Road Side Research Articles

Impact of biomass burning and non-exhaust vehicle emissions on PM10 levels in a mid-size non-industrial western Iberian city

PM10 aerosol was measured during one semester, simultaneously, at a roadside (RS) and an urban background (UB) location in Coimbra, Portugal. On average, the mass concentrations were 36% higher at the RS, compared to UB. Application of Positive Matrix Factorisation (PMF) and Ionic Mass Balance (IMB) methodologies permitted to source apportion the aerosol mass. During the cold season, biomass burning was a prevalent particulate matter source at both urban locations, contributing with up to 30% of PM10. At the RS, vehicle non-exhaust emissions (brake, tyre and road dust) doubled exhaust emissions by combustion engines, accounting for 18–19% of PM10. Unreacted and reacted sea salt was an important fraction of PM10, principally during the warm season, when it composed approximately 25% of the aerosol. Secondary pollution by ammonium salts and carbonaceous matter was important across the seasons, especially at the UB location, where they dominated the particulate mass.

An Enhance the Performance of Mining Vehicular and Machinery Security Systems Using Artificial Intelligence in VANET Cloud Computing

Over the recent decades, incorporating Vehicular Ad-hoc Network (VANET) into Cloud computing plays a vital role, since it provides a reliable safety journey to vehicular drivers, passengers, etc. However, attaining security and emergency message dissemination is still major bottleneck in VANET combined Cloud, due to the dynamic nature of vehicles and wireless communication. Our major intention is to provide high level security in VANET-Cloud environment. In addition to it, we also reduce delay in emergency dissemination. Our proposed Delay aware Emergency Message Dissemination and Data Retrieval in secure (DEMD22RS) VANET-Cloud is composed of four sequential processes: Authentication, Clustering, Data Retrieval and Data dissemination. In regard to maintaining security for both Road Side Unit (RSU) and Vehicles, we propose Hash based Credential Authentication Scheme (HCAS) that affords authentication using Secure Hash Algorithm-3 (SHA-3) and Elliptic Curve Points (ECP). To sustain a stable cluster, Firm Aware Clustering Scheme (FACS) is pursued where Stud Krill Herd (SKH) algorithm is exploited. In the data retrieval process, cloud provides requested information to the RSU in encrypted form using the Twofish algorithm. RSU discover the path to deliver received data through executing Artificial Neural Network (ANN) algorithm. In order to diminish delay in emergency message dissemination, best disseminator is selected by cluster head using Fuzzy-Topsis (FT) algorithm. Our DEMD22RS VANET-Cloud network is implemented in Network Simulator 3 tool. Finally, the evaluation of DEMD22RS work performance is achieved by computing consequent metrics that are Throughput, Packet Delivery Ratio, Transmission delay, Average delay, Key generation time, Encryption time and Decryption time.

Assessment of potentially toxic metals adsorbed on small macroplastics in urban roadside soils in southeastern Nigeria

Plastic pollution is a worldwide problem. In an urban setting, road dust is a major source of plastics, and their presence may be harmful to the health of the inhabitants. The distribution of small macroplastics (SMaPs; > 5mm to 10 cm) and adsorbed heavy metals in road side soils in Owerri, Imo state, were evaluated in this study. Top soils (< 5 cm) were collected along the Okigwe road at designated points (Orji flyover, Amawire Junction, Hardel junction, Orji Town primary school and Orji mission). The soil was characterized for physicochemical properties using standard methods. SMaPs were quantified and identified by visual counting and Fourier transform infrared (FTIR) spectroscopy. Adsorbed heavy metals (Pb, As, Cd, Cr, Ni and Cu) following weak acid digestion (20 % aqua regia) of SMaPs were quantified by Atomic absorption spectrophotometer. Results showed that the quantity of SMaPs particle /30 g of soil ranged from Orji mission (20.2 ± 2.59 to Orji town primary school (10.4 ± 2.6). FTIR confirmed the presence of polyethylene terephthalate (PET), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), polycarbonate (PC), polyethylene (PE) and macro rubber (Ma-R), which were mainly fragments (70 %). The mean distribution of the studied metals on the SMaPs showed Pb, As, Cd, Ni, Cr and Cu was highest on PET, PP, PE (Cd and Ni) and PVC (Cr and Cu) respectively. The plastic type showed variable adsorption behavior as revealed by Principal Component Analysis and multiple linear regression analysis reveals the influence of soil properties on adsorption. The ecological risks of plastics and metals were generally high. The SMaPs may serve as vector for these metals in soil. The source of the SMaPs can be attributed to local activities that appropriate regulatory bodies should monitor.

A Comprehensive Review on Advanced Driver Assistance System

In recent years, automotive industry is experiencing an unprecedented transformation with the rise of digital technologies. While in the past, acceleration, top speed, and mechanical design were the most essential factors for purchasing an automobile, electronics and software innovations define the characteristics of the future. One among such innovations is the Advanced Driver Assistance System (ADAS). This innovation is now considered as the major drive force of the automotive domain with the intelligent electronic and software architectures. ADAS is primarily designed with an objective to assist drivers by providing an alert/automate the manual tasks in any adverse conditions. ADAS functions will also have the capability to obtain the signals from roadways, Road Side Units (RSUs) and other vehicles. This research article attempts to provide a comprehensive review on the research developments and technologies used in design, development and implementation of advanced driver assistance systems. The sections included in the proposed study describe about the different ADAS methods such as adaptive and automated cruise control, smart navigation with collision warning and avoidance system, automated vehicle parking assistance and object detection. The primary goal of this research study is to achieve a collective knowledge of ADAS operational capabilities and limitations, as well as to suggest research requirements for future investigations.

Low complexity closed‐loop strategy for mmWave communication in industrial intelligent systems

Modern communication and computing technology is the basic support of the industrial intelligent systems (IIS). As a key component of IIS, the smart port is essential to be offered low-complexity and high-reliability communication service, especially for driverless engineering vehicles. However, it is combined and nonconvex to find the optimal association between vehicles and the road side units (RSUs). Besides, due to the mobility of vehicles and the severe path loss of mmWave links, beam switching and reassociation between vehicles and RSUs are required frequently, which brings a great challenge to the communication for the IIS. A low complexity closed-loop strategy based on distributed cooperation for mmWave communication in IIS is proposed in this study, in which user association and beam tracking with the assistance of beam pools is proposed. Many-to-many user association is established based on distributed multiagent reinforcement learning, where the vehicle can independently select the set of serving RSUs based on the local observation without information exchange with others, reducing the signaling overhead and computational complexity while improving system throughput. Furthermore, multipoint-cooperation soft switching of beams based on beam tracking improves the reliability of mmWave communication with the smaller training cost. Extensive analysis and simulation results demonstrate that the proposed solution significantly reduces the complexity of the mmWave communication while improving the throughput and stability in IIS.

A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

With the rise of the Internet of Vehicles (IoV) and the number of connected vehicles increasing on the roads, Cooperative Intelligent Transportation Systems (C-ITSs) have become an important area of research. As the number of Vehicle to Vehicle (V2V) and Vehicle to Interface (V2I) communication links increases, the amount of data received and processed in the network also increases. In addition, networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient. Thus, there is a need to augment them with intelligent network intrusion detection techniques. Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times. However, given the expected large network size, there is a necessity for extensive data processing for use in such anomaly detection methods. Deep learning solutions are lucrative options as they remove the necessity for feature selection. Therefore, with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario, the need for deep learning-based techniques is all the more heightened. This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs). The proposed Deep Learning Classification Engines (DCLE) comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers. Vehicular data received by the Road Side Units (RSUs) is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper. The proposed classifiers identify 18 different vehicular behavior types, the F1-scores ranging from 95.58% to 96.75%, much higher than the existing works. By running the classifiers on testbeds emulating edge servers, the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.

A Novel Contract Theory-Based Incentive Mechanism for Cooperative Task-Offloading in Electrical Vehicular Networks

The proliferation of compute-intensive services in next-generation vehicular networks will impose an unprecedented computation demand to meet stringent latency and resource requirements. Vehicular edge or fog computing has been a widely adopted solution to enhance the computational capacity of vehicular networks; however, the computation requirements of these compute hungry applications will surpass the capabilities of such a solution. To address this challenge, the on-board resources of neighboring mobile vehicles can be utilized. However, such resource utilization requires an incentive mechanism to motivate privately owned neighboring vehicles to participate in sharing their resources. In this paper, we propose a contract theory-based incentive mechanism that maximizes the social welfare of the vehicular networks by motivating neighboring vehicles to participate in sharing their resources. The proposed approach enables the Road Side Units (RSUs) to provide appropriate rewards by offering a tailored contract to each resource sharing vehicle based on their contribution and unique characteristics. Moreover, we derive an optimal contract scheme for computational task offloading, taking into account the individual rationality and incentive-compatible constraints. Finally, we perform numerical evaluations to demonstrate the effectiveness of our proposed scheme. The proposed scheme achieves up to 28% higher computing resource utilization, 17.2% lower energy consumption per computing resource utilization, and 17.1% lesser energy consumption per task completed when compared to the linear pricing incentive baseline.

Quantified Edge Server Placement With Quantum Encoding in Internet of Vehicles

In the edge computing-supported Internet of Vehicles (IoV), the edge servers (ESs) are distributed near the road side units (RSUs) to process the transmitted data for various IoV services in real time. Generally, due to the budget constraints, the ES scale is limited. If the ESs are not appropriately placed, it may cause the unbalanced load distribution of the ESs. Therefore, how to place a constant number of ESs with the goal of avoiding the risk of overload and improving the quality of services remains a challenge. To tackle this challenge, a quantified edge server placement strategy, named QESP, is fully investigated, to improve the coverage rate, the workload balance and reduce the average waiting time of services in the IoV. Technically, binary encoding and quantum encoding are fully leveraged to model the ES locations. Then, the niched pareto genetic algorithm II (NPGA-II) and the quantum rotation gate are adopted to obtain the appropriate solutions for the ES placement problem. Furthermore, an assessment function is devised to find the optimal scheme among the solutions obtained. Finally, the validity of QESP is evaluated by using real-world vehicular big data.

Secure V2V and V2I Communication in Intelligent Transportation Using Cloudlets

Intelligent Transportation System (ITS) is a vision which offers safe, secure and smart travel experience to drivers. This futuristic plan aims to enable vehicles, roadside transportation infrastructures, pedestrian smart-phones and other devices to communicate with one another to provide safety and convenience services. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication in ITS offers ability to exchange speed, heading angle, position and other environment related conditions amongst vehicles and with surrounding smart infrastructures. In this intelligent setup, vehicles and users communicate and exchange data with random untrusted entities (like vehicles, smart traffic lights or pedestrians) whom they don't know or have met before. The concerns of location privacy and secure communication further deter the adoption of this smarter and safe transportation. In this paper, we present a secure and trusted V2V and V2I communication approach using edge infrastructures where instead of direct peer to peer communication, we introduce trusted cloudlets to authorize, check and verify the authenticity, integrity and ensure anonymity of messages exchanged in the system. Moving vehicles or road side infrastructure are dynamically connected to nearby cloudlets, where security policies can be implemented to sanitize or stop fake messages and prevent rogue vehicles to exchange messages with other vehicles. We also present a formal attribute-based model for V2V and V2I communication, called AB-ITS, along with proof of concept implementation of the proposed solution in AWS IoT platform. This cloudlet supported architecture complements direct V2V or V2I communication, and serves important use cases such as accident or ice-threat warning and other safety applications. Performance metrics of our proposed architecture are also discussed and compared with existing ITS technologies.

Blockchain-Integrated Multiagent Deep Reinforcement Learning for Securing Cooperative Adaptive Cruise Control

Connected and Autonomous Vehicles (CAVs) are an emerging solution to the issues of safe and sustainable transportation systems in the future. One major transport technology for CAVs is Cooperative Adaptive Cruise Control (CACC), for which unsignalized autonomous intersection crossing is a growing use case. CACC relies heavily on inter-vehicular communication and is thus vulnerable to message forgery and jamming attacks. Most solutions for CACC focus exclusively on enhancing efficiency or security but do not offer an integrated framework for achieving both on a large scale. In this paper, we propose a Blockchain-integrated Multi-Agent Deep Reinforcement Learning (Block-MADRL) architecture for enhancing the efficiency of CACC while cooperatively detecting attacks, reducing the fuel efficiency of identified attackers and securely notifying the overall network. Our approach uses multi-agent deep reinforcement learning to find fuel and throughput optimizing solutions for CACC and a cooperative verification mechanism based on Extended Isolation Forest (EIF) for attack detection. Attacker data is securely stored in a Road Side Unit (RSU) level blockchain, and we design a low-latency, high throughput consensus protocol for speedy and secure data dissemination. Simulation results indicate over 29.5% better lane throughput with our approach during acceleration forgery attack, up to 23% induced reduction in fuel efficiency of malicious vehicles, 17.6% higher blockchain throughput through our consensus protocol and over 8% improvement in attack detection rate compared to the state-of-the-art.

A Fluid Mechanics-Based Model to Estimate VINET Capacity in an Urban Scene

Accurate estimation of network capacity is very important for Vehicular Infrastructure-based NETwork (VINET) in an urban scene that may involve greatly dynamic typology and complex driving conditions. The node mobility, network behavior, and network scale of a VINET are different from those of a wireless network, and, therefore, the existing capacity estimation methods of wireless networks cannot be used to estimate VINET capacity. In addition, most existing studies on VINET capacity only derive asymptotic descriptions when the number of nodes is large enough. In this work, a novel approach is proposed for the modeling and calculating VINET capacity. More specifically, we first analyze communication characteristics in a VINET, and introduce two transmission modes, i.e., a vehicle-based mode and a Road Side Unit (RSU)-based one. Then, we propose a probability-based transmission mode selecting strategy with which vehicle nodes can choose either transmission mode independently and such choice is probabilistic. Next, we analyze the characteristics of an RSU-based mode, divide a VINET into a number of communities according to the position and communication range of RSUs, and derive the capacity contributed by an RSU-based mode. Then, we calculate the capacity contributed by a vehicle-based mode based on fluid mechanics. Finally, the VINET capacity can be calculated. The proposed VINET capacity estimation approach is validated to be consistent with simulation results.

Intelligent Computation Offloading for MEC-Based Cooperative Vehicle Infrastructure System: A Deep Reinforcement Learning Approach

In the cooperative vehicle infrastructure system, the road side unit (RSU) equipped with a mobile edge computing (MEC) server and sensors could provide vehicle infrastructure cooperation services for vehicles, such as optimization and cooperative driving, enhanced visibility, and so on. In view of this, the MEC server needs to fuse the sensing information from sensors on the vehicles and RSU, respectively. In the case of bad channel conditions, uploading the raw sensing information from the vehicles results in high uplink transmission latency. To deal with it, the vehicles can process the information locally and just deliver the results to the RSU. However, due to the limited computing resources on the vehicles, the processing accuracy of the raw information on the vehicles is lower than that on the MEC server. Besides, processing locally leads to higher vehicle energy consumption. Thus, in this paper, we aim to jointly optimize execution latency, processing accuracy, and energy consumption of the MEC-based cooperative vehicle infrastructure system. Firstly, we design the terminal machine learning task model and the edge machine learning task model on the vehicle side and RSU side, respectively. Then, we formulate a long-term multi-objective optimization problem. Owing to the stochastic traffic and time-varying communication conditions, we reformulate it as a Markov decision process and propose a two-stage deep reinforcement learning-based offloading and resource allocation (TDORA) strategy to determine the task offloading and the transmit power of each vehicle. Simulation results demonstrate the efficacy of the proposed strategy.

Unique Signal System for Road Traffic

Abstract: The signal system designed here is intended to raise metal rods from the underground of road when red signal is energized. This scheme is adapted to restrict the road traffic strictly. When the red light turned in to green, automatically the metal rods will be lowered to below the road level. In addition after stopping the traffic, pedestrians are allowed to cross the road, for which an automatic announcement will be made to alert them. Since it is a prototype module, basic theme is presented for single road, but for real time applications it can be implemented for all sides of the cross roads . The demo module is constructed with single signal post which contains red, green &amp; yellow signals. The main processing unit constructed with 89C2051 controller chip is programmed to energize indicators one after another automatically with specific timings. Once the system is energized, initially green indicator will be glow, after some time green indicator will be switched off and yellow indicator will be glown, and then red indicator will be energized. As long as red indicator remains in glowing condition, walking person symbol simulated with green LED’s will be flashed continuously until the green indicator glows. During red indicator on, metal rods remains above the road level, after that these rods will be lowered automatically. DC motor is used to raise and lower the metal rods moving mechanism. The moving mechanism is constructed with Rack &amp; pinion. Voice record cum playback unit designed with ISD1820 chip is used to make an auto announcement such that during red signal condition “now pedestrians can cross the road” will be announced automatically. This message will be repeated until the red signal turned off. H Bridge IC is used to drive the DC motor. Especially this system is very useful for school zones.

TRAUMATIC PHACOCELE IN 55 YEARS OLD WOMAN FROM CHENNAI, INDIA

A 55-years-old woman presented with profound sudden loss of vision in left eye (LE) after an injury in a road side accident. She had normal right eye. Left eye had perception of light (PL) with accurate projection of light (PR) in all quadrants, total hyphema, chemosis in supero-nasal conjunctiva and normal intraocular pressure (IOP). B-scan of LE showed extrusion of the lens into the supero-nasal subconjunctival space called phacocele, and vitreous hemorrhage. Wound exploration was done with removal of expelled lens and the scleral wound was sutured with 6/0 vicryl, followed by 25G pars plana vitrectomy with fluid gas exchange. Secondary IOL implantation is planned in next visit.

Accident Data Analysis Using Machine Learning

Abstract: The main objective of this project is toanalyze the road side accidents by scrutinizing accident-prone or hotspot areas and their root causes. Accidents through roadways have been a greatthreat to developed as well asunderdeveloped countries. Roadaccidents and its safety have been a major concern for the world, and everyone is trying to handle this since years. Road traffic and reckless drivingoccur in every part of the world. Because of this, many pedestrians are affected too. With no fault, they become victims. Many road accidents occur because of numerous factors likeatmospheric changes, sharp curves, andhuman faults. Injuries caused by road accidents are major but sometimes imperceptible, which later on affect health too. This study aims to analyze road accidents in one of the popular metropolitan cities, i.e., Bengaluru,through Linear Regression, Polynomial Regression, Decision Tree Regressor, Support Vector Regressor, Random Forest Regressor algorithms and machine learning by scrutinizing accident-prone or hotspot areas and their root causes.

Literature Review on Fabrication of Battery-Operated Motorized Screw Jack

Abstract: Road side emergencies such as tyre punch, is a problem commonly observed in cars. Conventional car jacks uses mechanical advantage to allow a human to lift a vehicle by manual force. This paper is a modification of the current toggle jack by incorporating a servomotor whose motion as well as torque is transmitted via gear trains to the power screw, in order to make load lifting easier for emergency use with the aid of a 12V car battery as power source. Gear ratio is used to increase the lifting power. The significance and purpose of this work is to modify the existing car jack in order to make the operation easier, safer and more reliable in order to save individual internal energy and reduce health risks especially back ache problems associated with doing work in a bent or squatting position for a long period of time. Fabrication work has been done using milling, drilling, grinding, and welding machines. The developed car jack is tested on 1400kg Toyota Camry car. Loads were increased at intervals of 50 Kg using bags of cement and it was observed that, deformation of threads began at loads beyond 1550 Kg. this implies that, the electric powered jack mechanism is capable of liftingvehicles with maximum load of 1550 Kg.

Computational Soliton Modulated Nonlinear Microring Add-drop Multiplexer and Potential Applications

This paper proposed the design of the multiplexer node for the free-space transmission link. Various soliton modulated signal categories are numerically studied. The proposed system is a modified micro-optical add-drop multiplexer. The cross phase modulated signals induced into the main ring by the two side rings can give more signal complexity, which can lead to having the securely transmitted signal requirement. The high optical power obtained from the soliton aspect can give long-distance communication, where the required frequency band can be improved and suitable for the required applications. For an instant, the use for the side road connection of the vehicle-to-everything (V2X) can be implemented in either cable or wireless(free-space) connection. The results obtained have shown promising applications such as free-space transmission link in both above and underground nodes, quantum cryptography, and communication security based on nonlinear aspects. Moreover, the electro-optic conversion can also be used by the stacked layers of silicon-graphene-gold, in which the signal conversion can be applied suitably.

PENGARUH KEGIATAN SEKTOR EKONOMI TERHADAP KINERJA LALU LINTAS DI RUAS JALAN KELURAHAN VIM DISTRIK ABEPURA (Studi Kasus: Ruas Jalan Perkutut, Mako Brimob – Kantor Pos Kotaraja)

Economic activity becomes a benchmark in the assessment of the community to achieve prosperity, and becomes a supporter and driver of growth in the surrounding area. Vim Village, Abepura District is one of the service activity centers that has a land use that is so complex and strategically located that it triggers various forms of economic activity, becoming an attraction for people to travel for transportation, especially along Jalan Perkutut. This study examines the relationship of micro-economic activities with traffic performance obtained from side barriers, vehicle volume and road capacity. The research methods used to achieve the objectives are quantitative methods and qualitative methods. The quantitative method uses the analysis of the Indonesian Road Capacity Manual (MKJI, 1997) to obtain vehicle volume, road capacity and traffic performance. The qualitative method uses descriptive analysis that discusses the comparison of the distribution of micro-economic activities that affect side barriers, the comparison of two traffic performance results and spatial planning plans along the Perkutut road.&#x0D; The results showed that the increase in the number of micro-economic activities at night along Jalan Perkutut had an effect on increasing the number of side barriers and the volume of vehicles. In addition, the imbalance in the volume of vehicles and road capacity shows that Perkutut Road traffic performance is experiencing congestion as indicated by the Service Level Index (ITP) with a value of F. Under these conditions, spatial planning for the road network, pedestrian pedestrian arrangement and land use arrangement is needed, especially on microeconomic activities.

EFFECT OF SOCIAL MARKETING ON SUSTAINABLE BEHAVIOUR TOWARDS HOUSEHOLD WASTE DISPOSAL IN LAGOS STATE, NIGERIA

The study seeks to determine the influence of social marketing on sustainable behaviour in the area of household waste disposal in Lagos State, Nigeria. This study focuses on four dimensions of social marketing namely: social product, social price, social promotions and social place. Descriptive research design was employed using cross-sectional survey methods for data collection. Three hundred and ninety one (391) respondents were used for the study. Four research questions were raised and answered through corresponding hypotheses. Data were analyzed using descriptive and inferential statistics. Findings indicated a convincing relationship between sustainable behaviour and social marketing particularly the elements of price and place; and by extension, implications for improvement is that decrease in price associated with waste disposal billings might cause individual household to dispose waste appropriately and more importantly regular maintenance of waste collection site in other to discourage road side waste disposal.

Cyclist and pedestrian trust in automated vehicles: An on-road and simulator trial

Automated vehicles (AVs) need to be trusted by cyclists and pedestrians where they will share the road. To test trust, cyclists and pedestrians, and a comparison cohort of drivers, observed trials of both a road and simulator AV undertaking three common priority-based maneuvers: a right turn into a side road, overtaking a parked car, and passing over a pedestrian priority (zebra) crossing. The AV made the maneuvers either giving way, or not giving way, to a pedestrian or cyclist. One hundred and thirty-four participants aged 18 to 79 years were recruited based on being predominantly either a pedestrian, cyclist, or driver in their regular road use. For the on-road trials, the cyclist and pedestrian participants observed the AV maneuvers from the adjacent footway, and the driver participants observed from inside the AV. In the simulation environment, all participants were inside the automated vehicle. Trust scores were higher when participants observed a maneuver where the AV had to give way to a cyclist, and this can be linked with the re-assurance provided by the behavior of the AV in such an encounter. There was no significant difference in trust by road user type (cyclist, pedestrian or driver), age or driving experience, suggesting messaging to road users about the impacts of automated vehicles need not be differentiated by road user type. There was some evidence of differences in trust, especially for more complex maneuvers, between the on-road trial and the simulator, suggesting a need for caution in reliance on simulation-only experiments.