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1
Magnetodielectric Properties of La0.67Sr0.33MnO3 and Ba0.7Sr0.3TiO3 Thin Film Heterostructures

Arjun Tarale,Y.D. Kolekar,V.L. Mathe,S.B. Kulkarni,V.R. Reddy,Pradeep Joshi 대한금속·재료학회 2012 ELECTRONIC MATERIALS LETTERS Vol.8 No.4
The paper discuses synthesis and magnetodielectric properties of La0.67Sr0.33MnO3 (LSMO), Ba0.7Sr0.3TiO3 (BST),and BST/LSMO thin film heterostructures. The XRD spectra are determined for confirmation of the crystal structure of LSMO, BST and formation of a pure bi-phase composite. The paper presents variation of Cp and tanδ as a function of frequency between 100 Hz to 1 MHz and applied magnetic field up to 0.6 T. The observed variation of Cp, tanδ, magnetocapacitance and impedance spectra are analyzed in terms of a possible equivalent circuit model. The present analysis shows that the method of impedance spectra could be used to separate out the possible contributions.
2
Multi Label Deep Learning classification approach for False Data Injection Attacks in Smart Grid

( Prasanna Srinivasan. V ),( Balasubadra. K ),( Saravanan. K ),( Arjun. V. S ),( Malarkodi. S ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
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  ScienceON
  
  KISS
The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.
3
Prediction of Discharge Status and Readmissions after Resection of Intradural Spinal Tumors

Michael C. Jin,Allen L. Ho,Austin Y. Feng,Zachary A. Medress,Arjun V. Pendharkar,Paymon Rezaii,John K. Ratliff,Atman M. Desai 대한척추신경외과학회 2022 Neurospine Vol.19 No.1
Objective: Intradural spinal tumors are uncommon and while associations between clinical characteristics and surgical outcomes have been explored, there remains a paucity of literature unifying diverse predictors into an integrated risk model. To predict postresection outcomes for patients with spinal tumors. Methods: IBM MarketScan Claims Database was queried for adult patients receiving surgery for intradural tumors between 2007 and 2016. Primary outcomes-of-interest were nonhome discharge and 90-day postdischarge readmissions. Secondary outcomes included hospitalization duration and postoperative complications. Risk modeling was developed using a regularized logistic regression framework (LASSO, least absolute shrinkage and selection operator) and validated in a withheld subset. Results: A total of 5,060 adult patients were included. Most surgeries utilized a posterior approach (n = 5,023, 99.3%) and tumors were most commonly found in the thoracic region (n = 1,941, 38.4%), followed by the lumbar (n = 1,781, 35.2%) and cervical (n = 1,294, 25.6%) regions. Compared to models using only tumor-specific or patient-specific features, our integrated models demonstrated better discrimination (area under the curve [AUC] [nonhome discharge] = 0.786; AUC [90-day readmissions] = 0.693) and accuracy (Brier score [nonhome discharge] = 0.155; Brier score [90-day readmissions] = 0.093). Compared to those predicted to be lowest risk, patients predicted to be highest-risk for nonhome discharge required continued care 16.3 times more frequently (64.5% vs. 3.9%). Similarly, patients predicted to be at highest risk for postdischarge readmissions were readmitted 7.3 times as often as those predicted to be at lowest risk (32.6% vs. 4.4%). Conclusion: Using a diverse set of clinical characteristics spanning tumor-, patient-, and hospitalization-derived data, we developed and validated risk models integrating diverse clinical data for predicting nonhome discharge and postdischarge readmissions.