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Factors influencing the implementation of cardiopulmonary resuscitation among college freshmen: Based on the Theory of Planned Behavior

Objective Cardiac arrest is a leading cause of global mortality, for which timely cardiopulmonary resuscitation (CPR) is a critical intervention. However, public competence in CPR remains low. Grounded in the Theory of Planned Behavior (TPB), this study investigated the factors influencing CPR implementation among college freshmen by examining their behavioral intention. Methods A cross-sectional study was conducted among college freshmen undergoing a basic entrance physical health screening at Fuzhou First General Hospital between September 2 and 9, 2024. Participants were randomly selected to complete an electronic questionnaire, which included a general information sheet and the validated Public Behavior Intention Scale for Performing CPR, adapted with input from emergency medicine experts. Statistical analyses included correlation, regression, and mediation analysis. Results Among 4,929 valid questionnaires analyzed, a higher willingness to perform CPR was associated with undergraduate enrollment, good personal and family health status, prior CPR performance, and no history of transient loss of consciousness. Multiple linear regression identified behavioral attitude (β = 0.439), subjective norm (β = 0.272), CPR knowledge (β = 0.027), and perceived behavioral control (β = 0.070) as significant predictors of CPR behavioral intention (all P  < 0.01). Mediation analysis further revealed that CPR knowledge influenced behavioral intention primarily through its effects on behavioral attitude (79.43% of the total effect), subjective norm (61.31%), and perceived behavioral control (43.67%). Conclusion Behavioral attitude serves as the principal pathway through which CPR knowledge translates into intention to act. These findings underscore that targeted CPR training in universities should address both knowledge and psychosocial factors to strengthen students’ willingness to act.

Alternatives to animal research are not inherently more ethical

Sustainable carbon quantum dots synthesized from yeast β-glucan as a promising nanomaterial for biological applications

Dietary supplementation with Bacillus velezensis and Pichia guilliermondii improves growth performance through intestinal morphology and functionality enhancement in weaning piglets

The post-weaning period in piglets is often associated with significant changes in intestinal structure and function, which can negatively affect growth and performance. This study aimed to evaluate the effects of dietary supplementations with prebiotics and/or probiotics on intestinal morphology and functionality during the post-weaning phase. Piglets were supplemented with different diets, including combinations of Pichia guilliermondii (prebiotic, PRE) and Bacillus velezensis (probiotic, PRO), and their growth performance, intestinal histology, and functionality were assessed. Growth performance was evaluated in the post-weaning period, with a notable improvement in weight gain observed in piglets receiving the combined prebiotic/probiotic supplementation. Morphometric analysis revealed significant differences in intestinal morphology, particularly in the small intestine. The PRE/PRO combination was most effective in improving villus size in the duodenum and ileum at early post-weaning stages, while the jejunum showed a greater sensitivity to weaning stress. Molecular analysis indicated diet-dependent changes in the expression of key markers involved in nutrient absorption and mucosal integrity, including SLC15A1 and TJP1 . Additionally, 16S rRNA sequencing revealed diet-related changes in gut microbiota composition where α-diversity and β-diversity increased independently of the diets and, specific beneficial taxa (e.g., Lachnospiraceae) were enriched in the supplemented groups. These microbial shifts were consistent with the improved mucosal thickness observed in the colon, suggesting a potential contribution of microbial modulation to epithelial integrity of the large intestine. Overall, the results suggest that the synbiotic supplementation of prebiotics and probiotics can enhance intestinal integrity in both the small and large intestine, promote growth, and improve overall gut health in post-weaning piglets.

Bilateral transcutaneous auricular vagus nerve stimulation for the treatment of insomnia in breast cancer

Pharmacologic neuroprotective agents for the treatment of perinatal asphyxia in low-income and lower-middle-income countries: A systematic review and meta-analysis of randomised controlled trials

Background Perinatal asphyxia (PA) is a major contributor to neonatal mortality and long-term neurodevelopmental impairment, particularly in low- and middle-income countries (LMICs), where the effectiveness of therapeutic hypothermia remains limited. Pharmacologic neuroprotective agents have shown potential as alternative treatments, but their efficacy in low-income and lower-middle-income countries (LILMICs) is not well established. This systematic review aimed to assess the effectiveness of pharmacologic interventions in neonates with PA in LILMICs. Methods A systematic search of PubMed, Web of Science, CINAHL, and Google Scholar was conducted for randomised controlled trials (RCTs) published between 2000 and 2024. Eligible studies compared pharmacologic neuroprotective agents with placebo or standard care, excluding therapeutic hypothermia, among neonates diagnosed with PA in LILMICs. Data on survival and neurodevelopmental outcomes were extracted and synthesized; meta-analyses were conducted where appropriate. Results Twelve RCTs involving 1,008 neonates were included. The majority (91.7%) of studies were conducted in Asia, with only one study from Africa. Magnesium sulphate was the most frequently evaluated agent (66.7% of studies), followed by melatonin, topiramate, erythropoietin, and citicoline. Melatonin was associated with improved survival, and all agents showed improved short-term neurological outcomes. Neurodevelopmental outcomes at 3, 6, 12, and 19 months were generally favourable, though data remained limited. Conclusion Pharmacologic neuroprotective agents show promise in improving survival and neurological outcomes in neonates with PA in LILMICs. However, more robust, multi-center RCTs are needed to confirm their efficacy and establish them as feasible alternatives to therapeutic hypothermia in these settings.

Don’t downplay problems of bullying and harassment in academia

Epibrassinolide seed priming alleviates alkaline stress by enhancing antioxidant defense in dragonhead plants

Object location memories recruit distal CA1 and catecholaminergic inputs to proximodistal CA1

The hippocampus is thought to combine “what” and “where” information from the cortex so that objects and events can be represented within the spatial context in which they occur. Surprisingly then, these distinct types of information remain partially segregated in the output region of the hippocampus, area CA1. In this region, objects preferentially activate neurons in the distal segment (adjacent to the subiculum) while spatial locations are precisely represented by neurons in the proximal segment (adjacent to CA2). This difference likely results from distinct anatomical connections; proximal CA1 receives direct input from the medial entorhinal cortex (which encodes spatial context) whereas distal CA1 has reciprocal connections with the lateral entorhinal cortex (which encodes objects and events). Based on these findings, it has been proposed that CA1 contains two distinct representations; one that encodes the animal’s spatial location and another that encodes objects that are present in the environment. The current study aimed to determine the role of distal CA1 in learning the location of objects in an environment. To do this, we first demonstrated that distal CA1 is more responsive (higher levels of c-Fos) to objects while proximal is spatially tuned. Further, as previous studies indicate that catecholamines can regulate CA1 activity, we lesioned the catecholaminergic inputs to CA1 and observed a reduction in c-Fos levels in both segments of CA1, and an impairment in object location memory 24h after training. Together, these findings indicate that processing object location in an environment recruits distal CA1 and catecholaminergic inputs to CA1.

Cooking up a storm of air pollution

Machine learning framework for predicting the shear capacity of demountable bolted connectors in composite beams

Abstract Steel–concrete composite beams are increasingly adopted in modern construction owing to their high strength, stiffness, and efficiency. Conventional welded shear connectors, while effective, hinder disassembly and recycling, limiting their alignment with sustainable construction practices. To address this, demountable bolted connectors have emerged as a viable alternative, promoting reuse, reduced waste, and compatibility with modular construction. This study presents eight machine learning algorithms including linear, tree-based, and ensemble methods were trained on a hybrid dataset combining experimental that were collected from previous studies and numerical results. Among these ML models, the XGBoost Regressor exhibited the highest accuracy (R² ≈ 0.996) with consistently low error margins, while SHAP-based interpretability confirmed bolt diameter and reinforcement strength as the most influential predictors. The findings highlight the potential of combining advanced testing, finite element modeling, and machine learning to establish robust predictive tools for demountable connector systems. This multidisciplinary approach not only improves design accuracy but also supports the development of sustainable, reusable, and high-performance composite structures in line with circular economy principles.

Parents’ aversion to the possibility of having a gay or lesbian child predicts gendered parenting

Gendered parenting refers to parents’ tendency to raise their sons and daughters in accordance with gender stereotypes. Previous research showed that parents’ binary conceptions of gender are associated with gendered parenting. The goal of the current work was to test whether parents’ aversion to the possibility that their child might develop a same-sex orientation – an aversion rooted in a binary view of gender – can explain gendered parenting. Negative emotions towards such a possibility were identified in previous work, however rarely studied quantitatively and have yet to be linked to gendered parenting. We conducted two studies among parents of preschool children across Israel (Study 1) and the US (Study 2). Parents were asked to choose a gift for their child, through which gendered parenting behavior was assessed. We assessed known predictors of gendered parenting that are reflective of a binary view of gender (gender essentialism and gender ideology) and added parental aversion to the possibility of having a gay or lesbian child. We further asked parents to freely explain their responses to one of the gendered parenting indicators. The results, including both qualitative and quantitative analyses, showed that parents’ aversion to the possibility of having a gay or lesbian child, exclusively predicted gendered parenting, over and above predictors identified in previous work. Results are discussed in relation to relevant constructs such as heteronormativity, homophobia, and perceived masculinity.

Graphene battery as a viable alternative in electric vehicles for enhanced charging efficiency and thermal management

Abstract The transportation sector’s reliance on fossil fuels necessitates a transition towards sustainable alternatives like electric vehicles (EVs). While lithium-ion (Li-ion) batteries currently dominate the EV market, their limitations in charging time, thermal management, and resource sustainability motivate the exploration of advanced battery technologies. This research investigates the potential of graphene-enhanced batteries as a viable alternative for Li-ion batteries in EVs, focusing on enhancing charging efficiency and thermal management. A comparative analysis is conducted using a MATLAB-based simulation framework, modelling a graphene-enhanced battery system against a conventional Li-ion system based on considered reference of Tata Nexon EV Prime specifications. The simulations evaluate performance across various discharge rates (0.2 to 3 C), analysing charging time, temperature profiles, charging efficiency, and temperature coefficients. The results demonstrate that graphene-enhanced batteries exhibit significantly faster charging times (22% − 27%), maintain lower operating temperatures (0.1 to 5 °C lower), and also offer the potential for substantial weight reduction i.e. 53% in the modelled simulation). These advancements, stemming from graphene’s exceptional electrical and thermal conductivity, indicate a promising route toward the development of more efficient, safer, and higher-performing electric vehicles. This study provides quantitative insights into the benefits of graphene integration in EV battery technology, highlighting its potential to address key limitations of Li-ion batteries and contribute to a more sustainable transportation future.

Bearing fault diagnosis method based on enhanced VMD and adaptive-optimized SDAE

Motor rolling bearing is a fundamental component of industrial production, and its vibration signal extraction and fault diagnosis are challenging because of the effect of operating characteristics and external noise. This research initially proposes an adaptive variational mode decomposition approach based on dung beetle optimization algorithm to decompose and extract signals. At the same time, a composite optimization indicator function based on Tanimoto coefficient, permutation entropy and kurtosis are presented as the fitness function of decomposition to increase the flexibility and robustness of the technique. Next it combines with composite multiscale permutation entropy to finish feature extraction and create feature vectors. Finally, an enhanced inertia weights and Cauchy chaotic mutation-Sine Cosine Algorithm is utilized to optimize the hyperparameters of the stacked denoising auto-encoders network and construct a fault diagnosis model. The CWRU open bearing dataset is used to comprehensively evaluate the performance of the method, and the experimental results will be compared to show that the method proposed in this paper can effectively extract signal features in the situation of strong noise, while ensuring a high prediction accuracy, and has stronger adaptability and noise resistance compared with other methods.

MedShieldFL-a privacy-preserving hybrid federated learning framework for intelligent healthcare systems

A blended modeling framework for real-time design and verification of safety-critical embedded systems

Embedded systems often require multiple representations for design, verification, and implementation, ranging from low-level programming languages to high-level formal models and domain-specific abstractions. Generally, synchronization among different representations or notations is achieved manually, a process that is labor-intensive and prone to mistakes, adversely impacting productivity and time-to-market objectives. Despite existing tool support, there remains a lack of unified, automated mechanisms that ensure semantic consistency across heterogeneous modeling and programming notations. This article presents a scalable blended modeling framework that automates the synchronizations across an extensible set of notations using bidirectional transformations. This facilitates the system development, comprising design and verification aspects of safety-critical embedded systems, using various notations interchangeably. The applicability of the proposed framework is demonstrated using four distinct representations: C, SystemVerilog, Timed Automata, and a domain-specific modeling language. The framework supports a notation-agnostic design flow, allowing development to begin from any of the supported languages. This enables seamless transitions across notations based on design or verification needs. Validated through two industrial case studies, a ventilator system and a cruise control system, the framework achieved high round-trip transformation accuracy with minimal information losses in edge cases such as language-specific keywords. Performance evaluations revealed low transformation latency and modest memory consumption, supported by efficient Abstract Syntax Tree (AST) traversal. This research lays the groundwork for the standardization of model-to-code, code-to-model, and code-to-code transformations, significantly reducing manual engineering effort and improving the reliability and agility of embedded systems design and verification processes.

Nature at its weirdest: what metamorphosis reveals about science and ourselves

Lessons from a long road to a first-author paper

Association between anosognosia and neuropsychiatric symptoms in Alzheimer’s disease dementia patients

Abstract Anosognosia, the lack of awareness of memory decline, and Neuropsychiatric Symptoms (NPS) are prevalent and debilitating symptoms in Alzheimer’s disease (AD) dementia. Understanding the coexistence of these symptoms may help guide clinical interventions and treatment strategies. This study aimed to compare NPS prevalence in patients with and without anosognosia at baseline and to assess the association between anosognosia and NPS over time. We examined patients with AD dementia enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). To be included in the current study, patients had to have undergone baseline assessments and at least one subsequent follow-up evaluation. Furthermore, all patients had to have amyloid (as assessed using Positron Emission Tomography, PET), Mini-Mental State Examination (MMSE), Neuropsychiatric Inventory (NPI), and Everyday Cognition (ECog) variables available throughout the study. Anosognosia, our exposure of interest, was determined using Ecog scores from patients and study partners. Study partners evaluated the presence or absence of 12 NPS (our outcomes of interest) using the NPI. Cox proportional hazards models, excluding patients who had any symptoms of NPS at baseline, were used to evaluate NPS onset by group (anosognosia/no anosognosia) while adjusting for age, sex, years of education, and MMSE. 112 patients with follow-up data (mean = 1 year) were included in this study. Of these, 47.3% ( n  = 53) had anosognosia, while 52.7% ( n  = 59) did not. In those with anosognosia at baseline, we observed a trend toward greater prevalence of agitation and motor symptoms. Exploratory time-to-event analysis demonstrated that the patients with anosognosia had a faster onset of apathy (HR: 2.78, 95% CI: 1.37–5.62, p  = 0.01) compared to the patients without anosognosia. In this exploratory study, while there were no significant differences in frequency of NPS at baseline between the groups, patients with anosognosia demonstrated faster onset of apathy as compared to patients without anosognosia. These findings underscore the importance of longitudinal assessments and tailored interventions targeting the management of NPS in AD dementia patients with anosognosia. Further research is warranted to explain the underlying mechanisms driving these associations and to inform the development of targeted therapeutic strategies aimed at improving patient outcomes in this population.

Decoding brand sentiments: Leveraging customer reviews for insightful brand perception analysis using natural language processing and Tableau

Traditional survey-based feedback has given way to real-time online reviews, yet transforming this unstructured text into actionable knowledge remains difficult. Focusing on the highly competitive smartphone market, where customer sentiment shapes brand perception and product strategy, this study proposes an end-to-end analytics pipeline that combines Machine Learning (ML), Deep Learning (DL), topic modelling, and interactive visualisation. Reviews for ten smartphone brands from Amazon (n ≈ 68 k) were pre-processed and class-imbalanced data were mitigated through class weighting. Sentiment classification was performed with ML models (Decision Trees, Logistic Regression, SVM, Naive Bayes) and DL models (Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM)). CNN achieved the highest accuracy 85.07% and balanced performance across positive and negative classes, although all models struggled with neutral reviews. Underlying themes were extracted with Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF); quantitative evaluation using the Coherence Score (CS) showed that NMF produced more interpretable topics (CS = 0.54) than LDA (CS = 0.41). Topic-level sentiment was assessed with the Valence Aware Dictionary and Sentiment Reasoner (VADER), linking features such as battery life and camera quality to positive or negative customer attitudes. Results are delivered through an interactive Tableau dashboard that allows practitioners to track sentiment trends, drill into coherent topics, and compare brand performance. The study also discusses ethical considerations, such as potential bias from imbalanced or culturally nuanced language, and outlines future work on cross-domain generalisation and fairness auditing. Overall, the integrated pipeline demonstrates that coupling CNN-based sentiment analysis with high-coherence NMF topics provides richer, business-ready insights than sentiment analysis alone.