Large-scale Deployment Research Articles

Review: Uninterrupted Liquid Hydrogen (E-Methanol) Production framework to deliver Clean & Green Energy

Potential broad impact: Integrating solar, wind, and conventional energy to feed the electrolyze after completion of the electrochemical process will produce two streams of O2 and H2. The hydrogen stream will be stored and provided with uninterrupted feed to a catalytic reactor in the presence of CO2. It will produce EMethanol[1]. However, this depends on these resources' reliability, cost, and availability, with the highest potential for expansion to the size needed for large-scale deployment to produce e-methanol[2]. This is known as electro-fuel, which provides electricity and fuel for electric and diesel vehicles[3][4]. As witnessed the versatility of Hydrogen in these applications highlights its significance in various industrial sectors. As the world looks towards sustainable and clean energy solutions, the role of Hydrogen is expected to grow even further [5]. However, the central part of Hydrogen is produced from fossil fuels (known as gray and brown Hydrogen) [6], and only a minimal amount of almost 4% is delivered through an electrolysis process using renewable energy to complete the chemical process [7], known as green Hydrogen. The transition to a more sustainable hydrogen economy involves scaling up the production of green Hydrogen through electrolysis and advancing technologies to improve efficiency and reduce costs associated with this process [8],[9]. Recently research indicated that production and consumption of the Green Hydrogen is the future of sustainable power delivery by replacing fossil fuel [10]. The fossil and Biomass also catalyzing the GHG (Green House Gases) with high carbon intensity, this is biggest challenge to deliver clean energy [11]. In the first instance, this research proposed an integrated Modular, which incorporate Renewable(photovoltaic) and Conventional Power sources to complete electrolysis process and its port equipment(s) to deliver uninterrupted clean Energy inform of Liquid Green Hydrogen for Electricity and to replace diesel [12]]13].

Evaluating generative AI responses to real-world drug-related questions

Generative Artificial Intelligence (AI) systems such as OpenAI's ChatGPT, capable of an unprecedented ability to generate human-like text and converse in real time, hold potential for large-scale deployment in clinical settings such as substance use treatment. Treatment for substance use disorders (SUDs) is particularly high stakes, requiring evidence-based clinical treatment, mental health expertise, and peer support. Thus, promises of AI systems addressing deficient healthcare resources and structural bias are relevant within this domain, especially in an anonymous setting. This study explores the effectiveness of generative AI in answering real-world substance use and recovery questions. We collect questions from online recovery forums, use ChatGPT and Meta's LLaMA-2 for responses, and have SUD clinicians rate these AI responses. While clinicians rated the AI-generated responses as high quality, we discovered instances of dangerous disinformation, including disregard for suicidal ideation, incorrect emergency helplines, and endorsement of home detox. Moreover, the AI systems produced inconsistent advice depending on question phrasing. These findings indicate a risky mix of seemingly high-quality, accurate responses upon initial inspection that contain inaccurate and potentially deadly medical advice. Consequently, while generative AI shows promise, its real-world application in sensitive healthcare domains necessitates further safeguards and clinical validation.

Improve the efficiency of organic solar cells by creating a favorable charge transport channel with a halogen-free additive

In optimizing the mixed film morphology of organic solar cells (OSCs), conventional additives such as 1,8-diiodooctane (DIO) has an important role to play. Anyway, the toxicity inhibits their further development. Here, a new non-toxic, halogen-free additive called Methyl Benzoate (MBZ) has been added into the classic OSCs (PTB7-Th: PC71BM). A series of characterizations demonstrate that the MBZ additive effectively matches well with the active layer material and enhances the formation of network interpenetrating structures. This enhances charge transport and inhibits the recombination of excitons. Power conversion efficiency (PCE) of 8.76 % and fill factor (FF) of 70.9 % are achieved by the device with the MBZ additive. This research paves the way for future large-scale industrial deployment about organic solar cells (OSCs) by presenting a method for replacing conventional additives with halogen-free alternatives.

Joint UAV Deployment and Task Offloading in Large-Cale UAV-Assisted MEC: A Multiobjective Evolutionary Algorithm

With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms.

Main current legal and regulatory frameworks for carbon dioxide capture, transport, and storage in the European Economic Area

There is broad consensus on the key role that carbon dioxide (CO2) capture, transport, and storage (CCTS) systems will play in mitigating climate change, either by removing CO2 from the atmosphere and storing it permanently or by avoiding CO2 emissions generated by point sources, especially from hard-to-abate sectors (e.g., waste-to-energy, cement, shipping or aviation). Although CCTS is ready to be implemented from a technical standpoint, the legal and regulatory framework required for its implementation and regulation could be further improved. In this article, we summarize and critically discuss the provisions of the Convention for the Protection of the Marine Environment of the North-East Atlantic (the ‘OSPAR Convention’), and of the London protocol, as well as of the European CCS and ETS Directives. With a focus on the European Economic Area, we highlight existing gaps and hurdles that should be tackled in view of the large-scale deployment of CCTS. Furthermore, as the legal landscape for CO2 transport and geological storage is evolving rapidly, we provide an overview of recent clarifications on aspects of the existing legislation and a summary of new proposals presented by the European Commission in this space.

Predictive Modeling in Remote Sensing Using Machine Learning Algorithms

Predictive modeling in remote sensing using machine learning (ML) algorithms has emerged as a powerful approach for addressing various environmental and climatic challenges. This paper explores the integration of advanced ML techniques with remote sensing data to enhance predictive capabilities for applications such as land cover classification, crop yield prediction, climate change monitoring, and disaster management. We review related works and existing systems, highlighting platforms like Google Earth Engine (GEE), NASA Earth Exchange (NEX), and Sentinel Hub, which leverage cloud computing to handle large-scale data processing and model deployment. The proposed system incorporates data acquisition, preprocessing, feature extraction, model selection and training, and prediction and visualization to provide accurate and timely predictions. Future enhancements, including deep learning integration, real-time data processing, enhanced user interfaces, and collaboration with Internet of Things (IoT) devices, are discussed to further strengthen the system’s capabilities. The paper concludes by emphasizing the potential of ML algorithms in transforming remote sensing applications, supporting informed decision-making, and improving the management of Earth’s resources.

Semantic model-based large-scale deployment of AI-driven building management applications

Digitalization and Artificial Intelligent (AI) are revolutionizing building operation management. The abundance of data generated with the digitalization of buildings in the whole lifecycle can be harnessed to enhance building operational efficiency through data-driven control and optimization applications. However, the heterogeneity of data across building datasets hampers data interactivity and interoperability, presenting obstacles to the large-scale deployment of AI-enabled data-driven solutions. A semantic model-based framework is developed to integrate multi-sources data from buildings' air-conditioning system, supporting the large-scale deployment of AI-enabled data-driven building management applications. Both static and temporal data from multi sources are stored in the database guided by the semantic model. To demonstrate the framework's effectiveness, a building cooling load prediction application is implemented and evaluated across three typical buildings. The successful deployment of the proposed semantic model-based framework demonstrates its potential for facilitating large-scale deployment of AI-enabled data-driven applications in building sector.

Accuracy Low-Cost “Handshake” Two-Way Ranging Protocol Applied to Road Traffic Communications

Abstract The densification of the vehicle fleet leads to numerous challenges in road safety management. Wireless Sensor Networks (WSN) helps to manage vehicle-vehicle-infrastructure interactivity, in particular to predict dangers and avoid accidents, detect and penalize traffic violations. This requires a massive deployment of communicating nodes embedded in vehicles and in road infrastructure. The reliability of the systems built on these nodes lies in the accuracy of the ranging. In order to improve the factors that impact accuracy, various ranging protocols have been developed, but their implementations most often involve the use of expensive and specialized transceivers, which incorporate a dedicated IEEE 802.15.4a ranging layer. Our approach implements the basic and advanced functions integrated into low-cost transceivers and microcontrollers. As result, in two or three links according to the case, our protocol performs simultaneously the ranging and the data exchange, against a specific ranging session with a minimum of three exchanges for the other protocols. Moreover, each node knows the ranging data and has its clock frequency adapted to the controlled process. So, we meet the challenges of large-scale low-cost node deployment, while ensuring high reliability and performance of ranging.

Low-temperature welding engineering of ZnO nanoparticles films via sol-gel method

ZnO nanoparticles is a highly promising semiconductor material as an electron transfer layer layer (ETL) in various optoelectronic devices. The cost-effective and low-temperature preparation method renders it ideal for the development of flexible and large-scale modules, aligning seamlessly with the commercialization process. However, Solution-derived ZnO exhibits a significant number of surface defects, which affect the device's photoelectric conversion performance and stability. Herein, a simple strategy focusing on controlling the nano size of ZnO was employed to mitigate these surface defects. Welded ZnO films were prepared via spin coating with a precise amount of ZnO sol into dispersion. These films were characterized via techniques such as TEM, FSEM XRD, DLS, UV/vis and so on. PL spectra revealed a notable decrease in defect density with the increase in nano size. AFM reveals that films doped with 15 % sol exhibit root mean square (RMS) values of less than 1 nm, indicative of improved smoothness. Moreover, the four-point probe detected enhanced electrical conductivity attributed to the compact structure and improved integrity of the films. The presence of nanoparticles facilitates the crystallization of ZnO sol, allowing for an annealing temperature during the welding process to as low as around 90°C, compatible with flexible plastic substrates such as PET. The welded ZnO films produced at a low temperature in this study demonstrate high potential as ETL for flexible optoelectronic devices, which is conducive to their commercialization and large-scale deployment.

The Need for Adaptive Access Control System at the Network Edge

The emergence of edge computing, characterized by its distributed nature and real-time processing, necessitates a paradigm shift in access control mechanisms. Traditional, static methods struggle to adapt to the dynamic and heterogeneous environment of edge computing. This research addresses this gap by proposing an Adaptive Risk-Based Access Control (ARBAC) model specifically designed for edge environments. The objective of this research is to develop a robust access control system that dynamically responds to the changing security landscape of edge computing. The proposed ARBAC model integrates real-time data on user context, resource sensitivity, action severity, and risk history to dynamically assess the security risk associated with each access request. This approach ensures a balance between robust security and user experience by tailoring access controls based on the specific context. The research builds upon the growing recognition of the limitations of traditional access control methods in edge environments. Existing literature highlights the need for adaptive and risk-based access control models to address the dynamic nature of edge computing. This research contributes to this evolving field by proposing an ARBAC model that leverages real-time information for contextually relevant access decisions. The proposed ARBAC model offers several advantages. By dynamically adjusting access controls based on risk levels, the model enhances security and ensures compliance with regulatory requirements. Additionally, it improves network performance by reducing load and facilitating faster access to resources. Furthermore, the model's scalability makes it suitable for managing access in large-scale edge deployments. In conclusion, this research proposes an ARBAC model that aligns with the dynamic nature of edge computing environments. By leveraging real-time data and contextual information, the model offers a robust and adaptable approach to access control, promoting security, compliance, performance, and scalability in edge computing. This research paves the way for further exploration and implementation of ARBAC systems, empowering organizations to effectively manage access control in the evolving landscape of edge computing and IoT.

Towards early forest fire detection and prevention using AI-powered drones and the IoT

Wildfires are a significant natural hazard, resulting in financial losses, human deaths, and environmental damage. Due to the rising severity and frequency of wildfires, wildfire management and detection have recently received increased attention worldwide. Monitoring potential risk areas and early fire detection are critical factors for shortening the reaction time and reducing the potential damage. Conventional wildfire detection techniques like satellite imaging and remote camera-based sensing need more latency and low reliability. To tackle these limitations, this paper proposes a novel airborne UAV-based IoT (UIoT) system for wildfire sensing, detection, and extinguishing. It presents the design of low-cost and low-maintenance fire-detecting IoT nodes for large-scale deployment. It also proposes, investigates, and reports on several connectivity architectures using the LoRaWAN protocol for UIoT systems. We geolocate forest trees in a terrain-mapping exercise for precise fire localization. We then deploy an autonomous drone with a visual camera that utilizes our novel classification network to detect the presence of fire followed by fire detection and particle filter-based tracking to center fire at the center of the frame. We use a cloud back-end server for monitoring, analysis, and reporting. Our results show that our low-cost and long-range IoT nodes accurately detect fire within 1-5 min after fire ignition. Our fire classification network achieved an accuracy of 99.46% and a mean average precision of 99.64%. Numerical results suggest that the proposed UAV-IoT-based fire classification offers a fast and reliable wildfire recognition and extinguishing solution while minimizing power consumption and increasing the battery lifespan.

Assessing the impact of passenger compliance behavior in CAVs on environmental benefits

The potential of connected autonomous vehicles (CAVs) to offer significant environmental benefits is increasingly acknowledged. Nevertheless, it is debatable concerning CAV implementation as an eco-driving strategy, given that large-scale deployment has not yet been realized and studies relevant to emission reduction have yielded inconsistent results. Our study investigates the effects of implementing CAVs on emission reduction and focuses on passenger reactions regarding vehicular platooning. We employ microsimulation to examine the environmental effects associated with different levels of CAV penetration, complemented by a VR experiment that evaluates rider adherence to CAV systems. The findings indicate that higher CAV penetration rates and greater passenger compliance contribute to reductions in vehicle emissions, yet there is a concurrent rise in PM10 levels when the fleet size exceeds seven vehicles. This study reveals the trade-off between CAV deployment for emission reduction and on-road environmental protection, emphasizing the vital role of passenger compliance in environmental advantages.

Age of information minimization in UAV-assisted data harvesting networks by multi-agent deep reinforcement curriculum learning

Unmanned Aerial Vehicles (UAVs) are increasingly being used for data harvesting from Wireless Sensor Nodes (SNs). This study aims to minimize the Age of Information (AoI) during data collection, while also considering the energy sustainability of the UAVs. Addressing the challenge of optimizing performance in Multi-Agent Reinforcement Learning (MARL) systems as the number of agents increases, our study introduces a MARL approach combined with curriculum learning and evolutionary strategy. This innovation specifically targets the performance issue in traditional MARL setups when scaling up the number of agents. By applying curriculum learning and evolutionary strategies, our method not only enhances MARL scalability but also integrates energy-efficient charging mechanisms, effectively enhancing system performance in large-scale deployments. Numerical results show that our proposed algorithm outperforms baselines in terms of AoI and charging, proving its effectiveness in managing the complexities of large-scale MARL systems.

Incentive policies to realize large-scale deployment of CCS in China's power sector and its economy-wide impacts

Given the carbon capture and storage (CCS) technology is still in the demonstration phase, large-scale deployment of CCS is urgently needed in the coming decades, especially in the power sector. Therefore, we built a computable general equilibrium model with detailed electricity technology module and assessed the economy-wide impacts of multiple incentive policies on large-scale deployment of CCS in the power sector. Results show that, first, if only CCS is subsidized, without supplementing carbon pricing, it will lead to deviations from the core environmental objectives of CCS development. Secondly, when CCS subsidies are combined with carbon pricing, if the sectoral indirect tax is reduced, the GDP loss can be better alleviated, and it can also have an obvious positive impact on energy conservation and emission reduction. Finally, a gradual sector-coverage way of carbon pricing contributes to further mitigating the accumulated GDP loss, easing sectoral profit losses, and improving positive effects on energy indicators.

Devising a High-Level Command Language for the Teleoperation of Autonomous Vehicles

Autonomous vehicles (AVs) are rapidly evolving as a novel way of transportation. Nevertheless, there is a consensus that AVs cannot address all traffic scenarios independently. Consequently, there arises a need for remote human intervention. To pave the way for large-scale deployment of AVs onto public roadways, innovative models of remote operation must evolve. Such a paradigm is Tele-assistance, which posits that the low-level control of AVs should be delegated through high-level commands. Our work explores how such a command language should be constructed as a first step in designing a Tele-assistance user interface. Through interviews with 17 experienced teleoperators, we elicit a set of discrete commands that a remote operator can use to resolve various road scenarios. Subsequently, we create a scenario-command mapping and a thematic classification of the defined commands. Finally, we present an initial Tele-assistance interface design based on these commands.

Assessment of Potential and Techno-Economic Performance of Solid Sorbent Direct Air Capture with CO2 Storage in Europe.

Direct air capture with CO2 storage (DACCS) is among the carbon dioxide removal (CDR) options, with the largest gap between current deployment and needed upscaling. Here, we present a geospatial analysis of the techno-economic performance of large-scale DACCS deployment in Europe using two performance indicators: CDR costs and potential. Different low-temperature heat DACCS configurations are considered, i.e., coupled to the national power grid, using waste heat and powered by curtailed electricity. Our findings reveal that the CDR potential and costs of DACCS systems are mainly driven by (i) the availability of energy sources, (ii) the location-specific climate conditions, (iii) the price and GHG intensity of electricity, and (iv) the CO2 transport distance to the nearest CO2 storage location. The results further highlight the following key findings: (i) the limited availability of waste heat, with only Sweden potentially compensating nearly 10% of national emissions through CDR, and (ii) the need for considering transport and storage of CO2 in a comprehensive techno-economic assessment of DACCS. Finally, our geospatial analysis reveals substantial differences between regions due to location-specific conditions, i.e., useful information elements and consistent insights that will contribute to assessment and feasibility studies toward effective DACCS implementation.

Experiences of Deployed Physicians in Support of Operation Allies Refuge/Operation Allies Welcome: Lessons to Inform Improvements in Training.

Stability operations, including humanitarian assistance and disaster relief missions, are key functions of U.S. Military medicine and the Military Medical Humanitarian Assistance Course (MMHAC) is a 2-day course widely used to prepare military medical personnel for such missions. It focuses on caring for those most vulnerable in the wake of disasters, particularly children. The large-scale humanitarian deployment of military medical providers in support of Operation Allies Welcome/Operation Allies Refuge (OAW/OAR) presents an opportunity to evaluate the preparedness of these providers to care for the needs of the Afghan travelers, so we explored the experiences of military medical providers deployed in support of OAW/OAR to inform improvements in the MMHAC. We conducted a qualitative study of military medical providers who were deployed in support of OAW/OAR using a series of three virtual focus groups. Focus group questions were structured around the main topics covered in the MMHAC (patient care, ethical considerations, logistical concerns, and preventive and public health) and explicitly asked about adaptive leadership challenges faced and strategies used to overcome them. We analyzed transcripts using inductive thematic analysis within a constructivist paradigm, with adaptive leadership as a sensitizing concept. The study was approved by the Institutional Review Board of Uniformed Services University. We constructed 4 themes from participant responses, each addressing challenges that medical providers faced during their mission: (1) Medical providers navigated tension between medical and public health priorities and military mission priorities; (2) Chronic and complex care needs posed unique challenges for medical personnel; (3) Challenges in patient care were compounded by logistical and system-based barriers; and (4) Cultural barriers led to ethical dilemmas that physicians felt inadequately prepared to handle, most notably with respect to gender-related concerns. Within each theme, participants described which aspects of MMHAC training were most helpful and which areas were inadequate. Physicians found the OAR/OAW mission meaningful but also identified challenges related to medical care provision, public health, logistics, and ethical dilemmas that hindered their ability to carry out their medical mission. Lessons learned from OAW/OAR highlight several areas in which the MMHAC training could be augmented and improved to further mitigate these challenges.

Electromigration and electrical sintering in printed silver from high current at room temperature

Improved understanding of the reliability and failure physics of metal nanoparticle conductive inks would facilitate their large-scale deployment across a range of flexible electronics applications. We conduct room-temperature electromigration experiments on printed silver nanoparticle conductive ink test devices. We observe significant variation in failure time, location, and structure during these tests and during post-failure analysis with optical and electron microscopy. We use in situ Atomic Force Microscopy measurements to track volume changes in the sample as a function of time. These measurements provide additional data and understanding of the failure process within printed silver nanoparticle conductive inks.

EFACTLS: Effective Active TLS Fingerprinting for Large-Scale Server Deployment Characterization

EFACTLS: Effective Active TLS Fingerprinting for Large-Scale Server Deployment Characterization

Wake Effects in Lower Carbon Future Scenarios

In August 2022, the U.S. Congress passed the Inflation Reduction Act (IRA), which intended to accelerate U.S. decarbonization, clean energy manufacturing, and deployment of new power and end-use technologies. The National Renewable Energy Laboratory has examined possible scenarios for growth by 2050 resulting from the IRA and other emissions reduction drivers and defined several possible scenarios for large-scale wind deployment. These scenarios incorporate large clusters of turbines operating as wind farms grouped around existing or likely transmission lines which will result in wind farm wakes. Using a numerical weather prediction (NWP) model, we assess these wake effects in a domain in the U. S. Southern Great Plains for a representative year with four scenarios in order to validate the simulations, estimate the internal wake impact, and quantify the cluster wake effect. Herein, we present a validation of the ”no wind farm” scenario and quantify the internal waking effect for the ”ONE” wind farm scenario. Future work will use the “MID” scenario (more than 8000 turbines) and the “HI” scenario (more than 16,000 turbines) to quantify the effect of cluster wakes or inter-farm wakes on power production.