Pricing of Idiosyncratic Risk in an Intermediary Asset Pricing Model

Standard asset pricing theories suggest that only systematic risk is priced. Empirical studies report a relationship between idiosyncratic volatility or risk (IVOL) and asset price. The most common explanation for this anomaly is that households under-diversify creating a Bad Model problem. This paper uses an Intermediary Asset Pricing Model (IAPM) as a way to control for under-diversification in evaluating the relationship between IVOL and asset price. We find that IVOL premia is lower in an IAPM. Our findings indicate that under-diversification can explain the anomaly partially

Maternal and neonatal complications following Kielland's rotational forceps delivery: A systematic review and meta‐analysis

Background There is conflicting evidence regarding the safety of Kielland's rotational forceps delivery (KRFD) in comparison with other modes of delivery for the management of persistent fetal malposition in the second stage of labour. Objectives To derive estimates of risks of maternal and neonatal complications following KRFD, compared with rotational ventouse delivery (RVD), non-rotational forceps delivery (NRFD) or a second-stage caesarean section (CS), from a systematic review and meta-analysis of the literature. Search Strategy Standard search methodology, as recommended by the Cochrane Handbook for Systematic Reviews of Interventions. Selection Criteria Case series, prospective or retrospective cohort studies and population-based studies. Data Collection and Analysis A meta-analysis using a random-effects model was used to derive weighted pooled estimates of maternal and neonatal complications. Main Results Thirteen studies were included. For postpartum haemorrhage there was no significant difference between Kielland's and ventouse delivery; the rate was lower in Kielland's delivery compared with non-rotational forceps (RR 0.79, 95% CI 0.65–0.95) and second-stage CS (RR 0.45, 95% CI 0.36–0.58). There were no differences in the rates of anal sphincter injuries or admission to neonatal intensive care. Rates of shoulder dystocia were higher with Kielland's delivery compared with ventouse delivery (RR 1.79, 95% CI 1.08–2.98), but rates of neonatal birth trauma were lower (RR 0.49, 95% CI 0.26–0.91). There were no differences seen in the rates of 5-min APGAR score < 7 between Kielland's delivery and other instrumental births, but they were lower when compared with second-stage CS (RR 0.47, 95% CI 0.23–0.97). Conclusions Kielland's rotational forceps delivery is a safe option for the management of fetal malposition in the second stage of labour

Measurement of the top quark pair production cross section with a tau lepton in the final state at the center of mass energy of 8 TeV

This dissertation presents the measurement of the inclusive cross section of the top quark pair production in the final state of a tau lepton with associated jets. The dataset used in this measurement is collected by the ATLAS detector from proton-proton collisions during the 2012 operation of the Large Hadron Collider at the center-of-mass energy, $\sqrt{s} =$ 8~\tev. This dataset corresponds to an integrated luminosity of 20.2~\ifb. The inclusive \ttbar production cross section, \sigma_{\ttbar} is found to be \sigma_{\ttbar} = 231 \pm 3 {\rm (stat.)}^{+25}_{-25} {\rm (syst.)}\pm 3{\rm (lumi.)}~{\rm pb} for a top quark mass of 172.5~\gev. This result is consistent with the theoretical prediction \sigma^{\mathrm{SM}}_{\ttbar} = 253^{+13}_{-15}~{\rm pb}. Statistical analysis is performed to set a model independent upper limit on the visible cross section of any non Standard Model processes following frequentist probability. An upper limit on the branching ratio of the flavor changing neutral Higgs decay of the top quark to a charm quark is also calculated. The observed (expected) limit on the branching ratio at 95\% CL is $\text{BR}(t \rightarrow ch^0) <$ 10\% (15\%)

Oxygen Concentrators for Neonates in Low Resource Environments

ME450 Capstone Design and Manufacturing Experience: Fall 2020There is a need for low-resource portable, reliable, and robust oxygen provision device. The device must be appropriate to treat neonates experiencing RDS and hypoxia in clinical settings, and this project specifically targets Nepal. Requirements for this project were developed using the Target Product Profile from the World Health Organization.Caroline Soyars, Aim Tech Healthhttp://deepblue.lib.umich.edu/bitstream/2027.42/164437/1/Portable_Oxygen_Concentrator_Final_Report.pd

An Empirical Studies on How the Developers Discussed about Pandas Topics

Pandas is defined as a software library which is used for data analysis in Python programming language. As pandas is a fast, easy and open source data analysis tool, it is rapidly used in different software engineering projects like software development, machine learning, computer vision, natural language processing, robotics, and others. So a huge interests are shown in software developers regarding pandas and a huge number of discussions are now becoming dominant in online developer forums, like Stack Overflow (SO). Such discussions can help to understand the popularity of pandas library and also can help to understand the importance, prevalence, difficulties of pandas topics. The main aim of this research paper is to find the popularity and difficulty of pandas topics. For this regard, SO posts are collected which are related to pandas topic discussions. Topic modeling are done on the textual contents of the posts. We found 26 topics which we further categorized into 5 board categories. We observed that developers discuss variety of pandas topics in SO related to error and excepting handling, visualization, External support, dataframe, and optimization. In addition, a trend chart is generated according to the discussion of topics in a predefined time series. The finding of this paper can provide a path to help the developers, educators and learners. For example, beginner developers can learn most important topics in pandas which are essential for develop any model. Educators can understand the topics which seem hard to learners and can build different tutorials which can make that pandas topic understandable. From this empirical study it is possible to understand the preferences of developers in pandas topic by processing their SO post

INVESTIGATION OF GEOTHERMAL ENERGY POTENTIAL USING ELECTRICAL RESISTIVITY SURVEY AND CHEMICAL GEOTHERMOMETERS: A STUDY OF THE MANGHOPIR HOT SPRING KARACHI, SINDH PAKISTAN

Electrical resistivity survey and chemical geothermometers methods were used to find the geothermal gradient energy potential of the Manghopir hot spring which is located in Karachi, Sindh. Schlumberger electrode configurations were used to demarcate the two shallow potential subsurface aquifers. At various depths, three lithological units were encountered: alluvium, sandstone, and shale. The first thermal water aquifer lies below at the average depth of 10m and average thickness of 9 m lies in sandstone lithology of Nari Formation of Oligocene age. The second thermal water aquifer encountered at the average depth of 68 m and the average thickness of aquifer was 40.5m in sandstone lithology of Nari Formation. The surface water temperature was calculated with digital thermometer which shows the range in between 48 °C to 50 °C and subsurface temperature was calculated with the help of chemical geothermometers. The Na–K geothermometers indicate the subsurface equilibrium reservoir temperature in the range of 135.52 °C,125.54 °C, 172.964 °C and 184.08°C and the Na-K-Ca chemical geothermometers indicate the subsurface reservoir temperature 148.493°C. The Na-K-Ca geothermometers show a high temperature, but the reservoir temperature appears to be lower due to the mixing of sea water with the chemical composition of hot spring water within the subsurface aquifers

Chemical Modification: A unique solutions to Solubility problem

Almost 40% of the new chemical entities at present self find out poorly water soluble drugs. Badly water soluble drugs have solubility and dissolution related bioavailability problems. Solubility is one of the most important parameter to give desired concentration of drug in systemic circulation to get its pharmacological response. Orally administered drugs obtained completely absorb only when they show fair solubility in gastric medium and such drugs shows good bioavailability. The solubility and dissolution properties of drugs perform an valuable role in the process of formulation development. Enhancement of solubility of drug is the most challenging job in drug development process. Solubilization may be affected by co solvent water interaction, micellar solubilization, reduction in particle size, inclusion complexes, solid dispersion, and change in polymorph. This review highlight various techniques of solubility enhancement with special emphasis on Chemical modification methods like Salt formation, Co-crystallization, Co-solvency, Hydrotropy, use of novel solubilizer etc along with physical modification techniques. Keywords: Salt formation, Co-crystallization, Solubility, particle technologies, Milling solubility enhancement, Cosolvent, physical and chemical methods

MCNN-LSTM: Combining CNN and LSTM to classify multi-class text in imbalanced news data

Searching, retrieving, and arranging text in ever-larger document collections necessitate more efficient information processing algorithms. Document categorization is a crucial component of various information processing systems for supervised learning. As the quantity of documents grows, the performance of classic supervised classifiers has deteriorated because of the number of document categories. Assigning documents to a predetermined set of classes is called text classification. It is utilized extensively in a wide range of data-intensive applications. However, the fact that real-world implementations of these models are plagued with shortcomings begs for more investigation. Imbalanced datasets hinder the most prevalent high-performance algorithms. In this paper, we propose an approach name multi-class Convolutional Neural Network (MCNN)-Long Short-Time Memory (LSTM), which combines two deep learning techniques, Convolutional Neural Network (CNN) and Long Short-Time Memory, for text classification in news data. CNN's are used as feature extractors for the LSTMs on text input data and have the spatial structure of words in a sentence, paragraph, or document. The dataset is also imbalanced, and we use the Tomek-Link algorithm to balance the dataset and then apply our model, which shows better performance in terms of F1-score (98%) and Accuracy (99.71%) than the existing works. The combination of deep learning techniques used in our approach is ideal for the classification of imbalanced datasets with underrepresented categories. Hence, our method outperformed other machine learning algorithms in text classification by a large margin. We also compare our results with traditional machine learning algorithms in terms of imbalanced and balanced datasets

Implementation of ultrasonic sensing for high resolution measurement of binary gas mixture fractions

We describe an ultrasonic instrument for continuous real-time analysis of the fractional mixture of a binary gas system. The instrument is particularly well suited to measurement of leaks of a high molecular weight gas into a system that is nominally composed of a single gas. Sensitivity &#60; 5 × 10−5 is demonstrated to leaks of octaflouropropane (C3F8) coolant into nitrogen during a long duration (18 month) continuous study. The sensitivity of the described measurement system is shown to depend on the difference in molecular masses of the two gases in the mixture. The impact of temperature and pressure variances on the accuracy of the measurement is analysed. Practical considerations for the implementation and deployment of long term, in situ ultrasonic leak detection systems are also described. Although development of the described systems was motivated by the requirements of an evaporative fluorocarbon cooling system, the instrument is applicable to the detection of leaks of many other gases and to processes requiring continuous knowledge of particular binary gas mixture fractions