Reprogrammable reflection-transmission integrated coding metasurface for real-time terahertz wavefront manipulations in full-space.

In recent years, there has been notable advancement in programmable metasurfaces, primarily attributed to their cost-effectiveness and capacity to manipulate electromagnetic (EM) waves. Nevertheless, a significant limitation of numerous available metasurfaces is their capability to influence wavefronts only in reflection mode or transmission mode, thus catering to only half of the spatial coverage. To the best of our knowledge and for the first time, a novel graphene-assisted reprogrammable metasurface that offers the unprecedented capability to independently and concurrently manipulate EM waves within both half-spaces has been introduced in the THz frequency band. This intelligent programmable metasurface achieves wavefront control in reflection mode, transmission mode, and the concurrent reflection-transmission mode, all within the same polarization and frequency channel. The meta-atom is constructed with two graphene sections, enabling straightforward modification of wave behavior by adjusting the chemical potential distribution within each graphene segment via an external electronic source. The proposed functionalities encompass various programmable modes, including single and dual beam control in reflection mode, dual beam control in transmission mode, simultaneous control of dual beams in reflection mode-direct transmission, and vice versa, and control of beam steering in reflection mode-dual beams in transmission mode simultaneously. The proposed metasurface is expected to be reprogrammable due to wavefront manipulation in both half-spaces separately and continuously for various applications such as imaging systems, encryption, miniaturized systems, and next-generation wireless intelligent communications.

A reconfigurable graphene patch antenna inverse design at terahertz frequencies.

This article investigates the inverse design of a reconfigurable multi-band patch antenna based on graphene for terahertz applications to operate frequency range (2-5THz). In the first step, this article evaluates the dependence of the antenna radiation characteristics on its geometric parameters and the graphene properties. The simulation results show that it is possible to achieve up to 8.8 dB gain, 13 frequency bands, and 360[Formula: see text] beam steering. Then and due to the complexity of the design of graphene antenna, a deep neural network (DNN) is used to predict the antenna parameters by given inputs like desired realized gain, main lobe direction, half power beam width, and return loss in each resonance frequency. The trained DNN model predicts almost with 93% accuracy and 3% mean square error in the shortest time. Then, this network was used to design five-band and three-band antennas, and it has been shown that the desired antenna parameters are achieved with negligible errors. Therefore, the proposed antenna finds many potential applications in the THz frequency band.

A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties.

A mature and hybrid machine-learning model is verified by mature empirical analysis to measure county-level COVID-19 vulnerability and track the impact of the imposition of pandemic control policies in the U.S. A total of 30 county-level social, economic, and medical variables and a timeline of the imposed policies constitutes a COVID-19 database. A hybrid feature-selection model composed of four machine-learning algorithms is developed to emphasize the regional impact of community features on the case fatality rate (CFR). A COVID-19 vulnerability index (COVULin) is proposed to measure the county's vulnerability, the effects of model's parameters on mortality, and the efficiency of control policies. The results showed that the dense counties in which minority groups represent more than 45% of the population and those with poverty rates greater than 24% were the most vulnerable counties during the first and the last pandemic peaks, respectively. Highly-correlated CFR and COVULin scores indicated a close agreement between the model outcomes and COVID-19 impacts. Counties with higher poverty and uninsured rates were the most resistant to government intervention. It is anticipated that the proposed model can play an essential role in identifying vulnerable communities and help reduce damages during long-term alike disasters.

Multi-method simulation modelling of circular manufacturing systems for enhanced decision-making.

Circular manufacturing systems (CMS) constitute complex value networks comprising a large and diverse set of stakeholders that collaborate to close the loop of products through multiple lifecycles. Complex systems modelling and simulation play a crucial role in providing quantitative and qualitative insights into the behaviour of such systems. In particular, multi-method simulation modelling that combines agent-based, discrete-event, and system dynamics simulation methods is considered more suitable to model and simulate CMS as it allows to capture their complex and dynamic nature. This paper provides a step-by-step approach on how to build a CMS multi-method simulation model in order to assess their economic, environmental, and technical performance for enhanced decision-making. To model and simulate CMS three main elements need to be considered:•A multi-method model architecture where the CMS stakeholders with heterogeneous characteristics are modelled individually as autonomous agents using agent-based, discrete-event, and system dynamics.•An agent environment defined by a Geographic Information System (GIS) to establish connections based on agents' geographic location.•The product journey resulting from the product's interaction with various CMS stakeholders in the circular value network is traced throughout its multiple lifecycles.

Evaluation of Safety in Horizontal Curves of Roads Using a Multi-Body Dynamic Simulation Process.

Road transportation poses one of the significant public health risks. Several contributors and factors strongly link public health and road safety. The design and advancement of higher-quality roads can significantly contribute to safer roads and save lives. In this article, the safety aspect of the roads' horizontal curves under the standard of the American Association of State Highway Transportation Officials (AASHTO) is evaluated. Several factors, including vehicle weight, vehicle dimensions, longitudinal grades, and vehicle speed in the geometric design of the horizontal curves, are investigated through a multi-body dynamic simulation process. According to the AASHTO, a combination of simple circular and clothoid transition curves with various longitudinal upgrades and downgrades was designed. Three vehicles were used in this simulation, including a sedan, a bus, and a 3-axle truck. The analysis was based on the lateral friction between the tire and the pavement and also the safety margin parameter. The results showed that designers must differentiate between light and heavy vehicles, especially in curves with a high radius. Evaluation of longitudinal grade impacts indicated that the safety margin decreases when the vehicle is entering the curve. Safety margin reduction on the clothoid curve takes place with a lower grade toward the simple circular curve. By increasing the speed, the difference between lateral friction demand obtained from simulation and lateral friction demand proposed by AASHTO grows. The proposed novel methodology can be used for evaluating road safety.

Improving performances of the emergency department using discrete event simulation, DEA and the MADM methods.

This article presents a method by which performances at an emergency department (ED) in a large hospital in Iran could be improved, where the long waiting times and unbalanced utilization create problems for patients and ED staff. This method firstly simulates patient flow in the ED and finds bottlenecks that cause inefficiency in ED performance. In the simulation model, patient arrival is assumed to be non-homogenous and the operation of medical tests such as MRI, CT scan, pathology testing, laboratory testing, ultrasonography, and radiology are detailed and virtual queues of patients' specimens are considered separately from patient queues. Based on the simulation reports of the current situation and target criteria, what-if scenarios were used to design scenarios that could improve ED performance. This method used the data envelopment method (DEA) to determine efficient scenarios, analytic hierarchy process (AHP) to specify the weight of each criterion, the Delphi method to specify suitable utilization rates for various resources, and the extended Vlsekriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) method to compare data on 95% confidence intervals from efficient scenarios and to rank scenarios by considering conflicting criteria. Implementing the first scenario in the ranking would reduce acute patients' overall waiting time by approximately 5%, and it doesn't require any additional investments.