Paraneoplastic Fever, Leukemoid Reaction and Thrombocytosis in Transitional Cell Carcinoma of Kidney: A Rare Presentation

We report a rare presentation of transitional cell carcinoma of kidney with paraneoplastic fever, leukemoid reaction and thrombocytosis. Description of the case highlights an unusual clinical scenario where fever, leucocytosis, pyuria and raised procalcitonin levels at presentation in a patient with transitional cell carcinoma of kidney may mislead diagnostic work up toward an infective cause (i.e. pyelonephritis). This case will guide clinician to keep a high index of suspicion, in case they encounter such a situation

(R1889) Analysis of Resonant Curve in the Earth-Moon System under the Effect of Resistive Force and Earth’s Equatorial Ellipticity

In the present paper, we have determined the equations of motion of the Moon in spherical coordinate system using the gravitational potential of Earth. Using perturbation, equations of motion are reduced to a second order differential equation. From the solution, two types of resonance are observed: (i) due to the frequencies–rate of change of Earth’s equatorial ellipticity parameter and Earth’s rotation rate, and (ii) due to the frequencies–angular velocity of the bary-center around the sun and Earth’s rotation rate. Resonant curves are drawn where oscillatory amplitude becomes infinitely large at the resonant points. The effect of Earth’s equatorial ellipticity parameter and resistive force on the resonant curve is analyzed. From the graphs it is observed that the effect of Earth’s equatorial ellipticity on the resonant curve is very small while the effect of resistive force is significant. It is also observed that oscillatory amplitude decreases when the magnitude of resistive force increases. Finally, the phase portrait is analyzed when the system is free from forces. Orbits in the phase space are also studied by applying the method of Poincare section. A necessary condition for the bifurcation is derived at the end

Orthodontic Treatment Need among Nepalese High School Students

Objective: To assess the need for orthodontic treatment among Nepalese high school students. Material and Methods: This is a quantitative, cross-sectional descriptive study. The sample comprises 938 children (537 males and 401 females) with an age group above 14 years. The subjects were selected voluntarily from seven different schools of Kathmandu valley using a multistage sampling technique. The Index of Orthodontic Treatment Need comprises two components: Dental Health Component (DHC) and Aesthetic Component (AC). Two trained and calibrated examiners performed the oral examination. Results: On analysis of the DHC component, it was found that 21% had no need, 18.1% had mild/little need, 24.3% had moderate/borderline need, 35.8% had severe need, and 0.7% had extreme treatment need. Similarly on analysis of AC component, it was found that 33% were AC-1, 30.8% were AC-2, 7.2% were AC-3, 8.2% were AC-4, 2.1% were AC-5, 3.6% were AC-6, 1.8% were AC-7, 7.4% were AC-8, 1.8% were AC-9, and 3.9% were AC-10. Conclusion: The Index of Orthodontic Treatment Need can be used as a tool for planning dental health resources and prioritizing the treatment need of different populations

Analysing the Masked predictive coding training criterion for pre-training a Speech Representation Model

Recent developments in pre-trained speech representation utilizing self-supervised learning (SSL) have yielded exceptional results on a variety of downstream tasks. One such technique, known as masked predictive coding (MPC), has been employed by some of the most high-performing models. In this study, we investigate the impact of MPC loss on the type of information learnt at various layers in the HuBERT model, using nine probing tasks. Our findings indicate that the amount of content information learned at various layers of the HuBERT model has a positive correlation to the MPC loss. Additionally, it is also observed that any speaker-related information learned at intermediate layers of the model, is an indirect consequence of the learning process, and therefore cannot be controlled using the MPC loss. These findings may serve as inspiration for further research in the speech community, specifically in the development of new pre-training tasks or the exploration of new pre-training criterion's that directly preserves both speaker and content information at various layers of a learnt model

An experimental and simulation study on parametric analysis in turning of inconel 718 and GFRP composite using coated and uncoated tools

Process simulation is one of the important aspects in any manufacturing/production context because it generates the scenarios to gain insight into process performance in reasonable time and cost. With upcoming worldwide applications of Inconel 718 and Glass Fiber Reinforced Polymer (GFRP) composites, machining has become an important issue which needs to be investigated in detail. In turning of hard materials (such as Inconel 718), cutting tool environment features high-localized temperatures (~1000ºC) and high stress (~700 MPa) due to contact between cutting tool and work piece. The tool may experience repeated impact loads during interrupted cuts and the work piece chips may chemically interact with the tool materials. Therefore, the use of coated tool is preferred for turning of Inconel 718. It is observed that performance of machining process is influenced by different machining parameters such as spindle speed, depth of cut and feed rate as in case of turning. Material removal rate (MRR) and flank wear in turning of Inconel 718 using physical vapour deposition (PVD) and chemical vapour deposition (CVD) coated on carbide insert tool are reported. A simulation model based on finite element approach is proposed using DEFORM 3D software. The simulation results are validated with experimental results. The results indicate that simulation model can be effectively used to predict the flank wear and MRR in turning of Inconel 718. For simultaneous optimization of multiple responses, a fuzzy inference system (FIS) is used to convert multiple responses into a single equivalent response so that uncertainty and fuzziness in data can be addressed in an effective manner. The single response characteristics so generated is known as Multi Performance characteristic Index (MPCI). A non-linear empirical model has been developed using regression analysis between MPCI and process parameters. The optimal process parameters are obtained by a recent population-based optimization method known as imperialistic competitive algorithm (ICA). Analysis of variance (ANOVA) is performed to identify the most influencing factors for all the performance characteristics. The optimal conditions of process parameters during turning of Inconel 718 and GFRP composites are reported. It is observed that flank wear is combatively less when machined with PVD coated tool than CVD coated tool in turning of both Inconel 718 and GFRP composite

Assessment of machinability of inconel 718: A comparative study of CVD & PVD coated tools

281-297This paper highlights the parametric appraisal in turning of inconel 718 using fuzzy inference system coupled with imperialistic competitive algorithm (ICA) approach. The machining variables such as spindle speed, feed rate and depth of cut have been taken into consideration to analyse their effect on evaluation characteristics viz. material removal rate (MRR), flank wear and surface roughness. Fuzzy inference system (FIS) has been used to integrate aforementioned evaluation characteristics into a single response known as multi performance characteristic index (MPCI) to address the issue of impreciseness and uncertainties involved in decision making. Mathematical models have also been proposed for MPCI using non-linear regression analysis which acts as an objective function in ICA. ICA is new meta-heuristic based on social political theory which is used to obtain global optimal parametric combination in machining of Inconel 718. The results indicate that single layer (single coating: AlTiN) physical vapour deposition (PVD) coated tool is more efficient as compared to multi-layered (four coatings: TiN, TiCN, Al2O3 and TiN) chemical vapour deposition (CVD) coated tool

A Multimodal Approach to Predict Social Media Popularity

Multiple modalities represent different aspects by which information is conveyed by a data source. Modern day social media platforms are one of the primary sources of multimodal data, where users use different modes of expression by posting textual as well as multimedia content such as images and videos for sharing information. Multimodal information embedded in such posts could be useful in predicting their popularity. To the best of our knowledge, no such multimodal dataset exists for the prediction of social media photos. In this work, we propose a multimodal dataset consisiting of content, context, and social information for popularity prediction. Specifically, we augment the SMPT1 dataset for social media prediction in ACM Multimedia grand challenge 2017 with image content, titles, descriptions, and tags. Next, in this paper, we propose a multimodal approach which exploits visual features (i.e., content information), textual features (i.e., contextual information), and social features (e.g., average views and group counts) to predict popularity of social media photos in terms of view counts. Experimental results confirm that despite our multimodal approach uses the half of the training dataset from SMP-T1, it achieves comparable performance with that of state-of-the-art.Comment: Preprint version for paper accepted in Proceedings of 1st IEEE International Conference on Multimedia Information Processing and Retrieva