This paper presents the utilization of a developed pilot wireless-based Air Quality Index (AQI) monitoring system, reporting live geo-grid resolved air quality data, for the purposes of healthy route generation and recommendation to users. The generated routes are visualized on a map and recommended to users through a specially developed web-based application, as part of the client tier of the supporting IoT platform EMULSION. A distributed computing architecture is utilized for the generation of healthy (more precisely, ‘least air pollution exposure’) routes, performed in near real-time using the dynamic Dijkstra algorithm, based on the interpolated AQI values. In addition, the fastest and shortest routes for each journey, requested by a user, are generated as well. The importance of the presented work lies within the practical applicability of the proposed method for healthy route generation, either as a stand-alone version of the software application developed for the purpose or integrated into the existing popular navigation systems and applications alike.
This legal research explores the jurisprudential aspects of smart contracts in the context of Jordanian civil legislation. Smart contracts, which are digital programs based on blockchain technology, have emerged as a disruptive force with the potential to revolutionize traditional contractual relations. They autonomously execute binding agreements by adhering to pre-programmed instructions when specific conditions are met, thereby obviating the need for intermediaries. The growing global prominence of smart contracts makes their compatibility with and implications for the legal framework in Jordan an imperative subject of inquiry. Nevertheless, the integration of smart contracts into the existing legal framework presents distinctive challenges, especially within the milieu of Jordanian civil legislation. This research paper aims to conduct a comprehensive analysis of the stance of Jordanian civil legislation regarding smart contracts. By evaluating the compatibility between smart contracts and prevailing legal structures and an exploration of potential ramifications, this study contributes to the discussion concerning the convergence of technological innovation and legal frameworks within the Jordanian context. To achieve this goal, the study utilizes a descriptive, inductive, and analytical approach. The study concluded that the implementation of smart contracts presents legal challenges related to confirming digital mutual consent, aligning legal definitions with blockchain assets, and addressing enforceability concerns associated with self-execution. However, the study puts forth a range of recommendations, with the most significant being the development of mechanisms within smart contracts to confirm the mutual consent of contracting parties through the use of digital identity verification tools and electronic signatures.
As the level of modern technology development, namely autonomous robots, drones, robotics, etc., is high, the topic under study is highly relevant. Since the level of development of modern technologies, namely autonomous robots, drones, etc., is high, the topic under study is highly relevant. Due to the use of the Simultaneous Localisation and Mapping System (SLAM) in the industrial sector, ensuring and empirically verifying its robustness under challenging conditions is essential. The study aimed to evaluate and verify the reliability of the SLAM algorithm in real conditions. The following methods were used to conduct the study: deep learning methods and recurrent neural networks. ATE and RPE metrics were used to measure the accuracy of maps and trajectories. The study revealed a relatively high stability of the developed SLAM algorithm in changing lighting conditions and dynamic objects' presence. The ATE and RPE metrics were within acceptable limits. The study's scientific novelty and originality lie in considering the real conditions during the experiment, such as different lighting and dynamic objects, which were rarely considered in previous studies. The developed algorithm will be helpful for autonomous systems and in the context of the latest advanced technologies and robotics. A promising area for further research may be improving the SLAM algorithm for use in tough conditions.
Earthquake prediction is a challenge at this time. This is because the characteristics of earthquakes are very complex and dynamic. This study aims to create a new method that integrates the Non-linear Autoregressive with Exogenous Inputs (NARX) model and 1-dimensional Convolutional Neural Network (1D-CNN) to improve the accuracy of predicting the number of earthquake events in one month. We design the NARX architecture, based on 1D-CNN, to predict earthquake time series data from three different locations in Indonesia: Sunda Strait, South Java, and Bali. The training and testing process was carried out to predict the number of earthquake events in the coming month. The testing yielded the Mean Squared Error (MSE) metric, which demonstrates the good performance of the proposed model. The MSE values for each region of the Sunda Strait, South Java, and Bali are 2.130e-05, 6.018e-02, and 2.524e-02, respectively. The Mean Arctangent Absolute Percentage Error (MAAPE) metric at the prediction stage shows high accuracy in the first month, where the model is able to predict earthquakes in the short term. This research is expected to be able to answer the challenges of earthquake prediction in the field of seismology. Future developments use other deep learning methods for earthquake prediction.
Swarm intelligence is a branch of artificial and natural intelligence that studies systems with many characters that manage their activities through distributed control and self-organisation. The field focuses on the actions of social insects such as fish schools, bird flocks, ants, termites, bees, and wasp colonies. Particle swarm optimisation (PSO) and ant colony optimisation (ACO) are two of the most common systems reported by swarm intelligence. Ant Colony Optimization (ACO) is a probabilistic method used to find optimal pathways in computationally complex situations by condensing the problem. This paper analyses the use of ACO metaheuristics to find the initial basic feasible solution in an unbalanced and balanced transportation method. Then, it compares it to other traditional methods (The least cost method, Northwest Corner method, and Vogel’s approximation method). The primary goal of this study is to provide a helpful framework for understanding new trends in applying swarm intelligence in system optimisation and implementing/using the ACO algorithm in a real-life situation. Examples were generated online. At the end of the paper, for the unbalanced transportation problem, the Least Cost method, Northwest Corner method, Vogel’s Approximation method, and ACO method gave us (472, 547, 374, and 389) as the total cost, respectively. For the balanced transportation problem, the Least Cost, Northwest Corner, Vogel’s Approximation, and ACO methods gave us (2450, 3700, 2150, and 3650) as the total cost, respectively.
