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Research on stress release and pressure relief mechanism of underlying coal and rock under protective layer mining
Quantification of PFAS in soils treated with biosolids in ten northeastern US farms
Social processing distorts physical distance perception
Abstract Psychopathy is traditionally linked to higher-level cognitive processes, but its impact on low-level cognitive functions has remained largely unexplored. This study investigates the relationship between the fundamental cognitive function of physical distance estimation and our social predisposition towards others, as captured by psychopathic traits, grouped into the three facets of Fearless Dominance, Self-Centered Impulsivity and Coldheartedness. Using an innovative experimental design, participants estimated the distance of an approaching avatar agent. We demonstrate that social processing, as reflected in the scale of Psychopathy in the general population, significantly distorts egocentric space perception. Strikingly, this distortion is absent with non-anthropomorphic agents. The study also reveals that agent emotional expressions do not affect distance estimation, when they are task-irrelevant, suggesting a disocciation between the effect of psychopathic traits on social processing and emotion processing. These results uncover a direct top-down effect of psychopathic traits on basic spatial cognition, in the presence of others. These new insights into the basic cognitive mechanisms underlying social interactions have broader implications for any field involving interpersonal spatial behaviour.
Biomarkers of inflammation in sweat after myocardial infarction
Abstract ST-elevation myocardial infarction (STEMI) triggers a significant inflammatory response. Sweat may offer a novel, non-invasive medium for monitoring inflammation. In this prospective study, we characterized the inflammatory signatures in plasma and sweat collected from the skin surface of two patient groups: (1) 18 STEMI patients immediately following percutaneous coronary intervention (exposure) and (2) six patients who underwent outpatient angiography without subsequent intervention (control). Levels of 92 biomarkers were measured using a high-throughput proteomic assay and reassessed after 4–6 weeks in STEMI patients. Adjusting for patient group, sweat biomarkers did not show significant changes over time. In plasma, hepatocyte growth factor and interleukin-6 showed a significant decrease from the acute phase to follow-up, adjusted for patient group. STAM binding protein was significantly higher in the sweat of STEMI patients compared to controls, adjusted for time effects. While sweat was less sensitive than plasma for detecting biomarker levels in the setting of STEMI, its longitudinal analysis via wearable sensors holds promise for detecting specific markers. Trial registration: The trial is registered on www.clinicaltrials.gov with the trial registration number NCT05843006.
Solving the problem of power ripples for a multi-rotor wind turbine system using fractional-order third-order sliding mode algorithms
An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data
Disproportionality analysis of Raynaud’s phenomenon associated with calcitonin gene-related peptide inhibitors using the Food and Drug Administration adverse event reporting system
Selenium foliar application alleviates salinity stress in sweet william (Dianthus barbatus L.) by enhancing growth and reducing oxidative damage
Abstract Salinity is considered as one of the most important environmental stresses in plant growth and productivity around the world by arid and semi-arid areas; therefore, the development of an efficient strategy against salt stress in crops is urgently needed. Application of Se thus appeared to be an efficient approach for the improvement of plant growth and productivity under saline condition. This study investigated the effects of salinity stress by applying different NaCl levels (0, 30, 60, and 90 mM) in combination with foliar application of Se at different levels (0, 5, 10, and 15 µM) on morpho-physiological and biochemical traits of Dianthus barbatus. Done in a factorial design and completely randomized layout with three replications, the findings showed that salinity caused significant reduction in growth, increased electrolyte leakage and malondialdehyde levels, and increased activities of antioxidant enzymes. At an increase in growth defects among the saline treatments, a positive level of 90 mM NaCl was recorded, whereas the imposition of Se improved some growth traits in most aspects: phenolic and flavonoid contents; antioxidant capacity was boosted in Se-stressed plants. Indeed, at 10µM application level in most of salinity treatments and controls, enhancing the salinity tolerance was reflected. These evidences show cell membrane stabilization of Se through maintaining compounds with various protective functions coupled with enhancing their antioxidant enzyme capacity at efficient low doses. In conclusion, Se application through foliage was an effective method to enhance the plant’s tolerance capacity against salinity in sweet william and could turn out to be a sustained solution for agricultural production under salinity conditions.
Numerical tests on dynamic response of embankment with pile-supported foundation for high-speed railway in soft ground using soil–water coupling elastoplastic FEM
Application and exploration of surgical assistive arms in thoracoscopic surgery: a single-center retrospective study
Machine learning enhanced grey box soft sensor for melt viscosity prediction in polymer extrusion processes
Abstract Melt viscosity is regarded as a key quality indicator of the polymer melt in polymer extrusion processes. However, limitations such as disturbances to the melt flow and measurement delays of the existing in-line and side-stream rheometers prevent the monitoring and controlling of this key parameter in real time. Soft sensors can be employed to monitor physical parameters that are difficult to measure using hardware sensing instruments. This study presents a grey-box soft sensing solution to predict the melt viscosity in real time, which combines physics-based knowledge with machine learning. A fine-tuned physics-based mathematical model is used to make melt viscosity predictions, and a deep neural network is employed to compensate for its prediction errors. The proposed soft sensor model reported a normalised root mean square error of 2.2 $$\:\times\:$$ 10−3 (0.22%), outperforming fully data-driven soft sensor models based on multilayer perceptron and long short-term memory neural networks. Furthermore, it exhibited an improvement of approximately 95% in terms of predictive performance, compared to a soft sensor based on a radial basis function neural network reported in a previous study. The proposed soft sensor can monitor viscosity changes caused by changes in operating conditions but not suitable for detecting viscosity changes due to changes in material properties. The findings of this study can aid in enhancing process monitoring and control in polymer extrusion processes.
Comprehensive bioinformatic analysis reveals prognostic significance and functional insights of candidate gene expression in colorectal cancer
Genome-wide DNA methylation regulation analysis provides novel insights on post-radiation breast cancer
Structural identifiability of parameters of Anand material model
Abstract This article explores the structural identifiability of parameters within non-linear physical models, focussing specifically on the Anand material model. The proposed procedure is structured in two key steps: first, a local analysis identifies the most suitable parameters for testing. Subsequently, these selected parameters, along with their admissible value intervals, are evaluated for global structural dependence. Our innovative numerical approach systematically reduces the parameter count by substituting selected parameters with frozen values (constants). The study employs simulations of the finite element method of a simplified tensile test, utilising the Anand material model. The independent parameters identified through this method are then validated against a tensile test dataset. The validation results indicate a high probability that the use of a reduced parameter set yields unique values for the Anand model parameters in the tensile tests, underscoring the efficacy of our approach.
Effects of dietary live microbes intake on a newly proposed classification system for steatotic liver disease
Risk perception, travel intentions and self-protective behavior of chronically ill tourists under the protection motivation perspective
The impact of underwater waves on ship manoeuvrability: a case study in a fjord
Abstract In 2019, the Motor Vessel Jaeger Arrow collided with the Grande-Anse Terminal wharf (Saguenay Fjord, Canada) during docking from unknown causes. However, the timeline of the incident and the ship’s behavior during docking suggest that underwater waves may have caused the collision. Data collected in 2023 using a camera and thermometers confirmed that this area of the fjord regularly experiences underwater waves with wavelengths ranging from 50 to $$100~\textrm{m}$$ , wave heights of 1 and $$3~\textrm{m}$$ , and periods of around $$2~\textrm{min}$$ . These waves frequently collide with and reflect off the wharf, generating currents of 0.1 to $$0.3~\textrm{m}\,\textrm{s}^{-1}$$ . Numerical simulations further illustrate the interactions between the waves and the wharf, highlighting regions near the wharf where wave-induced currents, both inshore and offshore, occur, including areas with near-zero currents that could create a false sense of calm conditions. Importantly, our observations also revealed that large ships, such as the Jaeger Arrow, can generate their own underwater waves, potentially compromising docking operations. While we cannot definitively confirm that underwater waves caused the incident involving the Jaeger Arrow, our study offers a plausible explanation: the ship may have been caught in a wavetrain reflecting off the wharf, leading to unpredictable movement during docking. These results highlight the potential risks posed by underwater waves to ship safety and maneuverability during docking operations, a topic under-explored in existing scientific literature.