Porothermoelasticity of thermally shocked asphalt material under a multi-phase lag model.

This investigation represents porothermoelastic asphalt material with thermal shock due to multi-phase lag model of thermoelasticity. By applying proper boundary conditions to the normal mode approach, we were able to achieve the precise solution. The graphs provide numerical results for the physical quantities supplied in physical domain.

Evaluation of myocardial perfusion imaging techniques and artificial intelligence (AI) tools in coronary artery disease (CAD) diagnosis through multi-criteria decision-making method.

Background: Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years.

Quantifying Additional Procedures in Functionally Single-Ventricle Disease: A National Cohort Study.

Background: Given their importance as a metric for health care evaluation, this study's aim was to evaluate the rates of surgical and catheter reinterventions for children with functionally single-ventricle (f-SV) congenital heart disease (CHD) undergoing staged palliation.

Methods: We undertook a retrospective cohort study of children born with f-SV CHD between 2000 and 2018 in England and Wales, using the national registry, with survival ascertained in 2020. Competing risk analysis was used to describe the incidence of additional procedures that occurred first, during follow-up, accounting for competing events of death or transplantation.

Inferring structure of cortical neuronal networks from activity data: A statistical physics approach.

Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process.

Monitoring SARS-CoV-2 variants with complementary surveillance systems: risk evaluation of the Omicron JN.1 variant in France, August 2023 to January 2024.

BackgroundEarly detection and characterisation of SARS-CoV-2 variants have been and continue to be essential for assessing their public health impact. In August 2023, Santé publique France implemented enhanced surveillance for BA.2.

The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.

Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management.

Household water security is a mediator of household food security in a nationally representative sample of Mexico.

Objective: Explore the relationship between water insecurity and food security and their covariates in Mexican households.

Design: A cross-sectional study with nationally representative data from the National Health and Nutrition Survey-Continuous 2021 (in Spanish, ENSANUT-Continua 2021), collected data from 12,619 households.

Setting: Water insecurity was measured using the Household Water Insecurity Experiences (HWISE) Scale in Spanish and adapted to the Mexican context.

Becoming as Open-Minded and Organized as My Classmates? Peer Effects on Self-Reported Personality Trait Development in the Classroom.

Introduction: How does a student's personality development relate to the personality of their classmates? The school class builds a pertinent comparison group during adolescence that has been identified as a critical factor in students' development of abilities and self-perceptions. This study empirically tests the impact of classroom personality composition on changes in adolescents' Big Five personality traits. We hypothesized positive associations between class-level openness and conscientiousness and the individual development of these traits given their role in academic performance.

Nematic liquid crystal flow driven by time-varying active surface anchoring.

We demonstrate the generation of diverse material flow regimes in nematic liquid cells as driven by time-variable active surface anchoring, including no-net flow, oscillatory flow, steady flow, and pulsating flow. Specifically, we numerically simulate a passive nematic fluid inside a cell bounded with two flat solid boundaries at which the time-dependent anchoring is applied with the dynamically variable surface anchoring easy axis. We show that different flow regimes emerge as the result of different anchoring driving directions ( co-rotating or counter-rotating) and relative phase of anchoring driving.

Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables.

Background: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is urine culture which is a time-consuming and also an error prone method.

Assessing neonatal nurses: transitioning preterm infants to oral feeding - a multicenter cross-sectional study.

Background: In Egypt, approximately 10% of preterm deliveries occur between 32 and fewer than 37 weeks, leading to high neonatal intensive care unit (NICU) admissions. Preterm infants often face oral feeding difficulties due to immature development, which can lead to extended hospital stays and increased health risks.

Aim: To assess neonatal nurses' performance in terms of the transition to oral feeding in preterm infants, focusing on knowledge, practices, and attitudes.

Measuring a family sense of coherence: a rasch-based study extending dyadic data analyses.

Background: Family sense of coherence (FSOC) seems to reduce distress in the family and promote the well-being of the family. Therefore, getting accurate measurements for families with long-term illnesses is of particular interest. This study explores dyadic data analysis from the dyadic- and single-informant perspectives, and the measurement properties of the FSOC-S12 according to the Rasch model.

Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma.

Background: This study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).

Methods: In this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (n = 153). Sub-regions within the tumor were identified using the K-means method.

The indirect effects of school bullying on mathematics achievement: the mediating roles of teacher-student relationships, sense of belonging and differences between genders.

Background: Previous research paid more attention to the negative effects of the bullying on psychological distress, such as anxiety and depression. However, few studies explored the underlying mechanism between bullying and mathematics achievement. The purpose of this study was to explore the direct and indirect effects of school bullying on mathematics achievement among Chinese eighth grade students.

The prediction model of academic achievement based on cardiorespiratory fitness and BMI status for ninth-grade students.

The purpose of this study was to predict an academic achievement model based on cardiorespiratory fitness (CRF) and body mass index (BMI) in ninth-graders. The study sample included 6 530 adolescents from 341 public schools in Slovakia. Criterion-referenced competency tests measuring academic performance in mathematics and mother language (Slovak), CRF, and BMI were assessed in the academic year 2022-2023.

Automated stenosis estimation of coronary angiographies using end-to-end learning.

The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consuming and costly. Applying deep learning might yield a faster and more accurate stenosis assessment.

A global kernel estimator for partially linear varying coefficient additive hazards models.

We study kernel-based estimation methods for partially linear varying coefficient additive hazards models, where the effects of one type of covariates can be modified by another. Existing kernel estimation methods for varying coefficient models often use a "local" approach, where only a small local neighborhood of subjects are used for estimating the varying coefficient functions. Such a local approach, however, is generally inefficient as information about some non-varying nuisance parameter from subjects outside the neighborhood is discarded.

No winter halt in below-ground wood growth of four angiosperm deciduous tree species.

In the temperate zone, deciduous trees exhibit clear above-ground seasonality, marked by a halt in wood growth that represents the completion of wood formation in autumn and reactivation in spring. However, the growth seasonality of below-ground woody organs, such as coarse roots, has been largely overlooked. Here we use tree monitoring data and pot experiments involving saplings to examine the late-season xylem development of stem and coarse roots with leaf phenology in four common deciduous tree species in Western Europe.

Advanced control parameter optimization in DC motors and liquid level systems.

In recent times, there has been notable progress in control systems across various industrial domains, necessitating effective management of dynamic systems for optimal functionality. A crucial research focus has emerged in optimizing control parameters to augment controller performance. Among the plethora of optimization algorithms, the mountain gazelle optimizer (MGO) stands out for its capacity to emulate the agile movements and behavioral strategies observed in mountain gazelles.

Machine learning boosts wind turbine efficiency with smart failure detection and strategic placement.

The research study objective seeks to improve the efficiency of wind turbines using state-of-the-art techniques in the domain of ML, making wind energy the key player in fashioning a favorable future. Wind Turbine Health Monitoring (WTHM) is typically achieved through either vibration analysis or by using Supervisory Control and Data Acquisition (SCADA) data of wind turbines, wherein conventional fault pattern identification is a time-consuming, guesswork process. This work proposed an intelligent automated approach to early fault detection through the implementation of the HARO (Huber Adam Regression Optimizer) model, which combines Transformer networks with Lasso Regression and the Adam optimizer.

Smart furniture using radar technology for cardiac health monitoring.

The integration of radar technology into smart furniture represents a practical approach to health monitoring, circumventing the concerns regarding user convenience and privacy often encountered by conventional smart home systems. Radar technology's inherent non-contact methodology, privacy-preserving features, adaptability to diverse environmental conditions, and high precision characteristics collectively establish it a compelling alternative for comprehensive health monitoring within domestic environments. In this paper, we introduce a millimeter (mm)-wave radar system positioned strategically behind a seat, featuring an algorithm capable of identifying unique cardiac waveform patterns for healthy subjects.

Bioinspired self-flowing wood chemical treatment.

Wood has complex composition and structure, which make it difficult to achieve consistent and controllable treatment. A self-flowing process presented for the chemical treatment of wood is inspired by liquid transportation in trees during photosynthesis and tree growth, whereby liquid in the soil is brought through the natural vessels and/or fiber tracheids. In this process, wood lumbers are placed in a tank containing treatment chemicals such as preservatives, fire retardants, or reactive agents.

A scaling law to model the effectiveness of identification techniques.

AI techniques are increasingly being used to identify individuals both offline and online. However, quantifying their effectiveness at scale and, by extension, the risks they pose remains a significant challenge. Here, we propose a two-parameter Bayesian model for exact matching techniques and derive an analytical expression for correctness (κ), the fraction of people accurately identified in a population.

Hyperedge overlap drives explosive transitions in systems with higher-order interactions.

Recent studies have shown that novel collective behaviors emerge in complex systems due to the presence of higher-order interactions. However, how the collective behavior of a system is influenced by the microscopic organization of its higher-order interactions is not fully understood. In this work, we introduce a way to quantify the overlap among the hyperedges of a higher-order network, and we show that real-world systems exhibit different levels of intra-order hyperedge overlap.

Graft ischemia post cell transplantation to the brain: Glucose deprivation as the primary driver of rapid cell death.

Replacing cells lost during the progression of neurodegenerative disorders holds potential as a therapeutic strategy. Unfortunately, the majority of cells die post-transplantation, which creates logistical and biological challenges for cell therapy approaches. The cause of cell death is likely to be multifactorial in nature but has previously been correlated with hypoxia in the graft core.