http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Murali Dharan 소화기인터벤션의학회 2021 Gastrointestinal Intervention Vol.10 No.4
Double pigtail plastic stents (DPTPS) are routinely placed with the lumen apposing metal stent (LAMS) during endoscopic ultrasound-guided gallbladder drainage. Several reasons are postulated for this practice. This case report highlights a previously unreported benefit from use of DPTPS within LAMS.
Murali Dharan 소화기인터벤션의학회 2021 International journal of gastrointestinal interven Vol.10 No.4
Double pigtail plastic stents (DPTPS) are routinely placed with the lumen apposing metal stent (LAMS) during endoscopic ultrasound-guided gallbladder drainage. Several reasons are postulated for this practice. This case report highlights a previously unreported benefit from use of DPTPS within LAMS.
MDP-IoT: MDP based interest forwarding for heterogeneous traffic in IoT-NDN environment
Muralidharan, Shapna,Roy, Abhishek,Saxena, Navrati North-Holland 2018 Future generations computer systems Vol.79 No.3
<P><B>Abstract</B></P> <P>Internet of Things (IoT) a vision, being built today, holds a new rule for future “anything that can be connected will be connected”. IoT needs to support a multitude of heterogeneous objects extended with sensors, actuators, RFID’s, etc. These “Smart Objects” need unique identification, autonomous data transfer and communication with other objects. Consequently, these unique requisites of IoT need a promising future Internet architecture as it mostly revolves around data. Furthermore, the existing host-centric IP standards though advantageous, faces challenges like additional protocols for mobility, end-to-end security while deploying it with massive IoT applications. Named Data Networking (NDN) project is a new evolving data-centric internet architecture with innovative capabilities like caching, named data, security which mainly suits the specifications of IoT thereby proposed to solve the shortcomings of IP. NDN traditionally supports a PULL based traffic and its stateful forwarding engine despite its skillful nature need some modification while designing for an IoT system. In this paper, our foremost work is to classify and prioritize IoT traffic and enable delay-intolerant applications with low latency, to retrieve Data efficiently. Next, we propose a Markov Decision Process (MDP) based Interest scheduling for IoT traffic with varying priorities and measure the performance with different traffic probabilities. Our simulation results show that prioritizing and treating requests based on their traffic type can reduce network load by 30 % thereby improving QoS in an IoT-NDN environment. The MDP-based IoT model schedules’ the Interest to the best interface efficiently reducing the RTT values on an average of 20 % – 30 % than conventional forwarding strategies. The incurred delay is ∼ 30 % better than existing work and forwarding strategies.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A Markov Decision Process (MDP)-based Interest Scheduling in IoT-NDN scenario. </LI> <LI> The model is proposed to satisfy delay-intolerant IoT applications efficiently. </LI> <LI> Prioritizing IoT traffic, and then scheduling the Interests with low latencies to right interfaces. </LI> <LI> This results in less RTT, thereby meeting latency requirements. </LI> <LI> Efficient model to solve Interest scheduling as the IoT system has many uncertainties. </LI> </UL> </P>
Muralidharan, Ajith,Balasubramaniam, Krishnan,Krishnamurthy, C.V. Techno-Press 2008 Smart Structures and Systems, An International Jou Vol.4 No.4
An array based, outward monitoring, ultrasonic guided wave based SHM technique using a single transmitter and multiple receivers (STMR), with a small footprint is discussed here. The previous implementation of such SHM arrays used a phase-reconstruction algorithm (that is similar to the beam-steering algorithm) for the imaging of reflectors. These algorithms were found to have a limitation during the imaging of defects/reflectors that are present in the "near-field" of the array. Here, the "near-field" is defined to be approximately 3-4 times the diameter of the compact array. This limitation is caused by approximations in the beam-steering reconstruction algorithm. In this paper, a migration-based reconstruction algorithm, with dispersion correction in the frequency domain, is discussed. Simulation and experimental studies are used to demonstrate that this algorithm improves the reconstruction in the "near-field" without decreasing the ability to reconstruct defects in the "far-field" in both isotropic and anisotropic plates.
Muralidharan Murugan,Sangiliyandi Gurunathan,Kevin John Pulikotil Anthony,Muniyandi Jeyaraj,Navanietha Krishnaraj Rathinam 한국공업화학회 2014 Journal of Industrial and Engineering Chemistry Vol.20 No.4
In this paper, a simple and environmentally friendly method for synthesis of gold nanoparticles (AuNPs) using culture supernatant of Bacillus flexus as reductants and stabilizers is reported. The prepared AuNPs were characterized by various analytical techniques. UV–visible spectroscopy, X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDS) results confirmed that Au3+ ions reduced into Au0. Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) measurements confirmed that the chemical surface structure of AuNPs. TEM images showed the size and morphology of AuNPs. Moreover, the toxicity studies suggested that AuNPs were neither toxic nor inhibitory to human breast cancer cells (MCF-7).
PPT: A Push Pull Traffic Algorithm to Improve QoS Provisioning in IoT-NDN Environment
Muralidharan, Shapna,Sahu, Bharat J. R.,Saxena, Navrati,Roy, Abhishek IEEE 2017 IEEE communications letters Vol.21 No.6
<P>Internet of Things (IoT) is the convergence of connecting people, things, data, and processes. Named data networking (NDN) is a recent paradigm perceived for the future internet architecture to tackle the exponential increase in the volume of global IoT traffic. Traditionally, NDN supports PULL traffic, but IoT applications embrace both PULL and PUSH traffic. In this letter, we propose a hybrid PUSH-PULL traffic (PPT) model, for efficient data exchange in IoT applications. Simulation results indicate that our PPT algorithm can reduce the network load by 50% compared with the traditional IPv6, which in turn helps in almost 98% packet delivery ratio and no packet drop. The average throughput of our PPT model is 50% better than the IPv6 approach, which ensures a reliable IoT model.</P>
Mani, Muralidharan,Lee, Unn Hwa,Yoon, Nal Ae,Yoon, Eun Hye,Lee, Byung Ju,Cho, Wha Ja,Park, Jeong Woo Elsevier 2017 Biochemical and biophysical research communication Vol. No.
<P><B>Abstract</B></P> <P>Previously we have reported that developmentally regulated GTP-binding protein 2 (DRG2) localizes on Rab5 endosomes and plays an important role in transferrin (Tfn) recycling. We here identified DRG2 as a key regulator of membrane tubule stability. At 30 min after Tfn treatment, DRG2 localized to membrane tubules which were enriched with phosphatidylinositol 4-monophosphate [PI(4)P] and did not contain Rab5. DRG2 interacted with Rac1 more strongly with GTP-bound Rac1 and tubular localization of DRG2 depended on Rac1 activity. DRG2 depletion led to destabilization of membrane tubules, while ectopic expression of DRG2 rescued the stability of the membrane tubules in DRG2-depleted cells. Our results reveal a novel mechanism for regulation of membrane tubule stability mediated by DRG2.</P> <P><B>Highlights</B></P> <P> <UL> <LI> DRG2 localizes to membrane tubules enriched with PI(4)P. </LI> <LI> Tubular localization of DRG2 depends on Rac1 activity. </LI> <LI> DRG2 depletion destabilizes membrane tubules. </LI> <LI> Overexpression of DRG2 rescued the stability of membrane tubules in DRG2 depleted cells. </LI> </UL> </P>
Scalable Prediction Models for Airbnb Listing in Spark Big Data Cluster using GPU-accelerated RAPIDS
Samyuktha Muralidharan,Savita Yadav,허정우,이상훈,우종욱 한국정보통신학회 2022 Journal of information and communication convergen Vol.20 No.2
We aim to build predictive models for Airbnb’s prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.
Book review: State of Entrepreneurship Support through Incubators in India
Loganathan, Muralidharan Asian Society for Innovation and Policy 2021 Asian Journal of Innovation and Policy Vol.10 No.1
Entrepreneurial ecosystem is a very active area of research both conceptually and empirically, yet most literature that emerged over the last two decades predominantly pertain to developed economies. At the same time, transitioning and emerging economies have continued to grow rapidly, making a strong case to study entrepreneurial ecosystems in emerging economies (Bruton et al., 2018). Ecosystems are broad constructs and the constitutive elements of an ecosystem are themselves complex (Stam, 2015). Hence exploring key elements of the ecosystem in depth to understand the mechanisms of how entrepreneurship is supported through intermediary organizations like incubators is a fruitful exercise. In this context, we review the book "Technology Business Incubators in India Structure, Role and Performance" which is a timely synthesis for academic researchers and practitioners, looking to explore the topic as it pertains to emerging economies. The book is part of the De Gruyter Studies in Knowledge Management and Entrepreneurial Ecosystems series, that covers pertinent ecosystems issues around universities, and sustainability by leading authors.