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Sexual and Gender Diversity in Thailand: Associations with Recalled Childhood Sex-Typed Behavior and Adulthood Occupational Preferences
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Data are available publicly at https://doi.org/10.5683/SP3/4JUNKF.Supplementary Information is available online at: https://link.springer.com/article/10.1007/s10508-025-03121-6#Sec13 .Same-sex attracted individuals report greater levels of sex-atypical childhood behaviors and adulthood occupational preferences when compared with their heterosexual counterparts. While these sexual orientation differences are well established, the extent to which gender-role presentation relates to such differences is unclear. The present study examined recalled childhood sex-(a)typical behaviors (CSAB) and adulthood occupational preferences in a diverse Thai sample (N = 1294) of cisgender heterosexual men (n = 270) and women (n = 280), gay men (n = 199), lesbian women (n = 56), and unique Thai sexual orientation/gender categories: sao praphet song (i.e., feminine-presenting same-sex attracted males; n = 166), toms (i.e., masculine-presenting same-sex attracted females; n = 174), and dees (i.e., feminine-presenting females sexually attracted to toms; n = 149). Gay men and sao praphet song reported more CSAB and sex-atypical adulthood occupational preferences than heterosexual men, and sao praphet song were more sex-atypical than gay men. Toms reported more CSAB and sex-atypical adulthood occupational preferences than heterosexual women, lesbian women, and dees, whereas lesbian women were more sex-atypical than heterosexual women and dees in childhood but not adulthood. CSAB was associated with sex-atypical adulthood occupational preferences among heterosexual men and all same-sex attracted groups, indicating continuity in gender-role expression development. Overall, this study replicates previous findings indicating greater sex-atypical behaviors and interests during childhood and adulthood among same-sex attracted individuals. It also expands upon prior literature by showing how gender-role presentation relates to these sexual orientation differences among males and females.This study was funded by a Natural Sciences and Engineering Research Council (NSERC) of Canada Discovery Grant (RGPIN-2016–06446) awarded to DPV. FRGJ was funded by a Postdoctoral Fellowship from the Office of the Vice Principal of Research and Innovation, University of Toronto Mississauga
Artificial intelligence and robotics in the hydrogen lifecycle: A systematic review
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No data has been generated in this work.Hydrogen lifecycle, encompassing production, storage, and transportation, is crucial in the global transition to clean energy. Integrating artificial intelligence (AI) and robotics into hydrogen lifecycle offers promising solutions to enhance efficiency, safety, and scalability. This paper presents a comprehensive review of the current advancements published over the past two decades (2005–2025), analyzing AI and robotics applications across hydrogen production, storage, and transportation. We systematically examine the role of AI in optimizing hydrogen production processes, improving the safety and efficiency of storage systems, and enhancing transportation logistics through real-time monitoring and route optimization. Additionally, the paper explores the use of robotics to handle complex tasks in hazardous environments within the hydrogen lifecycle. We identify key challenges and gaps in the literature and propose future research directions to fully leverage AI and robotics across hydrogen technologies. This review serves as a foundation for researchers and practitioners seeking to advance the integration of AI and robotics in the hydrogen economy.PQ reports that financial support was provided by the Brunel Research Initiative and Enterprise Fund (BRIEF Award) 2024
How Does Generation Z Imagine the Future of “Laundry Care” Experiences? A Scenario-Based Exploration of User Expectations
No full textAmid the rapid evolution of technology, Generation Z, as digital natives, the unique perspectives have continually presented human–computer interaction research with challenges and innovation. This study aims to explore the expectations of Generation Z regarding the future “laundry care” experiences using mixed methods. The scenario-building approach and participatory experiments were used to elicit user expectations. The results revealed six dimensions of expected “laundry care” experiences. Additionally, expert designers were invited to subjectively evaluate the expectation themes, and the importance and prioritization of the user expectations were determined. The evaluations by expert designers also revealed consensus and differences in priorities and importance between designers and Generation Z. The significance of this study lies in the valuable insights into the expectations of Generation Z, offering implications and recommendations for innovation and experience design in future “laundry care.” The results provide a valuable design framework to guide designers and product developers.This research was supported by the National Social Science Foundation Project of Art “The Study of the Comprehensive Development Trends of Industrial Design in China from a Sociological Perspective” (NO.20BG129), “Postgraduate Research & Practice Innovation Program of Jiangsu Province” (NO. KYCX23_2431), and “CHINA SCHOLARSHIP COUNCIL” (NO.202206790035)
Growing belief: Developmental insights into the cognitive science of religion
No full textCommentary.White et al. (2025) provide a comprehensive overview of 30 years of research in the Cognitive Science of Religion (CSR) and offer insights on the field’s future directions. While their review spans a broad interdisciplinary literature, it primarily focuses on research involving adults. We propose that explaining development is essential for understanding CSR, as it illuminates how religious beliefs and behaviors emerge, evolve, and are transmitted over time (Legare & Nielsen, 2020). Below, we present evidence to support this perspective using the development of ritual as an example. ..
Visual Language Model based Cross-modal Semantic Communication Systems
Get PDFSemantic Communication (SC) has emerged as a novel communication paradigm in recent years. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low information density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: (1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure. (2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features. (3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 41904127 41904127, and 62132004), in part by the Hunan Provincial Natural Science Foundation of China under Grant 2024JJ5270, in part by the Open Project of Xiangjiang Laboratory under Grant 22XJ03011, in part by the Scientific Research Fund of the Hunan Provincial Education Department under Grant 22B0663, in part by the Changsha Natural Science Foundation under Grants kq2402098 and kq2402162, in part by the Jiangsu Major Project on Basic Researches under Grant BK20243059 and Gusu Innovation Project for under Grant ZXL2024360
Indirect news coverage and economic policy uncertainty
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The news intensity data underpinning this publication can be accessed
from Brunelfigshare: https://doi.org/10.17633/rd.brunel.27854760.The EPU data is obtained
from https://www.policyuncertainty.com. CPI and industrial production data are downloaded
from https://fred.stlouisfed.orgJEL classification: D8; C3; C8.• Dataset link: https://doi.org/10.17633/rd.brunel.27854760, https://www.policyuncertainty.com,
https://fred.stlouisfed.orgThis paper uses semantic fingerprints of news to measure news intensity for countries. Estimation results from DCC-GARCH models show that correlations between news intensity and economic uncertainty are mostly positive throughout time. The more relevant news about a country are published, the higher is the economic uncertainty in that country. News intensity also has a negative impact on correlations between uncertainty and inflation, and a positive impact on correlations between uncertainty and output growth.British Academy and Leverhulme (SG2122-210378
Integrating renewable energy resources in electricity distribution systems—A firm-level efficiency analysis for Sweden controlling for weather conditions
Get PDFSupplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S0140988324008570#appSC .Sweden is at the forefront of the transition of its energy sector to low-carbon technologies with profound consequences for both energy generation and its distribution. However, the impact of this transition on the performance of Electricity Distribution System Operators (DSOs) has not been thoroughly studied. The article addresses this gap by using a novel approach and detailed georeferenced firm-level, weather, and regional data in Sweden from 2014 to 2019. Our findings indicate that (i) an increase in the number of small-scale feeders and (ii) a higher degree of decentralized energy production (decentralization) both improve DSOs’ cost efficiencies. Additionally, we demonstrate that DSOs have adapted well to long-term weather variability. These results have significant implications for the effective implementation of renewable energy policies.The authors acknowledge financial support from the German Science Foundation for grant no. 411547861
Experimental investigation on an advanced thermosiphon-based heat exchanger for enhanced waste heat recovery in the steel industry
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Data will be made available on request.Industry sector within the European Union (EU) accounts for approximately 25 % of final energy use, where the steel industry accounts for 10 % of the total energy consumption in the industry sector. The steel industry and similar process industries are facing significant challenges to reduce their greenhouse gas emissions due to recent climate change legislations. One method to achieve this, is via the implementation of waste heat recovery systems. The paper presented focuses on a steel plant located in Slovenia, where significant amounts of thermal energy are lost through exhaust gases from a natural gas furnace. The novel multi-sink gravity-assisted Heat Pipe Heat Exchanger (HPHE) aims to recover and reuse waste heat and generates two useful heat sinks. The novel HPHE consists of air and water heat sink sections with an average energy recovery efficiency of 47 %. The recovered energy from the air section provides preheated combustion air to the burners, whereas the recovered energy from the water section opens the possibility for district heating. The thermosyphons in the exhaust-air section were arranged in a counterflow arrangement with Dowtherm A and distilled water as the working fluid, whereas the exhaust-water sections were arranged in a crossflow, with distilled water as the working fluid. The novel HPHE features a bypass, allowing complete flexibility for the end user to deactivate the exhaust-water section. To ensure replicability of the HPHE, a theoretical model has been developed and validated through experimental results, the model exhibited a good agreement with the results within an error of 15 %. Both air and water sections recovered 1677 MWh and 753 MWh annually, operating at 8050 and 5750 h respectively. The implementation of the HPHE equates to an overall reduction in CO2 emissions of 334 tCO2 per annum. Moreover, the unit highlights a benchmark for the technology due to its readiness within industry due to a reported Return on Investment (ROI) of under 10 months.The presented work is part of HEAT PIPE TECHNOLOGY FOR THERMAL ENERGY RECOVERY IN INDUSTRIAL APPLICATIONS — ETEKINA project. The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement NO 768772.
“Heat pipe technology for thermal energy recovery in industrial applications” (
https://www.etekina.eu/, H2020-EE-2017-PPP- 768772)
The legacy of a humble scientist: Edoardo Alesse, MD, PhD (1958–2025)
No full textObituary.As we walked down the long, dark corridor of the Coppito-II campus research facility at the University of L’Aquila, the air buzzed with anticipation and the faint rumours of laboratory equipment. A group of us, anxious graduate students, clutched our notebooks, hearts pounding with the uncertainty of meeting our supervisor for our final year theses. From the shadows, an imposing figure emerged, dressed in plastic goggles and personal protective equipment. Pausing briefly, his voice—warm yet authoritative—pierced the tension. “Welcome, everyone,” he announced. This was our introduction to Professor Edoardo Alesse on a chilly afternoon in L’Aquila in the late 1990s. In one gloved hand, he held an A3-sized X-ray film, its translucent surface illuminated by the radioactive signal from one of his experiments. Without hesitation and with a gleam in his eyes, he began to explain the significance of the results shown on the film. In that moment, standing in the corridor, we realized we were embarking on a transformative journey, guided by a mentor whose expertise promised to expand our understanding of scientific discovery. ..
Development of YOLO-based object detection and tracking systems for airport ground safety and simulation-based collision analysis
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonAirport ground incidents remain one of the major safety concerns in aviation, causing operational disruption and substantial economic losses. Although conventional surveillance and sensing can support surface awareness, cost, deployment constraints, and limited interpretability at close range motivate low-cost, vision-based alternatives. Key research gaps include (i) apron-specific, part-level, openly available labelled datasets, (ii) systematic and fair benchmarking of modern deep learning architectures under consistent evaluation conditions, and (iii) integrated risk analysis that progresses from perception to actionable early warning. This thesis addresses these gaps by developing and validating a scalable early-warning framework based on Computer Vision (CV) and Deep Learning (DL), designed for integration with existing Closed-Circuit Television (CCTV) infrastructures. The thesis follows a multi-stage methodology. First, a new detection and segmentation dataset was constructed with five aircraft classes (airplane, wing, nose, tail, and fuselage) to support part-level perception for apron safety. Using this dataset, twelve modern object detection and segmentation architectures were trained and evaluated under consistent experimental settings to establish a benchmarking baseline. YOLOv8-Seg (You Only Look Once, version 8-Segmentation) emerged as the most suitable backbone for the intended operational constraints. Second, to improve robust detection and segmentation of aircraft and their components, YOLOv8-Seg was systematically optimised through a six-step, ablation-driven pipeline, spanning parameter tuning, loss-function refinement, data augmentation, and inference-efficiency improvements. Third, a Multi-Object Tracking (MOT) dataset was created and annotated in the MOTChallenge format to benchmark leading trackers under identical evaluation settings. Finally, two complementary safety layers were developed: (i) a reactive module that issues immediate alerts using image-plane geometric proximity derived from segmentation outputs, and (ii) a proactive module that forecasts short-horizon conflicts by extrapolating past motion and evaluating future overlap using Intersection over Union (IoU). Experimental results show that the optimised YOLOv8-Seg model significantly improves segmentation performance by +8.04 points in [email protected]:0.95 (mean Average Precision averaged over IoU thresholds from 0.50 to 0.95) and +4.74 points in [email protected]. On the tracking benchmark with airplane-only ground truth, DeepSORT achieved the strongest overall performance, reaching 92.77% Multi-Object Tracking Accuracy (MOTA) with 93.27% recall. The framework was validated across multiple representative scenarios using both simulated and real-world video, supporting the feasibility of a low-cost, CCTV-compatible approach for enhancing apron safety through integrated perception, tracking, and early warning.Ministry of National Education, Republic of Türkiy
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