Countries with stronger support to gender equality tend to invest in policies supportingthe dual-earner family model or the earner/carer family model, while countrieswith stronger support to conservative gender norms tend to have policies supportingthe male breadwinner family model. However, gender equality norms can beendorsed by the majority of the population at the national level, whilst conservativenorms could still be largely supported at the sub-national level, potentially leadingto lower uptake of parental leave among fathers in more conservative areas.This study seeks to examine shifting norms in fathers’ parental leave uptakein Finnish municipalities in the 2010s, around the first reform that gave fathersan independent right to a 6-week quota of “solo” parental leave. We applied aBayesian spatio-temporal model on administrative data from Finnish municipalitiesand estimated local norms based on voting data and content analysis of politicalmanifestos. Furthermore, we used the proportion of Swedish residents as a proxyfor cross-border policy influences from the neighbouring country Sweden, wherepaternal leave-taking has been a longer phenomenon.Local support to de-familialising policies was found to predict higher leavetaking,but only before the 2013 reform when fathers’ quota use shortened mothers’leave. The proportion of Swedish-speaking residents was found to be increasinglyimportant for predicting paternal leave-taking. We interpret this result as a sign ofcross-border influences from Sweden. Interestingly, uptake increased the fastest inthe most conservative region, probably due to its strong linguistic and cultural linksto Sweden. Furthermore, we observed spatial dependencies between neighbouringmunicipalities, which supports our spillover hypothesis; that the interactions amongfamilies nearby lead them to adopt new practices.These results trigger new theoretical considerations concerning the role of gendernorms in affecting citizens’ behaviour in the family policy field and how these normsinteract with policy reforms, the role of national institutions in affecting welfare statepreferences at the local level, and the importance of community socialisation.

The degradation of solar photovoltaic (PV) modules over time, aggravated by defects, significantly affects the performance of utility-scale PV parks. This study presents a quantitative assessment of the power loss from module defects, and evaluates the cost-effectiveness of replacing defective modules at various stages of degradation. A module test site was established in Norway with six different defects, and continuous thermographic monitoring, combined with light IV measurements and electroluminescence (EL) imaging, provides partial support for further calculations on the long-term effects of the defects. The cumulative module energy loss is calculated over a 25-year park lifespan under both Norwegian and Chilean environmental conditions, the latter representing higher solar irradiation levels. The energy gain from replacing the defective modules at various stages of degradation is compared to the costs of replacement, both for infant-life failures and mid-life failures. Minor infant-life defects of 1\% power loss are likely not beneficial to replace in low-irradiation regions like Norway. For Chilean conditions it can be cost-effective, but primarily if the module is replaced around mid park life, which gives a larger yield when replaced with a new module. For more severe defects of 10\% loss the replacement gain is above the replacement cost for high-irradiation locations, and replacing the 33\% power loss defect is cost-effective for both locations, even when discovered late in the park lifetime. Mid-life defects are primarily beneficial to replace in high-irradiation locations.

This paper makes an in-depth analysis of consciousness phenomenon from three aspects: the essence of consciousness， the systematic analysis of conscious subjective content understanding and the essence of conscious intelligence. First of all， this paper discusses the essence of consciousness， puts forward the theoretical framework of subject angle， and points out that consciousness is the content change of subject angle， which corresponds to the structural change of entity one by one. Then， through concrete examples， such as animal conscious neural network， this paper expounds in detail how consciousness is related to the main structure， neural network structure and the objective world. Finally， this paper discusses the essence of conscious intelligence， and points out that conscious intelligence is a model in which multi-cells cooperate with the whole to record natural change data and form a data structure model， so that changes can be predicted in advance to avoid disadvantages through data structure model reasoning.

Background:Social media's rapid evolution has increased exposure to suspected suicides, driven by the democratisation of media content through polymediated transmission. There is a wide gap in our understanding of the impact of this phenomenon, and no research from preventive public and mental health perspectives. This study aimed to examine social media exposure to suspected suicide and its impact on communities from the perspectives of public and mental health. Methods:Knowledge Exchange workshops and in-depth interviews with public health and mental health professionals who work in a responding capacity following a suspected suicide informed the study. Their observations and experiences provided insights into polymediated exposure: how platforms operate, user behaviour, context of use, impact on communities and challenges and opportunities for intervention. A thematic and narrative analysis of qualitative data was conducted. Results:The study emphasises polymedia as a theoretical framework for understanding how the intertwining of diverse social media platforms transforms the way narratives relating to suspected suicides unfold. Analysis of the communicative ecology of social media highlighted the dual potential of social media content to both amplify and mitigate the risk of suicide. Data underscored the role of social media in fuelling increased exposure to harmful discourse in multiple and often unexpected ways, contributing to the copycat effect. Responsible content moderation and protective discourse can reverse these effects; however, these efforts may be compromised by algorithms that favour the Werther effect and social media’s connection to users’ reduced attention span, which affects their capacity to think before posting.Conclusion: This study contributes important new knowledge into the ways in which social media transforms exposure to suicide and compelling new evidence for direct impact on communities. As the public play an increasingly active role in generating new forms of media and narratives surrounding suspected suicides, Polymedia theory and the communicative ecology of social media emerge as pivotal factors that warrant a more prominent role in shaping suicide prevention policy. Responding to and managing social media risks requires a new, coordinated public health approach that goes beyond existing guidelines. There is a clear call for the augmentation of existing guidelines to encompass the dynamic nature of social media's influence, involving public health and allied organisations in this crucial endeavour.Keywords: suicide prevention, social media, exposure, polymedia, public health, mental health, postvention, communities, impact.

The ICJ has de facto adopted the functional approach to occupation with regard to Gaza. The Opinion is thus a critical point in the development of the law of occupation, in that it transcends a binary approach to the question of the existence of occupation, in favour of a more nuanced approach that enables holding that a territory is occupied, but not in an “all or nothing” way. More generally, the Opinion as rejects a more restrictive approach to the question of whether occupation exists in a territory or not in favour of a more flexible approach.

This article presents a compact visual colorimeter for the purpose of measuring colour-matching functions (CMFs) of individual observers through psychophysical experiments. Constructed from 3D-printed parts, optical elements, and LED-based light engines, the colorimeter facilitates the juxtaposition of two fields to create a bipartite field. The system underwent characterisation to evaluate factors that may impact colour-matching experiments, such as LED-light stability, spatial homogeneity of the bipartite field, and potential stray-light leakage. The study aimed to assess the accuracy and performance of the system in measuring individual observer CMFs. Results indicate that the system is stable enough to measure both intra- and inter-observer variations in CMFs.

The categorization of complex real-world stimuli, such as facial expressions, appears to vary greatly between people. This raises a crucial methodological challenge: how is it possible to elicit the mental representation of a complex category for a specific individual? Comprehensive category-elicitation methods such as Markov Chain Monte Carlo with People (MCMCP) work well across populations, but converge too slowly to be usable with individual participants. Here, we address the problem of slow convergence with a new method: combining MCMCP with an adapted Variational Auto-Encoder (VAE) acting as a “gatekeeper”. We tested this approach in a new experiment (N=90) on facial affect comparing MCMCP with the “gatekeeper” (MCMCPG) against baseline MCMCP and other variants. MCMCPG converged substantially faster than the other methods, in about 10 minutes for our task, with showing more representative recovered faces than its competitors. Further analyses captured participants’ substantial individual differences in a categorization task at an individual level. And, uniquely, the resulting model generalized these individual differences to real-world faces outside of our training set. Our study demonstrates the potential of MCMCPG for investigating generalizable human representations of complex stimuli at the individual level and illustrates the power of integrating Artificial Intelligence into psychological experiments.

We describe a system for identifying dog emotions based on the dogs' facial expressions and body posture. Towards that goal, we built a dataset with 2184 images of ten popular dog breeds, grouped into seven similarly sized primal mammalian emotion categories defined by neuroscientist and psychobiologist Jaak Panksepp that are ‘Exploring’, ‘Sadness’, ‘Playing’, ‘Rage’, ‘Fear’, ‘Affectionate’ and ‘Lust’. We modify the Contrastive Learning framework MoCo (Momentum Contrast for Unsupervised Visual Representation Learning) to train it on our original dataset and achieve an accuracy of 43.2% on a baseline of 14%. We also trained this model on a second publicly available dataset that resulted in an accuracy of 48.46% but had a baseline of 25%. We compared our unsupervised approach with a supervised model based on a ResNet50 architecture. This model when tested on our dataset having seven Panksepp labels resulted in an accuracy of 74.32%.