Browse Articles

Semi-transparent and stable In2S3/CdTe heterojunction photoanodes for unbiased photoelectrochemical water splitting

An attribute-enhanced relationship-aware neighborhood matching model with dual attention

The entity alignment task aims to match semantically corresponding entities in different knowledge graphs, which is important for knowledge fusion. Traditional graph-based methods often lose information due to insufficient use of attributes and imperfect relationship modeling, which makes it difficult to capture the deep semantic relationship between entities fully. To improve the effect of entity alignment, we propose a new model named ARNM-DAE2A, which strengthens the information aggregation capability of GCN by introducing a dual-attention mechanism to ensure a more balanced and comprehensive structural representation. The model contains the entity structure embedding module, the attribute structure embedding module, the joint alignment module and the relationship-aware neighborhood matching module. The entity structure embedding module optimizes the structure learning capability of GCN by introducing the pairwise attention mechanism. The attribute structural embedding module utilizes GCN to acquire entity attribute information. The joint alignment module weights and fuses the relationship structure information and attribute information as a comprehensive representation of entities. The relationship-aware neighborhood matching module then corrects the noise in the GCN aggregated information by comparing the neighborhood relationships of entity pairs. Experiments conducted on DBP15K and SRPRS datasets illustrate that the proposed ARNM-DAE2A outperforms baselines.

Small surface potential fluctuation near the valence band edge at nitrided 4H-SiC(0001)/SiO2 interfaces

Interface states near the valence band edge at a nitrided 4H-SiC(0001)/SiO2 interface were investigated by the conductance, C–ψS, and high-low methods. Consistent energy distribution of the interface state density was extracted by the conductance and C–ψS methods. The interface state density at EV+0.2 eV is 3.9×1012cm−2eV−1, which is comparable to that at EC−0.2 eV. The standard deviation of the surface potential fluctuation obtained by the conductance method is as small as 3–13 meV near the valence band edge, which is significantly smaller than that near the conduction band edge (∼100 meV). This finding indicates that the scattering caused by the surface potential fluctuation is much smaller in SiC p-channel metal-oxide-semiconductor field-effect transistors (MOSFETs) than in n-channel MOSFETs, which would be one of the reasons for the relatively high mobility in SiC p-channel MOSFETs.

Laser activation of single group-IV colour centres in diamond

Abstract Spin-photon interfaces based on group-IV colour centres in diamond offer a promising platform for quantum networks. A key challenge in the field is realising precise single-defect positioning and activation, which is crucial for scalable device fabrication. Here we address this problem by demonstrating a two-step fabrication method for tin vacancy (SnV−) centres that uses site-controlled ion implantation followed by local femtosecond laser annealing with in-situ spectral monitoring. The ion implantation is performed with sub-50 nm resolution and a dosage that is controlled from hundreds of ions down to single ions per site, limited by Poissonian statistics. Using this approach, we successfully demonstrate site-selective creation and modification of single SnV− centres. Our in-situ spectral monitoring opens a window onto materials tuning at the single defect level, and provides new insight into defect structures and dynamics during the annealing process. While demonstrated for SnV− centres, this versatile approach can be readily generalised to other implanted colour centres in diamond and wide-bandgap materials.

Patient satisfaction in regional referral hospitals of Bhutan: Insights from a cross-sectional study

Background Patient satisfaction is crucial for evaluating healthcare quality and guiding continuous quality improvement. Globally, patient satisfaction has been extensively studied; however, there is limited research on this topic in Bhutan, where the healthcare system is in the early stages of developing a quality-oriented culture. To address this gap, we aimed to evaluate patient satisfaction levels among different socio-demographic and clinical groups and identify the predictors of patient satisfaction in Bhutan. Methods We conducted a retrospective analysis of patient satisfaction survey responses archived in the quality assurance unit of two tertiary healthcare centres in Bhutan: Mongar Eastern Regional Referral Hospital and Gelephu Central Regional Referral Hospital. The routine surveys, administered throughout April 2024, utilised an adapted version of the Patient Satisfaction Questionnaire-18. The data were analysed using descriptive and inferential statistics. Results Our study revealed significant variations in patient satisfaction across socio-demographic and clinical groups. Ethnicity (P-value = 0.017), occupation (P-value = 0.014), and education level (P-value = 0.021) emerged as significant predictors of satisfaction. Sharchop and other ethnic groups (P-value= < 0.001); farmers, religious personnel, and other occupational groups (P-value= < 0.001); and illiterate (P-value= < 0.001) individuals exhibited significantly higher satisfaction levels. While patient type (P-value = 0.472), age (P-value = 0.553), and marital status (P-value = 0.448) influenced satisfaction levels, they did not emerge as significant predictors when considering other variables. Overall, patient satisfaction in Bhutan is 4.06 on a 5-point Likert scale. Satisfaction is highest in the financial domain, while accessibility and convenience received the lowest scores. Conclusions Overall, with a score of 4.06 on a 5-point Likert scale, patient satisfaction in Bhutan is high. However, our findings highlight the need to address socio-demographic disparities in patient satisfaction. As the Bhutanese socio-demographic landscape evolves, satisfaction levels may decline. To enhance overall satisfaction, healthcare policymakers should focus on improving accessibility and convenience. Strategies such as establishing dynamic limits on free services, exploring private sector engagement in advanced healthcare service, and strengthening the healthcare workforce are essential for sustainable and quality healthcare service delivery.

Deducing safety margin from boiling crisis for arbitrary solid/fluid combinations through the universality of temperature fluctuations

Despite decades of research, boiling heat transfer continues to be represented using dimensional quantities (heat flux vs wall superheat relative to liquid saturation temperature). Here, we show that non-dimensional representations of the nucleate boiling regime exist and can be extracted from the temperature fluctuations at different applied heat fluxes. High-speed temperature fluctuations of a platinum wire are measured along the pool boiling curve for a range of test conditions that yield critical heat flux values varying by a factor of five. Motivated by observations of long-term correlations in the fluctuations, we perform a multifractal analysis and show that the Hurst exponent of temperature fluctuations is a non-dimensional quantity that has a universal behavior along the boiling curve for multiple fluids and surface conditions.

Large live biomass carbon losses from droughts in the northern temperate ecosystems during 2016-2022

Influence of shot peening on the microstructure and friction-wear performance of CF53 steel

In order to further enhance the wear resistance of the camshaft surface, this study conducted shot peening reinforcement on CF53 steel. The research involves the analysis of microstructure, microhardness, and residual stress evolution. Additionally, a pin-on-disk friction and wear test machine was used to investigate the influence of shot peening on the friction-wear characteristics of CF53 steel. The results show that a certain depth of plastic deformation layer is formed on the surface after shot peening, accompanied by an increase in surface roughness. With the increase of shot peening pressure, the surface roughness, microhardness, and residual stress values of the samples correspondingly increase. Shot peening treatment significantly improves the friction-wear performance of CF53 steel, with a reduction of 22.5% and 54.6% in the friction coefficient and wear rate for SP3 and SP4 groups, respectively, compared to untreated samples. The wear mechanism of untreated samples is characterized by severe fatigue wear with prominent features of plowing grooves and cracks. In contrast, the wear mechanism of peened samples shifts to fatigue wear dominated by delamination.

The impact of defect evolution on the electrical performance of AlGaN/GaN HEMT after 14-MeV neutron irradiation

In this work, we study the electrical performance of AlGaN/GaN high-electron-mobility transistors following irradiation with 14 MeV neutrons at fluences of 3 × 1012, 7.4 × 1012, 1.2 × 1013, and 1.0 × 1014 n/cm2. The results reveal that at a neutron fluence of 7.4 × 1012 n/cm2, there is a notable increase in the saturation drain current, a negative shift in threshold voltage, and an enhancement in peak transconductance. As the fluence continues to increase, the electrical characteristics of the device begin to deteriorate. However, at a fluence of 1.0 × 1014 n/cm2, the electrical performance is still better than that before irradiation. The defect evolution induced by neutron irradiation is studied by utilizing low-frequency noise (LFN) and deep-level transient spectroscopy (DLTS) techniques. LFN analysis shows only slight changes in interface state density, while DLTS results reveal a significant reduction in deep-level defects after irradiation. We speculate that bulk defects in the GaN or AlGaN layers predominantly influence device performance variations. Neutron irradiation facilitates the recombination of original defects, thereby decreasing the concentration of deep-level defects in the device. This decrease in deep-level defects alleviates carrier trapping by defects, resulting in an increased carrier concentration and improved electrical performance of the device.

Cancer-fighting CAR T cells show promising results for hard-to-treat tumours

Performance of deep-learning-based approaches to improve polygenic scores

Abstract Polygenic scores, which estimate an individual’s genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. Neural-network based deep-learning has emerged as a method of intense interest to model complex, nonlinear phenomena, which may be adapted to exploit gene-gene and gene-environment interactions to potentially improve polygenic scores. We fit neural-network models to both simulated and 28 real traits in the UK Biobank. To infer the amount of nonlinearity present in a phenotype, we also present a framework using neural-networks, which controls for the potential confounding effect of linkage disequilibrium. Although we found evidence for small amounts of nonlinear effects, neural-network models were outperformed by linear regression models for both genetic-only and genetic+environmental input scenarios. In this work, we find that the usefulness of neural-networks for generating polygenic scores may currently be limited and confounded by joint tagging effects due to linkage disequilibrium.

Wavelet analysis text classification algorithm based on typical features of data samples

Currently, traditional text feature extraction methods fail to fully capture category-specific features when handling text data with existing category labels, thereby limiting classification performance. Meanwhile, text classification methods based on wavelet analysis have yet to achieve optimal performance due to the limitations of their feature extraction and analysis techniques. To address these issues, this paper proposes two novel algorithms: (1) Average Term Frequency-Document Frequency (ATF-DF), which adopts a forward-thinking approach to comprehensively extract category-specific features from labeled text samples, resulting in class feature vectors that effectively represent the text categories; (2) Average Term Frequency-Document Frequency-Wavelet Analysis (ATF-DF-WA), which transforms class feature vectors into waveforms and utilizes wavelet analysis to extract typical class feature layer waveforms and feature layer waveforms of the text to be classified. Text classification is then performed by calculating waveform similarity. Experimental results on the THUCHNews dataset demonstrate that compared to two baseline algorithms, ATF-DF improves Precision, Recall, and F1-score by 13.71%, 28.94%, and 20.74%, respectively. Furthermore, experimental results on the THUCHNews, Sogou, and CNTC datasets indicate that ATF-DF-WA outperforms four baseline algorithms, achieving an average Precision improvement of 2.80% to 80.36%, an average Recall improvement of 0.10% to 54.65%, and an average F1-score improvement of 2.62% to 60.82%. Additionally, experimental results on the THUCHNews dataset reveal that ATF-DF-WA demonstrates advantages in both classification performance and training speed compared to baseline algorithms based on pre-trained models, highlighting its promising potential for practical applications.

Impulsive excitation of squeezed phonons in single crystal germanium by an x-ray laser

In this Letter, we present the experimental observation of squeezed phonon generation in semiconductor germanium (Ge) induced by x-ray excitation. Prior x-ray pump, x-ray probe studies reported coherent longitudinal acoustic phonon generation in insulating oxides like strontium titanate and potassium tantalate. In contrast, such signals were not observed in semiconductors likely due to limited signal-to-noise ratio. Now, with an improved experimental setup, we observe a phonon response in single-crystal germanium. Utilizing x-ray split-delay optics with enhanced stability, we extract the phonon dispersion relation, which shows strong agreement with the calculated transverse acoustic phonon mode. Our results reveal that responses to x-ray excitations in semiconductors are of a similar nature to optical excitations. This suggests that the initial response to x-ray core–hole excitations rapidly diffuses to a non-local excitation, similar to what is observed with optical laser valence excitation on a femtosecond timescale.

Single-molecule direct RNA sequencing reveals the shaping of epitranscriptome across multiple species

Changes in HIV incidence during the COVID-19 pandemic (2020–22) compared with the pre-pandemic period (2015–19) in Peru: An observational study

Introduction During the COVID-19 pandemic, non-pharmaceutical interventions affected the screening of sexually transmitted infections. We investigated the incidence of HIV infection during the COVID-19 pandemic compared with incidence in the pre-pandemic period. Methods In this observational study, we analyzed HIV surveillance data for all age-groups from 25 geographically diverse regions in Peru from Jan 1, 2015 to Dec 31, 2022. HIV incidence during the COVID-19 pandemic (2020, 2021, and 2022) was compared with pre-pandemic rates (2015–19). Results Overall, there were 65,166 new cases of HIV infection from January 1, 2015 to December 31, 2022. HIV incidence risk ratio (IRR) was 26% lower in 2020 (IRR = 0.74; 95% CI, 0.71–0.76), 5% higher in 2021 (IRR = 1.05; 95% CI, 1.02–1.08) and 16% higher in 2022 (IRR = 1.16; CI, 1.13–1.20), compared with the pre-pandemic period. Furthermore, compared with the pre-pandemic period, the annual incidence of HIV among men was 29% lower in 2020 (IRR = 0.71; 95% CI, 0.68–0.73), 4% higher in 2021 (IRR = 1.04, 95% CI, 1.01–1.08) and 10% higher in 2022 (IRR = 1.10; 95% CI, 1.06–1.14). In the age-stratified analysis, the annual HIV incidence in 2020 was 21 and 33% lower for those aged 18–29 (IRR = 0.79; 95% CI, 0.75–0.83) and 30–59 (IRR = 0.67; 95% CI, 0.64–0.70), respectively, compared with the pre-pandemic period. Finally, annual HIV incidence has decreased in 11 out of 25 regions in 2020, compared with the pre-pandemic period. Conclusions Our study showed that during the COVID-19 pandemic in 2020, the incidence of HIV infection in the population of Peru decreased. However, this incidence began to return to pre-pandemic rates in 2021, coinciding with the easing or elimination of non-pharmaceutical interventions. By 2022, the incidence of HIV infection was higher than in the pre-pandemic period, especially in regions of the Peruvian Amazon.

Daily briefing: CAR-T proves its worth in hard-to-treat solid tumours

High mobility group A1 (HMGA1) promotes esophageal squamous cell carcinoma progression by inhibiting STING-mediated anti-tumor immunity

A study protocol for neonatal sepsis and gut microbiomics among preterm infants admitted at Muhimbili National Hospital, Tanzania

Background Neonatal mortality remains high in many low- and middle-income countries (LMICs), with neonatal sepsis and antimicrobial resistance (AMR) posing significant threats to newborns, particularly in sub-Saharan Africa (SSA). Tanzania is among the countries with the highest neonatal mortality rates, with sepsis being a major contributor. Gut dysbiosis has been identified as a risk factor for neonatal sepsis in high-income countries, due to factors like abundance of pathogenic bacteria, decrease in microbiome diversity, intestinal barrier defects and bacterial translocation. Understanding gut dysbiosis in the local setting and its role in sepsis development may offer new prevention strategies, such as probiotics for high-risk preterm infants. Objectives This prospective neonatal cohort, established at Muhimbili National Hospital (MNH) in Dar es Salaam, Tanzania, aims to analyze the gut microbiome of preterm infants and explore associations with neonatal late-onset sepsis (LOS). Additionally, data on bacterial pathogens of bloodstream infections and AMR prevalence will be identified. Secondary endpoints include clinical LOS, sepsis-related death, death from any cause, and hospital discharge outcomes. Methods Eligible preterm neonates (28 + 0 to <34 weeks of gestational age, birth weight ≥ 1000g) will be recruited with maternal consent. Socio-demographic and clinical data, microbiological details of blood pathogens, and a set of fresh frozen fecal samples during the 28 days observation period will be collected. The study targets a sample size of 1350 participants and we expect 72–135 culture-proven LOS during a study period of 18 months. Fecal samples will undergo next-generation sequencing (NGS) to analyze microbial community functions in comparison to matched controls. Discussion This collaborative study between universities in Tanzania and Germany, aims to analyze the neonatal microbiome in relation to sepsis development and AMR of blood culture isolates to enhance neonatal sepsis care, improve diagnostics and treatment. The project will offer insights into potential therapeutic strategies for the future, promote academic exchange, capacity building and research on African microbiomes.

Methionine cycle in C. elegans serotonergic neurons regulates diet-dependent behaviour and longevity through neuron-gut signaling

RETRACTED: Consumption quota compilation based on BP artificial neural network algorithm in mechanical and electrical installation engineering of prefabricated buildings

The traditional quota compilation method has a large workload and requires a lot of manpower and material resources, making it difficult to apply to the consumption quota compilation in mechanical and electrical installation engineering of prefabricated buildings. Therefore, a consumption quota compilation model on the basis of artificial neural network is built. On the basis of the traditional quota formulation model based on statistical theory, artificial neural networks are introduced, and regularization techniques and particle swarm optimization algorithms are taken to optimize the model performance. The experiment was validated using project datasets covering different regions, scales, and types of prefabricated components. The results showed that the mean squared errors on the training and testing sets were 1.2% and 1.1%, and the average absolute errors were 8.3% and 8.1%, respectively. In addition, the determination coefficients on the training and testing sets were 95.1% and 92.8%, and the accuracy was 92.3% and 91.4%. Further case analysis also showed that the prediction error rates of the research model for material consumption, labor hours, and mechanical equipment usage were relatively low, not exceeding 2.48%, 1.25%, and 4.1%, respectively. In addition, in terms of quota compilation efficiency and economic benefits, the proposed model achieved a quota compilation efficiency value of 90.1%. The return on investment in material consumption, labor hours, and mechanical equipment use was 5.03, 6.09, and 5.92, respectively, and the cost savings rates were 6.21%, 4.85%, and 5.48%, respectively, all of which were better than traditional models. Overall, the designed model can optimize the accuracy of engineering budgeting and the ability to control costs.