How using aerial drones to deliver projects during Covid lockdown led to a 'new normal'

The paper illustrates how unmanned aerial vehicle surveys were used to support the designs for refurbishment of seven rural bridges in Wales during a Covid-19 lockdown in 2020. The hundreds of high-resolution photos captured for each structure were used to produce photo-realistic three-dimensional photogrammetry models using automated processes. Although born of necessity, the reduction in cost, duration and disruption together with the elimination of risks associated with conventional surveys show the case for wider adoption beyond the Covid-19 pandemic.

Modelling the Heating Process in the Transient and Steady State of an In Situ Tape-Laying Machine Head

As the use of composite materials increases, the search for suitable automated processes gains relevance for guaranteeing production quality by ensuring the uniformity of the process, minimizing the amount of scrap generated, and reducing the time and energy consumption. Limitations on production by traditional means such as hand lay-up, vacuum bagging, and in-autoclave methods tend not to be as efficient when the size and shape complexity of the part being produced increases, motivating the search for alternative processes such as automated tape laying (ATL). This work aims to describe the process of modelling and simulating a composite ATL with in situ consolidation by characterizing the machine elements and using the finite differences method in conjunction with energy balances in order to create a digital twin of the process for further control design. The modelling approach implemented is able to follow the process dynamics when changes are made to the heating element and to predict the composite material temperature response, making it suitable for use as a digital twin of a production process using an ATL machine.

Automation and Augmentation

This chapter examines the nature of work where human labor is a complement to machines and considers its import for social wellbeing. While dominant portrayals about the effects of work automation are often characterized by discourses of fear and hype, these have limited utility. The chapter proposes moving beyond fear and hype to consider the ways in which automation alters the organization of work and the human role. It asserts that, although essential, the human role in automation is often obscured. Drawing on the concepts of “fauxtomation,” “heteromation,” and human infrastructures, the chapter makes visible hidden forms of human labor in automated work and maintains that a positive strategy for social well-being is the recognition and revaluation of human work in automated processes.

Knowing the unknown: The hunting loop

There are several ways to improve an organization’s cybersecurity protection against intruders. One of the ways is to proactively hunt for threats, i.e., threat hunting. Threat Hunting empowers organizations to detect the presence of intruders in their environment. It identifies and searches the tactics, techniques, and procedures (TTP) of the attackers to find them in the environment. To know what to look for in the collected data and environment, it is required to know and understand the attacker's TTPs. An attacker's TTPs information usually comes from signatures, indicators, and behavior observed in threat intelligence sources. Traditionally, threat hunting involves the analysis of collected logs for Indicator of Compromise (IOCs) through different tools. However, network and security infrastructure devices generate large volumes of logs and can be challenging to analyze thus leaving gaps in the detection process. Similarly, it is very difficult to identify the required IOCs and thus sometimes makes it difficult to hunt the threat which is one of the major drawbacks of the traditional threat hunting processes and frameworks. To address this issue, intelligent automated processes using machine learning can improve the threat hunting process, that will plug those gaps before an attacker can exploit them. This paper aims to propose a machine learning-based threat-hunting model that will be able to fill the gaps in the threat detection process and effectively detect the unknown adversaries by training the machine learning algorithms via extensive datasets of TTPs and normal behavior of the system and target environment. The model is comprised of five main stages. These are Hypotheses Development, Equip, Hunt, Respond and Feedback stages. This threat hunting model is a bit ahead of the traditional models and frameworks by employing machine learning algorithms.

PARAMETRIC FORMULATION OF THE FLOODABLE LENGTH CURVE: APPLICATION CASE TO OFFSHORE PATROL VESSELS

The residual buoyancy of vessels after damage has a fundamental role in their survivability and it is implemented through adequate ship internal subdivision. Traditionally the number and the position of transverse watertight bulkheads are selected for most ships early in the design phase by means of the “floodable length curve” coupled with the concept of “margin line”. However, for naval vessels, it is more and more common during the acquisition process to explore a wide domain of feasible ships, identified with the assistance of automated processes and assessed also in terms of capabilities, among which is survivability. The generation and the comparison of a considerable number of different ship configurations is very time consuming. Therefore recourse to a parametric expression of the floodable length curve is considered to be a very efficient approach and would thus enable characterisation of the ship, in terms of survivability performance. In this paper such an approach is presented, using an offshore patrol vessel (OPV) as the case study.

Universal Credit and Automated Decision Making: A Case of the Digital Tail Wagging the Policy Dog?

Intended to simplify the benefit system and ’make work pay’, Universal Credit (UC) is the UK’s first ‘digital by design’ benefit. Proponents of UC highlight the greater efficiency and effectiveness of digitalisation, while critics point to costly IT write-offs and the ‘digital divide’ between people with the skills and resources to access digital technologies, and those without. Less attention has been paid to automation in UC and its effects on the people subject to these rapidly developing technologies. Findings from research exploring couples’ experiences of claiming UC suggest that automated processes for assessing entitlement and calculating payment may be creating additional administrative burdens for some claimants. Rigid design parameters built into UC’s digital architecture may also restrict options for policy reform. The article calls for a broadening of thinking and research about digitalisation in welfare systems to include questions of administrative burden and the wider effects and impacts on claimants.

Taxation of artificial intelligences and taxable cases

In order to tax the facts emerging from the computer economy, it is necessary not only to verify whether artificial intelligence is endowed with an autonomous tax subjectivity, but also to ascertain its compatibility with the principle of ability to pay, the basis and limit of taxation. This twofold requirement applies in particular to machines with cognitive skills similar to those of human beings, capable of taking decisions independently and increasing their knowledge. In any event, it must be the case that the subjective suitability of the machine for assuming the tax obligation can be inferred. Within these limits, the provision of a robot tax that does not alter the structure of the tax system could privilege the compensatory view of the social damage caused by technological innovation, in order to take into account the negative externalities related to the automation of production processes in terms of employment and financing of public expenditure. The taxation of robotics would thus affect the production of technological companies, due to the negative externalities resulting from the adoption of automated processes, since these are activities that pursue economic growth objectives.

Preliminary Impact of a Social Connection Program on Older Adults

Background Research demonstrates social connections decrease loneliness and improves life satisfaction among older adults. Unfortunately, the COVID-19 pandemic has limited social connectedness, specifically for older adults. Thus, programs aiming to increase social connectedness among older adults are imperative. Purpose The primary objective of this study was to determine if the telephonic Peer-to-Peer (P2P) program can improve social connectedness and loneliness among older adults. A secondary objective was to determine whether additional improvements in life satisfaction and perception of aging were achieved. Methods Eligible older adults (age 65+) were recruited via outbound calls and/or a mailer. Participants were mailed a T1 survey, completed intervention training, and matched into a dyad. The matched dyad engaged in weekly telephone calls for 12 weeks. Post 12 weeks, participants completed a T2 survey, and a T3 four weeks later. Results Overall, 7,544 individuals were contacted to participate, and 759 expressed interest in participation. A total of 475 participants (62%) completed a T1, 372 (78%) completed training, and 348 (94%) were matched. Gender distribution was skewed towards females (74%), and most were 65-74 years old (53%). Preliminary results show significant differences between lonely and not lonely participants, with lonely participants reporting more negative health associations across all measures. Conclusion Once agreeing to participate, results showed a high likelihood of continuing in P2P, thus demonstrating a social connectedness opportunity for older adults. Delay in mailing and scheduling training may contribute to challenges in attrition. However, developing automated processes utilizing technology may decrease lag time for future phases.

Modelling the Heating Process at Transient and Steady State of an In Situ Tape Laying Machine Head

As use of composite materials increases, the search for suitable automated processes gains relevance to guarantee production quality by ensuring uniformity of the process, minimizing the amount of generated scrap and reducing time and energy consumption. Limitations on production by traditional means such as hand lay-up, vacuum bagging and in-autoclave methods, tend not to be as efficient when the size and shape complexity of the part being produced increases, motivating the search for alternative processes such as the Automated Tape Laying (ATL). This work aims to describe the process of modelling and simulating a composite ATL with in situ consolidation by characterizing the machine elements, using the finite differences method in conjunction with energy balances, in order to create a digital twin of the process for further control design. The modelling approach implemented is able to follow the process dynamics when changes to the heating element are imposed as well as to predict the composite material temperature response, making it suitable to work as a digital twin of a production process using an ATL machine.