The applications of magnesium oxychloride cement (MOC) have been extensively studied recently due to its eco-friendly and high-strength nature. However, one of the significant limitations of MOC is its poor water resistance. To address this limitation, this study explored the prospect of incorporating biochar particles (up to 25 % of the dry mass of MgO) to form lightweight MOC with improved water resistance. The compressive (fc) and flexural (ff) strengths were investigated after 28-day curing and under 56-day water attack. The fc of MOC after immersion was determined under both wet (directly after immersion) and dry (air-dried to constant weights) conditions. The results indicated that the inclusion of 5 % and 10 % biochar increased the 28-day fc, while the addition of biochar decreased ff regardless of its dosage. Microscopic examination uncovered that the increase in strength resulted from the promoted production of phase 5 (5 Mg(OH)2·MgCl2·8H2O) and the reduction in unreacted MgO. The inclusion of 5 % and 10 % biochar increased the compressive and flexural strength retention ratios after 56-day immersion. The ff with 5 % biochar inclusion after immersion was higher compared to that of pure MOC. Moreover, the inclusion of biochar had minimal effects on the thermal degradation of MOC. The above results suggest that biochar can be a potential additive to enhance the mechanical behaviour and water resistance of MOC. As fc of immersed MOC increased during air-drying, a new equation was developed to describe variations in fc of MOC subject to different degrees of saturation during drying.
Background: Peripheral inflammation has been associated with major depression, however there is a paucity of studies examining whether inflammatory profiles differ across depressive subtypes. The current study sought to compare peripheral inflammatory markers in patients with melancholic versus non-melancholic depression and with healthy controls. Method: Eighty outpatients with a current major depressive episode (MDE) were assigned as having a melancholic or a non-melancholic depressive subtype based on clinician diagnosis and the Sydney Melancholic Prototypic Index (SMPI). Participants provided peripheral venous blood from which plasma levels of cytokines and other inflammatory markers (C-reactive protein (CRP), neutrophil/lymphocyte ratio, plasma cytokines) were compared across the two patient groups and also to a group of 81 age-matched healthy controls. Results: Patients with melancholic and non-melancholic depression demonstrated increased CRP and decreased interferon-gamma (IFN-γ) levels compared to controls. Using clinician diagnosis of subtype, interleukin-12 (IL-12) and interleukin-10 (IL-10) levels were elevated in melancholic patients versus non-melancholic and control groups, with no differences found for the other measured markers of inflammation. Conclusion: Study findings demonstrate shared inflammatory changes across certain inflammatory markers (CRP and IFN-γ) and increases in IL-12 and IL-10 levels specific to melancholic depression. While generally supportive of previous work, our peripheral inflammation findings in melancholic depression are relatively novel and suggest this subgroup may benefit from anti-inflammatory therapies. Further studies are required to replicate these findings.
Although visible light communication (VLC) can provide high throughput and low latency, the inherent limited bandwidth of light-emitting diode (LED) remains its performance bottleneck. Thus, an effective equalizer with low complexity and good pervasiveness is indispensable to tackle this issue. In this letter, a hybrid active-passive single-order equalizer is proposed to expand LED bandwidth and support the design of resource-constrained transmitter front-end. Different from other active-passive equalizers, the active part of the proposed method can provide a lower output impedance for equalizer. Then, the effectiveness of the proposed equalizer is verified based on a practical experimental test-bed and it can extend the 3-dB bandwidth of the LED from 9 MHz to 181 MHz even with a trivial transistor. Moreover, the established red–blue–green–yellow (RGBY) LEDs-based VLC system is designed to verify the pervasiveness of the proposed equalizer. In particular, an achievable overall rate of 3.12 Gbps with a transmission distance of 4 m is obtained in the VLC system and the corresponding bit error ratio performance is below 1.2 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> .
The utilization of sustained and green energy is believed to alleviate increasing menace of global environmental concerns and energy dilemma. Interfacial assembly of two‐dimensional graphene‐derived ion channels (2D‐GDIC) with tunable ion/fluid transport behavior enables efficient harvesting of renewable green energy from ubiquitous water, especially for osmotic energy harvesting. In this Review, we summarize various interfacial assembly strategies for fabricating diverse 2D‐GDICs and discuss their ion transport properties. We analyze how particular structure and charge density/distribution of 2D‐GDIC can be modulated to minimize internal resistance of ion/fluid transport and enhance energy conversion efficiency, and we highlight stimuli‐responsive functions and stability of 2D‐GDIC and further examine the possibility of integrating 2D‐GDIC with other energy conversion systems. Notably, the presented preparation and applications of 2D‐GDIC also inspire and guide other 2D materials to fabricate sophisticated ion channels for targeted applications. Finally, we analyze potential challenges in this field and offer a prospect to future developments toward high‐performance or large‐scale real‐word applications. This article is protected by copyright. All rights reserved
The development of advanced solar energy technologies, which efficiently convert solar energy to heat and then to electricity, remains a significant challenge in the pursuit of clean energy production. Here, we address this challenge by designing a photothermal absorber composed of liquid gallium particles and a natural polyphenol‐based coordination ink. The design of this composite takes advantage of the tuneable light absorption properties of the polyphenol inks and can also be applied onto flexible substrates. While the ink utilizes two types of coordination complexes to absorb light at different wavelengths, the liquid gallium particles with high thermal and electrical properties provide enhanced thermoelectric effect. As such, the photothermal composite exhibits a broad‐spectrum light absorption and highly efficient solar‐to‐heat conversion. A thermoelectric generator coated with the photothermal composite exhibits an impressive voltage output of ∼185.3 mV when exposed to 1 Sun illumination, without requiring any optical concentration, which sets a new record for a power density at 345.5 μW cm ⁻² . This work showcases the synergistic combination of natural compound‐based light‐absorbing coordination complexes with liquid metals to achieve a strong photothermal effect and their integration into thermoelectric devices with powerful light harvesting capabilities. This article is protected by copyright. All rights reserved
Rapid proton transport in solid‐hosts promotes a new chemistry in achieving high‐rate Faradaic electrodes. Exploring the possibility of hydronium intercalation is essential for advancing proton‐based charge storage. Nevertheless, this is yet to be revealed. Herein, we report a new host of hexagonal molybdates, (A 2 O) x ·MoO 3 ·(H 2 O) y (A = Na ⁺ , NH 4 ⁺ ), and demonstrate hydronium (de)intercalation with experiments. Hexagonal molybdates show a battery‐type initial reduction followed by intercalation pseudocapacitance. Fast rate of 200 C (40 A g ⁻¹ ) and long lifespan of 30000 cycles are achieved in electrodes of monocrystals even over 200 μm. Solid‐state NMR confirms hydronium intercalations, and operando measurements using electrochemical quartz crystal microbalance and synchrotron XRD disclose distinct intercalation behaviours in different electrolyte concentrations. Remarkably, characterizations of the cycled electrodes show nearly identical structures and suggest equilibrium products are minimally influenced by the extent of proton solvation. These results offer new insights into proton electrochemistry and will advance correlated high‐power batteries and beyond. This article is protected by copyright. All rights reserved
Objectives and importance of the study: Most older Aboriginal peoples live in urban locations. Many of these people were displaced by the policies and practices that produced the Stolen Generations. As a result, access to ‘Country’ and cultural landscapes that are minimally impacted by urbanisation can be limited for older Aboriginal peoples, restricting the health and wellbeing benefits these environments promote. Study type: Qualitative study. Methods: Our study worked collaboratively with Aboriginal traditional cultural knowledge holders to observe and analyse how participation in a ‘cultural camp’ on a Yuwaalaraay sacred site in New South Wales (NSW), Australia, impacted wellbeing and connection to place among older Aboriginal people who were survivors or descendants of the Stolen Generations. Results: Eight participants (three women; five men) attended the cultural camp and took part in the yarning circle. Thematic analysis of a yarning circle uncovered memories of traumatic experiences of institutionalisation, including abuse and loss of Country, community, and culture. Experiences of the cultural camp generated a sense of reconnection, cultural pride, wellbeing and place attachment. The sensory experience of Country emphasised a sense of belonging and healing. Conclusions: Our findings reflect the importance of sensory-led experiences on Country for older urban Aboriginal peoples and reinforce previous evidence on the ‘therapeutic’ aspects of culture and natural landscapes minimally impacted by colonisation. Policies and resources supporting grassroots initiatives such as Aboriginal cultural camps are needed to ensure accessibility for older Aboriginal peoples living in urban places.
Background: Historically the voices of people with intellectual disability have been occluded by barriers imposed by research practice. More recently, adaptive research approaches have been proposed to enhance the inclusion of people with intellectual disability in qualitative research. Method: This article presents an adaptive interviewing approach employed with five people ageing with intellectual disabilities in rural South Australia. The interviews were conducted within a broader participatory action research project in which tools and resources were co-designed for post-parental care planning. Results: We describe our adaptive interviewing approach incorporating multiple methods: (i) responsive communication techniques; (ii) the inclusion and support of family carers; (iii) visual tools; (iv) walking interviews. Conclusion: Findings contribute knowledge about how an adaptive interview approach supports the participation of people with an intellectual disability in qualitative research. KEYWORDS: adaptive interviewing, inclusive research, intellectual disability, qualitative research, visual tools
Disadvantaged socioeconomic position (SEP) is an important predictor of poor health in children with chronic kidney disease (CKD). The time course over which SEP influences the health of children with CKD and their carers is unknown. This prospective longitudinal study included 377 children, aged 6–18 years with CKD (stages I–V, dialysis, and transplant), and their primary carers. Mixed effects ordinal regression was performed to assess the association between SEP and carer-rated child health and carer self-rated health over a 4-year follow-up. Adjusted for CKD stage, higher family household income (adjusted odds ratio (OR) (95% CI) 3.3, 1.8–6.0), employed status of primary carers (1.7, 0.9–3.0), higher carer-perceived financial status (2.6, 1.4–4.8), and carer home ownership (2.2, 1.2–4.0) were associated with better carer-rated child health. Household income also had a differential effect on the carer’s self-rated health over time (p = 0.005). The predicted probabilities for carers’ overall health being ‘very good’ among lower income groups at 0, 2, and 4 years were 0.43 (0.28–0.60), 0.34 (0.20–0.51), and 0.25 (0.12–0.44), respectively, and 0.81 (0.69–0.88), 0.84 (0.74–0.91), and 0.88 (0.76–0.94) for carers within the higher income group. Carers and their children with CKD in higher SEP report better overall child and carer health compared with those in lower SEP. Carers of children with CKD in low-income households had poorer self-rated health compared with carers in higher-income households at baseline, and this worsened over time. These cumulative effects may contribute to health inequities between higher and lower SEP groups over time. A higher resolution version of the Graphical abstract is available as Supplementary information.
Cryptocurrency has become very popular and widely used by major businesses as digital currency for online investments and services. However, the price prediction of such digital currencies as Bitcoin and Ethereum is challenging. It involves financial indicators and nonfinancial indicators, such as historical data and social media data, respectively. In this paper, we propose deep learning and hybrid models that effectively incorporate both types of indicators and introduce the optimal algorithms for long-term price prediction of Bitcoin and Ethereum. We conduct extensive experimental evaluations on real data we extracted from financial dataset comprising Yahoo Finance data, and non-financial data consisting of Google Trends data and approximately 30 million related Bitcoin and Ethereum. Our experimental results show that the hybrid models involving LSTM/1D-CNN with ARIMA/ARIMAX outperformed the individual models for the long-term prediction of cryptocurrency prices.
Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings.
Network fraud detection, specifically identifying abnormal users on rating platforms, has attracted considerable interests of researchers due to its wide applicability. However, the performance of existing detection systems suffer from several challenging problems such as class imbalance, lack of annotated data and network sparsity. To address above challenges, in this paper, we propose a novel unsupervised fraud detection algorithm FD-SpaN based on network structure exploration, to effectively rank users based on computed probabilities of being fraudulent and identify abnormal users on sparse networks. Firstly, we model ratings networks as graphs in mathematical manner with introduced metrics. Then, we add variable smoothing terms accordingly when inferring the quality and trustworthiness of each item and rating respectively, to tackle network sparsity on entity level. Meanwhile, for active users, we integrate their rating patterns into our developed formulations as a critical term to avoid overfitting. In addition, our proposed FD-SpaN is scalable to large-scale rating networks in real world due to its linear time complexity with respect to the size of network. Extensive experiments on two real-world datasets show the effectiveness of FD-SpaN under extreme class imbalance and network sparsity, as it outperforms other state-of-the-art baselines in terms of all evaluation metrics.
Inspired by recent progress in text-conditioned image generation, we propose a model for the problem of text-conditioned graph generation. We introduce the Vector Quantized Text to Graph generator (VQ-T2G), a discrete graph variational autoencoder and autoregressive transformer for generating general graphs conditioned on text. We curate two multimodal datasets of graph-text pairs, a real-world dataset of subgraphs from the Wikipedia link network and a dataset of diverse synthetic graphs. Experimental results on these datasets demonstrate that VQ-T2G synthesises novel graphs with structure aligned with the text conditioning. Additional experiments in the unconditioned graph generation setting show VQ-T2G is competitive with existing unconditioned graph generation methods across a range of metrics.
KRAS G12C mutation is prevalent in ~4% of colorectal cancer (CRC) and is associated with poor prognosis. Divarasib, a KRAS G12C inhibitor, has shown modest activity as a single agent in KRAS G12C-positive CRC at 400 mg. Epidermal growth factor receptor has been recognized as a major upstream activator of RAS–MAPK signaling, a proposed key mechanism of resistance to KRAS G12C inhibition in CRC. Here, we report on divarasib plus cetuximab (epidermal growth factor receptor inhibitor) in patients with KRAS G12C-positive CRC (n = 29) from arm C of an ongoing phase 1b trial. The primary objective was to evaluate safety. Secondary objectives included preliminary antitumor activity. The safety profile of this combination was consistent with those of single-agent divarasib and cetuximab. Treatment-related adverse events led to divarasib dose reductions in four patients (13.8%); there were no treatment withdrawals. The objective response rate was 62.5% (95% confidence interval: 40.6%, 81.2%) in KRAS G12C inhibitor-naive patients (n = 24). The median duration of response was 6.9 months. The median progression-free survival was 8.1 months (95% confidence interval: 5.5, 12.3). As an exploratory objective, we observed a decline in KRAS G12C variant allele frequency associated with response and identified acquired genomic alterations at disease progression that may be associated with resistance. The manageable safety profile and encouraging antitumor activity of divarasib plus cetuximab support the further investigation of this combination in KRAS G12C-positive CRC. ClinicalTrials.gov identifier: NCT04449874
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