Cybernet Systems Corporation (Canada)

The article explores the global issue of water scarcity. Methods are being applied to eliminate water pollution, which can lead to water shortages. Azerbaijan is also one of the countries suffering from this problem. A global approach to the issue is required. Considering the complexity of the problem, a prototype is being developed, and potential solutions are demonstrated on it. The JWR is taken as the prototype object. The conceptual model, functional model, and system architecture have been presented.

Abstract The Strangways Springs artesian mound spring complex in South Australia reveals a layered history in which resources, technology, labor, and culture are significant and changing variables. The site exists in Arabana country, and for thousands of years provided a location for human shelter, artesian waters, and life sustaining resources. The arrival of sheep stations in the “Far North” of South Australia represented a significant rupture and the creation of a new kind of economy based on wool. The establishment of an overland telegraph repeater station brought the latest technological developments to this remote frontier, which had the information of the world available instantly. Other developments such as the railway and wool scouring further secured the importance of locations like Strangways Springs in the continent's colonial infrastructure. This microhistory uses archaeology, archival research, and photography to explore these technological transitions and their impacts at Strangways Springs in the nineteenth century, providing important insights into the sociotechnical nexus that characterized emerging colonial worlds and new forms of modernity in settler Australia.

<p>Endoscopic cameras attached to miniaturised snakelike continuum robotic arms can improve dexterity, accessibility, and visibility in minimally invasive surgical tasks. This steerable camera can expand the field of view and enhance the surgical experience with additional degrees of freedom. However, it also creates more control options that complicate human-machine interaction. This challenge presents an opportunity to develop novel human-machine control strategies using visual sensing to complement the dexterous actuation of a steerable surgical manipulator. This study presents a semi-autonomous controller to assist teleoperation by steering a camera such that it keeps an arbitrary target in the centre of the field of view, thus assisting in surveying different orientations about the target with imagebased information. Two controllers are proposed using classical image-based visual servoing techniques and optimal visual predictive control techniques. These techniques are simulated and validated on our robot SnakeRaven: a 3D-printed patient-specific end-effector attached to the RAVEN II surgical platform. Both systems, most notably the visual predictive approach, operated successfully with robustness to a lack of information about the target. A video demonstrating the main results of this paper can be found via https://youtu.be/fiUM9qYdl1U</p>

Artificial intelligence applications in education have attracted the attention of multiple parties, including scholars, educators, governments, and researchers. The article aims to conduct a comprehensive and inclusive review of the proliferation and impact of Artificial intelligence on Higher Learning. Chronologically, the focus has been artificial intelligence applications in higher learning since the early 1950s. There needs to be more literature regarding the adoption of AI in higher education, resulting in a substantial limitation of this article. This article also discusses the role of cyber security in adopting AI in higher education. The authors have also discussed various applications of AI in higher education and the challenges. This article presents some recommendations. More research is needed as the recommendations are based on a limited number of scholarly articles. A Georgia State University case study conducted in 2015 substantiates AI adoption's benefits in higher education.

The latter half of the twentieth century yielded two tools of unprecedented power, both of which took decades to mature to their current states. The purpose of this research is to apply these to a theory of gravity and develop the consequences of the model based on these tools. This paper presents such results without mathematical details, which are presented elsewhere. The tools are: Geometric Calculus, developed by David Hestenes, circa 1965 and Mathematica, released in 1988 by Steven Wolfram. Both tools have steep learning curves, requiring several years to acquire expertise in their use. This paper explains in what sense they are optimal.

Clarifying duct noise is gathering attention for various industries. Duct noise is one of the noise problems in the equipment such as air conditioner and ventilation system. In the duct noise, the noise generated by the sources due to the fan or motor are included, and these noise propagate in the L-shape duct with flow. However the propagation mechanism of these noise are not clarified, especially in the case of the propagation in the duct with flow. In this paper, we investigated the propagation and attenuation of the noise generated by the speaker in the duct with or without a flow both experimentally and numerically. The results show that in the case of the duct with the flow, the noise generated by the speaker at frequency more than 1200 Hz are damped more than in the case of the duct without the flow.In the numerical simulations, the results shows that the strength of the noise sources on the duct wall decreases in the case of the duct with a flow more than in the case of the duct without a flow. In the numerical simulations, we applied the coupling method using CFD and acoustic BEM solvers.

Abstract Offshore oil and gas production platforms are complex and hazardous process facilities which are usually attended by a permanent human crew to run the daily operations. In recent years, the oil and gas industry has demonstrated strong commitment to change this traditional operations approach and move towards inherently safer philosophy in offshore facilities design and operations, i.e. removing human crew from the facility and operating it remotely from a safe location over extended periods. This paper aims to demonstrate the readiness of robotics technologies coupled with digitalization technologies in process control and facility automation in transforming offshore oil and gas production platforms into smart unmanned installations. This paper is focused on the application of smart robotics, with highly dexterous capabilities and equipped with multiple sensing instruments, in maintaining an offshore oil and gas production facility in full operation without a permanent human crew, and with planned visits to the platform at 12-week intervals, in a case study. The robots are developed to be remotely operated from an onshore control center and/or may be programmed to function autonomously for routine missions on the offshore facility.

Nowadays, customer analytics plays a fundamental role in the commercial activity of a financial company, which is interested to deliver the best products and services to consumers. However, sometimes things may possibly go wrong, resulting in inconveniences experienced by the clients, who may eventually decide to end the relationship with a bank. This study uses a dataset which contains a bank's customer base who churned, with a series of variables regarding sociodemographic characteristics, member activity, balance, estimated salary and tenure. An issue of general interest in banking field is predicting the probability of churn or attrition, a phenomenon which may lead to significant decreasing of revenues if the company fails to act on time. Therefore, in this paper we examine which are the main characteristics of customers that influence the propensity to churn by means of an exploratory data analysis. Moreover, we employ two machine learning algorithms and use an optimization technique called Grid Search to obtain the optimal predictive model, which can estimate the likelihood of a customer leaving a bank in the future. Ultimately, we use the Area Under the Curve (AUC) as model performance metric to compare the outcomes of Logistic Regression and Random Forest classifiers on the test dataset. Final results show that Random Forest classifier outperformed Logistic Regression model in terms of AUC values and, at the same time,the outcomes emphasize different behaviors between countries.

In this paper, we analyze the state of the art in distributed ledger technologies and blockchains and investigate potential applications in the Internet of Things (IoT) domain. Afterwards, we discuss interoperability of blockchains, and their use in smart contracts, and artificial intelligence.