AUTHOR]

Eco-driving has significant potential to reduce fuel consumption and emissions from transit operations. Analyses were conducted of 68 thousand miles of real-world operations data from 26 buses, collected from local transit service provided by the Metropolitan Atlanta Rapid Transit Authority (MARTA), and express bus service provided by the Georgia Regional Transportation Authority (GRTA). The analysis utilized second-by-second operations data collected via global positioning system (GPS) devices from buses operated by these transit agencies. The researchers simulated the implementation of transit eco-driving strategies, based on the modal emissions modeling framework employed by the Motor Vehicle Emission Simulator (MOVES) designed to reduce engine load and emissions. This algorithm seeks to minimize fuel consumption by limiting instantaneous vehicle specific power (VSP), while maintaining average speed and conserving total distance. Fuel consumption and fuel-cycle emissions were compared across the monitored driving cycles and their modified eco-driving cycles.The savings from eco-driving were also compared against expected fuel and emissions reductions via conversion of the transit fleets to compressed natural gas (CNG), which is another popular fuel conservation strategy. The transit eco-driving strategy showed a 5% reduction in fuel consumption and fuel cycle greenhouse gas (GHG) emissions for MARTA’s 508-bus fleet (~35% diesel/65% CNG), and a 7% reduction in fuel consumption for GRTA’s 166-bus diesel fleet. The fuel savings translate to about 300,000 gallons of diesel fuel equivalent per year for MARTA and 55,000 gallons of diesel per year for GRTA. Eco-driving was also shown to reduce fuel use and emissions for CNG fleets. Eco-driving training can readily be implemented if speed/acceleration activity is monitored. Because eco-driving does not require significant capital investment it is a potentially very cost-effective strategy for local and express bus transit operations.View the NCST Project Webpage