Revenue Elasticity of the Main federal Taxes in Mexico

An inelastic tax system increases the uncertainty associated with tax revenue collection. This results in continuous short-term adjustments to maintain the stability of tax collection. In this paper, we estimate the revenue elasticity of the principal taxes in Mexico, finding a much greater elasticity than that found in previous studies. A cointegration model between the revenue and taxes is used which satisfies strong exogeneity, providing a basis for congruent and reliable projections. Using this model, the tax revenue projected for 2011 is much lower than the estimates prepared by Mexico’s federal government.Federal taxes, long-term revenue elasticity, cointegration, strong exogeneity, forecasts

Fuzzy set theory for cumulative trauma prediction

A widely used fuzzy reasoning algorithm was modified and implemented via an expert system to assess the potential risk of employee repetitive strain injury in the workplace. This fuzzy relational model, known as the Priority First Cover Algorithm (PFC), was adapted to describe the relationship between 12 cumulative trauma disorders (CTDs) of the upper extremity, and 29 identified risk factors. The algorithm, which finds a suboptimal subset from a group of variables based on the criterion of priority, was adopted to enable the inference mechanism of a constructed knowledge-based system to predict CTD occurrence

A deep learning-based dirt detection computer vision system for floor-cleaning robots with improved data collection

Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application.publishe

Integrated Hybrid System Architecture for Risk Analysis

A conceptual design has been announced of an expert-system computer program, and the development of a prototype of the program, intended for use as a project-management tool. The program integrates schedule and risk data for the purpose of determining the schedule applications of safety risks and, somewhat conversely, the effects of changes in schedules on changes on safety. It is noted that the design has been delivered to a NASA client and that it is planned to disclose the design in a conference presentation

Direct determination of Cu and Fe in jet fuel by electrothermal atomic absorption spectrometry with injection of sample as detergent emulsions

AbstractThis paper reports the development of a method for the determination of copper and iron in jet fuels employing the electrothermal atomic absorption spectrometry (ETAAS). In order to allow the direct determination of the analytes, the samples were injected into the graphite furnace as detergent emulsions in order to avoid their volatilization during analysis. The results obtained in this work indicated that a stable emulsion can be formed by mixing 1mL of a 7% m/v Triton X-100 solution containing 10% v/v HNO3 with 4mL of jet fuel. The injection of emulsions provided integrated absorbance signals with suitable sensitivity and precision for 300min at least. The addition of chemical modifier was not necessary because background values were always very low, allowing the use of pyrolysis temperature around 1000°C for both analytes. Both Triton X-100 and HNO3 concentrations in the solution used to form the emulsion had remarkable influence on the sensitivity as well as the heating rate employed in the drying step. Under the best conditions established in the present work, limits of detection of 0.50 and 0.46μgL−1 were observed for copper when oil-based and aqueous standards were added to the emulsions for calibration, respectively. For iron, the limits of detection were 0.88 and 0.90μgL−1 for oil-based and aqueous standards, respectively. The method was applied in the determination of Cu and Fe in five samples of jet fuels and a recovery test was performed, producing recovery percentages between 95% and 105%

Personal Data Broker Instead of Blockchain for Students’ Data Privacy Assurance

Data logs about learning activities are being recorded at a growing pace due to the adoption and evolution of educational technologies (Edtech). Data analytics has entered the field of education under the name of learning analytics. Data analytics can provide insights that can be used to enhance learning activities for educational stakeholders, as well as helping online learning applications providers to enhance their services. However, despite the goodwill in the use of Edtech, some service providers use it as a means to collect private data about the students for their own interests and benefits. This is showcased in recent cases seen in media of bad use of students’ personal information. This growth in cases is due to the recent tightening in data privacy regulations, especially in the EU. The students or their parents should be the owners of the information about them and their learning activities online. Thus they should have the right tools to control how their information is accessed and for what purposes. Currently, there is no technological solution to prevent leaks or the misuse of data about the students or their activity. It seems appropriate to try to solve it from an automation technology perspective. In this paper, we consider the use of Blockchain technologies as a possible basis for a solution to this problem. Our analysis indicates that the Blockchain is not a suitable solution. Finally, we propose a cloud-based solution with a central personal point of management that we have called Personal Data Broker

Protected Users: A Moodle Plugin To Improve Confidentiality and Privacy Support through User Aliases

[EN]The privacy policies, terms, and conditions of use in any Learning Management System (LMS) are one-way contracts. The institution imposes clauses that the student can accept or decline. Students, once they accept conditions, should be able to exercise the rights granted by the General Data Protection Regulation (GDPR). However, students cannot object to data processing and public profiling because it would be conceived as an impediment to teachers to execute their work with normality. Nonetheless, regarding GDPR and consulted legal advisors, a student could claim identity anonymization in the LMS, if adequate personal justifications are provided. Per contra, the current LMSs do not have any functionality that enables identity anonymization. This is a big problem that generates undesired situations which urgently requires a definitive solution. In this work, we surveyed students and teachers to validate the feasibility and acceptance of using aliases to anonymize their identity in LMSs as a sustainable solution to the problem. Considering the positive results, we developed a user-friendly plugin for Moodle that enables students' identity anonymization by the use of aliases. This plugin, presented in this work and named Protected users, is publicly available online at GitHub and published under GNU General Public License