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Full vehicle road testing of magnetorheological-based intelligent suspension for sport utility vehicle
Full text linkVibration control in intelligent vehicle suspension utilizing magnetorheological (MR) dampers has attracted increasing attention. However, a practical real-time controller with easy implementation for sport utility vehicles (SUVs) and experimental validation have not been fully developed. To address this issue, the present study proposes a novel control algorithm for SUVs equipped with double-wishbone MR-based intelligent suspension. This algorithm directly calculates the desired current for the MR suspension based on vehicle sensor signals, eliminating the need for a complex inverse model of the MR damper and associated force tracking challenges. Firstly, the working principle of MR suspension systems is discussed, followed by experimental investigations of the dynamic behavior of a manufactured MR fluid damper. Next, a controller designed to balance comfort and attitude compensation for full-vehicle vibration suppression is proposed. Its control effectiveness for vertical comfortability is validated by a quarter SUV with a double-wishbone MR damper, and finally its effectiveness for vertical and attitude mitigation is evaluated through comprehensive practical road testing of an SUV across various road profiles. The results demonstrate significant reductions in the vehicle body acceleration and the pitch and roll angles when utilizing the proposed controller. This study provides a straightforward control algorithm for mitigating vertical motion and improving the dynamic responses of SUVs with MR-based intelligent suspension systems
International Law and Justice
No full textThis chapter examines queer perspectives on international law and justice, highlighting how cis-heteronormativity and Eurocentrism underpin contemporary legal systems, marginalising LGBTQIA+ identities. It explores two key approaches: one advocating for inclusion within existing legal frameworks, and another critiquing and deconstructing the power structures that sustain international law. The chapter argues that international law perpetuates violence by regulating populations through normative gender and racial assumptions, thereby excluding LGBTQIA+ individuals from mechanisms such as the UN Security Council. It also addresses the tension between seeking inclusion and questioning the foundations of legal systems historically built on exclusion. Lastly, queer scholarship envisions a future where international law is either radically reformed or dismantled to foster more equitable global governance.</p
Enhancing Cognitive Clarity through Drill-Down Structuring in Data Videos
Full text linkData videos are widely used in media and education, but can overwhelm viewers if poorly organized. We assess whether a hierarchical drill-down structure improves comprehension and reduces extraneous cognitive load in linear, non-interactive data videos. Building on cognitive load theory and narrative visualization research, we propose a conceptual model that divides a narrative into successive layers of detail. We conducted an online between-subjects experiment (N = 100) comparing a drill-down video with an equivalent flat baseline. To isolate visual-structuring effects and reflect common sound-off contexts (e.g., autoplay feeds, public displays), we used short, caption-free videos without audio. Independent-samples t-tests showed slightly better recall with drill-down but no statistically significant differences in recall, cognitive load, or self-reported comprehension. Qualitative feedback highlighted that fast pacing and high visual density in both videos imposed substantial cognitive demands, likely overshadowing any structural benefits. Our findings encourage designs that combine drill-down structuring with adaptive pacing, persistent visual anchors, and multimedia cues
Comparative assessment of antibiofilm and antioxidant activities between curcuma longa silver nano particles and ethanolic extract of curcuma longa
Full text linkBiofilms demonstrate multidrug resistance and present challenges for therapies. Notably, organisms like Staphylococcus aureus and Pseudomonas aeruginosa are major contributors to biofilm-related infections. Various strategies are employed to address antibiotic resistance vulnerabilities. Researchers are investigating the potential of plants and phytochemicals to overcome these challenges, with green nanosynthesis emerging as a promising strategy against antibiotic resistance. Turmeric (Curcuma longa) is a commonly used spice valued for its potent medicinal properties, such as antibacterial, anti-inflammatory, antioxidant, and anti-biofilm activities. This study encompasses the ethanolic extraction of Curcuma longa (C. longaE) and the qualitative identification of its constituents using Thin Layer Chromatography (TLC) and Gas Chromatography-Mass Spectrometry (GC–MS). Additionally, it also includes the synthesis and characterization of Curcuma longa silver nanoparticles (C. longaAgNPs), along with a comparative assessment of the antioxidant and antibiofilm activities of C. longaE and C. longaAgNPs. The TLC and GCMS– results show the presence of curcuminoids, Ar-tumerone, and curlone as major constituents in C. longaE. The C. longaAgNPs were characterized by Ultraviolet (UV) Spectroscopy, Fourier Transform Infrared Radiation (FTIR), X-ray Diffraction (XRD), and DLS analysis. The DLS analysis shows that C. longaAgNPs had a hydrodynamic diameter of 153 ± 0.75 nm and a polydispersity index (PDI) of 0.199 ± 0.0043. The IC50 value for the antioxidant activity of C. longaAgNPs was significantly lower at 19.972 ± 0.148 µg/ml when compared with the C. longaE, which measured 63.262 ± 0.928 µg/ml. The IC50 values for the antibiofilm activity of C. longaAgNPs were significantly lower at 0.1963 ± 0.0120 mg/ml for P. aeruginosa and 0.1681 ± 0.0259 mg/ml for S. aureus compared to the IC50 values of C. longaE, which measured 2.043 ± 0.0831 mg/ml and 0.8758 ± 0.0325 mg/ml, respectively. The C. longaAgNPs show significantly higher antioxidant and antibiofilm properties than C. longaE
Depth-aware RGB-D concrete crack segmentation and quantification using progressive cross-modal attention
Full text linkCracks in infrastructures, such as concrete structures and pavements, pose significant risks to structural safety and durability. The development of crack geometry provides critical information of structural reliability hence there is need, recommended by standards, to precisely quantify the crack details, such as crack length, width or depth. Although deep learning has inspired automated crack detection, its capacities in profiling the crack geometry is still in doubt since most methods that rely solely on RGB images face challenges in field conditions with low contrast, surface contamination, and complex textures. Such conditions often result in blurred boundaries and unreliable geometric measurements, limiting their applicability in practice. To address these challenges, this study proposes a progressive Cross-Modal Fusion Transformer (CMF-Former) that integrates RGB and depth modalities through hierarchical representation and adaptive feature interaction. The network separately models RGB and depth representations to retain modality-specific features, and introduces a progressive cross-modal attention mechanism to adaptively fuse complementary information across semantic stages. A multi-scale decoder is used to further facilitate accurate crack localization and restoration. Additionally, a depth-assisted quantification method is developed by leveraging depth information to automatically estimate distance and spatial scale, enabling direct measurement of crack geometric features. Experimental results show that CMF-Former achieves a highest mIoU of 86.51%, outperforming other RGB-based and RGB-D based models. In addition to segmentation performance, the proposed RGB-D framework notably enhances geometric quantification. For crack width estimation, the proposed method achieved an average Root Mean Square Error (RMSE) of 1.167, representing a substantial improvement compared to other RGB-based methods. Moreover, the relative error rates for crack length and depth estimation are 2.19% and 6.188%, respectively, demonstrating improved accuracy in capturing crack morphology
Global soybean trade dynamics: Drivers, impacts, and sustainability
Full text linkSince the 20th century, the global soybean trade has undergone major changes, shaped by rising demand, climate-related risks, and shifting international dynamics. Despite its global importance, important gaps remain in understanding the complex drivers and sustainability challenges of this transformation. This review synthesizes both direct and indirect forces reshaping trade flows, spanning market dynamics, supply chain logistics, policy shifts, and technological innovation. We examine how soybean trade expansion has impacted deforestation, inequality, and food security, and assess the responses of governments and companies to address these challenges. Finally, we provide a forward-looking perspective on the strategic pathways needed to ensure a more resilient and sustainable global soybean system. The integrated insights offered in this review can inform sustainable trade strategies and foster cross-scale policy coordination for a more resilient global agri-food system
Exposure to trauma in pregnant women and its association with previous perinatal complications, IPV and antenatal service satisfaction in rural Ethiopia: a cross-sectional facility-based study.
Full text linkBACKGROUND: We aimed to describe the prevalence of exposure to traumatic events and post-traumatic stress disorder (PTSD) in pregnant women attending antenatal care (ANC) in rural Ethiopia. We hypothesised that antenatal PTSD symptoms would be associated with previous obstetric complications and intimate partner violence (IPV) and impact negatively on women´s satisfaction with ANC. METHODS: The design was a facility-based cross-sectional study in primary health centres providing ANC in southern Ethiopia. Trauma events were assessed using the Life Events Checklist (LEC) and PTSD checklist for DSM-5 (PCL-5). Previous obstetric complications were extracted from clinical records. IPV was measured using the 'Non-Graphic Language' screening test and ANC satisfaction was measured using a locally validated adapted version of the Mental Health Service Satisfaction Scale. Generalized linear mixed-effects regression models were used to calculate prevalence ratios between PTSD, IPV and ANC satisfaction. RESULTS: Out of 2079 interviewed women, 52.3% (n = 1,087) reported one or more traumatic life events on the LEC. Physical assault was the most common traumatic event experienced (n = 485; 23.3%) and witnessed (n = 1,176; 56.6%) but only 289 (13.9%) screened positive for IPV. One hundred and six women (5.1%) met DSM-5 criteria for PTSD. Women meeting diagnostic criteria for PTSD had five times increased prevalence of IPV in their current pregnancy [prevalence ratio (PR) 4.34, 95%CI 3.01-6.30; p < 0.001]. Only twenty-six women had a record of previous obstetric complications (0.01%). Overall, women with PTSD reported less satisfaction with antenatal care. CONCLUSIONS: Despite high exposure to traumatic life events, particularly physical violence, among pregnant women attending ANC in Southern Ethiopia, the prevalence of PTSD is relatively low. Previous obstetric complications and IPV were under-reported, relative to known prevalence estimates. Our study highlights the challenges of detection of psychosocial needs in the ANC setting and the need for targeted interventions to support women's disclosure of difficulties in maternity care settings
The effect of exercise on left ventricular global longitudinal strain: a systematic review and meta-analysis
No full textAbstract
Funding Acknowledgements
Type of funding sources: None.
OnBehalf
Cancer Research Institute, Allied Health and Human Performance, University of South Australia
Background/Introduction
Left ventricular global longitudinal strain (LVGLS) can detect early myocardial changes prior to clinical abnormalities arising, and is a strong prognostic indicator of future cardiovascular (CV) dysfunction and mortality. It is well established that exercise improves CV function and reduces risk of CV disease. However, the impact of exercise on LVGLS is currently unclear. If LVGLS increases in response to habitual exercise, it could offer a sensitive measure that can determine the effectiveness of an exercise regime on CV health.
Purpose
The aim of this systematic review and meta-analysis was to determine whether exercise impacts LVGLS across a range of healthy and diseased populations.
Methods
Four databases (Medline, Scopus, eMbase, SPORTDiscus) were searched in November 2020. Included studies assessed LVGLS before and after an exercise intervention (minimum 2 weeks) in adults aged 18 years and over, and were published in English from 2000 onwards. Random-effects meta-analyses were performed at a study level using Stata (v16.1) to calculate summary standardized mean differences (SMD) and 95% confidence intervals (CI). 39 studies met selection criteria, with 35 included in meta-analyses (1765 participants).
Primary meta-analyses included only studies that compared outcomes between one or more intervention arms to a standard (non-exercising) control arm (RCT’s, N-RCT’s, randomised crossover). Secondary analyses included data from studies with intervention arms only (single group pre-post studies, intervention group from RCT’s, N-RCT’s, randomised crossover).
Results
Primary:
In populations with CV disease, a moderate effect of exercise was observed compared to non-exercising controls (SMD = 0.59; 95% CI, 0.16-1.02; p = 0.01 – figure 1a). No significant effect of exercise was observed for CV risk (SMD = 0.07; 95% CI, -0.15-0.29; p = 0.56 – figure 1b) and healthy (SMD = -0.20; 95% CI, -0.73-0.33; p = 0.45) populations compared to non-exercising controls.
Secondary
In secondary meta-analyses, significant effects of exercise were observed in CV disease (SMD = 0.26; 95% CI, 0.07-0.46; p = 0.01), CV risk (SMD = 0.54; 95% CI, 0.15-0.93; p = 0.01), chronic kidney disease (SMD = 0.65; 95% CI, 0.03-1.28; p = 0.04) and athletic (SMD = 0.30; 95% CI, 0.20-0.41; p= <0.001) populations.
Conclusion(s)
Increases in LVGLS observed in populations with CV disease may assist the prevention of secondary CV events. Secondary findings may support the use of exercise across a range of populations to increase LVGLS and enhance CV function. Future research must address the methodological limitations that currently exist, including improving upon study designs and reporting of individual data. Abstract Figure
Are We Really Making Recommendations Robust? Revisiting Model Evaluation for Denoising Recommendation
Full text linkImplicit feedback data has emerged as a fundamental component of modern recommender systems due to its scalability and availability. However, the presence of noisy interactions - such as accidental clicks and position bias - can potentially degrade recommendation performance. Recently, denoising recommendation have emerged as a popular research topic, aiming to identify and mitigate the impact of noisy samples to train robust recommendation models in the presence of noisy interactions. Although denoising recommendation methods have become a promising solution, our systematic evaluation reveals critical reproducibility issues in this growing research area. We observe inconsistent performance across different experimental settings and a concerning misalignment between validation metrics and test performance caused by distribution shifts. Through extensive experiments testing 6 representative denoising methods across 4 recommender models and 3 datasets, we find that no single denoising approach consistently outperforms others, and simple improvements to evaluation strategies can sometimes match or exceed state-of-the-art denoising methods. Our analysis further reveals concerns about denoising recommendation in high-noise scenarios. We identify key factors contributing to reproducibility defects and propose pathways toward more reliable denoising recommendation research. This work serves as both a cautionary examination of current practices and a constructive guide for the development of more reliable evaluation methodologies in denoising recommendation
Simplified Swarm Learning Framework for Robust and Scalable Diagnostic Services in Cancer Histopathology
No full textThe complexities of healthcare data, including privacy concerns, imbalanced datasets, and interoperability issues, necessitate innovative machine learning solutions. Swarm Learning (SL), a decentralized alternative to Federated Learning, offers privacy-preserving distributed training, but its reliance on blockchain technology hinders accessibility and scalability. This paper introduces a Simplified Peer-to-Peer Swarm Learning (P2P-SL) Framework tailored for resource-constrained environments. By eliminating blockchain dependencies and adopting lightweight peer-to-peer communication, the proposed framework ensures robust mo-del synchronization while maintaining data privacy. Applied to cancer histopathology, the framework integrates optimized pre-trained models, such as TorchXRayVision, enhanced with DenseNet decoders, to improve diagnostic accuracy. Extensive experiments demonstrate the framework’s efficacy in handling imbalanced and biased datasets, achieving comparable performance to centralized models while preserving privacy. This study paves the way for democratizing advanced machine learning in healthcare, offering a scalable, accessible, and efficient solution for privacy-sensitive diagnostic applications
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