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Over the past decade, machine learning (ML) and artificial intelligence (AI) have attracted great interest in research and various practical applications. Currently, smart, fast, and high sensitivity with excellent selectivity are becoming increasingly interesting due to the high need for environmental safety and medical applications. The main challenge is to improve sensor selectivity, which requires the combination of interdisciplinary research areas to successfully develop smart gas/chemical sensing devices with better performance. In this review, we present a few principles of gas sensing based on low-cost interdigital electrodes (IDEs), such as electrochemical, resistive, capacitive, and acoustic sensors. In addition, the most important current methods for improving gas sensing performance, the different materials, the different techniques used to fabricate IDE gas sensors, and their advantages and limitations are presented. In addition, a comparison between different ML and AI algorithms for pattern recognition and classification algorithms is also discussed. The discussion then establishes application cases of smart ML algorithms, which provide efficient data processing methods, for the design of smart gas sensors that are highly selective. In addition, the challenges and limitations of ML in gas sensor applications are critically discussed. The study shows the importance of ML with the need for structural optimization to develop and improve smart, sensitive, and selective sensors.

In this paper, a theoretical study of the single electron molecular transistor (MSET) as a gas sensor has been presented. Using the Quantum ATK simulator, density functional theory coupled with the green nonequilibrium function was applied to calculate the carrier transport properties and adsorption effects on the pentacene-based single electron transistor (C22H14-MSET). The simulation results show good sensitivity to detect ammonia equal to 58% at a concentration of 200 ppb with a detection limit of 40 ppb. The sensitivity as a function of concentration shows a linear evolution which proves that MSET sensor has a good sensitivity. The present results make the MSET sensor is a very promising candidate to detect a toxic gas at low concentration and to fabricate a small size gas sensor.

ABSTRACTIn this work, we present the different steps in the modeling and implementation of a first-order macroscopic road traffic simulation tool, using Lighthill–Whitham–Richards approach with a Godunov scheme (cell transmission model) and bounded acceleration. The origin–destination traffic movement is based on the definition of partial streams according to their chosen paths or ‘routes’ from an origin to its destination. The resolution of the traffic conflicts at the nodes level is achieved using a modified dynamic node model. The dynamic node model proposed in this work introduces a new supply function which follows from the extension of the Highway Capacity Manual (2010) model for use in dynamic and instantaneous regime. The method is then applied to reconstruct the traffic at a roundabout in the city of Sousse, Tunisia, known to be over-jammed at rush hour. The results are satisfactory and can be generalized to other intersection types with different priority levels.

A vehicle's energy consumption and emissions are two major constraints in sustainable development. Both of them have proportionally raised in recent decades with the exponential growth of world traffic demands. The reduction of road traffic-generated energy consumption and emissions have thus become unprecedentedly challenging and worth examining. This paper investigates energy consumption and environmental problems present at roundabout and signalized intersection to analyze the impact of the hybridization level's fleet and intersection type on vehicle consumption and pollution. Instantaneous fuel consumption and emission models coupled with simulation of urban mobility (SUMO) are in this study. The authors started with modeling energy consumption. Then, an emission model emissions from traffic (EMIT) was implemented to quantify vehicle emissions of CO2, CO and NOx. These models help investigate the influence of intersection type on energy consumption and environmental conditions. The authors implemented a signalized intersection and roundabout using SUMO. The input data are collected from the roundabout of Sousse (Tunisia) using video data collection. Since there is a lack of econometric models that emulate hybridized stream behavior near intersections, two energy consumption models for the roundabout and crossroad are developed using traffic flow and hybridization level as the input variables. Compared to crossroads, a roundabout can obtain more environmental improvements and substantial reductions in energy consumption and road traffic emissions.

In this paper, the results of a comparative study between experimental measurements and technology computer-aided design (TCAD) simulations of graphene field-effect transistors (GFET) are presented. Our simulations were performed to study the electrical properties of few-layer graphene, and the physical approach to the simulation tools is described by using the basics of band theory, Poisson’s equation, the continuity equation and the drift diffusion equations that are suitable for devices with small active regions. A correct formulation of the carrier density was performed to take into account the quantum capacitance. The modeled current was compared to the measured results for a prototype and was shown to be accurate and to have a predictive behavior.