Development of 2024 p/m aluminium alloy–SiCp nanocomposites via mechanical alloying

Aluminum alloy 2024 is the best known most widely used aircraft structural alloy. Now a days it is also gaining ground in automotive industry. In the present studies, 2024 P/M Al alloy and SiC particulates have been chosen as matrix and reinforcement materials, respectively. Mechanical alloying (MA) was used to obtain uniform SiCp dispersion in the matrix. MA powder was vacuum degassed and consolidated by hot pressing and subsequent hot forging. Thereafter the composites were heat treated to give T-6 temper. Optical and scanning electron microscopy of the composite was carried out and evaluation of mechanical properties was done. The principal objective of the present investigations was to determine how the particulate volume fraction and extent of mechanical alloying affect the microstructure and tensile properties of 2024 P/M Al alloy-SiCp composites. In these efforts it could be possible to develop nano composites of 2024 P/M Al alloy-SiCp having tensile strength of 504 MPa and modulus of elasticity of 105 GPa

MODELING THE LEADERSHIP OF LANGUAGE CHANGE FROM DIACHRONIC TEXT

Natural languages constantly change over time. These changes are modulated by social factors such as influence which are not always directly observable. However, large-scale computational modeling of language change using timestamped text can uncover the latent organization and social structure. In turn, the social dynamics of language change can potentially illuminate our understanding of innovation, influence, and identity: Who leads? Who follows? Who diverges? This thesis contributes to the growing body of research on using computational methods to model language change with a focus on quantifying linguistic leadership of change. A series of studies highlight the unique contributions of this thesis: methods that scale to huge volumes of data; measures that quantify leadership at the level of individuals or in aggregate; and analysis that links linguistic leadership to other forms of influence. First, temporal and predictive models of event cascades on a network of millions of Twitter users are used to show that lexical change spreads in the form of a contagion and influence from densely embedded ties is crucial for the adoption of non-standard terms. A Granger-causal test for detecting social influence in event cascades on a network is then presented, which is robust to both the presence of confounds such as homophily and can be applied to model both linguistic or non-linguistic change in a network. Next, a novel scheme to score and identify documents that lead semantic change in progress is introduced. This linguistic measure of influence on the documents is strongly predictive of their influence in terms of the number of citations that they receive for both US court opinions and scientific articles. Subsequently, a measure of lead on any semantic change between a pair of document sources (e.g. newspapers) and a method to aggregate multiple lead-lag relationships into a network is presented. Analysis on an induced network of nineteenth century abolitionist newspapers, following the proposed method, reveals the important yet understated role of women and Black editors in shaping the discourse on abolitionism. Finally, a method to induce an aggregate semantic leadership network using contextual word representations is proposed to investigate the link between semantic leadership and influence in the form of citations among publication venues that are part of the Association of Computational Linguistics. Taken together, these studies illustrate the utility of finding leaders of language change to gain insights in sociolinguistics and for applications in social science and digital humanities.Ph.D

Development and Validation of Method for the Estimation of Telmisartan as Active Pharmaceutical Ingredient in Tablet Dosage form and Prepared Spherical Agglomerates by RP-HPLC

The Present work was designed to develop and validate an accurate, precise and rapid method for the estimation of Telmisartan as Active Pharmaceutical Ingredient (API) as well as in tablet dosage form and prepared spherical agglomerates by RP-HPLC. The developed method was found to be simple, accurate, precise and sensitive. The separation was achieved on an Isocratic High Pressure Liquid Chromatography (HPLC) (Thermo Scientific) using pumps Jasco PU 2080 Plus, UV detector, column oven (Jasco), and a Reverse Phase C-18 (phenyl) Column (25 cm x 4.6 mm) i.d., particle size 5 μm. The HPLC system was run with flow rate: 0.8 ml/min Injection Volume: 10μl and run time: 10 min, Detector temp: 40 oC. The method was validated for specificity, precision, linearity, and accuracy, robustness, LOD and LOQ parameters. The recovery range was within the range of 99.0–102.0% and the method could be successfully applied for the routine analysis of the drug substance as well as the spherical agglomerates prepared by crystallo coagglomeration technique

A study on pregnancy outcome following previous one spontaneous abortion

Background: Pregnancy plays a unique role in the transformation of women towards completeness. Pregnancy should be considered a unique normal physiological episode in a woman’s life. However in some cases many twists and turns occur which alter the good outcome of pregnancy into a disaster. For those women who have had a previous unsuccessful outcome, pregnancy may bring a lot of inevitable negative emotions. The main objective of our study was to determine pregnancy outcome following previous one spontaneous abortionMethods: A prospective study was done on 756 patients. There were 252 patients in the case group consisting of pregnant females with the history of previous one spontaneous abortion (group A). There were two control groups - primigravida women (group B) & second gravida with history of previous successful pregnancy outcome (group C) who delivered subsequent to our study group. All the antenatal, post natal complications and modes of delivery were noted and compared between the three groups.Results: Pregnancy complications included: threatened miscarriage, premature rupture of membranes (PROM), preterm delivery, intra uterine growth restriction (IUGR), diabetes mellitus, abruption, placenta praevia, preeclampsia, eclampsia and postpartum hemorrhage (PPH). Statistical analysis was carried out using Statistical Package for Social Scientists (SPSS) version 20. Statistical analysis showed that all the complications except preeclampsia, eclampsia, placenta praevia and diabetes were more in the study group than both the control groups (p<0.05). Risk of preeclampsia was more in primigravidae. Rate of caesarean section and instrumental delivery was also significantly increased in women with previous one spontaneous abortion. Conclusions: Women with a history of previous one spontaneous abortion are at an increased risk of complications in the next pregnancy. So careful surveillance should be provided to such women and not to be restricted only to females with history of recurrent pregnancy loss

Mechanistic Modeling of Mycobacterium tuberculosis Infection in Murine Models for Drug and Vaccine Efficacy Studies.

Tuberculosis (TB) drug, regimen, and vaccine development rely heavily on preclinical animal experiments, and quantification of bacterial and immune response dynamics is essential for understanding drug and vaccine efficacy. A mechanism-based model was built to describe Mycobacterium tuberculosis H37Rv infection over time in BALB/c and athymic nude mice, which consisted of bacterial replication, bacterial death, and adaptive immune effects. The adaptive immune effect was best described by a sigmoidal function on both bacterial load and incubation time. Applications to demonstrate the utility of this baseline model showed (i) the important influence of the adaptive immune response on pyrazinamide (PZA) drug efficacy, (ii) a persistent adaptive immune effect in mice relapsing after chemotherapy cessation, and (iii) the protective effect of vaccines after M. tuberculosis challenge. These findings demonstrate the utility of our model for describing M. tuberculosis infection and corresponding adaptive immune dynamics for evaluating the efficacy of TB drugs, regimens, and vaccines

Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop

Although information workers may complain about meetings, they are an essential part of their work life. Consequently, busy people spend a significant amount of time scheduling meetings. We present Calendar.help, a system that provides fast, efficient scheduling through structured workflows. Users interact with the system via email, delegating their scheduling needs to the system as if it were a human personal assistant. Common scheduling scenarios are broken down using well-defined workflows and completed as a series of microtasks that are automated when possible and executed by a human otherwise. Unusual scenarios fall back to a trained human assistant who executes them as unstructured macrotasks. We describe the iterative approach we used to develop Calendar.help, and share the lessons learned from scheduling thousands of meetings during a year of real-world deployments. Our findings provide insight into how complex information tasks can be broken down into repeatable components that can be executed efficiently to improve productivity.Comment: 10 page

Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4

In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query. We find that OpenAI models have memorized a wide collection of copyrighted materials, and that the degree of memorization is tied to the frequency with which passages of those books appear on the web. The ability of these models to memorize an unknown set of books complicates assessments of measurement validity for cultural analytics by contaminating test data; we show that models perform much better on memorized books than on non-memorized books for downstream tasks. We argue that this supports a case for open models whose training data is known.Comment: EMNLP 2023 camera-ready (16 pages, 4 figures

Regenerative Braking: Review Paper

A brief review of research in theRegenerative braking system is presented. The regenerative braking system isan energyretrieval mechanism which stops a moving vehicle or object by converting its kinetic energy into electrical energy and store it in batteries or capacitors. When conventional brakes are applied, kinetic energy is wasted into heat energy due to friction between the brakes and wheels. This heat is carried away in the environment and the energy is effectively wasted. The total amount of energy lost in this way depends on how often, how hard and for how long the brakes are applied. The aim of this project was to store the energy which is wasted during braking, and monitor it over a display. An Electric Motor is a device which is used to convert this Kinetic Energy into electrical energy.It increases the efficiency of the electric vehicle by saving the energy