This paper explores the use of mindset material in terrorism cases. Mindset material is a broad category of evidence, including social media activity and internet search histories, relied on to infer or imply that the accused is affiliated or aligned with terrorists. Although mindset material plays a central role throughout the justice process in terrorism cases, no work to date has explored and discussed its use in depth. In this paper I draw on doctrinal and empirical findings, including interviews, to examine how and why it has come to play such a central role in terror cases. While mindset material is a well-intentioned tool used to selectively enforce broadly drafted and vaguely defined terrorism offences, it is also a blunt tool, and sees great emphasis placed on the accused's inferred status as terrorist (or not). In this paper I explore and problematise the use of mindset material and the inferences drawn from it, specifically in relation to to fantasists, the merely curious, and young autistic defendants. I posit that mindset material is symptomatic of a need to revisit the substantive law and to rethink the proper role of the criminal law in preventing terrorism.

Understanding how to improve the physical and cognitive accessibility of visitor economy businesses and organisations wanting to offer nature-based outdoor pursuits for people with dementia is key to supporting their inclusion and agency. The aim of this qualitative study was to understand the experiences, needs and preferences of people with dementia participating in nature-based outdoor pursuits in their leisure time. Semi-structured interviews were conducted with 15 people with dementia and 15 family members and subjected to thematic analysis. Four themes related to inclusion for people with dementia and their family members reflected diversity in individual needs and preferences for engaging with nature-based outdoor pursuits, their own adaptations to maintain access including accommodating risk, how cognitive and physical accessibility can be supported by businesses, and which practical and psychosocial barriers prevent inclusion. Learning from people with dementia and their family members has helped bridge the gap to their inclusion in nature-based outdoor pursuits. Their insights will inform the development of such pursuits by businesses and organisations as well as future work into risk decision-making.

We present the Pilot Survey Phase 2 data release for the Wide-field ASKAP L-band Legacy All-sky Blind surveY (WALLABY), carried-out using the Australian SKA Pathfinder (ASKAP). We present 1760 H i detections (with a default spatial resolution of 30′′) from three pilot fields including the NGC 5044 and NGC 4808 groups as well as the Vela field, covering a total of $\sim 180$ deg$^2$ of the sky and spanning a redshift up to $z \simeq 0.09$. This release also includes kinematic models for over 126 spatially resolved galaxies. The observed median rms noise in the image cubes is 1.7 mJy per 30′′ beam and 18.5 kHz channel. This corresponds to a 5$\sigma$ H i column density sensitivity of $\sim 9.1\times10^{19}(1 + z)^4$ cm$^{-2}$ per 30′′ beam and $\sim 20$ km s$^{-1}$ channel and a 5$\sigma$ H i mass sensitivity of $\sim 5.5\times10^8 (D/100$ Mpc)$^{2}$ M$_{\odot}$ for point sources. Furthermore, we also present for the first time 12′′ high-resolution images (“cut-outs”) and catalogues for a sub-sample of 80 sources from the Pilot Survey Phase 2 fields. While we are able to recover sources with lower signal-to-noise ratio compared to sources in the Public Data Release 1, we do note that some data quality issues still persist, notably, flux discrepancies that are linked to the impact of side lobes associated with the dirty beams due to inadequate deconvolution. However, in spite of these limitations, the WALLABY Pilot Survey Phase 2 has already produced roughly a third of the number of HIPASS sources, making this the largest spatially resolved H i sample from a single survey to date.

This study investigates the stigma against people with mental illness in Bangladesh through in-depth interviews with 14 patients and 9 healthcare professionals, and 33 focus group discussions with people without mental illness. The research has delved into the understanding of different types of stigma against mental illness in the context of Bangladesh. The findings revealed four types of stigma which were categorized into four themes namely self-stigma, public stigma, professional, and institutional stigma. Patients had internalized negative attitudes, thereby discriminated toward themselves. The public discriminated against patients because of believing in prejudices against them. Other health professionals had negative conceptions toward patients, and they devalued mental health professionals (MHPs). A culture of negative attitude and belief had emerged in institutional settings which encouraged discrimination. Policymakers and healthcare professionals can use the findings to develop a mental health service by addressing the stigma. Mental health practitioners can assess the impact of stigma to improve the mental well-being of their patients. Students and workplace staff will benefit from intentional or unintentional discrimination in educational institutions and workplace settings by addressing the effects of stigma. Importantly, other health care providers will be aware of their thoughts against patients and MHPs.

We develop a novel method for image segmentation of 3D confocal microscopy images of emerging hematopoietic stem cells. The method is based on the theory of persistent homology and uses an optimal threshold to select the most persistent cycles in the persistence diagram. This enables the segmentation of the image’s most contrasted and representative shapes. Coupling this segmentation method with a meshing algorithm, we define a pipeline for 3D reconstruction of confocal volumes. Compared to related methods, this approach improves shape segmentation, is more ergonomic to automatize, and has fewer parameters. We apply it to the segmentation of membranes, at subcellular resolution, of cells involved in the endothelial-to-hematopoietic transition (EHT) in the zebrafish embryos.

The procreation asymmetry is a widely held view in ethics, claiming that one should make existing people happy but has no reason to make happy people. Here, I shall present a new objection demonstrating from modest premises that one has a reason to take a sequence of actions that simply creates a happy person; yet this judgment in combination with plausible principles about sequences of actions entails that one has some reason to simply create a happy person. Additionally, I will argue that one's reasons to create a happy person are quite strong.

What explains voter attitudes toward immigration in Latin America? This article argues that increased refugee arrivals moderate the impact of social identities on immigration attitudes. We propose that informational cues associated with increased immigration make cosmopolitan identities less important—and exclusionary national identities more important—determinants of immigration preferences. Analyzing 12 Latin American countries from the 2017–2022 wave of the World Values Survey, we demonstrate that cosmopolitanism is positively associated with pro-immigration attitudes, but only in countries experiencing low-to-moderate refugee inflows. Conversely, nationalism is negatively associated with pro-immigrant attitudes, and increasingly so as refugee inflows increase. The uneven distribution of refugee migration has therefore reshaped public opinion in Latin America by moderating the effects of competing social identities (i.e., cosmopolitanism and nationalism). These findings contribute to broader debates on the behavioral impacts of immigration by highlighting an indirect mechanism by which increased immigration may generate anti-immigrant hostility.

In this paper, we consider a delayed discrete single population patch model in advective environments. The individuals are subject to both random and directed movements, and there is a net loss of individuals at the downstream end due to the flow into a lake. Choosing time delay as a bifurcation parameter, we show the existence of Hopf bifurcations for the model. In homogeneous non-advective environments, it is well known that the first Hopf bifurcation value is independent of the dispersal rate. In contrast, for homogeneous advective environments, the first Hopf bifurcation value depends on the dispersal rate. Moreover, we show that the first Hopf bifurcation value in advective environments is larger than that in non-advective environments if the dispersal rate is large or small, which suggests that directed movements of the individuals inhibit the occurrence of Hopf bifurcations.

We present a dynamic pay-how-you-drive pricing scheme for motor insurance using telematics signals. More specifically, our approach allows the insurer to apply penalties to a baseline premium on the occurrence of events such as hard acceleration or braking. In addition, we incorporate a bonus-malus system (BMS) adapted for telematics data, providing a credibility component based on past telematics signals to the claim frequency predictions. We purposefully consider a weekly setting for our ratemaking approach to benefit from the signal’s high-frequency rate and to encourage safe driving via dynamic premium corrections. Moreover, we provide a detailed structure that allows our model to benefit from historical records and detailed telematics data collected weekly through an onboard device. We showcase our results numerically in a case study using data from an insurance company.

The gastrointestinal (GI) tract plays a critical role in nutrition and the pathophysiology of disease, and there is an increasing variety of methodologies available for the assessment of various aspects of GI physiology. Advancements in assessment methods, including techniques to study gut motility, fermentation, permeability, and microbiota composition, have provided researchers with powerful tools to investigate the impact of diet on GI tract physiology and the microbiota-gut-brain axis. Mechanistic evidence from reverse translational studies, which apply findings from human studies to preclinical models in a ‘bedside-to-bench’ approach, have also enhanced our understanding of the bidirectional interactions and candidate signalling molecules among the diet-gut-brain relationship. Interpreting data from these advanced techniques and study designs requires a thorough understanding of their principles, applications, and limitations. This review aims to summarise the methodological advances in GI tract physiology measurements and their application in nutritional studies, focusing on gut motility, fermentation, and permeability. We will present examples of how these techniques have been utilised in recent research, discuss their advantages and limitations, and provide insights on their use and interpretation in research. Understanding the capabilities and limitations of these tools is crucial for designing robust studies and elucidating the complex interplay between diet and the GI tract. The scope of this review encompasses recent advancements in GI tract assessment methodologies and their implications for nutritional research, providing a comprehensive overview for researchers in the field.

The Prof pegmatite is located NW of Revelstoke, British Columbia, Canada on Boulder Mountain. Due to the abundance of petalite, the pegmatite is classified as a petalite subtype Li-Cs-Ta pegmatite or a Group one pegmatite. The Prof pegmatite contains a suite of minerals indicative of a highly evolved pegmatite melt including petalite, elbaite, lepidolite and Nb–Ta oxides. Four textural zones are present: (1) border; (2) intermediate, including (2.1) graphic texture dominant and (2.2) overgrowth dominant, where diverse minerals form rims around one another; (3) central; and (4) quartz. The border zone has a similar mineralogy to the intermediate zone and is interpreted to represent a chilled margin. The intermediate zone has a feldspar, mica, garnet and dravite–schorl dominant composition. The central zone hosts an evolved pegmatite core, which contains the majority of the lithium mineralisation composed of petalite, elbaite and lepidolite. The tourmaline, Nb–Ta oxides and mica within the pegmatite record the geochemical evolution of the melt from more primitive Fe- and Mg-rich minerals to a Li-, Mn- and Nb-rich assemblage indicative of a highly evolved geochemical system. The various pegmatitic textures and extremely fractionated geochemical composition of the pegmatite indicate that the melt was undercooled and crystallised rapidly. Three phases of metasomatism are recognised in the Prof pegmatite: an albitisation event observed cutting primary orthoclase; followed by a transition to a Na–Li–F-rich event mostly containing secondary albite, trilithionite and elbaites; and a sericitisation event.

The Prof pegmatite has a similar mineralogy to known pegmatites at Mount Begbie, 15 km to the south, in particular the notable presence of the rare mineral qitianlingite, petalite, lepidolite and elbaite. Together, these pegmatite bodies form part of an extensive, poorly mapped pegmatite field. Additional work is required to assess the extent and nature of mineralisation within this field.

Precipitation is one of the most relevant weather and climate processes. Its formation rate is sensitive to perturbations such as by the interactions between aerosols, clouds, and precipitation. These interactions constitute one of the biggest uncertainties in determining the radiative forcing of climate change. High-resolution simulations such as the ICOsahedral non-hydrostatic large-eddy model (ICON-LEM) offer valuable insights into these interactions. However, due to exceptionally high computation costs, it can only be employed for a limited period and area. We address this challenge by developing new models powered by emerging machine learning approaches capable of forecasting autoconversion rates—the rate at which small droplets collide and coalesce becoming larger droplets—from satellite observations providing long-term global spatial coverage for more than two decades. In particular, our approach involves two phases: (1) we develop machine learning models which are capable of predicting autoconversion rates by leveraging high-resolution climate model data, (2) we repurpose our best machine learning model to predict autoconversion rates directly from satellite observations. We compare the performance of our machine learning models against simulation data under several different conditions, showing from both visual and statistical inspections that our approaches are able to identify key features of the reference simulation data to a high degree. Additionally, the autoconversion rates obtained from the simulation output and satellite data (predicted) demonstrate statistical concordance. By efficiently predicting this, we advance our comprehension of one of the key processes in precipitation formation, crucial for understanding cloud responses to anthropogenic aerosols and, ultimately, climate change.