Methodology for the Selection of Technological Modes for the Synthesis of Transparent Conducting Oxides with Desired Properties

Transparent conducting oxides (TCOs) are widely used as a transparent electrode in various fields of opto-and semiconductor electronics. The main materials used today are indium-tin oxide, tin-antimony oxide and zinc-aluminum oxide. The authors have developed and improved the spray-pyrolysis method, which is one of the most promising methods of implementation in production. In this work, the study of tin dioxide doped with antimony coatings and the development of a methodology for the controlled synthesis of TCO, taking into account the effect of technological modes of deposition on the TCO parameters. The results of the performed studies contribute to the development of an automated technology for the synthesis of transparent conducting oxides with desired properties.

Diabetic retinopathy classification for supervised machine learning algorithms

Background Artificial intelligence and automated technology were first reported more than 70 years ago and nowadays provide unprecedented diagnostic accuracy, screening capacity, risk stratification, and workflow optimization. Diabetic retinopathy is an important cause of preventable blindness worldwide, and artificial intelligence technology provides precocious diagnosis, monitoring, and guide treatment. High-quality exams are fundamental in supervised artificial intelligence algorithms, but the lack of ground truth standards in retinal exams datasets is a problem. Main body In this article, ETDRS, NHS, ICDR, SDGS diabetic retinopathy grading, and manual annotation are described and compared in publicly available datasets. The various DR labeling systems generate a fundamental problem for AI datasets. Possible solutions are standardization of DR classification and direct retinal-finding identifications. Conclusion Reliable labeling methods also need to be considered in datasets with more trustworthy labeling.

Technology of training a neural-network model for ontology population

The paper considers one of the subtasks of ontology learning - the ontology population, which implies the extension of existing ontology by new instances without changing the structure of its classes and relations. A brief overview of existing ontology learning approaches is presented. A highly automated technology for ontology population based on training and application of the neural-network language model to identify and extract potential instances of ontology classes from domain texts is proposed. The main stages of its application, as well as the results of its experimental evaluation and the main directions of its further improvement are considered.

Analysis of the Security of Modern Automated Technological Processes Control Systems: Attack on a SCADA Object

(……) Modern automated technology process control systems and the chances of attacks on them are examined in this article. It studies worm virus, Stuxnet, and its detection at the Bushehr Nuclear Power Plant. It also analyzes ways of carrying out attacks on critically important objects, and provides analytical tools for the security of technological process systems. The ways for discovering nodes compromised by the Stuxnet virus are proposed. The article considers technological network of typical topology and its typical vulnerabilities; it analyzes the Modbus protocol, the routing system, and passwords on Cisco routers.

Exo Journalism: A Conceptual Approach to a Hybrid Formula between Journalism and Artificial Intelligence

The irruption of artificial intelligence (AI) and automated technology has substantially changed the journalistic profession, transforming the way of capturing, processing, generating, and distributing information; empowering the work of journalists by modifying the routines and knowledge required by information professionals. This study, which conceptualizes the “exo journalism” on the basis of the impact of AI on the journalism industry, is part of a research project of the Observatory for Information Innovation in the Digital Society (OI2). The results, derived from documentary research supported by case studies and in-depth interviews, propose that AI is a source of innovation and personalization of journalistic content and that it can contribute to the improvement of professional practice, allowing the emergence of a kind of "exo journalist", a conceptual proposal that connects the possibilities of AI with the needs of journalism’s own productive routines. The end result is the enhancement of the journalist’s skills and the improvement of the news product. The research focuses on conceptualizing a kind of support and complement for journalists in the performance of their tasks based on the possibilities of AI in the automatic generation of content and data verification.

New Automated MPD Riser System Allowed an Operator to Reduce Manual Working Hours in the Moonpool by 85% and Installation Time by 60%

In pursuit of efficiency and well construction cost optimization, the oil and gas industry demand continuous improvements and constant evolution of the service providers’ hardware and software, including Managed Pressure Drilling (MPD) technologies. Recently deployed in the Caspian Sea, the new automated riser system enabled an operator to reduce manual working hours in the moonpool by 85% and installation time by 59%. The improved efficiencies represent an additional saving of 19.5 hours rig time compared to the previous generation MPD below tension ring (BTR) systems, which are currently used on more than 19 floaters around the world. Lessons learned over the past 10 years led to the design and release of the new automated technology that resulted in this time and cost savings. The operator currently targets deep reservoirs that cannot be drilled using conventional drilling techniques because of very narrow operating windows. This paper discusses the service delivery process, engineering, and operational challenges that culminated in the flawlessly executed first deployment of the automated MPD riser system.

A Randomized Controlled Trial Using Automated Technology for Improving Ototoxicity Monitoring in VA Oncology Patients

Purpose Determine the efficacy of ototoxicity monitoring (OM) administered as automated protocols with the Oto-ID mobile audiometer (automated ototoxicity monitoring [A-OM]), compared with usual care (UC) OM in cancer patients receiving cisplatin. Method Participants were patients ( n = 46, mean age 64.7 years; range: 30–78 years) receiving cisplatin-based chemotherapy at the Department of Veterans Affairs Portland Health Care System. A randomized controlled trial contrasted A-OM and UC at up to three program evaluations (PEs) conducted by the study audiologist who was blinded to arm through PE1. PE1 occurred before randomization or oncology treatment; PE2 and PE3 occurred during and/or after treatment at 35 and 365 days postrandomization. The A-OM group ( n = 24) used Oto-ID to screen their hearing before each cisplatin dose. Oto-ID results were sent to the study audiologist for interpretation, follow-up, and care coordination. The UC group ( n = 22) received a consult for OM services through the audiology clinic. Outcomes included hearing shift near each patient's high-frequency hearing limit, revised hearing-handicap inventory score, and survival time from the start of treatment. Adherence to OM protocols, patients' use of aural rehabilitation services, and oncologists' treatment decisions were also examined. Results Ototoxicity was identified at a high overall rate (46% and 76% at 35 and 365 days, respectively, postrandomization). Adherence to monitoring prior to each cisplatin dose was 83.3% for those randomized to A-OM compared with 4.5% for UC. Randomization to A-OM was not associated with reduced ototoxic hearing shifts or self-reported hearing handicap relative to UC; neither did it compromise participants' survival. Half of participants in each arm accessed aural rehabilitation services. One in each arm had a documented ototoxicity-related cisplatin dose reduction. Conclusions Auditory impairment was an actionable concern for the participants and their oncology providers. A dedicated surveillance program using the Oto-ID's automated protocols improved adherence to OM recommendations over a traditional UC service delivery model. Supplemental Material https://doi.org/10.23641/asha.16649602

LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is categorized as a neurodevelopmental disability. Having an automated technology system to classify the ASD trait would have a huge influence on paediatricians, which can aid them in diagnosing ASD in children using a quantifiable method. A novel autism diagnosis method based on a bidirectional long-short-term-memory (LSTM) network's deep learning algorithm is proposed. This multi-layered architecture merges two LSTM blocks with the other direction of propagation to classify the output state on the brain signal data from an electroencephalogram (EEG) on individuals; normal and autism obtained from the Simon Foundation Autism Research Initiative (SFARI) database. The accuracy of 99.6% obtained for 90:10 train:test data distribution, while the accuracy of 97.3% was achieved for 70:30 distribution. The result shows that the proposed approach had better autism classification with upgraded efficiency compared to single LSTM network method and potentially giving a significant contribution in neuroscience research.

This Is Your Brain on Autopilot: Neural Indices of Driver Workload and Engagement During Partial Vehicle Automation

Objective This research explores the effect of partial vehicle automation on neural indices of mental workload and visual engagement during on-road driving. Background There is concern that the introduction of automated technology in vehicles may lead to low driver stimulation and subsequent disengagement from the driving environment. Simulator-based studies have examined the effect of automation on a driver’s cognitive state, but it is unknown how the conclusions translate to on-road driving. Electroencephalographic (EEG) measures of frontal theta and parietal alpha can provide insight into a driver’s mental workload and visual engagement while driving under various conditions. Method EEG was recorded from 71 participants while driving on the roadway. We examined two age cohorts, on two different highway configurations, in four different vehicles, with partial vehicle automation both engaged and disengaged. Results Analysis of frontal theta and parietal alpha power revealed that there was no change in mental workload or visual engagement when driving manually compared with driving under partial vehicle automation. Conclusion Drivers new to the technology remained engaged with the driving environment when operating under partial vehicle automation. These findings suggest that the concern surrounding driver disengagement under vehicle automation may need to be tempered, at least for drivers new to the experience. Application These findings expand our understanding of the effects of partial vehicle automation on drivers’ cognitive states.