The malicious use of artificial intelligence is growing rapidly, creating major security threats for individuals and the healthcare sector. Individuals with mental illness may be especially vulnerable. Healthcare provider data are a prime target for cybercriminals. There is a need to improve cybersecurity to detect and prevent cyberattacks against individuals and the healthcare sector, including the use of artificial intelligence predictive tools.

The Lower Pleistocene bentonite deposits of Eastern Milos, Greece have been formed at the expense of volcaniclastic rocks under submarine conditions. Systematic variation of the major chemical elements reveals that the deposits were formed from different precursors which were erupted from different volcanic centers belonging to at least two separate volcanic provinces. The volcanic eruptions were probably subaqueous. The major authigenic phases are smectite, K-feldspar, opal-CT and the zeolites mordenite and clinoptilolite. The deposits have a complex history and have been affected by hydrothermal alteration.

The geological features of bentonites coupled by the presence of abundant authigenic K-feldspar indicate that alteration of the parent volcanoclastic rocks took place under low temperatures and is probably not related to hydrothermal alteration, which is a separate event. Hydrothermal alteration has modified both the mineralogical characteristics and the properties of bentonites. Alteration of the parent rocks to bentonites was favoured by high water: wall rock ratios and fluid flow and is associated with leaching and subsequent removal of Na, K and Ca. The source of Mg was the parent rocks and only small scale Mg-uptake from the sea water has probably taken place. The formation of authigenic K-feldspar has probably been favoured by a high K+/H+ activity ratio and high Si activity of the pore fluid. Such conditions might have been favoured by the pH conditions and the cooling history of the parent rocks.

With the recent advances in artificial intelligence (AI), patients are increasingly exposed to misleading medical information. Generative AI models, including large language models such as ChatGPT, create and modify text, images, audio and video information based on training data. Commercial use of generative AI is expanding rapidly and the public will routinely receive messages created by generative AI. However, generative AI models may be unreliable, routinely make errors and widely spread misinformation. Misinformation created by generative AI about mental illness may include factual errors, nonsense, fabricated sources and dangerous advice. Psychiatrists need to recognise that patients may receive misinformation online, including about medicine and psychiatry.

Armenian (or , /hɑjeˈɾen/, ISO 639-1 hy) comprises an independent branch of the Indo-European language family.1 Its earliest attested ancestor is Classical Armenian in the fifth century CE (see Godel 1975; Thomson 1989; DeLisi 2015; Macak 2016). Modern Armenian is classified into two dialect families: Eastern Armenian (ISO 639-3 hye) and Western Armenian (ISO 639-3 hyw). Eastern Armenian is spoken in modern-day Armenia, and large speaker communities also exist in Georgia, Russia and Iran (shown in Figure 1). Western Armenian was historically spoken in the Ottoman Empire, but now includes varieties spoken throughout the Armenian diaspora in the Middle East, Europe, and the Americas (Donabédian 2018). There are substantial Western Armenian speaker communities in Turkey (Istanbul), Lebanon (Beirut), Syria (Aleppo, Damascus), California (Fresno, Los Angeles County), France (Marseilles), Australia (Sydney) and Argentina (Buenos Aires). There are also recent diaspora communities of Eastern Armenian speakers in California (Karapetian 2014), as well as communities of Western Armenian speakers in Armenia who escaped the Armenian genocide during World War I, who repatriated after World War II, or who fled the ongoing Syrian civil war. UNESCO lists Western Armenian as an endangered language in Turkey, and there are significant language promotion efforts in many diaspora communities that are intended to combat declining use by speaker generations born in the Americas and Europe (Al-Bataineh 2015; Chahinian & Bakalian 2016).

Eigenvectors of the observability and controllability Gramians represent responsive and receptive flow structures that enjoy a well-established connection to resolvent forcing and response modes. However, whereas resolvent modes have demonstrated great potential to guide sensor and actuator placement, observability and controllability modes have been leveraged exclusively in the context of model reduction via input and output projections. In this work, we introduce interpolatory, rather than orthogonal, input and output projections, that can be leveraged for sensor and actuator placement and open-loop control design. An interpolatory projector is an oblique projector with the property of preserving certain entries in the vector being projected. We review the connection between the resolvent operator and the Gramians, and present several numerical examples where we perform both orthogonal and interpolatory input and output projections onto the dominant forcing and response subspaces. Input projections are used to identify dynamically relevant disturbances, place sensors to measure disturbances, and place actuators for feedforward control in the linearized Ginzburg–Landau equation. Output projections are used to identify coherent structures and place sensors aiming at state reconstruction in the turbulent flow in a minimal channel at $Re_{\tau }=185$. The framework does not require data snapshots and relies only on knowledge of the steady or mean flow.

The next generation of high-power lasers enables repetition of experiments at orders of magnitude higher frequency than what was possible using the prior generation. Facilities requiring human intervention between laser repetitions need to adapt in order to keep pace with the new laser technology. A distributed networked control system can enable laboratory-wide automation and feedback control loops. These higher-repetition-rate experiments will create enormous quantities of data. A consistent approach to managing data can increase data accessibility, reduce repetitive data-software development and mitigate poorly organized metadata. An opportunity arises to share knowledge of improvements to control and data infrastructure currently being undertaken. We compare platforms and approaches to state-of-the-art control systems and data management at high-power laser facilities, and we illustrate these topics with case studies from our community.