Identifying Alterability States of a Single Track Railway Line Control System

In the context of automation and deployment of computer based control systems, a specific application on French railway line is proposed on low traffic single track railway lines. The issue of updates requires thorough consideration. In the case of low traffic single track railway lines, handling the removal of a shunting track, which role is to allow trains to circulate in both directions of a same line, the issue of timing the update to the control system is particularly critical. Indeed, a wrongly timed update could lead to a deadlock, while one or more trains are expected to travel while respecting safety constraints on the blocked infrastructure. This paper studies the application of works from the field of dynamic software updating, specifically the works of Panzica La Manna et al. [12]. Using their results on a graph based model of a single track rail line, it identifies alterability states that ensure safety constraints are respected at all times without causing deadlocks. These results are then used to discuss the pertinence of using concepts from dynamic software updating in the context of railway systems

Efficient Classification of Satellite Image with Hybrid Approach Using CNN-CA

Today, satellite imagery is being utilized to help repair and restore societal issues caused by habitats for a variety of scientific studies. Water resource search, environmental protection simulations, meteorological analysis, and soil class analysis may all benefit from the satellite images. The categorization algorithms were used generally and the most appropriate strategies are also be used for analyzing the Satellite image. There are several normal classification mechanisms, such as optimum likelihood, parallel piping or minimum distance classification that have presented in some other existing technologies. But the traditional classification algorithm has some disadvantages. Convolutional neural network (CNN) classification based on CA was implemented in this article. Using the gray level Satellite image as the target and CNN image classification by the CA’s selfiteration mechanism and eventually explores the efficacy and viability of the proposed method in long-term satellite remote sensing image water body classification. Our findings indicate that the proposed method not only has rapid convergence speed, reliability but can also efficiently classify satellite remote sensing images with long-term sequence and reasonable applicability. The proposed technique acquires an accuracy of 91% which is maximum than conventional methods

VOODOO AND HUMAN TRAFFICKING IN NIGERIA AS IMPEDIMENTS TO EFFECTIVE ADMINISTRATION OF JUSTICE

Voodoo is the major factor that makes human trafficking to thrive in Nigeria. Voodoo is regularly used by traffickers in human trafficking to exert pressure over the victims. The use of voodoo is a form of mental coercion aim at reducing the need to use physical violence. The adverse power exerted over the trafficked victims is so enormous that they dare not disobey the trafficker. In this manner, the Nigerian networks can control their victim from a distance and no additional person is required to supervise them. This paper concludes that the use of voodoo by traffickers impedes effective administration of justice. Voodoo does not allow victims of human trafficking to reveal the identities of the trafficker so that the law enforcement agencies would not arrest and prosecute them

Data Processing by Fuzzy Methods in Social Sciences Researches. Example in Hospitality Industry

Likert-type scales are a common technique used in social science. Plus, the Likert scale is among the most frequently used psychometric tools in social sciences and educational research. Despite its frequently used, the Likert scale raises up many questions mark. We can say that the use of the Likert scale in its classical form is too rigid and loses valuable information. Li (2013, p. 1613) calls on previous studies that "have claimed that fuzzy scales are more accurate than traditional scales due to the continuous nature of fuzzy sets". The aim of this research is to reduce the inaccuracy caused by the use of the Likert scale, by proposing a method of more appropriate processing of data collected in this way. As shown in this paper, fuzzy methods can be a good alternative. The research methodology consists of using the usual technique on the set of fuzzy numbers by considering the input data as linguistic variables, subsequently identified by triangular fuzzy numbers. The obtained scale is more elastic with respect to the input data, therefore it better captures the reality. The newly proposed method is applied in the concrete example of the competitors in the hotel field. The Importance-Performance Competitor Analysis is utilized. A weakness of the method is due to the use in its application of data collection with the Likert scale. The results conclude on the situation of the competitors regarding each attribute considered as in the crisp version of the method, but the identification and processing of data correspond better to the aspects of subjectivity and uncertainty specific to human thinking. A novelty is also the obtaining of a hierarchy within each category of attributes from the quadrants proposed by the Important-Performance Analysis in relation to the competition

IoT-inspired Framework for Real-time Prediction of Forest Fire

Wildfires are one of the most devastating catastrophes and can inflict tremendous losses to life and nature. Moreover, the loss of civilization is incomprehensible, potentially extending suddenly over vast land sectors. Global warming has contributed to increased forest fires, but it needs immediate attention from the organizations involved. This analysis aims to forecast forest fires to reduce losses and take decisive measures in the direction of protection. Specifically, this study suggests an energy-efficient IoT architecture for the early detection of wildfires backed by fog-cloud computing technologies. To evaluate the repeatable information obtained from IoT sensors in a time-sensitive manner, Jaccard similarity analysis is used. This data is assessed in the fog processing layer and reduces the single value of multidimensional data called the Forest Fire Index. Finally, based on Wildfire Triggering Criteria, the Artificial Neural Network (ANN) is used to simulate the susceptibility of the forest area. ANN are intelligent techniques for inferring future outputs as these can be made hybrid with fuzzy methods for decision-modeling. For productive visualization of the geographical location of wildfire vulnerability, the Self-Organized Mapping Technique is used. Simulation of the implementation is done over multiple datasets. For total efficiency assessment, outcomes are contrasted in comparison to other techniqueS

Online Healthcare Privacy Disclosure User Group Profile Modeling Based on Multimodal Fusion

With the spread of COVID-19, online healthcare is rapidly evolving to assist the public with health, reduce exposure and avoid the risk of cross-infection. Online healthcare platform requires more information from patients than offline, and insufficient or incorrect information may delay or even mislead treatment. Therefore, it is valuable to predict users’ privacy disclosure behaviors while fully protecting their information, which can provide healthcare services for users accurately and realize a personalized online healthcare environment. Compared with the traditional static online healthcare platform user privacy disclosure behavior influence factor analysis, this paper uses multimodal fusion and group profile technology to build a user privacy disclosure model and lay the foundation for personalized online healthcare services. This paper proposes a cross-modal fusion modeling approach to address the problem that the information of each modality cannot be fully utilized in the current online healthcare privacy disclosure modeling. A multimodal user profile approach is used to construct personal and group profiles, and the privacy disclosure behavioral characteristics reflected by both are integrated to realize accurate personalized services for online healthcare. The case study shows that compared with the static unimodal privacy disclosure model, the accuracy of our method gains significant improvement, which is helpful for precision healthcare services and online healthcare platform development

Evolutionary Computation Paradigm to Determine Deep Neural Networks Architectures

Image classification is usually done using deep learning algorithms. Deep learning architectures are set deterministically. The aim of this paper is to propose an evolutionary computation paradigm that optimises a deep learning neural network’s architecture. A set of chromosomes are randomly generated, after which selection, recombination, and mutation are applied. At each generation the fittest chromosomes are kept. The best chromosome from the last generation determines the deep learning architecture. We have tested our method on a second trimester fetal morphology database. The proposed model is statistically compared with DenseNet201 and ResNet50, proving its competitiveness

A Personalized mHealth Monitoring System for Children and Adolescents with T1 Diabetes by Utilizing IoT Sensors and Assessing Physical Activities

The problem of diabetes mellitus is becoming alarming due to the increase in morbidity among children. Patients are undergoing vital insulin replacement therapy, the dose depends on the level of glucose in the blood. The glucose level prediction program, taking into account the impact of physical activity on the body, the use of mobile health capabilities will allow us to develop personalized tactics for a child patient and minimize the risks of a critical health condition. The target group of this study are children and adolescents with type 1 diabetes. This study provides an IoT based mHealth monitoring system, including sensors, medical bracelets, mobile devices with applications. The mobile healthcare application for personalized monitoring can implement the functions of more effectively targeting young users to support their own health and improve the quality of life. In addition to monitoring blood glucose levels, the effect of physical activity on the condition of patients is also taken into account. The use of the proposed method for calculating the probable change in the patient’s blood glucose level after the end of physical activity will allow the doctor to make individual recommendations for the diet before the start of physical activity and its intensity

ANALYSIS OF THE FRENCH DOCTRINE REGARDING THE NORMATIVE POWER OF THE OPINIONS OF THE COURT OF CASSATION

In this article I continue to research the decisions of the supreme courts, which have the constitutional role of unifying the interpretation of the law at the national level, and implicitly of the judicial practice, by studying the French legal doctrine regarding the legal nature of the notices for appeals of the Court of Cassation

Approximating the Level Curves on Pascal’s Surface

It is well-known that in general the algorithms for determining the reliability polynomial associated to a two-terminal network are computationally demanding, and even just bounding the coefficients can be taxing. Obviously, reliability polynomials can be expressed in Bernstein form, hence all the coefficients of such polynomials are fractions of the binomial coefficients. That is why we have very recently envisaged using an extension of the classical discrete Pascal’s triangle (which comprises all the binomial coefficients) to a continuous version/surface. The fact that this continuous Pascal’s surface has real values in between the binomial coefficients makes it appealing as being a mathematical concept encompassing all the coefficients of all the reliability polynomials (which are integers, as resulting from counting processes) and more. This means that, the coefficients of any reliability polynomial can be represented as discrete steps (on level curves of integer values) on Pascal’s surface. The equation of this surface was formulated by means of the gamma function, for which quite a few approximation formulas are known. Therefore, we have started by reviewing many of those results, and have used a selection of those approximations for the level curves problem on Pascal’s surface. Towards the end, we present fresh simulations supporting the claim that some of these could be quite useful, as being both (reasonably) easy to calculate as well as fairly accurate