Low wage inflation

Inflation (Finance) ; Wages

The great American job machine

Unemployment ; Unemployment insurance

Price-Matching Guarantees

Are price-matching guarantees anticompetitive? This paper examines the incentives for price-matching guarantees in markets where information about prices is costly. Under some conditions the conventional explanation of price-matching announcements as facilitating collusion finds support, and is even strengthened. But our model provides an additional explanation for the practice. A price-matching guarantee may be a credible and easily understood means of communicating to uninformed consumers that a firm is low-priced. The credibility of the signal to uninformed consumers is assured by the behaviour of informed consumers. We contrast the testable implications of our model with those of the anticompetitive theories and discuss supportive evidence from an illustrative sample of retailers.

Stellar Populations in Bulges

We present line strengths in the bulges and inner disks of 38 galaxies in the local universe, including several galaxies whose bulges were previously identified as being disk-like in their colors or kinematics, to see if their spectral properties reveal evidence for secular evolution. We find that red bulges of all Hubble types are similar to luminous ellipticals in their central stellar populations. They have large luminosity-weighted ages, metallicities, and alpha/Fe ratios. Blue bulges can be separated into a metal-poor class that is restricted to late-types with small velocity dispersion and a young, metal-rich class that includes all Hubble types and velocity dispersions. Luminosity-weighted metallicities and alpha/Fe ratios are sensitive to central velocity dispersion and maximum disk rotational velocity. Red bulges and ellipticals follow the same scaling relations. We see differences in some scaling relations between blue and red bulges and between bulges of barred and unbarred galaxies. Most bulges have decreasing metallicity with increasing radius; galaxies with larger central metallicities have steeper gradients. Where positive age gradients (with the central regions being younger) are present, they are invariably in barred galaxies. The metallicities of bulges are correlated with those of their disks. While this and the differences between barred and unbarred galaxies suggest that secular evolution cannot be ignored, our results are generally consistent with the hypothesis that mergers have been the dominant mechanism responsible for bulge formation.Comment: 30 pages, 21 figures; submitted to MNRA

Predicting college basketball match outcomes using machine learning techniques: some results and lessons learned

Most existing work on predicting NCAAB matches has been developed in a statistical context. Trusting the capabilities of ML techniques, particularly classification learners, to uncover the importance of features and learn their relationships, we evaluated a number of different paradigms on this task. In this paper, we summarize our work, pointing out that attributes seem to be more important than models, and that there seems to be an upper limit to predictive quality

removal of metal ions from ethanoic acid producing plant waste water effluent,using ion exchange resins and activated carbon

This study is conducted using effluents taken from BP PETRONAS Acelyls Plant which the outcomcs of this project may give new alternative for effluent treatment oil-site not only for BBPA plant but to other industries as well This is to ensure the quality of the environment is maintained and at the same time may help in reducing cost of having to treat the effluent off site

Aprendizaje automático basado en mezcla Gaussiana mejorada Modelo para IoT en tiempo real: Análisis de los datos

Introduction: The article is the product of the research “Due to the increase in popularity of Internet of Things (IoT), a huge amount of sensor data is being generated from various smart city applications”, developed at Pondicherry University in the year 2019. Problem:To acquire and analyze the huge amount of sensor-generated data effectively is a significant problem when processing the data. Objective:  To propose a novel framework for IoT sensor data analysis using machine learning based improved Gaussian Mixture Model (GMM) by acquired real-time data.  Methodology:In this paper, the clustering based GMM models are used to find the density patterns on a daily or weekly basis for user requirements. The ThingSpeak cloud platform used for performing analysis and visualizations. Results:An analysis has been performed on the proposed mechanism implemented on real-time traffic data with Accuracy, Precision, Recall, and F-Score as measures. Conclusions:The results indicate that the proposed mechanism is efficient when compared with the state-of-the-art schemes. Originality:Applying GMM and ThingSpeak Cloud platform to perform analysis on IoT real-time data is the first approach to find traffic density patterns on busy roads. Restrictions:There is a need to develop the application for mobile users to find the optimal traffic routes based on density patterns. The authors could not concentrate on the security aspect for finding density patterns.Introducción: el artículo es producto de la investigación "Debido al aumento en la popularidad de Internet de las cosas (IoT), se está generando una gran cantidad de datos de sensores a partir de varias aplicaciones de ciudades inteligentes", desarrollado en la Universidad de Pondicherry en el año 2019. Problema: adquirir y analizar datos generados por sensores de manera efectiva pues es un problema importante al procesar los datos. Objetivo: proponer un marco novedoso para el análisis de datos del sensor IoT utilizando el aprendizaje automático basado en mejoras desde el Modelo de mezcla gaussiana (GMM) por datos adquiridos en tiempo real. Metodología: en este documento, los modelos GMM basados en agrupamiento se utilizan para encontrar los patrones de densidad en un día o semanalmente para los requisitos del usuario. La plataforma en la nube ThingSpeak utilizada para realizar análisis y visualizaciones. Resultados: se realizó un análisis sobre el mecanismo propuesto implementado en datos de tráfico en tiempo real con precisión, recuperación y F-Score como medidas. Conclusiones: los resultados indican que el mecanismo propuesto es eficiente en comparación con el estado de esquemas de arte. Originalidad: la aplicación de la plataforma GMM y ThingSpeak Cloud para realizar análisis de datos en tiempo real de IoT es el primer enfoque para encontrar patrones de densidad de tráfico en carreteras transitadas. Limitación: existe la necesidad de desarrollar la aplicación para que los usuarios móviles encuentren las rutas de tráfico óptimas basadas en patrones de densidad. Los autores no pudieron desarrollar el aspecto de seguridad para encontrar patrones de densidad

Involvement Of Mitochondria In Diclofenac – And Ibuprofen- Induced Hepatotoxicity

Diclofenac and ibuprofen are commonly used non-steroidal anti-inflammatory drugs (NSAIDs) in the treatment of rheumatic diseases. However, these drugs are known to cause hepatotoxicity in patients. Recent in vitro studies indicated that the hepatotoxic effects of these NSAIDs are related to their ability to induce apoptosis by targeting the mitochondria. This study was carried out to investigate and to compare possible liver perturbation following diclofenac and ibuprofen administration to rats. Male Sprague-Dawley rats (n=144) were treated with 3mg/kg, 5mg/kg and 1Omg/kg diclofenac and ibuprofen in normal saline, intraperitonealJy at 500~I/rat/day for 15 days. The control group was administered with saline in a similar manner. Four rats from each group were euthanised every 3 consecutive days. While 200mg/kg diclofenac and ibuprofen-treated rats (n=4) were euthanised following a single dose 10 hours post-treatment. Upon euthanisation, the livers were removed and cleaned with normal saline. A section across the right lobe was taken and fixed in 10% (v/v %) formal saline and 4% (v/v) glutaraldehyde for light (H&E staining and TUNEL assay) and transmission electron microscopy, respectively. The remaining samples were kept under -80°C for Western blotting analysis. The three mg/kg diclofenac administered group at day 15 showed significant presence of microvesicles and lymphocytic infiltration. The five mg/kg diclofenac-treated rats revealed significant presence of microvesicles, lymphocytic and neutrophilic infiltrations at day 15. Liver sections obtained from rats administered with 10 mg/kg diclofenac showed significant presence of microvesic1es, mild lymphocytic and neutrophilic infiltration and inflammation. The five mg/kg and 10mg/kg ibuprofeninjected rats showed significant presence of microvesicles and mild focal lymphocytic and neutrophilic infiltrations. These observations were mainly seen around central veins (CVs). In TUNEL assay, 5mg/kg and IOmg/kg diclofenac and IOmg/kg ibuprofen administered rats, showed apoptotic cells around the CVs at day 15. Ultrastructural study revealed swollen and ruptured mitochondrial membranes in rats treated with 5mg/kg diclofenac, 10mg/kg diclofenac and 10mg/kg ibuprofen on day 15. Western blotting analysis showed constant expression of cytochrome c in liver homogenate and mitochondrial fraction on day 3,6,9, 12 and 15. However no cytochrome c expression was detected in the cytosolic fraction. In 200 mg/kg diclofenac and ibuprofen-treated rats, cytochrome c was detected in all 3 fractions; homogenate, mitochondrial and cytosol. The expression of cytochrome c is higher density in the cytosol from rats administered with diclofenac when compared to the expression in cytosol from rats treated with ibuprofen. It can be concluded that diclofenac is probably more potent in inducing changes in mitochondrial membrane leading to apoptosis. However, at therapeutic dosage both drugs did not induce prominent alteration in the mitochondria and the hepatocytes in general

Robot weed killers - no pain more gain

Weed destruction plays a significant role in crop production, and its automation has both economic and environmental benefits by minimizing the usage of chemicals in the fields. Our aim is to design a small low-cost versatile robot allowing the destruction of weeds that lie between the crop rows by navigating in the field autonomously. Major challenges foreseen are: mapping the unknown geometry of the field, high-level planning of efficient and complete coverage of the field, and controlling the low-level operations of the robot. Traditionally, sensors like odometer have been used for localisation of robots but without much success in real-world scenarios. Specialized sensors like cameras will therefore be investigated and the plethora of image recognition algorithms will be explored and fine-tuned to enable Simultaneous Localisation And Mapping (SLAM) even on resource constrained robotic platforms. Vision-based localisation is not always viable because of the varying weather conditions of the environment and to overcome that, intelligent stochastic data fusion and machine learning algorithms will be utilized to combine data from heterogenous sensor. The image sensors for localisation will be re-used to differentiate crop rows from the weeds, which are cut when they grow. Finally, logics and reinforcement learning techniques will be explored, to exploit the generated map of the field and other sensorial information, to efficiently plan and execute weed elimination