We study the singularities of varieties obtained as infinitesimal quotients by $1$-foliations in positive characteristic. (1) We show that quotients by (log) canonical $1$-foliations preserve the (log) singularities of the MMP. (2) We prove that quotients by multiplicative derivations preserve many properties, amongst which most F-singularities. (3) We formulate a notion of families of $1$-foliations, and investigate the corresponding families of quotients.

The paper outlines Czech constitutional law and the development of emergency law. Initially, the legislature did not expect emergencies to occur, perhaps due to the idealistic optimism associated with the general atmosphere of the collapse of the Eastern Bloc in 1989 and the “End of History” thesis. As a result, emergencies were not regulated by Czech law in the 1990s. This changed after the great floods at the end of the 1990s, when “history returned,” and the need for some special rules for emergencies became clear. The first decades of this century showed that Czech emergency law worked well for short-term natural disasters. The game-changer came in 2020, with the emergence of the COVID-19 pandemic and the need for a long-term state of emergency. It soon became clear that the rules that worked for floods and other disasters did not work for long-term global pandemics. In other words, the legal system was not prepared for a situation in which emergencies were the rule rather than the exception. Legislators were unable to prepare a long-term legislative response to fill this gap. The memory of COVID-19 is fading fast, and there are no plans to reform the relevant legislation. Accordingly, any new pandemic or similar event will lead to the same problems that the Czech legal system had to deal with from 2020 to 2022.

Ketamine, originally synthesized in 1962, has gained increasing attention due to its rapid and sustained antidepressant effects in treatment-resistant depression (TRD). Unlike traditional antidepressants, ketamine acts on multiple neurotransmitter systems, including NMDA receptor antagonism and AMPA receptor potentiation, offering a novel mechanism for mood regulation. Beyond psychiatry, ketamine remains a valuable anesthetic and analgesic agent with applications in acute and chronic pain management. Its anti-inflammatory, neuroprotective, and potential antitumor properties further underscore its versatility in clinical medicine. Despite its therapeutic promise, ketamine poses challenges due to its dissociative and hallucinogenic effects, contributing to widespread recreational use. Chronic misuse is associated with adverse health outcomes, including bladder dysfunction and hepatobiliary complications. As a result, ketamine has been subject to increasing regulatory scrutiny, balancing its medical utility with concerns about abuse potential. The rediscovery of ketamine’s antidepressant effects in the early 2000s has revolutionized psychiatric treatment, particularly in TRD and suicidality. Recent studies have also explored its efficacy in bipolar depression, posttraumatic stress disorder, obsessive-compulsive disorder, and substance use disorders, expanding its therapeutic applications. Additionally, ongoing research aims to elucidate its long-term effects and optimize dosing regimens to maximize clinical benefits while minimizing risks. This review highlights ketamine’s multifaceted pharmacological properties, its evolution from an anesthetic to a novel psychiatric treatment, and its broader medical applications. As research advances, a comprehensive understanding of ketamine’s mechanisms and clinical implications will be crucial for optimizing its therapeutic potential while mitigating its risks.

This study addresses challenges in sensor fusion for accurate and robust joint orientation estimation in human movement analysis using wearable inertial measurement units (IMUs). A magnetometer-free refined Kalman filter (KF) approach is presented and validated to address various indoor environmental constraints and challenges posed by human movement. These include variability in motion and dynamics, as well as magnetic disturbances caused by ferromagnetic materials or electronic interferences. Our proposed approach utilizes a Kalman-filter-based framework that analyzes the accelerometer’s alignment with the Earth’s frame to estimate orientation and correct gyroscope readings, eliminating reliance on magnetometer inputs. The algorithm was tested on both controlled robotic movements and real-world upper-limb-motion-monitoring scenarios. First, a comparative analysis was conducted on the double-stage Kalman filter (DSKF) and complementary filter using the collected robot motion encoder data. The results demonstrated superior performance in orientation estimation, particularly in yaw measurements, where the proposed method significantly improved accuracy. It achieved a lower root mean square error (RMSE = $ {2.447}^{\circ } $) and mean absolute error (MAE = $ {2.006}^{\circ } $), outperforming both the DSKF and complementary filter approaches. Additionally, the study’s findings were validated against a standard motion capture system, revealing error metrics within generally acceptable ranges ($ \le 12.4\% $ of the joint range of motion [ROM]) and strong correlation coefficients ($ {r}^2>0.89 $). However, some deviations were observed during complex motion cycle intervals, highlighting opportunities for further refinement. These findings suggest that the proposed approach presents a promising alternative for human joint orientation estimation in industrial settings with magnetic distortions.

Paternity leave may promote greater gender equality in domestic labour. Though numerous studies show that paternity leave promotes greater fathers’ involvement in childcare, less is known about whether paternity leave-taking may facilitate fathers’ involvement in other forms of domestic labour such as housework. Using repeated cross-sectional data on different-gender partnered US parents from the Study on Parents’ Divisions of Labor During COVID-19 (SPDLC), this study examines the extent to which paternity leave-taking and length of paternity leave are associated with US fathers’ shares of, and time spent on, housework. Findings suggest that paternity leave-taking is positively associated with fathers’ shares of, and time spent on, housework tasks. Longer paternity leaves are also associated with fathers performing greater shares of housework. Overall, this study indicates that the benefits of paternity leave likely extend to fathers’ greater participation in housework, providing additional support for the belief that increased use of paternity leave may help to promote gender equality in domestic labour.

Against the proliferation of large language model (LLM) based Artificial Intelligence (AI) products such as ChatGPT and Gemini, and their increasing use in professional communication training, researchers, including applied linguists, have cautioned that these products (re)produce cultural stereotypes due to their training data. However, there is a limited understanding of how humans navigate the assumptions and biases present in the responses of these LLM-powered systems and the role humans play in perpetuating stereotypes during interactions with LLMs. In this article, we use Sequential-Categorial Analysis, which combines Conversation Analysis and Membership Categorization Analysis, to analyze simulated interactions between a human physiotherapist and three LLM-powered chatbot patients of Chinese, Australian, and Indian cultural backgrounds. Coupled with analysis of information elicited from LLM chatbots and the human physiotherapist after each interaction, we demonstrate that users of LLM-powered systems are highly susceptible to becoming interactionally entrenched in culturally essentialized narratives. We use the concepts of interactional instinct and interactional entrenchment to argue that whilst human–AI interaction may be instinctively prosocial, LLM users need to develop Critical Interactional Competence for human–AI interaction through appropriate and targeted training and intervention, especially when LLM-powered tools are used in professional communication training programs.

The use of camera traps in wildlife conservation and ecological research is a popular method of data capture due in large part to the perceived low interference levels for the animals being studied. However, evidence exists that some species alter their behaviour when exposed to this technology. The primary aim of this study was to address whether researchers working with this technology in the ecology and forestry fields are making considerations for the possible impacts of cameras on animal behaviour. A secondary aim was to investigate how the use of this technology is framed in recent publications. In this rapid systematic literature review, we conducted a search on Web of Science and we identified 267 papers published in the last five years, in the fields of ecology and forestry, that met our inclusion criteria. We screened the studies for mentions of the impact of camera traps on the welfare of wildlife. Surprisingly, only 7.5% of the papers considered the possible animal welfare impacts of camera use on the wildlife species of interest in their study, with most comparing it to invasive methods and therefore framing this technology positively. We strongly encourage researchers working in this field to consider the impact of this technology on the specific species being studied. Whilst we recognise that the use of camera traps avoids direct handling of the animals, the short- and long-term effects of using this technology should not be ignored and should, at a minimum, be acknowledged in the limitations.