Agora University Editing House: Journals
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Dynamic Traffic Light System to Reduce The Waiting Time of Emergency Vehicles at Intersections within IoT Environment
Full text linkTraditional traffic light system, which works based on fixed cycle can be a main reason for traffic jam, due to lack of adaptation to road conditions. Traffic jam has a bad impact on drivers and road users due to the time delay it causes for road users to reach their destinations. This delay can cause a life threat in case of emergency vehicles, such as ambulance vehicles and police cars. One key solution to solve traffic jam on intersections is the dynamic traffic lights, where traffic light operation adapts based on the intersection traffic conditions. Since few of researches projects in the literature interested in solving traffic jam problem for emergency vehicles, the contribution of this paper is to introduces a novel approach to operate traffic light system. The new approach consists of two algorithms which are pure operation mode and hybrid operation mode. These operation modes aim to reduce the waiting time of emergency vehicles on traffic intersections. They assume that there is a smart infrastructure system uses Internet of Things (IoT) that can detect emergency vehicles arrival to an intersection. The smart infrastructure system switches traffic light operation from fixed cycle mode to dynamic mode. The dynamic mode manages traffic lights at intersections to reduce the waiting time of emergency vehicles. The paper presents a simulation of the proposed algorithms, highlights their advantages. In order to evaluate the efficiency of the new technique, we compared our approach with Wen algorithm in the literature and the Traditional traffic light system. Our evaluation study indicated that the proposed algorithms outperformed Wen technique and the Traditional system under different traffic scenario
Survey of Cubic Fibonacci Identities When Cuboids Carry Weight
Full text linkThe aim of this paper is to present a comprehensive survey of cubic Fibonacci identities, trying to uncover as many as possible. From the outset, our rationale for a very careful search on an apparently obscure problem was not only a matter of mathematical curiosity, but also motivated by a quest for 3D Fibonacci spirals.
As we were not able to find any survey on the particular topic of cubic Fibonacci identities we decided to try to fill this void. We started by surveying many Fibonacci identities and recording cubic ones. Obviously, tracing all Fibonacci identities (for identifying a handful) is a daunting task. Checking several hundred we have realized that it is not always clear who the author is. The reason is that in many cases an identity was stated in one article (sometimes without a proof, e.g., as an open problem, or a conjecture) while later being proven in another article, or effectively rediscovered independently by other authors. However, we have done our best to present the identities chronologically. We have supplied our own proof for one identity, having tried, but failed, to find a published proof. For all the other identities, we either proved them on a computer, or else verified by hand their original published proofs. Somehow unexpectedly, our investigations have revealed only a rather small number of cubic Fibonacci identities, representing a tiny fraction of all published Fibonacci identities (most of which are linear or quadratic). Finally, out of these, only a handful of cubic Fibonacci identities are homogeneous
Covid-19 Patients' Hospital Occupancy Prediction During the Recent Omicron Wave via some Recurrent Deep Learning Architectures
Full text linkThis paper described a suggested model to predict bed occupancy for Covid-19 patients by country during the rapid spread of the Omicron variant. This model can be used to make decisions on the introduction or alleviation of restrictive measures and on the prediction of oxygen and health human resource requirements. To predict Covid-19 hospital occupancy, we tested some recurrent deep learning architectures. To train the model, we referred to Covid-19 hospital occupancy data from 15 countries whose curves started their regressions during January 2022. The studied period covers the month of December 2021 and the beginning of January 2022, which represents the period of strong contagion of the omicron variant around the world. The evolution sequences of hospital occupancy, vaccination percentages and median ages of populations were used to train our model. The results are very promising which could help to better manage the current pandemic peak
An Efficient Approach towards Network Routing using Genetic Algorithm
Full text linkThe network field has been very popular in recent times and has aroused much of the attention of researchers. The network must keep working with the varying infrastructure and must adapt to rapid topology changes. Graphical representation of the networks with a series of edges varying over time can help in analysis and study. This paper presents a novel adaptive and dynamic network routing algorithm based on a Regenerate Genetic Algorithm (RGA) with the analysis of network delays. With the help of RGA at least a very good path, if not the shortest one, can be found starting from the origin and leading to a destination. Many algorithms are devised to solve the shortest path (SP) problem for example Dijkstra algorithm which can solve polynomial SP problems. These are equally effective in wired as well as wireless networks with fixed infrastructure. But the same algorithms offer exponential computational complexity in dealing with the real-time communication for rapidly changing network topologies. The proposed genetic algorithm (GA) provides more efficient and dynamic solutions despite changes in network topology, network change, link or node deletion from the network, and the network volume (with numerous routes)
Evolutionary Computation Paradigm to Determine Deep Neural Networks Architectures
Full text linkImage 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 Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning
Full text linkThe resource optimization of ultra-dense networks (UDNs) is critical to meet the huge demand of users for wireless data traffic. But the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. This paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (DRL), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep Q-learning process. To effectively allocate resources in UDNs, the DRL algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep Q-network (DQN), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the Q value. Simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. Thus, it is very meaningful to introduce the DRL to the research of UDN resource allocation
A Boundary Determined Neural Model for Relation Extraction
Full text linkExisting models extract entity relations only after two entity spans have been precisely extracted that influenced the performance of relation extraction. Compared with recognizing entity spans, because the boundary has a small granularity and a less ambiguity, it can be detected precisely and incorporated to learn better representation. Motivated by the strengths of boundary, we propose a boundary determined neural (BDN) model, which leverages boundaries as task-related cues to predict the relation labels. Our model can predict high-quality relation instance via the pairs of boundaries, which can relieve error propagation problem. Moreover, our model fuses with boundary-relevant information encoding to represent distributed representation to improve the ability of capturing semantic and dependency information, which can increase the discriminability of neural network. Experiments show that our model achieves state-of-the-art performances on ACE05 corpus
DEFENDING FAMILY LAW VALUES VIA CRIMINAL LAW NORMS
Full text linkThe economic and social challenges of the modern society necessarily imply the existence of a well-trained human resource, able to efficiently manage the various tasks related to the workplace, but also to efficiently cope with the evolution (revolution) of the informatics.In this respect, at present, each European state seeks, through various specific policies, to develop a high social capital capable of successfully fulfilling these goals.Thus, the protection of the family, as a matrix of the primary development of everyone, but also of the values related to it, has a primordial role in this context. The protection mechanisms are diverse, being regulated by both the rules of public and private law.In the following we have proposed to review the criminal law norms that protect certain values of family law, such as the crime of abandoning the family (art. 378 Criminal Law), and the offense of preventing access to mandatory general education (art. 380 Criminal Law)
CRIMINAL MEANS TO COMBAT DOMESTIC VIOLENCE — GUARANTOR OF THE PERSON’S SAFETY. STATE OF THE ART AND PERSPECTIVES
Full text linkThe present work is part of the research steps dedicated to the writing of my PhD thesis on the topic of combating domestic violence by criminal means, dealing mainly with the effect of criminal instruments on the safety of victims. The latter is an important social problem, especially in the current context of spending longer periods of time at home, which, together with a relatively more difficult access to justice, has led all over the world to an escalation of domestic violence.Specifically, I will briefly deal with the presentation of the current legislative framework, insisting on the elements of criminal legislation and I will highlight the weaknesses in relation to guaranteeing the safety of the person, respectively, I will advance proposals for harmonization and improvement of the legislative framework.The conclusions of the study present the neuralgic elements of the criminal legislation, regarding the protection of the life and integrity of the victims of domestic violence, as well as the changes that I consider appropriate for improving the legal intervention in the field
MIGRANTS SMUGGLING EUROPEAN PHENOMENON OR CRIME?
Full text linkBecause, in the context of the crisis of illegal migration in Europe, the notion of migrants smuggling is increasingly used, especially in the field of judicial cooperation in criminal matters between romanian and other member states judicial authorities, we intend to determine whether this term is regulated as an independent crime or whether it is just the name of a new phenomenon at european level. At the same time, we try cu establish whether the notion of migrants smuggling is part of smuggling or migrants trafficking crime