Categorical color perception shown in a cross-lingual comparison of visual search

Categorical perception (CP) for colors entails that hues within a category look more similar than would be predicted by their perceptual distance. We examined color CP in both a UK and a remote population (Himba) for newly acquired and long-established color terms. Previously, the Himba language used the same color term for blue and green but now they have labels that match the English terms. However, they still have no color terms for the purple areas of color space. Hence, we were able to investigate a color category boundary that exists in the Himba language but not in English as well as a boundary that is the same for both. CP was demonstrated for both populations in a visual search task for one different hue among 12 otherwise similar hues; a task that eliminated concerns of label matching. CP was found at the color-category boundaries that are specific to each language. Alternative explanations of our data are discussed and, in particular, that it is the task-dependent use of categorical rather than non-categorical (perceptual) color networks which produces CP. It is suggested that categorical networks for colors are bilaterally represented and are the default choice in a suprathreshold similarity judgment

XGBoost Model for Predicting Property Prices in the UK Real Estate Market

Abstract. This study contributes to the enhancement of the predictive models and mitigating regression problem, using data science machine learning approach. It critically evaluates studies that have been done within the predictive valuation, forecasting models, using the current technological trends in machine learning, through the proposed framework: Sustainable Feature Machine Learning Agile Framework (SFMLAF). SFMLAF suggests that our proposed model based on XGBoost demonstrated better accuracy based on these results; utilizing the following validation metrics: XGBoost RMSE: 0.444, MAPE: 1.94%, MAE: 0.234. The required datasets were sourced online from the UK government property sold dataset, under the Open Government licence v.3.0, focusing on four UK cities with the following data observations: London: 658,337, Peterborough: 44,635, Leeds: 102,984, and Manchester: 154,626. In conclusion, the proposed predictive model aims to deliver practical benefits for real estate professionals, homebuyers and sellers by enhancing the accuracy and reliability of property valuation in the UK market

Performance of Higher-Order Networks in Reconstructing Sequential Paths: from Micro to Macro Scale

Activities such as the movement of passengers and goods, the transfer of physical or digital assets, web navigation and even successive passes in football, result in timestamped paths through a physical or virtual network. The need to analyse such paths has produced a new modelling paradigm in the form of higher-order networks which are able to capture temporal and topological characteristics of sequential data. This has been complemented by sequence mining approaches, a key example being sequential motifs measuring the prevalence of recurrent subsequences. Previous work on higher-order networks has focused on how to identify the optimal order for a path dataset, where the order can be thought of as the number of steps of memory encoded in the model. In this paper, we build on these approaches to consider which orders are necessary to reproduce different path characteristics, from path lengths to counts of sequential motifs, viewing paths generated from different higher-order models as null models which capture features of the data up to a certain order, and randomise otherwise. Furthermore, we provide an important extension to motif counting, whereby cases with self-loops, starting nodes, and ending nodes of paths are taken into consideration. Conducting a thorough analysis using path lengths and sequential motifs on a diverse range of path datasets, we show that our approach can shed light on precisely where models of different order overperform or underperform, and what this may imply about the original path data

Subjectivity Without Sex? The Materialist Trans Feminist Potential in Monique Wittig’s Non-Fiction

In proposing that the categories of sex must be transcended, Monique Wittig’s non-fiction is ripe with trans feminist potential. Yet her arguments are beset by a paradox. On the one hand, ‘male’ and ‘female’ are presented as purely relational categories with no fixed content. On the other, the category of ‘man’ is essentialized as possessing a uniquely oppressive consciousness which no ‘woman’ can achieve. After exploring Wittig’s insurrectory ‘lesbian’ as a category of subjectivity without sex, this article highlights the implicit racism and transition phobia animating Wittig’s representation of sex difference and raises broader concerns about radical feminist projects of gender abolition

Theoretical investigation of functionalized diamond-like carbon with COOH, OH and NH2: a comprehensive DFT-D study

In this study, the functionalization of diamond-like carbon (DLC) with carboxyl (COOH), hydroxyl (OH) and amine (NH₂) groups was investigated to understand its impact on the structural, electronic and nonlinear optical (NLO) properties. Dispersion-corrected density functional theory (DFT-D) calculations using the B3LYP-D3(BJ) exchange–correlation functional were performed in all calculations. The results indicated that functionalization with these groups enhanced the reactivity of the DLC surface. Molecular reactivity descriptors revealed that COOH − DLC exhibited the highest softness (S = 0.25 eV), significant electrophilicity (ω = 2.55 eV) and a reduced energy gap (∆Eg = 3.97 eV). Time-dependent DFT (TD-DFT) analysis showed that COOH − DLC achieved the maximum absorption wavelength among the systems investigated. Additionally, functionalization improved the NLO properties, including increased polarity, with COOH − DLC displaying the highest first hyperpolarizability value. Natural bond orbital (NBO) analysis indicated significant orbital delocalization between the functional groups and the pristine DLC surface. Quantum theory of atoms in molecules (QTAIM) and non-covalent interaction (NCI) analyses, based on the reduced density gradient (RDG), provided a detailed characterization of interactions, highlighting the presence of van der Waals forces

Disaggregating Time-Series with Many Indicators: An Overview of the DisaggregateTS Package

Low-frequency time-series (e.g., quarterly data) are often treated as benchmarks for interpolating to higher frequencies, since they generally exhibit greater precision and accuracy in contrast to their high-frequency counterparts (e.g., monthly data) reported by governmental bodies. An array of regression-based methods have been proposed in the literature which aim to estimate a target high-frequency series using higher frequency indicators. However, in the era of big data and with the prevalence of large volumes of administrative data-sources there is a need to extend traditional methods to work in high-dimensional settings, i.e., where the number of indicators is similar or larger than the number of low-frequency samples. The package DisaggregateTS includes both classical regressions-based disaggregation methods alongside recent extensions to high-dimensional settings. This paper provides guidance on how to implement these methods via the package in R, and demonstrates their use in an application to disaggregating CO2 emissions

Schizophrenia more employable than depression? Language-based artificial intelligence model ratings for employability of psychiatric diagnoses and somatic and healthy controls

Artificial Intelligence (AI) assists recruiting and job searching. Such systems can be biased against certain characteristics. This results in potential misrepresentations and consequent inequalities related to people with mental health disorders. Hence occupational and mental health bias in existing Natural Language Processing (NLP) models used in recruiting and job hunting must be assessed. We examined occupational bias against mental health disorders in NLP models through relationships between occupations, employability, and psychiatric diagnoses. We investigated Word2Vec and GloVe embedding algorithms through analogy questions and graphical representation of cosine similarities. Word2Vec embeddings exhibit minor bias against mental health disorders when asked analogies regarding employability attributes and no evidence of bias when asked analogies regarding high earning jobs. GloVe embeddings view common mental health disorders such as depression less healthy and less employable than severe mental health disorders and most physical health conditions. Overall, physical, and psychiatric disorders are seen as similarly healthy and employable. Both algorithms appear to be safe for use in downstream task without major repercussions. Further research is needed to confirm this. This project was funded by the London Interdisciplinary Social Science Doctoral Training Programme (LISS-DTP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Putting the Femme in Feminist: Trans Feminism and the ‘Male Lesbian’ in the American Second Wave

A slur, a joke or a post-structuralist case of mistaken identity. To the extent that the male lesbian has been discussed, she has figured dismissively. Yet throughout the period historicised as American feminism's second wave, potentially thousands of trans femmes organised under this identity. Despite being entirely overlooked in scholarship, the lesbian feminism articulated by a community of femme-for-femme trans femmes in the 1970s constitutes one of the most enduring and intellectually significant subsets of lesbian feminism to come out of the second wave. That they have yet to be historicised and theorised represents an injustice at the level of epistemology itself, wherein trans women are able to speak as trans, but not as lesbians. Reconstructing the archive of trans lesbian feminism that was developed by Sally Douglas in 1970 and then popularised through her organisation the Salmacis Society the year after, this article proposes that the existence of Salmacis disrupts dominant ideas of necessary antagonisms between ‘trans’ and ‘lesbian’ in the 1970s, and we highlight how the distinctly trans, sex-positive, lesbian femme-inism of the organisation can reanimate lesbian feminism today

The impact of the Russia-Ukraine war on global supply chains: a systematic literature review

This systematic review examines the multifaceted impacts of the Russia-Ukraine war on global supply chains. Following PRISMA methodology, we analyze 22 peer-reviewed studies published between 2022 and 2025 to identify key disruption patterns, sectoral vulnerabilities, regional impacts, and adaptive strategies. Our findings reveal significant disruptions across food, energy, and critical materials sectors, with asymmetric regional vulnerabilities particularly affecting developing economies. The review identifies five major impact domains: (1) food security disruptions, (2) energy market volatility, (3) critical material shortages, (4) transportation bottlenecks, and (5) financial market responses. We document emerging adaptation strategies including supply diversification, strategic reserves development, and accelerated digitalization. The findings suggest permanent shifts in global supply chain configurations and trade relationships that will persist beyond the conflict’s resolution. This review contributes to both academic understanding of supply chain vulnerability to geopolitical shocks and provides practical insights for logistics professionals developing resilience strategies