Internet of Robotic Things Intelligent Connectivity and Platforms

The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the Internet and “things” have evolved significantly. “Things” now range from simple Radio Frequency Identification (RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and autonomous vehicles operating in consumer, business, and industrial environments. The emergence of “intelligent things” (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms, trustworthiness frameworks, and platforms for the integration of applications across different business and industrial domains. These new applications accelerate the development of autonomous system design paradigms and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can communicate with other things, learn autonomously, interact safely with the environment, humans and other things, and gain qualities like self-maintenance, self-awareness, self-healing, and fail-operational behavior. IoRT applications can make use of the individual, collaborative, and collective intelligence of robotic things, as well as information from the infrastructure and operating context to plan, implement and accomplish tasks under different environmental conditions and uncertainties. The continuous, real-time interaction with the environment makes perception, location, communication, cognition, computation, connectivity, propulsion, and integration of federated IoRT and digital platforms important components of new-generation IoRT applications. This paper reviews the taxonomy of the IoRT, emphasizing the IoRT intelligent connectivity, architectures, interoperability, and trustworthiness framework, and surveys the technologies that enable the application of the IoRT across different domains to perform missions more efficiently, productively, and completely. The aim is to provide a novel perspective on the IoRT that involves communication among robotic things and humans and highlights the convergence of several technologies and interactions between different taxonomies used in the literature.publishedVersio

AI-Based Edge Acquisition, Processing and Analytics for Industrial Food Production

This article presents a novel approach to the acquisition, processing, and analytics of industrial food production by employing state-of-the-art artificial intelligence (AI) at the edge. Intelligent Industrial Internet of Things (IIoT) devices are used to gather relevant production parameters of industrial equipment and motors, such as vibration, temperature and current using built-in and external sensors. Machine learning (ML) is applied to measurements of the key parameters of motors and equipment. It runs on edge devices that aggregate sensor data using Bluetooth, LoRaWAN, and Wi-Fi communication protocols. ML is embedded across the edge continuum, powering IIoT devices with anomaly detectors, classifiers, predictors, and neural networks. The ML workflows are automated, allowing them to be easily integrated with more complex production flows for predictive maintenance (PdM). The approach proposes a decentralized ML solution for industrial applications, reducing bandwidth consumption and latency while increasing privacy and data security. The system allows for the continuous monitoring of parameters and is designed to identify potential breakdown situations and alert users to prevent damage, reduce maintenance costs and increase productivity.publishedVersio

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio

An Intelligent Real-Time Edge Processing Maintenance System for Industrial Manufacturing, Control, and Diagnostic

This paper presents an artificial intelligence (AI) based edge processing real-time maintenance system for the purposes of industrial manufacturing control and diagnostics. The system is evaluated in a soybean processing manufacturing facility to identify abnormalities and possible breakdown situations, prevent damage, reduce maintenance costs, and increase production productivity. The system can be used in any other manufacturing or chemical processing facility that make use of motors rotating equipment in different process phases. The system combines condition monitoring, fault detection, and diagnosis using machine learning (ML) and deep learning (DL) algorithms. These algorithms are used with data resulting from the continuous monitoring of relevant production equipment and motor parameters, such as temperature, vibration, sound/noise, and current/voltage. The condition monitoring integrates intelligent Industrial Internet of Things (IIoT) devices with multiple sensors combined with AI-based techniques and edge processing. This is done to identify the parameter modifications and distinctive patterns that occur before a failure and predict forthcoming failure modes before they arise. The data from production equipment/motors is collected wirelessly using different communication protocols - such as Bluetooth low energy (BLE), Long range wide area network (LoRaWAN), and Wi-Fi - and aggregated into an edge computing processing unit via several gateways. The AI-based algorithms are embedded in the processing unit at the edge, allowing the prediction and intelligent control of the production equipment/motor parameters. IIoT devices for environmental sensing, vibration, temperature monitoring, and sound/ultrasound detection are used with embedded signal processing that runs on an ARM Cortex-M4 microcontroller. These devices are connected through either wired or wireless protocols. The system described addresses the components necessary for implementing the predictive maintenance (PdM) strategy in soybean industrial processing manufacturing environments. Additionally, it includes new elements that broaden the possibilities for prescriptive maintenance (PsM) developments to be made. The type of ML or DL techniques and algorithms used in maintenance modeling is dictated by the application and available data. The approach presented combines multiple data sources that improve the accuracy of condition monitoring and prediction. DL methods further increase the accuracy and require interpretable and efficient methods as well as the availability of significant amounts of (labeled) data.publishedVersio

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

Recent developments in Tuberculous meningitis pathogenesis and diagnostics [version 2; peer review: 1 approved]

The pathogenesis of Tuberculous meningitis (TBM) is poorly understood, but contemporary molecular biology technologies have allowed for recent improvements in our understanding of TBM. For instance, neutrophils appear to play a significant role in the immunopathogenesis of TBM, and either a paucity or an excess of inflammation can be detrimental in TBM. Further, severity of HIV-associated immunosuppression is an important determinant of inflammatory response; patients with the advanced immunosuppression (CD4+ T-cell count of 150 cells/μL. Host genetics may also influence outcomes with LT4AH genotype predicting inflammatory phenotype, steroid responsiveness and survival in Vietnamese adults with TBM. Whist in Indonesia, CSF tryptophan level was a predictor of survival, suggesting tryptophan metabolism may be important in TBM pathogenesis. These varying responses mean that we must consider whether a “one-size-fits-all” approach to anti-bacillary or immunomodulatory treatment in TBM is truly the best way forward. Of course, to allow for proper treatment, early and rapid diagnosis of TBM must occur. Diagnosis has always been a challenge but the field of TB diagnosis is evolving, with sensitivities of at least 70% now possible in less than two hours with GeneXpert MTB/Rif Ultra. In addition, advanced molecular techniques such as CRISPR-MTB and metagenomic next generation sequencing may hold promise for TBM diagnosis. Host-based biomarkers and signatures are being further evaluated in childhood and adult TBM as adjunctive biomarkers as even with improved molecular assays, cases are still missed. A better grasp of host and pathogen behaviour may lead to improved diagnostics, targeted immunotherapy, and possibly biomarker-based, patient-specific treatment regimens

Long-term variability of proglacial groundwater-fed hydrological systems in an area of glacier retreat, Skeioararsandur, Iceland

Proglacial groundwater‐fed features, such as seeps, substantially impact proglacial geomorphology, hydrology, and ecology. However, there is a paucity of research on the impacts of climate change and glacier retreat on the extent of these important features. This paper aims to investigate the impact of glacier retreat on proglacial groundwater levels and on the extent of groundwater‐fed seeps. Research has taken place in western Skeiðarársandur, the large proglacial outwash plain of Skeiðarárjökull, a retreating temperate glacier in southeast Iceland. Changes in the extent of proglacial groundwater seeps were mapped using historical aerial photographs from 1986, 1997, and 2012. Proglacial groundwater levels were monitored in shallow boreholes between 2000 and 2012. The western margin of Skeiðarárjökull has retreated approximately 1 km beyond its position in 1986. However, this retreat was punctuated by short periods of readvance. The geomorphology and groundwater systems at the site were substantially impacted by the November 1996 jökulhlaup, whose deposits altered approximately 18% of the area of groundwater seeps. The surface areas of groundwater seeps and lakes in the study area have declined by ~97% between 1986 and 2012. Most of the decline took place after 1997, when the mean annual rate of retreat increased three‐fold. Groundwater levels also declined substantially between 2000 and 2012, although this trend varies spatially. The paper provides a conceptual model of the controls on proglacial shallow groundwater systems. Direct impacts of glacier retreat are suggested as the main cause for the declines in proglacial groundwater levels and in the extent of groundwater seeps. These declines are expected to adversely impact sandur ecology

Recent Developments in Tuberculous Meningitis Pathogenesis and Diagnostics

The pathogenesis of Tuberculous meningitis (TBM) is poorly understood, but contemporary molecular biology technologies have allowed for recent improvements in our understanding of TBM. For instance, neutrophils appear to play a significant role in the immunopathogenesis of TBM, and either a paucity or an excess of inflammation can be detrimental in TBM. Further, severity of HIV-associated immunosuppression is an important determinant of inflammatory response; patients with the advanced immunosuppression (CD4+ T-cell count of &lt;150 cells/μL) having higher CSF neutrophils, greater CSF cytokine concentrations and higher mortality than those with CD4+ T-cell counts &gt; 150 cells/μL. Host genetics may also influence outcomes with LT4AH genotype predicting inflammatory phenotype, steroid responsiveness and survival in Vietnamese adults with TBM. Whist in Indonesia, CSF tryptophan level was a predictor of survival, suggesting tryptophan metabolism may be important in TBM pathogenesis. These varying responses mean that we must consider whether a “one-size-fits-all” approach to anti-bacillary or immunomodulatory treatment in TBM is truly the best way forward. Of course, to allow for proper treatment, early and rapid diagnosis of TBM must occur. Diagnosis has always been a challenge but the field of TB diagnosis is evolving, with sensitivities of at least 70% now possible in less than two hours with GeneXpert MTB/Rif Ultra. In addition, advanced molecular techniques such as CRISPR-MTB and metagenomic next generation sequencing may hold promise for TBM diagnosis. Host-based biomarkers and signatures are being further evaluated in childhood and adult TBM as adjunctive biomarkers as even with improved molecular assays, cases are still missed. A better grasp of host and pathogen behaviour may lead to improved diagnostics, targeted immunotherapy, and possibly biomarker-based, patient-specific treatment regimens.</ns4:p

Biological Insights into the Expression of Translation Initiation Factors from Recombinant CHOK1SV Cell Lines and their Relationship to Enhanced Productivity

Translation initiation is on the critical pathway for the production of monoclonal antibodies (mAb) by mammalian cells. Formation of a closed loop structure comprised of mRNA, a number of eukaryotic initiation factors and ribosomal proteins has been proposed to aid re-initiation of translation and therefore increase global translational efficiency. We have determined mRNA and protein levels of the key components of the closed loop; eukaryotic initiation factors (eIF3a, eIF3b, eIF3c, eIF3h, eIF3i and eIF4G1), poly(A) binding protein (PABP) 1 and PABP interacting protein 1 (PAIP1) across a panel of 30 recombinant mAb-producing GS-CHOK1SV cell lines with a broad range of growth characteristics and production levels of a model recombinant mAb. We have used a multi-level statistical approach to investigate the relationship between key performance indicators (cell growth and recombinant antibody productivity) and the intracellular amounts of target translation initiation factor proteins and the mRNAs encoding them. We show that high-producing cell lines maintain amounts of the translation initiation factors involved in the formation of the closed loop mRNA, maintaining these proteins at appropriate levels to deliver enhanced recombinant protein production. We then utilise knowledge of the amounts of these factors to build predictive models for, and use cluster analysis to identify, high-producing cell lines. This study therefore defines the translation initiation factor amounts that are associated with highly productive recombinant GS-CHOK1SV cell lines that may be targets for screening highly productive cell lines or to engineer new host cell lines with the potential for enhanced recombinant antibody productivity