Rational solutions of polynomail-exponential equations

We give description of rational solutions of polynomial-equations

Reroute Prediction Service

The cost of delays was estimated as 33 billion US dollars only in 2019 for the US National Airspace System, a peak value following a growth trend in past years. Aiming to address this huge inefficiency, we designed and developed a novel Data Analytics and Machine Learning system, which aims at reducing delays by proactively supporting re-routing decisions. Given a time interval up to a few days in the future, the system predicts if a reroute advisory for a certain Air Route Traffic Control Center or for a certain advisory identifier will be issued, which may impact the pertinent routes. To deliver such predictions, the system uses historical reroute data, collected from the System Wide Information Management (SWIM) data services provided by the FAA, and weather data, provided by the US National Centers for Environmental Prediction (NCEP). The data is huge in volume, and has many items streamed at high velocity, uncorrelated and noisy. The system continuously processes the incoming raw data and makes it available for the next step where an interim data store is created and adaptively maintained for efficient query processing. The resulting data is fed into an array of ML algorithms, which compete for higher accuracy. The best performing algorithm is used in the final prediction, generating the final results. Mean accuracy values higher than 90% were obtained in our experiments with this system. Our algorithm divides the area of interest in units of aggregation and uses temporal series of the aggregate measures of weather forecast parameters in each geographical unit, in order to detect correlations with reroutes and where they will most likely occur. Aiming at practical application, the system is formed by a number of microservices, which are deployed in the cloud, making the system distributed, scalable and highly available.Comment: Submitted to the 2023 IEEE/AIAA Digital Aviation Systems Conference (DASC

Big data-driven prediction of airspace congestion

Air Navigation Service Providers (ANSP) worldwide have been making a considerable effort for the development of a better method to measure and predict aircraft counts within a particular airspace, also referred to as airspace density. An accurate measurement and prediction of airspace density is crucial for a better managed airspace, both strategically and tactically, yielding a higher level of automation and thereby reducing the air traffic controller's workload. Although the prior approaches have been able to address the problem to some extent, data management and query processing of ever-increasing vast volume of air traffic data at high rates, for various analytics purposes such as predicting aircraft counts, still remains a challenge especially when only linear prediction models are used. In this paper, we present a novel data management and prediction system that accurately predicts aircraft counts for a particular airspace sector within the National Airspace System (NAS). The incoming Traffic Flow Management (TFM) data is streaming, big, uncorrelated and noisy. In the preprocessing step, the system continuously processes the incoming raw data, reduces it to a compact size, and stores it in a NoSQL database, where it makes the data available for efficient query processing. In the prediction step, the system learns from historical trajectories and uses their segments to collect key features such as sector boundary crossings, weather parameters, and other air traffic data. The features are fed into various regression models, including linear, non-linear and ensemble models, and the best performing model is used for prediction. Evaluation on an extensive set of real track, weather, and air traffic data including boundary crossings in the U.S. verify that our system efficiently and accurately predicts aircraft counts in each airspace sector.Comment: Submitted to the 2023 IEEE/AIAA Digital Aviation Systems Conference (DASC

Effects of muscular dystrophy, exercise and blocking activin receptor IIB ligands on the unfolded protein response and oxidative stress

Protein homeostasis in cells, proteostasis, is maintained through several integrated processes and pathways and its dysregulation may mediate pathology in many diseases including Duchenne muscular dystrophy (DMD). Oxidative stress, heat shock proteins, endoplasmic reticulum (ER) stress and its response, i.e. unfolded protein response (UPR), play key roles in proteostasis but their involvement in the pathology of DMD are largely unknown. Moreover, exercise and activin receptor IIB blocking are two strategies that may be beneficial to DMD muscle, but studies to examine their effects on these proteostasis pathways are lacking. Therefore, these pathways were examined in the muscle of mdx mice, a model of DMD, under basal conditions and in response to seven weeks of voluntary exercise and/or activin receptor IIB ligand blocking using soluble activin receptor-Fc (sAcvR2B-Fc) administration. In conjunction with reduced muscle strength, mdx muscle displayed greater levels of UPR/ER-pathway indicators including greater protein levels of IREloc, PERK and Atf6b mRNA. Downstream to IREloc and PERK, spliced Xbpl mRNA and phosphorylation of elF2oc, were also increased. Most of the cytoplasmic and ER chaperones and mitochondrial UPR markers were unchanged in mdx muscle. Oxidized glutathione was greater in mdx and was associated with increases in lysine acetylated proteome and phosphorylated sirtuin 1. Exercise increased oxidative stress when performed independently or combined with sAcvR2B-Fc administration. Although neither exercise nor sAcvR2B-Fc administration imparted a clear effect on ER stress/UPR pathways or heat shock proteins, sAcvR2B-Fc administration increased protein expression levels of GRP78/BiP, a triggering factor for ER stress/UPR activation and TxNIP, a redox-regulator of ER stress-induced inflammation. In conclusion, the ER stress and UPR are increased in mdx muscle. However, these processes are not distinctly improved by voluntary exercise or blocking activin receptor IIB ligands and thus do not appear to be optimal therapeutic choices for improving proteostasis in DMD. (C) 2016 Elsevier Inc. All rights reserved.Peer reviewe

Bilgi Okuryazarlığı Ulusal Konferansı, 30-31 Mart 2009, İstanbul Teknik Üniversitesi Süleyman Demirel Kültür Merkezi

Yaratıcı Kütüphane Girişimleri Tanıtım Grubu (ILIPG), ilkokul ve lise çağındaki gençlerin içinde bulunduğumuz bilgi çağını yakalamalarının çok önemli olduğu inancından yola çıkarak, 12-18 yaş arası gençlerin bilgi okuryazarlığı konusuna dikkatlerini çekmek ve kişisel yeteneklerini geliştirmelerine yardımcı olmak amacıyla bir proje başlatmıştır. Bu Proje’nin amacı; İstanbul İl Milli Eğitim Müdürlüğü’nün de desteğiyle, “bilgi okuryazarlığı” konusunda toplumumuzda bir farkındalık yaratmak ve özellikle birincil hedef kitlemiz olan 12–18 yaş grubundaki ilköğretim ikinci kademe ve lise çağındaki gençlerimizde “bilgi okuryazarlığı” bilinci oluşturarak eleştirel yeteneklerinin ve kişiliklerinin gelişimine yardımcı olmaktır.ILIP

Modification of surface morphology of UHMWPE for biomedical implants

Ion-Beam-Based Nanofabrication; San Francisco, CA; United States; 10 April 2007 through 12 April 2007Ultra High Molecular Weight Polyethylene (UHMWPE) samples were implanted with metal and metal-gas hybrid ions (Ag, Ag+N, C+H, C+H+Ar, Ti+O) by using improved MEVVA Ion implantation technique [1,2]. An extraction voltage of 30 kV and influence of 1017 ions/cm2 were attempted in this experiment, to change their surface morphologies in order to understand the effect of ion implantation on the surface properties of UHMWPEs. Characterizations of the implanted samples with RBS , ATR - FTIR, spectra were compared with the un-implanted ones . Implanted and unimplanted samples were also thermally characterized by TGA and DSC. It was generally observed that C-H bond concentration seemed to be decreasing with ion implantation and the results indicated that the chain structure of UHMWPE were changed and crosslink density and polymer crystallinity were increased compared to unimplanted ones resulting in increased hardness. It was also observed that nano size cracks (approx.10nm) were significantly disappeared after Ag implantation, which also has an improved antibacterial effect. Contact angle measurements showed that wettability of samples increased with ion implantation. Results showed that metal and metal+gas hybrid ion implantation could be an effective way to improve the surface properties of UHMWPE to be used in hip and knee prosthesis.Center for Irradiation of Materials, Alabama A&M Universit

A foundation model for generalizable disease detection from retinal images

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders 1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications 2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.</p

Performance of the CMS Cathode Strip Chambers with Cosmic Rays

The Cathode Strip Chambers (CSCs) constitute the primary muon tracking device in the CMS endcaps. Their performance has been evaluated using data taken during a cosmic ray run in fall 2008. Measured noise levels are low, with the number of noisy channels well below 1%. Coordinate resolution was measured for all types of chambers, and fall in the range 47 microns to 243 microns. The efficiencies for local charged track triggers, for hit and for segments reconstruction were measured, and are above 99%. The timing resolution per layer is approximately 5 ns

A foundation model for generalizable disease detection from retinal images

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging