Browse Articles

Effect of technological parameters on mechanical properties and microstructure of heat-assisted friction stir welded joints of 6061 aluminum alloy

This study investigates the influence of key technological parameters on the mechanical characteristics and microstructure of heat-assisted friction stir welded (FSW) joints of AA6061 aluminum alloy pipes. Specifically, the effect of tool rotation speed, transverse speed, and tool shoulder diameter was evaluated. An experimental campaign was conducted using a three-level, three-factor composite design, with the primary objective of determining the optimal combination of these parameters to maximize the tensile strength of the weldments. AA6061 aluminum alloy pipes, with a thickness of 5 mm and an outer diameter of 80 mm, were joined using a resistance preheating FSW (RPFSW) process. The input parameters were varied at three distinct levels: rotation speed (1250, 1500, 1750 rpm), transverse speed (75, 87.5, 100 mm/min), and shoulder diameter (12, 15, 18 mm). Tensile tests were conducted to evaluate mechanical strength, and optical microscopy together with scanning electron microscopy (SEM), was employed to examine the microstructure of the weldments. The results indicate that the weldments achieved a tensile strength ranging from 49.7% to 72.4% of the base material. The optimal processing parameters were identified to achieve the highest predicted tensile strength of 198.15 MPa, corresponding to a transverse speed of 100 mm/min, a rotation speed of 1629 rpm, and a shoulder diameter of 13 mm. Microstructural analysis revealed that appropriate RPFSW parameters lead to suitable temperature and material flow, which in turn reduces weld defects and enhances the overall mechanical properties of the joint.

Enhancing C ₂₊ Product Faradaic Efficiency in CO ₂ Reduction Using Fluorine-Stabilized Superhydrophobic Copper (δ+)

Generative artificial intelligence models outperform students on divergent and convergent thinking assessments

Unpleasant but effective: Newspaper coverage of cancer screening and cancer in the Netherlands from 2010 to 2022

Participation rates in cancer screening programs (CSPs) have shown a declining trend, and research suggests that news media reports may contribute to public opinion. Therefore, we aimed to understand how Dutch news media report on CSPs. We mainly focused on breast, colorectal, and cervical CSPs but did not exclude reports on other cancer types. Through a systematic content analysis 5,503 news articles from 2010 to 2022 were analyzed for key characteristics such as topic and reported cancer type. Results showed that most news reports framed CSPs as effective and beneficial for public health. The reliability of screening methods was sometimes criticized. In these cases, reports discussed overdiagnosis or medicalization. Although reports were positive about CSPs’ effectiveness, they were sometimes negative about organizational, psychological, and physiological aspects. Early detection and diagnosis of cancer are portrayed as having benefits that outweigh the costs. These findings show that news media often describe CSPs as a ‘necessary evil’ and that participation may be inconvenient and stressful, but that early detection and diagnosis of cancer are benefits that seem to outweigh this necessary evil.

Ground subsidence monitoring and analysis of Lairong railway during its entire construction cycle based on SBAS-InSAR

Correction: Chip-Based Comparison of the Osteogenesis of Human Bone Marrow- and Adipose Tissue-Derived Mesenchymal Stem Cells under Mechanical Stimulation

Research on the similarity calculation of short text in the terminology domain based on siamese BERT model

Correction: Biomarkers of professional cybersportsmen: Event related potentials and cognitive tests study

AMMI and GGE biplot analysis for seed yield and stability performance of selected buckwheat genotypes under multi-environmental trials

Research on computational propagation and identification of mine microseismic signals based on deep learning

In the mining field, hydraulic fracturing of coal - seam boreholes generates a large number of weak microseismic signals. The accurate identification of these signals is crucial for subsequent positioning and inversion. However, when dealing with such signals, traditional automatic microseismic waveform identification algorithms have difficulty in accurately identifying weak waveforms and are prone to misjudging background noise. This study innovatively introduces the deep - learning convolutional neural network (CNN), integrating the concepts and methods of computational communication to analyze microseismic signals. 8,341 pieces of background noise data and 5,860 pieces of microseismic data are carefully selected from the data of coal - seam borehole hydraulic fracturing. After adding noise at 12 levels and performing translation with 10 different degrees of displacement, 101,123 pieces of background noise and 102,546 effective waveforms are obtained. Subsequently, by applying the information - propagation dynamics model of computational communication, microseismic signals are regarded as information carriers. A signal - propagation network is constructed, and features such as network degree distribution are extracted. These features, combined with traditional time - domain and frequency - domain features, are converted into time - domain and Fourier images and then input into a two - dimensional CNN model. Experiments show that the time - domain CNN model achieves a precision rate of 100% and a recall rate of 68% in microseismic event identification, significantly outperforming traditional methods such as AIC, STA/LTA, and the Fourier CNN model. Furthermore, the time-frequency fusion CNN model—integrating time-domain waveforms, Fourier frequency-domain features, and time-frequency characteristics (e.g., short-time Fourier transform)—achieves an identical precision rate of 100% and a higher recall rate of 72%, outperforming the single-domain time-domain CNN model. The integration of computational communication concepts (e.g., signal propagation network topological features) and multi-domain features enables the model to capture comprehensive spatiotemporal and dynamic signal characteristics, further validating its superiority in identifying weak microseismic signals with low signal-to-noise ratios (SNR).This indicates that the combination of time - domain images and computational - communication technology is more suitable as the input data for the CNN model. It can effectively distinguish microseismic waveforms from background noise, opening up a new path for the identification of mine microseismic signals and demonstrating the application potential of computational communication in this field.

Double Disguise: Camouflaging Photocages for Bioorthogonally Controlled Conditional Activation

Integrating artificial intelligence and sustainable materials for smart eco innovation in production

Cuts or carcasses? Diet form affects fecal microbial and animal fiber fractions in a large carnivore, the Asiatic lion

The care of exotic felids in zoos involves numerous factors, and the dietary management is currently considered particularly critical. This study investigated the effects of four different dietary regimens on the fecal microbiota and fecal characteristics of four female Asiatic lions ( Panthera leo persica ) at the Rotterdam Zoo, the Netherlands. The lions were sequentially fed beef meat on bone (BM01, 4 weeks), degutted and skinned cattle carcasses (CC, 4 weeks), degutted but unskinned banteng carcasses (BC, 2 weeks), and then returned to beef meat on bone (BM02, 4 weeks). Feces were collected at day 28, 56, 70 and 98, and represented the total feces of the group between the last feeding and the next feeding. 16S rRNA gene sequencing showed significant microbial shifts at phylum level, including, between CC and the subsequent diets, a decrease in Proteobacteria abundance and increases in Actinobacteria and Fusobacteria (p < 0.05). Fecal characteristics varied by diet. CC resulted in the highest proportion of visible bone, and BC in the lowest. Fecal particle size was highest on BC, and fecal volume on BC was about twice that of other feeding regimes, suggesting a dilution by indigestible skin and fur components. Significantly lower levels of fecal ash, calcium and phosphorus on BC (p < 0.001) supported the dilution hypothesis. On all diets, ash was shown to be a significant part of the animal fiber in the feces (p < 0.001). Although unskinned carcass may have required greater chewing effort, potentially increasing bone fragmentation, the reason for the reduced bone intake remains unclear. While the exact intake of animal fiber could not be quantified, differences in microbial composition and fecal characteristics were associated with variations in the type of animal fiber, such as bone versus skin.

Balanced CO/OH Intermediates for Efficient and CO-Resilient Electrocatalytic Methanol Oxidation via Pt Supported on La-Doped α-MoC

Layered structure of cortex explains reversal dynamics in bistable perception

Behavioural, immunological and transcriptomic consequences of post-weaning social isolation and chronic celecoxib administration in mouse

Early life stress (ELS) and chronic low-grade inflammation are associated with psychiatric disease risk, but their neurobiological consequences are poorly understood. Here, we aim to investigate the behavioural, immunological and molecular consequences of ELS in mice. C57Bl6 mice were subjected to post-weaning social isolation (SI - PD21−40) with or without chronic celecoxib (CEL) (PD21−61). ELS-induced behavioural changes were assessed using the open field test (OFT) and three-chambered test (3CT). The anti-inflammatory effects of celecoxib were assessed by enzyme-linked immunosorbent assay (ELISA) of IL-6, TNF-α and IL-10 cytokines released by stimulated splenocytes. Gene expression changes in the hippocampus and amygdala were assessed using RNA-sequencing. Neither SI nor CEL affected OFT time in centre or 3CT discrimination ratio. However, SI induced locomotor changes in both tests. CEL significantly reduced IL-6, TNF-α and IL-10 release from splenocytes. SI induced significant gene expression changes in both hippocampus and amygdala, while CEL only induced gene expression changes in the hippocampus. Differentially expressed genes (DEGs) induced by SI were enriched for ontologies relating to gamma-aminobutyric acid activity and insulin binding in the hippocampus and neurogenesis in the amygdala. CEL-induced DEGs in the hippocampus were enriched for neurogenesis. Cell type enrichment implicated choroid plexus and vascular leptomeningeal cells in SI DEGs and medium spiny neurons (MSNs) in CEL DEGs. CEL-induced DEGs were enriched for heritability for psychiatric disorders and cognitive ability. In conclusion, gene expression changes show convergence with human psychiatric disorders through both enrichments in common genetic heritability and enrichment of previously implicated cell populations.

Predicting self-healing efficiency in recycled aggregate concrete using optimized machine learning models

Insights from the ground: A qualitative investigation of retailer perspectives of the challenges and opportunities in the legal cannabis market in Newfoundland and Labrador, Canada

Background The legalization of recreational cannabis in Canada has resulted in varying regulatory and market environments across provinces and territories. These differences shape how retail markets develop and how retailers perceive their opportunities, challenges, and roles in advancing public health objectives. In Newfoundland and Labrador (NL), cannabis retail operates within a distinctive framework shaped by centralized distribution, licensing requirements, and pricing regulations. This qualitative study explores how licensed and prospective retailers perceived the factors influencing the cannabis retail market in NL. Methods Semi-structured virtual interviews were conducted with nine licensed and nine prospective cannabis retailers in NL. A thematic analysis, using Wright-Brown et al.’s Comprehensive Cannabis Retail Framework and Ritchie and Spencer’s framework analysis, was conducted. Both deductive and inductive coding were applied to identify framework-aligned and emergent themes. Results Licensed retailers reported challenges such as restrictive advertising rules, high taxation, and supply chain inefficiencies, which they viewed as constraints on profitability and growth. At the same time, access to quality products, positive customer relationships, and informal mentorship networks were seen as enablers of success. Prospective retailers identified high licensing fees, limited access to opportunities, and financing difficulties as significant barriers to entering the legal market. Conclusion This study highlights how NL’s cannabis retail system, designed to balance public health protection with market development, may inadvertently limit participation and business sustainability. The study illustrates how regulatory design can shape retailer experiences and market dynamics, underscoring the need to assess whether current regulations are achieving their intended outcomes. While focused on NL, these findings offer valuable insights for other jurisdictions with similar regulatory models, emphasizing the importance of aligning policy design with retailers’ experiences to foster a more inclusive, sustainable, and public health–oriented cannabis retail sector.

Effects of melatonin on advanced glycation end products, inflammation, and oxidative stress in peritoneal dialysis patients: a randomized controlled trial

Facial expression recognition reveals students’ engagement in online class: Correlations with six engagement measurements

Student engagement assessment in online learning faces critical limitations: traditional methods fail to capture engagement’s dynamic, multidimensional nature, particularly in second language (L2) contexts where emotional factors are paramount. This study introduces a novel multi-method framework that combines real-time facial expression recognition with dynamic self-reporting and observational measures to provide comprehensive, temporally-sensitive engagement assessment in synchronous online classrooms. Through analyzing a focused sample of Chinese L2 learners, we examined correlations between automatically detected happiness expressions and six established engagement measurements across both static and dynamic scales. Results revealed a significant and robust correlation between happiness expressions and self-reported emotional engagement, representing the study’s primary validated finding. Additional correlations were found with specific mood indicators (happy and loving items). However, no significant correlations were observed with behavioral engagement, cognitive engagement, or flow experience. The facial expression recognition successfully captured dynamic engagement fluctuations, showing modest but significant correlations with both retrospective self-reports and classroom observations. Our findings demonstrate that facial expressions serve as valuable indicators of specific engagement dimensions in online L2 learning, offering a targeted tool for emotional engagement assessment within comprehensive, multi-method evaluation frameworks in digital education.