Design and Evaluation of Remote Driving Architecture on 4G and 5G Mobile Networks

Despite the progress in the development of automated vehicles in the last decade, reaching the level of reliability required at large-scale deployment at an economical price and combined with safety requirements is still a long road ahead. In certain use cases, such as automated shuttles and taxis, where there is no longer even a steering wheel and pedals required, remote driving could be implemented to bridge this gap; a remote operator can take control of the vehicle in situations where it is too difficult for an automated system to determine the next actions. In logistics, it could even be implemented to solve already more pressing issues such as shortage of truck drivers, by providing more flexible working conditions and less standstill time of the truck. An important aspect of remote driving is the connection between the remote station and the vehicle. With the current roll-out of 5G mobile technology in many countries throughout the world, the implementation of remote driving comes closer to large-scale deployment. 5G could be a potential game-changer in the deployment of this technology. In this work, we examine the remote driving application and network-level performance of remote driving on a recently deployed sub-6-GHz commercial 5G stand-alone (SA) mobile network. It evaluates the influence of the 5G architecture, such as mobile edge computing (MEC) integration, local breakout, and latency on the application performance of remote driving. We describe the design, development (based on Hardware-in-the-Loop simulations), and performance evaluation of a remote driving solution, tested on both 5G and 4G mobile SA networks using two different vehicles and two different remote stations. Two test cases have been defined to evaluate the application and network performance and are evaluated based on position accuracy, relative reaction times, and distance perception. Results show the performance of the network to be sufficient for remote driving applications at relatively low speeds (<40 km/h). Network latencies compared with 4G have dropped to half. A strong correlation between latency and remote driving performance is not clearly seen and requires further evaluation taking into account the influence of the user interface.

Hyperthermia: A Potential Game-Changer in the Management of Cancers in Low-Middle-Income Group Countries

Loco-regional hyperthermia at 40–44 °C is a multifaceted therapeutic modality with the distinct triple advantage of being a potent radiosensitizer, a chemosensitizer and an immunomodulator. Risk difference estimates from pairwise meta-analysis have shown that the local tumour control could be improved by 22.3% (p < 0.001), 22.1% (p < 0.001) and 25.5% (p < 0.001) in recurrent breast cancers, locally advanced cervix cancer (LACC) and locally advanced head and neck cancers, respectively by adding hyperthermia to radiotherapy over radiotherapy alone. Furthermore, thermochemoradiotherapy in LACC have shown to reduce the local failure rates by 10.1% (p = 0.03) and decrease deaths by 5.6% (95% CI: 0.6–11.8%) over chemoradiotherapy alone. As around one-third of the cancer cases in low-middle-income group countries belong to breast, cervix and head and neck regions, hyperthermia could be a potential game-changer and expected to augment the clinical outcomes of these patients in conjunction with radiotherapy and/or chemotherapy. Further, hyperthermia could also be a cost-effective therapeutic modality as the capital costs for setting up a hyperthermia facility is relatively low. Thus, the positive outcomes evident from various phase III randomized trials and meta-analysis with thermoradiotherapy or thermochemoradiotherapy justifies the integration of hyperthermia in the therapeutic armamentarium of clinical management of cancer, especially in low-middle-income group countries.

Multipath Wireless Network Coding: A Scheduling Constraint in Game Perspective

This paper considers wireless networks in which various paths are obtainable involving each source and destination. It is allowing each source to tear traffic among all of its existing paths, and it may conquer the lowest achievable number of transmissions per unit time to sustain a prearranged traffic matrix. Traffic bound in contradictory instructions in excess of two wireless hops can utilize the “reverse carpooling” advantage of network coding in order to decrease the number of transmissions used. These call such coded hops “hyper-links.” With the overturn carpooling procedure, longer paths might be cheaper than shorter ones. However, convenient is an irregular situation among sources. The network coding advantage is realized only if there is traffic in both directions of a shared path. This project regard as the problem of routing amid network coding by egotistic agents (the sources) as a potential game and develop a method of state-space extension in which extra agents (the hyper-links) decouple sources’ choices from each other by declaring a hyper-link capacity, allowing sources to split their traffic selfishly in a distributed fashion, and then altering the hyper-link capacity based on user actions. Furthermore, each hyper-link has a scheduling constraint in stipulations of the maximum number of transmissions authorized per unit time. Finally these project show that our two-level control scheme is established and verify our investigative insights by simulation.

Leveraging Game AI to Transform Integrated Brownfield Well Planning

PDO is in the process of transforming its well and urban planning by adopting digital technologies and Artificial Intelligence (AI) to improve organizational efficiency and maximize business value through faster quality decision. In 2020, PDO collaborated with a third-party contractor to provide a novel solution to an industry-wide problem: "how to effectively plan 100's of wells in a congested brownfield setting?". This paper describes an innovative AI-assisted well planning method that is a game-changer for well planning in mature fields, providing efficiency in urban and well trajectory planning. It was applied in one of PDO's most congested fields with a targeted infill of 43m well spacing. The novel well planning method automatically designs and optimizes well trajectories for 100-200 new wells while considering surface, subsurface and well design constraints. Existing manual workflows in the industry are extremely time consuming and sequential (multiple man-months of work) - particularly for fields with a congested subsurface (350+ existing wells in this case) and surface (limited options for new well pads). These conventional and sequential ways of working are therefore likely to leave value on the table because it is difficult to find 100+ feasible well trajectories, and optimize the development in an efficient manner. The implemented workflow has the potential to enable step change in improvements in time and value for brownfield well and urban planning for all future PDO developments. The innovative AI assisted workflow, an industry first for an infill development of this size, evaluates, generates and optimizes from thousands of drillable trajectories to an optimized set for the field development plan (based on ranked value drivers, in this case, competitive value, cost and UR). The workflow provides a range of drillable trajectories with multi-scenario targets and surface locations, allowing ranking, selection and optimization to be driven by selected metrics (well length, landing point and/or surface locations). The approach leads to a step change reduction in cycle time for well and urban planning in a complex brownfield with 100-200 infill targets, from many months to just a few weeks. It provides potential game-changing digital solutions to the industry, enabling improved performance, much shorter cycle times and robust, unbiased well plans. The real footprint and innovation from this AI-assisted workflow is the use of state-of-the-art AI to enhance team collaboration and integration, supporting much faster and higher quality field development decisions. This paper describes a novel solution to integrated well planning. This is a tangible example of real digital transformation of a complex, integrated and multi-disciplinary problem (geologists, well engineers, geomatics, concept engineers and reservoir engineers), and only one of very few applied use cases in the industry. This application also gives an example of "augmented intelligence", i.e. how AI can be used to truly support integrated project teams, while the teams remain fully in control of the ultimate decisions. The success of this approach leans on the integrated teamwork across multiple technical disciplines, not only involving PDO's resources, but also WhiteSpace Energy as a 3rd party service provider. The enhanced collaboration allowed all parties to highlight their constraints in an integrated way from the start, strengthening the technical discussion between disciplines and learning from each constraint impact and dependencies. (e.g. dog leg severity). In summary, the change in process flow moving from a sequential well planning and urban planning method to an iterative and fast AI solution – including all technical considerations from beginning represented for PDO an added value of over 6 months of direct cycle time HC acceleration.

Study of potential games using Ising interaction

Problems can be handled properly in game theory as long as a countable number of players are considered, whereas, in real life, we have a large number of players. Hence, games at the thermodynamic limit are analyzed in general. There is a one-to-one correspondence between classical games and the modeled Hamiltonian at a particular equilibrium condition, usually the Nash equilibrium. Such a correspondence is arrived for symmetric games, namely the Prisoner’s Dilemma using the Ising Hamiltonian. In this work, we have shown that another class of games known as potential games can be analyzed with the Ising Hamiltonian. Analysis of this work brings out very close observation with real-world scenarios. In other words, the model of a potential game studied using Ising Hamiltonian predicts behavioral aspects of a large population precisely.

Are humanized IgE reporter systems potential game changers in serological diagnosis of human parasitic infection?

Immunoglobulin E (IgE) is thought to have evolved to protect mammalian hosts against parasitic infections or toxins and plays a central role in the pathogenesis, diagnosis, and therapy of IgE-mediated allergy. Despite the prominence of IgE responses in most parasitic infections, and in stark contrast to its use in the diagnosis of allergy, this isotype is almost completely unexploited for parasite diagnosis. Here, we discuss the perceived or real limitations of IgE-based diagnosis in parasitology and suggest that the recent creation of a new generation of very sensitive cellular IgE-based reporters may represent a powerful new diagnostic platform, but needs to be based on a very careful choice of diagnostic allergens.