Simulation Modeling Tools Research Articles

Development of 1 ×1 km gridded emission inventory for air quality assessment and mitigation strategies from open biomass burning in Karnataka, India

In this work, we have developed a high-resolution (1 ×1 km grid) emission inventory of greenhouse gases and air pollutants from open biomass burning (OBB) in Karnataka for 2000, 2005, 2010, 2015, 2020, and 2022. Secondary and field datasets were used to validate the emission inventories. The GIS-based statistical model and activity data were used to estimate greenhouse and air pollutant gas emissions from OBB (Forest, grassland, and crop fires). The uncertainty analysis of the emissions were done using the Monte Carlo Simulation (MCS) model and Low Emission Analysis Platform (LEAP) tool was used for projecting the emissions to 2050 based on the annual average growth rate. According to our results, the total OBB burned areas (%) in Karnataka during the aforementioned years were 24.43, 15.69, 15.95, 11.41, 26.73, and 5.78. The results showed that total annual average OBB emissions in Karnataka for 2000–2022 in Gg were SO2 (6.67), NOx (9.48), NH3(9.80), CO (670.12), OC (59.78), BC (5.09), CO2 (11071 .11), CH4(33.68), PM10(84.29), PM2.5(81.08), NMVOC (74.13), and NO2 (4.80) respectively. The districts of Kalburgi, Raichur, Bagalkot, Uttara Kannada, and Shivamogga have the highest emissions from OBB in Karnataka from 2000 to 2022. The emission uncertainty results showed ±26.92, ±28.17, and ±30.27 % for the crop, grassland, and forest fires, respectively. Under the business-as-usual (BAU) scenarios, the CH4 emissions from forest, grassland, and crop fires are projected at 8823, 2.64, and 5262 metric tonnes by 2030 and 58046, 10, and 30700 metric tonnes by 2050, respectively. The developed high-resolution inventories are the most up-to-date surface emission datasets with hotspots in Karnataka from OBB emission. National Clean Air Programme (NCAP) and India’s Net-Zero Emission target by 2070, our high-resolution emission inventories and mitigation strategies may be helpful for air quality monitoring, health benefits analysis, and policy-making studies in Karnataka, India.

Generative artificial intelligence of things systems, multisensory immersive extended reality technologies, and algorithmic big data simulation and modelling tools in digital twin industrial metaverse

Research background: Multi-modal synthetic data fusion and analysis, simulation and modelling technologies, and virtual environmental and location sensors shape the industrial metaverse. Visual digital twins, smart manufacturing and sensory data mining techniques, 3D digital twin simulation modelling and predictive maintenance tools, big data and mobile location analytics, and cloud-connected and spatial computing devices further immersive virtual spaces, decentralized 3D digital worlds, synthetic reality spaces, and the industrial metaverse. Purpose of the article: We aim to show that big data computing and extended cognitive systems, 3D computer vision-based production and cognitive neuro-engineering technologies, and synthetic data interoperability improve artificial intelligence-based digital twin industrial metaverse and hyper-immersive simulated environments. Geolocation data mining and tracking tools, image processing computational and robot motion algorithms, and digital twin and virtual immersive technologies shape the economic and business management of extended reality environments and the industrial metaverse. Methods: Quality tools: AMSTAR, BIBOT, CASP, Catchii, R package and Shiny app citationchaser, DistillerSR, JBI SUMARI, Litstream, Nested Knowledge, Rayyan, and Systematic Review Accelerator. Search period: April 2024. Search terms: “digital twin industrial metaverse” + “artificial Intelligence of Things systems”, “multisensory immersive extended reality technologies”, and “algorithmic big data simulation and modelling tools”. Selected sources: 114 out of 336. Published research inspected: 2022–2024. PRISMA was the reporting quality assessment tool. Dimensions and VOSviewer were deployed as data visualization tools. Findings & value added: Simulated augmented reality and multi-sensory tracking technologies, explainable artificial intelligence-based decision support and cloud-based robotic cooperation systems, and ambient intelligence and deep learning-based predictive analytics modelling tools are instrumental in augmented reality environments and in the industrial metaverse. The economic and business management of the industrial metaverse necessitates connected enterprise production and big data computing systems, simulation and modelling technologies, and virtual reality-embedded digital twins.

On the importance of mooring cable dynamics in response analysis of submerged floating tunnel using quasi-static and fully coupled dynamic models

Submerged floating tunnels are considered an economical alternative to those conventional bridges in crossing deep and long-span water bodies. The development of accurate modeling methods and simulation tools is the primary requirement for the safe design of submerged floating tunnels. Mooring cables are the primary source of stability of submerged floating tunnels against hydrodynamic loadings, and are commonly modelled using the computationally efficient quasi-static catenary approach. The dynamics of mooring cables can be of significant importance in dynamic responses of the submerged floating tunnel. Therefore, in this study, a discrete catenary element is presented with the aim of its implementation in the fully coupled dynamic analysis of submerged floating tunnels. To study the effect of mooring cable dynamics, the prototype of Qiandao Lake submerged floating tunnel has been used in a comparative study between quasi-static and fully coupled dynamic models. The numerical examples show differences in displacements and cable tensions due to the mooring cable dynamics. Furthermore, the mooring cables dynamics have significant effect on the tunnel shear forces and bending moments. The quasi-static coupled modeling significantly underestimates the tunnel internal forces.

Development and verification of a discrete event simulation tool for high-fidelity modelling of offshore wind and solar farm decommissioning campaigns

The number of offshore wind turbines to be decommissioned is expected to grow in the next decade but some of the oldest wind farms in the North Sea region are already approaching the end of their service lives. This means that several detailed decommissioning plans will need to be prepared in the coming 2-5 years. So far, only small-scale wind farms consisting of relatively small turbines located in shallow, near-shore, or confined waters have been decommissioned. The limited experience and data available yield high uncertainty in the estimation of costs, duration, and environmental footprint of upcoming offshore wind farm decommissioning campaigns. High-fidelity logistic simulation tools as well as high-level cost models are both needed to support end-of-life decision-making. While several holistic cost models have been proposed, the literature lacks high-fidelity models to support the detailed planning and execution of offshore wind farm decommissioning projects. Given that offshore operations are the greatest source of costs, emissions, and safety risks in decommissioning campaigns, these require special attention when seeking optimization opportunities. In this work, a high-fidelity logistic simulation module for the decommissioning of offshore wind and solar farms has been developed within the discrete event simulation platform UWiSE. The new UWiSE Decommission module enables detailed assessment of alternative decommissioning scenarios taking into account offshore operation complexities like weather and resource dependencies, which are not included or heavily approximated in holistic cost models. The new tool has been verified against publicly available analytical cost model results from recent literature. A short assessment of the impact of weather delays on cost and duration estimates for the considered offshore wind farm decommissioning campaign has also been performed as an example of how UWiSE Decommission may bring value to the planning of offshore decommissioning projects.

DESIGN AND IN SILICO VALIDATION OF VITAMIN B5 AND ITS DERIVATIVES AS A POTENTIAL TARGET AGAINST CYCLOPHILIN A (CyPA)

The title molecule (N-(2,4-dihydroxy-3,3-dimethylbutyryl)b-alanine), DDBBA and its derivatives were selected for theoretical investigations viz geometry optimization, ADME profiling, binding affinity using quantum-mechanical calculations and modelling simulation tools. Geometry optimization by Gaussian 09 program revealed the stability and electrophilic nature of the investigated molecules. In order to depict the charge density distributions that may be related to biological activity, the contour maps of HOMO-LUMO as well as the associated chemical descriptors such as chemical potential (µ), electronegativity (χ), electrophilicity (ω), hardness (η) and softness (σ) were explored. Utilizing molecular docking, the antiviral, antibacterial, and anticancer activities was examined. The docked molecules showed strong propensity for binding to 2HQ6 cancer protein active sites. Vit B5-CH=CF2 and VitB5-CCl3 showed lowest binding energies (-5.861 and -5.478 kcal/mol) and low inhibition constant values (1.43 M). Studies on the (NBO) natural bond orbital, the Mulliken population, and the Fukui function were all analyzed. Further, the interactions between the derivatives and other molecules were studied using Hirshfeld surface analysis.

Development of a provably secure and privacy-preserving lightweight authentication scheme for roaming services in global mobility network

The accelerated development of Internet of Things services surfaced the way for providing uninterrupted and adaptable smart mobility services for mobile clients to communicate without any zone or time constraints. The global mobility network is an appropriate contender to be adopted as a communication platform to offer roaming services for swift data exchange and fast movement of mobile users in smart mobility. Nevertheless, the confidentiality and secrecy of the exchanged messages have become major concerns due to the insecure nature of the public communication channel. Additionally, it is worth monitoring that enhanced security measures must not increase the burden on limited-powered, small computationally efficient mobile devices. To fulfill these purposes, multiple authentication and key agreement protocols in the context of roaming services have been proposed in recent days. This research demonstrates that most of the previous authentication schemes for the GLOMONET environment have failed to achieve the aforesaid secrecy goals. To satisfy the purposes, this article has proposed a mutual authentication and session key negotiation scheme for roaming services that resists all security susceptibilities and provides essential requirements like anonymity with minimized computation and communication overhead. To support our claim, the security of the proposed scheme is formally verified using the well-known random oracle model and aProVerif simulation tool. Moreover, the implementation of the protocol using the broadly acknowledged NS-2 simulation tool visualizes the practical efficiency of the protocol.

A framework for assessing the impacts of land-use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale.

Irrigation is used extensively to enhance grain production and ensure food security. Many studies have used crop models and global climate models to study the variation of irrigated crop yield in the context of climate change. But most considered the influence of direct climate change but neglected the influence of irrigation water availability, which is affected by land-use/cover change (LUCC) and indirect climate change, on irrigated crop yield. This study therefore developed a framework including Patch-generating Land Use Simulation model, Soil and Water Assessment Tool, Agricultural Production Systems sImulator Model, and global climate models for exploring the impacts of LUCC, direct climate change, and indirect climate change on wheat yield in a typical watershed. Both LUCC and climate change caused increased runoff from October to May, and thus increased the irrigation water availability, by 51.6 and 30.7 mm per growing season under 1.5 and 2.0 °C warming, respectively. The combined influence of LUCC, direct, and indirect climate change increased wheat yield by about 18.5% and 15.5% in the context of 1.5 and 2.0 °C warming, respectively. The relative contribution of LUCC, indirect climate change and direct climate change to yield was 4.7%, 41.2%, and 54.1% under 1.5 °C warming, and 13.1%, 28.7%, and 58.2% under 2.0 °C warming, respectively. We suggest that changes in irrigation water availability should be considered from a watershed perspective when simulating the influence of climate change on crop yield, especially regional crop production estimation. © 2023 Society of Chemical Industry.

Simulation modeling of infrastructure objects as a management tool of intelligent transport system

Nowadays, the formation of intelligent transportation systems is taking place, allowing to increase the efficiency of transportation through the use of innovative technologies. Methods and means of simulation modeling have gained considerable popularity among them, allowing to increase the accuracy of forecasting and efficiency of management decisionmaking. The purpose of this article is to develop the directions of application of simulation modeling in the field of passenger transport infrastructure management. The article presents the results of applied research using simulation modeling tools of passenger transport infrastructure objects. Based on these results, the effects of using simulation modeling methods in the management of passenger transport system are systematized. In order to achieve the research goal, a review of scientific works was conducted, the areas of application of simulation modeling in the management of passenger transport infrastructure were specified, the effects of using simulation modeling methods and tools were analyzed and generalized. The results of the research can be useful to transport organizations, state regulation bodies on transport and organizers of transportation. They can be used in the construction of scenarios for the development of transport infrastructure objects to improve the quality of service to the population and to ensure the effectiveness of management decisions on transport.

Simuleau: a tool for hybrid and batches Petri nets

Simuleau is a modeling, analysis and simulation tool for discrete event models expressed by batches Petri nets, a formalism that enriches hybrid Petri nets of David and Alla. Batches PNs incorporate timed-transition discrete PNs, constant continuous PNs, and new types of nodes called batch nodes. This formalism and Simuleau have been exploited among diverse application areas such as manufacturing systems, communication networks, and traffic road. After presenting batches PN formalisms and their application domains, this paper focuses on the main characteristics of Simuleau, including the tool structure, its menu interface, the input model description and the outputs obtained after simulation.

ИМИТАЦИОННОЕ МОДЕЛИРОВАНИЕ БИЗНЕС-ПРОЦЕССОВ В УСЛОВИЯХ ЦИФРОВОЙ ТРАНСФОРМАЦИИ ЭКОНОМИКИ

The article is devoted to the problem of teaching skills of simulation modeling of business processes of an enterprise in the conditions of its digital transformation. An example of a training case shows the possibilities of using simulation modeling tools to visualize and quantify the results of a university admissions campaign. The direction of further research on the development of methodological materials for the development of digital twins, taking into account simulation models of business processes of the enterprise, is outlined.

IMPROVING STRATEGIC MARKETING PLANNING IN THE FORMATION OF DEVELOPMENT PRIORITIES FOR MONOTOWNS IN THE REPUBLIC OF KAZAKHSTAN

The importance of the research is highlighted by the special role of monotowns, which are closely monitored by the government as they face growing socio-economic issues while also playing a crucial role in the country’s economy. This article aims to develop and test a methodological approach to strategic marketing planning for establishing development priorities for monotowns in Kazakhstan. The study presents monotowns analyzed over different periods: 27, 20 and 9 monotowns of Kazakhstan. Current and future priorities for 9 monotowns are defined based on available statistical socio-economic indicators grouped by areas of territorial marketing. The research objectives include developing a meth-odological approach to strategic marketing planning, conducting typology, identifying the advantages and disadvantages of monotown development, creating a simulation modeling tool for socio-economic development using artificial intelligence technology, and determing strategic priorities development of monotowns. Data collection, processing, and analysis methods include content, comparative, factor, and cluster analysis, typology, and artificial intelligence modeling methods (supervised learning, er-ror correction, BFGS optimization). The STATISTICA software version 13 was utilized for data analysis and forecasting. The results indicate that the strategic marketing planning tools developed are useful for establishing scientifically grounded development priorities for monotowns in Kazakhstan. This re-search is beneficial for government bodies aiming to devise socio-economic development strategies for monotowns.

Tasarım optimizasyonu ile geliştirilmiş MEMS mikrofon frekans yanıtı

Microphone main characteristic is to faithfully detect and transform incoming acoustic signal in electric one. Semiconductor based capacitive MEMS microphones, despite their limited dimensions, offer remarkable performances (frequency response, SNR). The purpose of this article is to introduce some design optimizations in the current SDM (Sealed-Dual-Membrane) capacitive MEMS microphone mainly concerning the position of the ventilation hole. The effects of the suggested modifications on the microphone’s performances were evaluated using Lumped Model simulation tool. A clear improvement in the device frequency response within audio-band was obtained even if a less significant improvement in the general performances of microphone (SNR) was achieved.

A smart contract-based robotic surgery authentication system for healthcare using 6G-Tactile Internet

An intelligent robotic surgical system, utilizing the capabilities of 6G-enabled Tactile Internet (TI) and blockchain technology, holds remarkable promise for providing remote healthcare services in real-time. This system has the potential to deliver high-quality and reliable healthcare services with a focus on responsiveness. It holds considerable benefits for society, particularly in terms of achieving exceptionally accurate surgical diagnoses. Yet, contemporary robotic surgery systems face challenges such as security, privacy, reliability, latency, and the costly implications of blockchain-based storage. These issues curtail the immediate global implementation of tactile internet in surgical procedures. In order to address the previously mentioned concerns, we present a new solution referred to as “A Smart Contract-Based Robotic Surgery Authentication System for Healthcare Using 6G-Tactile Internet”. It is an innovative framework for intelligent and blockchain-driven tactile internet. By incorporating a smart contract, we aim to effectively tackle the challenges associated with security, privacy, and the transparency. The introduced approach also effectively thwarts the implementation of malicious commands that might be transmitted by an unauthorized individual. The utilization of the 6G communication channel substantially reduces the latency challenges associated with exchanging surgical commands. The security of the suggested protocol is formally showcased through the utilization of the Real-Or-Random Oracle (ROR) model and Scyther simulation tool. Furthermore, an informal security analysis is conducted to validate and support the findings from the formal security analyses. We conduct simulations of the proposed protocol utilizing the Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL). Moreover, the performance and comparative analysis illustrate that the presented solution outperforms currently existing approaches.

Muscle Architecture of Leg Muscles: Functional and Clinical Significance.

Architectural properties of the muscles are the prime predictors of functional attributes and force-generating capacity of the muscles. This data is vital for musculoskeletal modelling and selecting the appropriate muscle-tendon units for tendon transfers.Cadaveric data for architectural properties is the gold standard and primary input for musculoskeletal modelling. There is a paucity of these datasets, especially in the leg muscles. Sixty muscles of the anterior and lateral compartments from twelve formalin-fixed lower limbs were studied for gross architecture, including the peculiar fibre arrangements and architectural properties of muscles. Muscle weight, muscle length, fibre length, pennation angle and sarcomere length were measured. Normalised fibre length, fibre length to muscle length ratio (FL/ML ratio), and the physiological cross-sectional area (PCSA) were calculated from the obtained data. Muscles displayed a combination of architectural strategies and were partly fusiform and partly pennate. The tibialis anterior and peroneus longus were the heaviest muscles in their respective compartments and showed more extensive origin from the nearby deep facial sheets.Long fibre length and less pennation angle were seen in muscles of the extensor compartment. Potential muscle power was highest in the tibialis anterior and peroneus longus and least in the extensor hallucis longus. Arching of the foot and eversion are peculiar to humans and recent in evolution. Due to the functional demand of maintaining the medial longitudinal arch and eversion, the tibialis anterior and peroneus longus have more muscle weight and larger physiological cross-sectional area and are potentially more powerful.Extensor compartment muscles were architecturally more suited for excursions because of the long fibre length and less pennation angle.This study contributes baseline normative data for musculoskeletal modelling platforms and simulation tools - an emerging area in biomechanics and tendon transfers.

DFIG versus PMSG for marine current turbine applications

Emerging technologies for marine current turbine are mainly relevant to works that have been carried out on wind turbines and ship propellers. It is then obvious that many electric generator topologies could be used for marine current turbines. As in the wind turbine context, doubly-fed induction generators and permanent magnet generators seems to be attractive solutions to be used to harness the tidal current energy. In this paper, a comparative study between these two generators type is presented and fully analyzed in terms of generated power, maintenance and operation constraints. This comparison is done for the Raz de Sein site (Brittany, France) using a multi physics modeling simulation tool. This tool integrates, in a modular environment, the resource model, the turbine hydrodynamic model and the generators models.

Building information modelling-enabled multi-objective optimization for energy consumption parametric analysis in green buildings design using hybrid machine learning algorithms

Green buildings (GB) have been widely promoted in various nations. However, the post occupancy evaluation suggests many GB cannot well fulfill the expected targets. To overcome the mismatch among GB’s multi-objectives, this paper develops an efficient and intelligent hybrid method using BIM-DesignBuilder (BIM-DB), Grey wolf optimization (GWO), random forest (RF) and non-dominated sorting genetic algorithm II (NSGA-II) to achieve the optimization of design parameters. The BIM model and DB simulation tool were used to obtain data samples of envelope and air conditioning system design parameters, and their life cycle carbon emission (LCCE), economic cost (EC) and predicted mean vote (PMV). The RF model was used to achieve high precision prediction. The GWO was used in the hyper-parameter optimization. The NSGA-II algorithm was applied to multi-objective optimization to obtain optimal design parameters. A building case shows: (1) The RF model had an excellent prediction performance for LCCE, EC and PMV. (2) BIM-DB can be used to obtain low error and high reliability building simulation data sets. (3) The RF-NSGA-II intelligent algorithm can reduce the LCCE of the building in the entire cycle by 16.6%, reduce the EC per square meter by 2.0%, and greatly improve the thermal comfort by 18.3%, representing good application value. This research provides a way of thinking for the multiobjective optimization of green buildings from the perspective of data mining and guidance for the parameter selection of the envelopes and air conditioning systems of new and existing buildings to more scientifically and effectively design green buildings.

Clustering of logistics supply chains in the process of Ukraine's eurointegration

This article delves into the concept of clusters within the nation's economy, the role of logistics management in effective cluster operation, and avenues for its enhancement. The research focuses on the socio-economic processes that facilitate the clustering of logistics supply chains for the company "PAPIR SERVICE SK" - one of Ukraine's largest companies involved in the procurement and distribution of paper and cardboard in rolls. The subject of investigation encompasses a comprehensive range of scientific, methodological, and practical aspects related to the establishment of a mechanism for forming cluster entities within the context of Ukraine's integration with Europe. Particular attention is devoted to defining the concept of an economic cluster, exploring the role of logistics management for cluster participants, examining prospects for logistics companies to become integral parts of a cluster, analyzing contemporary trends in the development of logistics management for supply chains. An arsenal of simulation modeling tools is proposed for designing supply chains within the market of printing services in Ukraine. The feasibility of proposing necessary alterations to the functioning of logistical systems and enterprises within an economic cluster is demonstrated. Implementing the results obtained expedites the processes of creating cluster entities within Ukraine's transportation and logistics sector, facilitating the formulation of strategies for their activities based on the application of scientific tools.

Automation program for optimum design of electric vehicle powertrain systems based on artificial neural network

Many studies have been conducted on various powertrain systems, such as multi-motor, multi-speed, or both, to enhance the energy efficiency and dynamic performance of electric vehicles (EVs). This study developed an automated design program to obtain the optimal design of EVs for various powertrain systems. The program consists of an EV simulation and artificial neural network (ANN) modeling and optimization tools. The EV simulation tool employs an integrated EV model that can analyze the efficiency and performance of various powertrain systems in a single environment. The ANN modeling and optimization tool first constructs an ANN model, and then performs optimization using the ANN model to address excessive computational efforts arising from the multi-objective genetic algorithm. This study verified the developed program by conducting analysis and optimization of five powertrain systems with the same EV specifications. A multi-objective optimization problem was formulated by considering the design variables as the torque distribution between the motors and gear shifting patterns and ratios of the transmission, and the objectives as the energy consumption and acceleration time. A comparison of the optimization results among the five powertrain systems quantitatively showed the positive effects of the multi-motor and multi-speed powertrain systems. Furthermore, the ANN-based multi-objective optimization process allowed for the efficient determination of the optimum design solutions for the proposed EV powertrain systems. Consequently, these results demonstrated the effectiveness of the automation program in rapid decision-making on EV powertrain system configurations, satisfying each designer’s requirements.

A SysML-centric integration framework for helicopter fuel system development

Data and model exchange between SysML (Systems Modeling Language) Modeling tools and other system development tools is a critical aspect of Model-Based Systems Engineering (MBSE). This paper introduces an application of the SysML-centric integration framework, which allows integrating multi-disciplinary engineering models into a SysML Modeling environment. A design practice of helicopter fuel system is then presented to illustrate how to combine the external simulation models within the SysML Models to explore the performance and verify corresponding requirements. In this case, FMI (Functional Mock-up Interface) based co-simulation creates a bridge between SysML Modeling tools and simulation tools in the SysML-centric integration framework. According to the practice, we believe it will significantly improve model consistency and traceability to provide early evaluation of concepts during conceptual design phase.

Multi-channel capacitive sensing system for cross bore detection and classification by machine learning

Cross bore is an intersection of an existing underground pipeline such as a sewer pipeline by a second utility such as a gas pipeline installed using the trenchless technique, which could lead to severe incidents like explosions. There are hundreds of thousands of existing cross bores in the U.S., at an estimated average rate of 0.4 per mile of pipeline reported by Cross Bore Safety Association, which is at high risk and a significant threat to public safety if not well detected, characterized, and mitigated. The research on high-accuracy nondestructive evaluation (NDE) methods that can identify cross bore areas more effectively and efficiently under challenging field environment is of vital importance. This paper proposes a multi-channel capacitive sensing system for cross bores detection and classification, which passes through the gas pipe to inspect the dielectric property changes of surrounding materials and indicates the existence of cross bores. A 3-D simulation modeling tool was developed to optimize the footprint of multi-channel sensor, and the effect of four cross bore types on the received capacitance values was investigated. The lab-scale experiments were performed using the developed multi-channel capacitive sensing system through a soil box setup, and the experimental results indicated that the developed system can identify the four types of cross bores. To achieve an automated decision making for the cross bores detection and classification, machine learning (ML) algorithms were developed through the experimental dataset, and it was found that the subspace k-nearest neighbors (SKNN) performed better with a high classification accuracy. Finally, field test validation was performed at the pipe farm (Gas Technology Institute, IL, USA) and the superior capability of the developed sensing system in identifying cross bores as well as other key parts in gas pipelines including butt fusion and saddle fitting with coil was demonstrated.