Impact of Sulfur and Carbonaceous Emissions from International Shipping on Aerosol Distributions and Direct Radiative Forcing

Abstract in HTML and technical report in PDF available on the MIT Joint Program on the Science and Policy of Global Change website (http://mit.edu/globalchange/www/).We describe in this report an effort using the MIT/NCAR three-dimensional aerosol-climate model to study the impact of ship emissions on chemical composition and radiative forcing of aerosols. Our results indicate that international shipping can be a non-negligible factor in determining the radiative forcing of aerosols over specific regions with intensive ship activities. These places include the European, eastern Asian, and American coastal regions. The global mean aerosol radiative forcing caused by the ship emissions ranges from -12.5 to -23 mW/m^2, depending on whether the mixing between black carbon and sulfate is included in the model. However, over the aforementioned places, the radiative forcing resulting from ship emissions can be much more important in the total regional aerosol forcing.MIT Joint Program for the Science and Policy of Global Change; NSF (Climate Dynamics and Atmospheric Chemistry); and NASA (IDS and Atmospheric Composition)

Update on GOCART Model Development and Applications

Recent results from the GOCART and GMI models are reported. They include: Updated emission inventories for anthropogenic and volcano sources, satellite-derived vegetation index for seasonal variations of dust emission, MODIS-derived smoke AOT for assessing uncertainties of biomass-burning emissions, long-range transport of aerosol across the Pacific Ocean, and model studies on the multi-decadal trend of regional and global aerosol distributions from 1980 to 2010, volcanic aerosols, and nitrate aerosols. The document was presented at the 2013 AEROCENTER Annual Meeting held at the GSFC Visitors Center, May 31, 2013. The Organizers of the meeting are posting the talks to the public Aerocentr website, after the meeting

The Research of Effective Factors on is Planning Capability of IT Organization

In fast-paced business environments, most businesses rely on IT. Business units continuously require planning, development and management of IS aligning with their business strategies. In this continuous process, an IS organization performs business analyses as well as planning and application functions for IS environments in a position of mediator between both business and IT units. In recent years, monitoring and evaluation of developed information systems has become an important tasks, which is inevitable and essential for making IT investment decision. This organization is generally referred to as an \u27IS strategic planning team\u27, \u27IT planning team\u27, \u27information strategic team\u27, and \u27IT strategy planning team\u27, etc., and is collectively referred to as an \u27information strategic organization\u27. This paper aims to identify ‘IS Planning Capability’ as the most important critical factor for information strategic organizations and examined how different factors that can affect planning capability, and further impacts on IS planning satisfaction in business units

Inference of SNP-Gene Regulatory Networks by Integrating Gene Expressions and Genetic Perturbations

In order to elucidate the overall relationships between gene expressions and genetic perturbations, we propose a network inference method to infer gene regulatory network where single nucleotide polymorphism (SNP) is involved as a regulator of genes. In the most of the network inferences named as SNP-gene regulatory network (SGRN) inference, pairs of SNP-gene are given by separately performing expression quantitative trait loci (eQTL) mappings. In this paper, we propose a SGRN inference method without predefined eQTL information assuming a gene is regulated by a single SNP at most. To evaluate the performance, the proposed method was applied to random data generated from synthetic networks and parameters. There are three main contributions. First, the proposed method provides both the gene regulatory inference and the eQTL identification. Second, the experimental results demonstrated that integration of multiple methods can produce competitive performances. Lastly, the proposed method was also applied to psychiatric disorder data in order to explore how the method works with real data

A Multiple Radar Approach for Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programming, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is to utilize our multi-radar technique and determine the optimal number of radars needed to detect and classify targets.Comment: 8 pages, 9 figures, International Conference for Data Intelligence and Security (ICDIS

Ab initio study of the effect of water adsorption on the carbon nanotube field-effect transistor

We perform density-functional calculations to investigate the effect of adsorbed water molecules on carbon nanotubes (CNTs). Noting that the H2 O molecule has much wider energy gap than the CNT, we find that the charge transfer between them is negligible. We discuss that several recent publications, which claimed a substantial electron transfer from the water molecule to the CNT, have been based on incautious interpretations of the Mulliken population analysis. We suggest that the effect of humidity on nanotube devices may be attributed to various indirect effects enhanced by water vapors, rather than the carrier generations by the physisorbed H2 O molecules.open292

Aerosol Modulation of Ultraviolet Radiation Dose over Four Metro Cities in India

This paper discusses the influence of aerosols on UV erythemal dose over four metro cities in India. Tropospheric Emission Monitoring Internet Service (TEMIS), archived UV-index (UV-I), and UV daily erythemal dose obtained from SCIAMACHY satellite were used in this study during June 2004 and May 2005 periods covering four important Indian seasons. UV-Index (UV-I), an important parameter representing UV risk, was found to be in the high to extreme range in Chennai (8.1 to 15.33), moderate to extreme range in Mumbai and Kolkata (5 to 16.5), and low to extreme over Delhi (3 to 15). Average UV erythemal dose showed seasonal variation from 5.9 to 6.3 KJm−2 during summer, 2.9 to 4.4 KJm−2 during postmonsoon, 3 to 4.5 KJm−2 during winter, and 5.1 to 6.19 KJm−2 during premonsoon seasons over the four cities. To estimate the influence of aerosols on reducing UV dose, UV aerosol radiative forcing and forcing efficiency were estimated over the sites. The average aerosol forcing efficiency was found to be from -1.38±0.33 to -3.01±0.28 KJm−2 AOD−1 on different seasons. The study suggests that aerosols can reduce the incoming UV radiation dose by 30–60% during different seasons

Handcrafted Microwire Regenerative Peripheral Nerve Interfaces with Wireless Neural Recording and Stimulation Capabilities

A scalable microwire peripheral nerve interface was developed, which interacted with regenerated peripheral nerves in microchannel scaffolds. Neural interface technologies are envisioned to facilitate direct connections between the nervous system and external technologies such as limb prosthetics or data acquisition systems for further processing. Presented here is an animal study using a handcrafted microwire regenerative peripheral nerve interface, a novel neural interface device for communicating with peripheral nerves. The neural interface studies using animal models are crucial in the evaluation of efficacy and safety of implantable medical devices before their use in clinical studies. 16- electrode microwire microchannel scaffolds were developed for both peripheral nerve regeneration and peripheral nerve interfacing. The microchannels were used for nerve regeneration pathways as a scaffolding material and the embedded microwires were used as a recording electrode to capture neural signals from the regenerated peripheral nerves. Wireless stimulation and recording capabilities were also incorporated to the developed peripheral nerve interface which gave the freedom of the complex experimental setting of wired data acquisition systems and minimized the potential infection of the animals from the wire connections. A commercially available wireless recording system was efficiently adopted to the peripheral nerve interface. The 32-channel wireless recording system covered 16-electrode microwires in the peripheral nerve interface, two cuff electrodes, and two electromyography electrodes. The 2-channel wireless stimulation system was connected to a cuff electrode on the sciatic nerve branch and was used to make evoked signals which went through the regenerated peripheral nerves and were captured by the wireless recording system at a different location. The successful wireless communication was demonstrated in the result section and the future goals of a wireless neural interface for chronic implants and clinical trials were discussed together

Real-Time Road Hazard Information System

Infrastructure is a significant factor in economic growth for systems of government. In order to increase economic productivity, maintaining infrastructure quality is essential. One of the elements of infrastructure is roads. Roads are means which help local and national economies be more productive. Furthermore, road damage such as potholes, debris, or cracks is the cause of many on-road accidents that have cost the lives of many drivers. In this paper, we propose a system that uses Convolutional Neural Networks to detect road degradations without data pre-processing. We utilize the state-of-the-art object detection algorithm, YOLO detector for the system. First, we developed a basic system working on data collecting, pre-processing, and classification. Secondly, we improved the classification performance achieving 97.98% in the overall model testing, and then we utilized pixel-level classification and detection with a method called semantic segmentation. We were able to achieve decent results using this method to detect and classify four different classes (Manhole, Pothole, Blurred Crosswalk, Blurred Street Line). We trained a segmentation model that recognizes the four classes mentioned above and achieved great results with this model allowing the machine to effectively and correctly identify and classify our four classes in an image. Although we obtained excellent accuracy from the detectors, these do not perform particularly well on embedded systems due to their network size. Therefore, we opted for a smaller, less accurate detector that will run in real time on a cheap embedded system, like the Google Coral Dev Board, without needing a powerful and expensive GPU

How Well Does NASA GEOS Model Perform in Simulating Dust Deposition into the Tropical Atlantic Ocean?

Massive dust emitted from North Africa can transport long distances across the tropical Atlantic Ocean, reaching the Americas. Dust deposition along the transit adds microorganisms and essential nutrients to marine ecosystem, which has important implications for biogeochemical cycle and climate. However, assessing the dust-ecosystemclimate interactions has been hindered in part by the paucity of dust deposition measurements and large uncertainties associated with oversimplified representations of dust processes in current models. We have recently produced a unique dataset of seasonal dust deposition flux and dust loss frequency into the tropical Atlantic Ocean at a nominal resolution of 200 km x 500 km by using the decade-long (2007-2016) record of aerosol three-dimensional distribution from four satellite sensors, namely CALIOP, MODIS, MISR, and IASI. On the basis of the ten-year average, the yearly dust deposition into the tropical Atlantic Ocean is estimated at 98-153 Tg. The dust deposition shows large spatial and temporal (on seasonal and interannual scale) variability. The satellite observations also yield an estimate of annual mean dust loss frequency of 0.052 ~ 0.078 d-1, a useful diagnostic that makes it possible to disentangle the dust transport and removal processes from the dust emissions when identifying the major factors contributing to the uncertainties and biases in the model simulated dust deposition. In this study, we use the dataset along with in situ and remote sensing observations to assess how well NASA GEOS model performs in simulating trans-Atlantic dust transport and deposition. We found that the GEOS modeling of dust deposition falls within the range of satellite-based estimates. However, this reasonable agreement in dust deposition is a compensation of the model's underestimate of dust emissions and overestimate of dust removal efficiency. Further, the overestimate of dust removal efficiency results largely from the model's overestimate of rainfall rate. Our results provide insights into the model's deficiencies at process level, which could better guide model improvements