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The intersection of artificial intelligence and assistive technologies in the diagnosis and intervention of mental health conditions
No full textopen access articleMental health disorders are becoming a major global health concern and pose a significant burden on global healthcare systems. Nearly one billion people suffer from mental disorders, accounting for 13% of the global disease burden and $1 trillion in annual productivity loss. Depression is the leading cause of disability and suicide is the second leading cause of death among young individuals. Economic uncertainty, social isolation, climate change, shifting societal norms, political conflict, and increasing violence are key factors contributing to the high prevalence of mental health issues. In the future, increasing poverty and inequality are likely to worsen this trend, resulting in a greater incidence and burden of mental illness. Therefore, timely diagnosis and intervention are a high priority. Traditional diagnostic and intervention methods, such as self-report questionnaires, clinical interviews, psychotherapy, medication, electroconvulsive therapy, and occupational therapy, have drawbacks including subjectivity, time commitment, and the potential for prolonged treatment. Due to these limitations, advanced approaches are needed to improve diagnostic accuracy and precision and to develop more effective interventions. This review aims to explore and evaluate the applications of Artificial Intelligence in the diagnosis and treatment of mental health conditions. This study provides a thorough analysis of various artificial intelligence-driven techniques and their advancements in the diagnosis of mental health conditions. Artificial intelligence has the potential to greatly improve the accuracy and effectiveness of mental health conditions. Moreover, this work consolidates the research gaps in current techniques and provides research hypotheses on how to overcome the gaps using a proposed 3-tier solution
The Impact of Sociocultural Aspects on Energy Consumption in Residential Buildings in Riyadh, Saudi Arabia
No full textopen access articleThis study explores the intersection of sociocultural factors, particularly privacy, with energy consumption patterns in residential buildings in Riyadh, Saudi Arabia. While cultural values around privacy have long been recognised as influential in residential design, the impact of these values on energy consumption is underexplored. This re-search aims to fill this gap by examining how privacy needs, residents' preferences, and open layouts affect energy efficiency, particularly in terms of natural light and ventilation. A mixed-methods approach was employed, including semi-structured interviews with engineers, data collected from 108 respondents via an online survey, a case study of a residential building in Riyadh, and building performance simulations using IES software. The study also assessed actual energy consumption data and in-door lighting as potential implications of privacy concerns, causing changes in behavioural control of systems (e.g., windows, blinds, lighting, etc.). It focuses on the relationship between privacy needs, energy use, and natural daylight distribution. The IES simulation results for the studied residential building show an annual energy consumption of 24,000 kWh, primarily due to cooling loads and artificial lighting caused by privacy measures applied by the residents. The findings reveal that privacy-driven design choices and occupant behaviours, such as the use of full window shutters, frosted glazing and limited window operation, significantly reduce daylight availability and natural ventilation, leading to increased reliance on artificial lighting and air conditioning. This study highlights the need for human-centric design approaches that address the interplay between sociocultural factors, particularly reinforcing cultural sensitivity, and building performance, offering insights for future sustainable housing developments in Riyadh and similar contexts
Feature Compression for Cloud-Edge Multimodal 3D Object Detection
No full textThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Machine vision systems, which can efficiently manage extensive visual perception tasks, are becoming increasingly popular in industrial production and daily life. Due to the challenge of simultaneously obtaining accurate depth and texture information with a single sensor, multimodal data captured by cameras and LiDAR is commonly used to enhance performance.
Additionally, cloud-edge cooperation has emerged as a novel computing approach to improve user experience and ensure data security in machine vision systems. This paper proposes a pioneering solution to address the feature compression problem in multimodal 3D object detection. Given a sparse tensor-based object detection network at the edge device, we introduce two modes to accommodate different application requirements: Transmission-Friendly Feature Compression (T-FFC) and Accuracy-Friendly Feature Compression (A-FFC). In T-FFC mode, only the output of the last layer of the network’s backbone is transmitted from the edge device. The received feature is processed at the cloud device through a channel expansion module and two spatial upsampling modules to generate multiscale features. In A-FFC mode, we expand upon the T-FFC mode by transmitting two additional types of features. These added features enable the cloud device to generate more accurate multiscale features. Experimental results on the KITTI dataset using the VirConv-L detection network showed that T-FFC was able to compress the features by a factor of 4933 with less than a 3% reduction in detection performance. On the other hand, A-FFC compressed the features by a factor of about 733 with almost no degradation in detection performance. We also designed optional residual extraction and 3D object reconstruction modules to facilitate
the reconstruction of detected objects. The reconstructed objects effectively reflected the shape, occlusion, and details of the original objects. Our source code is released on GitHub at:
https://github.com/yuanhui0325/FC3DOD
Can industrial helmets protect the head in simulated falls and trips?
No full textPurpose: Falls and trips are a leading cause of work-related traumatic brain injuries, yet the protective performance of industrial helmets in such scenarios remains poorly understood. This study assesses the effectiveness of different industrial helmet designs under impact conditions representative of falls and trips.
Methods: Six industrial helmets with different designs were tested. Four were suspension-based models compliant with EN 397, including two versions of the same model, one with and one without the rotation reduction system, MIPS. Two additional helmets were foam-based, meeting both EN 397 and EN 12492 standards. Helmets were dropped onto angled anvils at different speeds and impact locations to simulate trips and falls. Tests were conducted on two surface types: P80 abrasive papers and roof shingles. The new EN 17950 headform was used.
Results: Helmet performance varied by design and impact condition. Foam-based helmets offered better protection against impacts than suspension-based helmets, which showed greater sensitivity to impact location. Front impacts near the rim at 5.5 m/s produced the highest severity, with peak linear accelerations exceeding 700 g for some suspension-based helmets, followed by rear impacts. In the single helmet model evaluated, MIPS reduced peak rotational acceleration. Finally, the influence of the surface type on peak head kinematics was borderline significant, with P80 papers producing larger peak kinematics.
Conclusion: Helmet design has a key role in protection against trip and fall impacts, with foam-based helmets providing added benefits. These findings highlight the need for improvements in helmet safety standards and helmet designs to better prevent work-related brain injuries
Geochemical characterisation of the Ellsworth-Whitmore Mountains crustal block: a critical piece in the puzzle to unravel ice retreat in West Antarctica
No full textThe West Antarctic Ice Sheet (WAIS) is prone to major retreat. Identifying when previous WAIS retreats occurred could improve sea level rise projections, but detection of these events in marine sediments is limited by knowledge of the geochemical signature of key sediment source regions located further inland - primarily the Ellsworth-Whitmore Mountains (EWM). This is because a smaller WAIS would likely result in enhanced erosion of the EWM and permit more widespread transport of EWM detritus offshore. We here characterise the provenance signature of the EWM, showing that different stratigraphic units exhibit distinct geochemical characteristics based on unique combinations of zircon Usingle bondPb ages and Nd and Sr isotope ratios. The oldest Heritage Group displays dominant Grenville Orogeny (1000–1250 Ma) ages, with mean εNd values of ~ − 8 and 87Sr/86Sr ratios of ~0.719. The overlying Crashsite Group and Whiteout Conglomerate exhibit dominant Ross Orogeny ages (490–580 Ma), with mean εNd values of ~ − 12 and − 18 and 87Sr/86Sr ratios of ~0.740 and ~ 0.725, respectively. The youngest Polarstar Formation has common Permian zircon Usingle bondPb ages and εNd values of ~ − 4 and 87Sr/86Sr ratios of ~0.711. Outlying nunataks are assigned to the Heritage and Crashsite groups, extending our geological knowledge of the Ellsworth Subglacial Highlands. Together, our data suggest that the Ellsworth Mountains crustal block is characterised by a mean εNd value of ~ − 10, 87Sr/ 86Sr ratio of ~0.728, and bimodal Ross and Grenville orogeny zircon Usingle bondPb ages. This “provenance fingerprint” should be identifiable in offshore sediments recording times of major WAIS retreat
Diversity analysis of indoor and outdoor fungal bioaerosols in UK households: a longitudinal study
No full textBackground
Prolonged exposure to indoor fungal bioaerosols is a recognised risk factor for respiratory illness, particularly in damp and poorly ventilated housing. However, the diversity and seasonal variability of these communities are poorly understood. This study as part of WellHome, aimed to characterise the composition, diversity, and temporal dynamics of indoor fungal bioaerosols in urban UK homes compared with outdoor air, to inform future exposure baselines and policy development.
Methods
In this prospective, community-based observational study, 118 households were recruited across West London, UK, via community networks and partner organisations, prioritising families with children aged 5–17 years with asthma or allergies from diverse socioeconomic backgrounds. Sampling occurred between 3rd October 2022 and 14th June 2024. Participant data was collected via questionnaires completed by household members, capturing demographics, building characteristics, and respiratory health. Passive air samplers were deployed in living rooms for 28 days during two seasonal campaigns, with concurrent outdoor sampling at four fixed community sites. Fungal bioaerosols were identified by ITS2 amplicon sequencing and quantified using broad-range qPCR targeting the 18S rRNA gene. Diversity indices and temporal dynamics were analysed using ecological statistics and generalised additive models.
Findings
118 households were enrolled, comprising 504 residents (263 female, 237 male, 4 not reported). Of these, 104 households completed both seasonal campaigns and 14 completed one, yielding 262 air samples (222 indoor, 40 outdoor). DNA was successfully recovered from all samples, identifying 2,027 fungal genera. Indoor environments showed significantly higher richness (mean 646 vs 495 ASVs; p<0.0001) and Shannon diversity (4.21 vs 3.53; p<0.0001) than outdoors. Community composition differed markedly (PERMANOVA p<0.0001), with Penicillium, Aspergillus, and Wallemia enriched indoors. Indoor fungal communities exhibited stronger seasonal cycling (R²=0.203) than outdoors (R²=0.012). Fungal burden across all homes had a median 11,043 Genomic Equivalence (GE); IQR 4,598 - 20,579 GE. The highest levels were observed in homes with visible mould; one household showed elevated Aspergillus exposure linked to repeated asthma hospitalisations in a sensitised resident.
Interpretation
Indoor fungal bioaerosols are more diverse and dynamic than outdoor communities in urban UK homes. These findings establish foundational exposure data and highlight the need for incorporating fungal bioaerosol monitoring into public health policy to mitigate mould-related health risks.
Funding
UKRI SPF Clean Air Programme
Mortality in children with fulminant myocarditis: a six-year multicenter retrospective study
No full textBackground
Fulminant myocarditis (FM) in children can progress rapidly to cardiogenic shock, with high risk of mortality. Early recognition of prognostic markers is critical to guide timely escalation of circulatory support. This multicenter study sought to characterize clinical features and identify early predictors of in-hospital mortality in pediatric FM.
Methods
We conducted a retrospective cohort study of patients <18 years with FM admitted to eight ECMO-capable pediatric intensive care units between January 2018 and August 2023. Clinical, biochemical, electrocardiographic, and echocardiographic variables were analyzed. Logistic regression was used to identify predictors of mortality, and receiver operating characteristic (ROC) curves were generated to assess discriminatory performance.
Results
A total of 187 children were included; 157 (84.0%) required ECMO. In-hospital mortality was 16.6% (31/187). Univariate analysis identified elevated CK-MB, higher peak lactate, and ventricular tachycardia as associated with mortality. In multivariate analysis, peak lactate (AUC 0.791) and CK-MB (AUC 0.774) remained independent predictors. A combined model of peak lactate and ventricular tachycardia demonstrated moderate discrimination (AUC 0.772), whereas a composite model incorporating CK-MB, peak lactate, and ventricular tachycardia achieved the best predictive performance (AUC 0.815). Elevated lactate measured 12 h after initiation of extracorporeal membrane oxygenation or intensive conventional therapy further increased mortality risk (OR 1.219, 95% CI 1.004–1.481).
Conclusion
Peak lactate, CK-MB, and ventricular tachycardia are early independent predictors of in-hospital mortality in pediatric FM. Persistent hyperlactatemia within 12 h of advanced support provides additional prognostic value and may assist clinicians in early risk stratification
From dissipativity property to data-driven gas certificate of degree-one homogeneous networks with unknown topology
No full textIn this work, we propose a data-driven divide and conquer strategy for the stability analysis of interconnected nonlinear homogeneous networks of degree one with unknown models and an unknown topology. The proposed scheme leverages joint dissipativity -type properties of subsystems described by storage functions, while providing a stability certificate over unknown interconnected networks. In our data-driven framework, we begin by formulating the required conditions for constructing storage functions as a robust convex program (RCP). Given that unknown models of subsystems are integrated into one of the constraints of the RCP, we collect data from trajectories of each unknown subsystem and provide a scenario convex program (SCP) that aligns with the original RCP. We solve the SCP as a linear program and construct a storage function for each subsystem with unknown dynamics. Under some newly developed data-driven compositionality conditions, we then construct a Lyapunov function for the unknown interconnected network utilizing storage functions derived from data of individual subsystems. We show that our data-driven divide and conquer strategy provides correctness guarantees (as opposed to probabilistic confidence) while significantly mitigating the sample complexity problem existing in data-driven approaches. To illustrate the effectiveness of our proposed results, we apply our approaches to three different case studies involving interconnected homogeneous (nonlinear) networks with unknown models. We collect data from trajectories of unknown subsystems and verify the global asymptotic stability (GAS) of the interconnected system with a correctness guarantee
Sourcing critical metal from critical habitat: is the trade-off worth making?
No full textThis study analyzes the environmental impact of nickel mining on biodiversity in Indonesia’s Wallacea region, using habitat quality as a proxy. It employs the Integrated Valuation of Eco-system Services and Trade-offs (InVEST) Habitat Quality Model to project current and future habitat quality and degradation. Findings confirm that nickel mining significantly threatens habitat quality. Under a future scenario, 10% (513 km²) of excellent-quality habitat is projected to be lost across the study area. Specifically, mining zones face severe degradation and a future absence of excellent habitat, though protected areas are expected to maintain excellent quality. The study highlights Indonesia's core dilemma between economic nickel dominance and severe environmental destruction, stressing the need for equitable global risk-sharing. We recommend three strategies: 1) an Integrated Land-Sparing Strategy, 2) Responsible Mining Practices, and 3) Risk Mapping with Equitable Global Risk-Sharing Policies
Cloud droplet number enhancement from co-condensing NH₃, HNO₃, and organic vapours: boreal case study
No full textSemi-volatile compounds such as organics, nitrate, chloride, and ammonium are ubiquitous in atmospheric aerosols. Their gaseous precursors (organics, HNO3, HCl, NH3) co-condense with water vapour when
ambient relative humidity (RH) increases, thus enhancing hygroscopic growth under sub-saturated conditions
and facilitating activation as cloud condensation nuclei (CCN) to cloud droplets. In this study, we investigate the
co-condensation effect on CCN activation for inorganics, organics, and their combination in a boreal forest site
in autumn with our cloud parcel model that includes non-ideality of organic-inorganic mixtures. The volatility
distribution of organics is highly uncertain but critically important to estimate the co-condensation effect. We
compare two distinct volatility basis sets (VBS) established from experimental and modelling data at 25 °C, which we amended with a volatility bin of saturation concentration C∗ = 10⁴µg m−3, which proved to be highly
relevant for CCN activation. The combined co-condensation of organics and inorganics increases CDNC by up to 44 % in simulations initialized with RH of 80 %, depending on VBS distribution and updraft velocity during the air parcel uplifts. Non-ideality of the system is relevant for considering the co-condensation effect realistically. For the ideal case, the maximum CDNC enhancement due to the combined co-condensation effect is 53 % while it is 44 % for the non-ideal case. The combined enhancement in CDNC of inorganic and organic species exceeds the sum of individual effects and should be further constrained in different environments in cloud parcel models as a basis for regional and global simulations