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Heat and mass transfer of micropolar fluid flow over a stretching sheet by legendre collocation method
Abstract The integration of micropolar fluid in extrusion processes is critical for understanding and improving the manufacturing of materials that reveal microstructural effects. Extrusion is a commonly used process in sectors such as polymer, food, and metal processing, where a material is pushed through a die to form a product with a desired cross-section (e.g., films, sheets, fibers, tubes). The consequences of magnetic field, thermal radiation, and chemical reaction on the quality of the extruded product constitute a complicated and vital area of research. Hence, the current study is conducted to examine the flow associated with the transport of heat and mass of micropolar fluid across an expandable sheet in the company of an external magnetic field, thermal radiation, and chemical reaction. The problem is controlled by the energy equation for heat transfer, the species transport equation for mass transfer, and the Navier-Stokes equations for momentum. Following some conversions, the subsequent scheme of ordinary differential equations (ODEs) is numerically worked out by applying the Legendre-collocation approach. The velocity, temperature, and concentration profiles are analyzed in relation to the effects of thermal radiation, magnetic field strength, and chemical reaction rate. The findings reveal that the magnetic field will decrease the velocity but on the other hand it will increase the microrotation velocity. The magnetic field and the thermal radiation will enhance the temperature. Finally, the magnetic field will improve the concentration slightly but on the other hand the chemical reaction will decrease it.
Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides
Digital pathology enables automatic analysis of histopathological sections using artificial intelligence. Automatic evaluation could improve diagnostic efficiency and find associations between morphological features and clinical outcome. For development of such prediction models in breast cancer, identifying invasive epithelial cells, and separating these from benign epithelial cells and in situ lesions would be important. In this study, we trained an attention gated U-Net for segmentation of epithelial cells in hematoxylin and eosin stained breast cancer sections. We generated epithelial ground truths by immunohistochemistry, restaining hematoxylin and eosin sections with cytokeratin AE1/AE3, combined with pathologists’ annotations. Tissue microarrays from 839 patients, and whole slide images from two patients, were used for training and evaluation of the models. The sections were derived from four breast cancer cohorts. Tissue microarray cores from a fifth cohort of 21 patients was used as a second test set. In quantitative evaluation, mean Dice scores of 0.70, 0.79, and 0.75 were achieved for invasive epithelial cells, benign epithelial cells, and in situ lesions, respectively. In qualitative scoring (0-5) by pathologists, the best results were reached for all epithelium and invasive epithelium, with scores of 4.7 and 4.4, respectively. Scores for benign epithelium and in situ lesions were 3.7 and 2.0, respectively. The proposed model segmented epithelial cells well, but further work is needed for accurate subclassification into benign, in situ, and invasive cells.
Optimal planning of integrated nuclear-hybrid renewable energy systems for electrical distribution networks based on artificial intelligence
Abstract In recent years, small-scale nuclear power plants, particularly micro nuclear reactors, have emerged as viable alternatives, gaining importance in the technical and economic operation of electrical distribution systems. As consumer demand for electricity continues to rise, the use of renewable energy sources and nuclear energy has become essential, especially as dependence on conventional energy sources grows increasingly unsustainable from an environmental standpoint. In this study, mathematical models for various Hybrid Energy Systems (HES) are developed using both single and multi-objective functions. Active Power Loss (APL) is selected as the first single-objective fitness function, while the total Net Present Cost (NPC) serves as the second. These two objectives are also considered together in a multi-objective optimization framework. The White Shark Optimizer is employed to determine the optimal configuration that achieves an improved voltage profile, reduces power losses, and minimizes both cost and greenhouse gas (GHG) emissions. The proposed modeling and simulations are conducted using MATLAB software, and the optimization methodology is applied to three types of HES on two standard radial distribution networks; the IEEE 33-bus and IEEE 69-bus systems. The three HES configurations analyzed are; Nuclear-Renewable Hybrid Energy System (N-R HES), Stand-alone Fossil Fuel-based Thermal Generators (FFTGs), and Renewable-Fossil Fuel Hybrid Energy System. Among the three, the N-R HES demonstrates the most favorable between system performance, cost efficiency, and environmental impact. Results and analysis prove that N-R HES is the most effective solution for sustainable energy generation and decarbonization, offering the lowest NPC and APL.
Cuproptosis-related genes associated with mitochondrial dysfunction in Parkinson’s disease
Parkinson’s disease (PD), a neurodegenerative condition characterized by the loss of dopamine neurons and motor deficits, has recently been associated with cuproptosis, a process potentially leading to mitochondrial dysfunction. This study utilized six PD datasets from the GEO database, designating one for internal training and the remaining five for external validation. Various analytical methods, such as Gene Set Enrichment Analysis (GSEA), immune infiltration studies, and differential expression analysis, were employed to pinpoint differentially expressed genes (DEGs). The research also applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify module genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. By intersecting DEGs with cuproptosis-related genes (CRGs), differentially expressed cuproptosis-related genes (DECRGs) were identified and assessed using Receiver Operating Characteristic (ROC) curves. Further analysis led to the discovery of differentially expressed cuproptosis-mitochondrial dysfunction-related genes (DEC-MDRGs), which were validated and subjected to additional scrutiny. The study concluded with predictions of potential therapeutic drugs. The findings revealed 6685 DEGs and 31 distinct modules, with gene functions predominantly enriched in immune-related pathways. Twelve DECRGs, recognized as high-diagnostic-potential hub genes (AUC > 0.9), were identified for early PD diagnosis. Additionally, eight DEC-MDRGs were found to be expressed across various neural cells. The miRNA network highlighted the significance of miR-4632 and miR-637. In a MPTP-induced mouse model of PD, differential gene expression was confirmed through gene and protein analysis. Transmission electron microscopy (TEM) uncovered mitochondrial alterations in SH-SY5Y cells. Potential PD treatments, including NADH, Radicipol, and Glycine, were also identified. In summary, advancements in PD prevention, diagnosis, and treatment can be achieved by modulating copper metabolism and mitochondrial function, thereby enhancing the quality of life for patients.
Modeling the relationships among physical education teachers’ technological pedagogical content knowledge, technology integration self-efficacy, employability, and the mediating role of job passion
Psychometric validation of an Arabic version of the WHO-5 wellbeing index among Lebanese adolescents
Introduction Wellbeing in adolescence is frequently associated with positive developmental outcomes. The WHO-5 is widely recognized for its brevity, clarity, robust reliability, and cultural validity. In this study, we aimed to assess the psychometric properties of an Arabic version of the WHO-5 scale among Lebanese adolescents. Methods This cross-sectional study involved 700 Lebanese school students aged 14−19 years. Participants were assessed using WHO-5 wellbeing index (WHO-5) (wellbeing), PHQ-9 (depression), and GAD-7 (anxiety) Arabic questionnaires at two-time points, 3 months apart. Results We found that the 5 items of the WHO-5 converged into a single factor. Internal consistency of scores was adequate in the total sample (ω = .83/ α = .83). The convergent validity for this model was satisfactory. We were able to show the invariance across gender at the configural, metric, and scalar levels, with males showing a higher level of wellbeing compared to females. The pre-posttest assessment for the WHO-5 scale was conducted on 358 participants; the intraclass correlation coefficient was adequate = 0.78 [95% CI .73; .82]. Our analyses also show that wellbeing was negatively correlated with depression (r = −.54; p < .001) and anxiety (r = −.52; p < .001). Conclusion The Arabic WHO-5 among Lebanese adolescents displayed highly satisfactory psychometric properties, which are evidence of its validity. It could be used to better track positive mental health in this vulnerable age group and could highlight efficiency in interventions aiming to promote wellbeing in adolescents. It could also potentially identify at risk individuals.
Adaptation and validation of the Multidimensional Measure of Parasocial Relationships (MMPR) in Poland
Abstract Parasocial relationships, one-sided bonds with media figures, have grown with Internet/social media use and are linked to various well-being outcomes. For example, parasocial relationships on social media may foster connection and healthy behaviors while also prompting negative self-comparisons. The Multidimensional Measure of Parasocial Relationships (MMPR), developed by Garcia and colleagues (2022), assess parasocial engagement across affective, behavioral, cognitive, and decisional dimensions. While the MMPR has demonstrated robust psychometric properties in its original Swedish version, its cross-cultural applicability remains unexplored. To addresses this gap, we adapted and validated the MMPR in a Polish sample. A total of 371 Polish young adults (255 women, 116 men; age range 18–48 years) completed the survey. The adaptation process involved translation, back-translation, and review by expert judges. Confirmatory factor analysis (CFA) tested the four-factor model. We also calculated Internal consistency (Cronbach’s alpha and McDonald’s omega), four-week test–retest reliability, and examined convergent validity via correlations with theoretically related measures (i.e., early maladaptive schemas and emotional well-being). CFA supported the four-dimensional structure: χ2/df = 2.74; RMSEA = .069 (90% CI = .060–.077); SRMR = .069; CFI = .855. Cronbach’s α ranged between .59 (Behavioral dimension) and .75 (Decisional dimension) (.83 for the whole scale). Test–retest correlations were moderate to strong (r = .47-.81). Convergent validity revealed expected, but weak, associations (e.g., higher parasocial engagement was linked to lower abandonment schemas and lower negative emotions). Our findings support the MMPR as a psychometrically sound instrument for assessing parasocial relationships in a digital Polish context. Despite limitations such as the sample’s demographic composition and modest internal consistency for the Behavioral subscale, the study contributes meaningful evidence for the scale’s applicability beyond its original cultural setting. The results underscore the importance of culturally adapted tools to capture the complex interplay between media engagement and psychosocial functioning, particularly in an era of increasing online interaction. Future research should further refine the measure and explore its use across diverse populations and platforms.
Accurate full-scale patient-specific Circle of Willis models including aneurysms: A novel manufacturing approach
Background Accurate physical replicas of the Circle of Willis (CoW) are valuable for planning neuroendovascular interventions, validating computational simulations, evaluating medical devices and training physicians. Existing methods often replicate only segments of the CoW or lack geometric precision, which is critical for realistic hemodynamic simulations. Objective We introduce a novel, cost-effective manufacturing approach to create full-scale, patient-specific CoW models using fused deposition modeling (FDM) 3D printing and lost core silicone casting. We aim to evaluate the accuracy and reproducibility of this manufacturing process. Methods A patient-specific 3D model of the CoW with four saccular aneurysms was generated from time-of-flight magnetic resonance angiography (TOF-MRA) data. Three identical models were printed using FDM with acrylonitrile styrene acrylate (ASA) for the vascular structure and butenediol vinyl alcohol co-polymer (BVOH) as a water-soluble support material. The printed models were encased in a clear silicone block and the ASA core was then dissolved using acetone. Computed tomography (CT) scans were used to assess geometric accuracy through cloud-to-mesh distance calculations and centerline analysis. Results The median absolute surface deviation between the replicas and the initial model was approximately 309 µm for the entire CoW, with interquartile ranges (IQR) between 360 µm and 444 µm. The aneurysm surfaces exhibited lower deviations, averaging 90 µm. Centerline analysis showed median absolute deviations in vessel radius ranging from 48 µm to 114 µm across key vascular pathways. Statistical analysis confirmed minimal discrepancies between replicas and the initial model. Each replica costs approximately €100 in materials and requires five days to produce. Conclusion The manufacturing approach produces accurate, reproducible full-scale, patient-specific CoW models, including four aneurysms. This method simplifies the production process, reduces costs and maintains high geometric accuracy, making it suitable for hemodynamic studies, device evaluation, and clinical training.
Study on the failure behavior of saturated sandstone based on AE and avalanche characteristics
Enhanced SVM-based model for predicting cyberspace vulnerabilities: Analyzing the role of user group dynamics and capital influx
Amid substantial capital influx and the rapid evolution of online user groups, the increasing complexity of user behavior poses significant challenges to cybersecurity, particularly in the domain of vulnerability prediction. This study aims to enhance the accuracy and practical applicability of cyberspace vulnerability prediction. By incorporating the dynamics of user behavioral changes and the logic of platform scaling driven by investment, two representative cybersecurity datasets are selected for analysis: the Canadian Institute for Cybersecurity Intrusion Detection System 2017 and the Network-Based Intrusion Detection Evaluation Dataset 2015. A standardized data preprocessing pipeline is constructed, including redundancy elimination, feature selection, and sample balancing, to ensure data representativeness and compatibility. To address the limited adaptability of traditional support vector machine (SVM) models in identifying nonlinear attacks, this study introduces a distribution-driven, dynamically adaptive kernel optimization approach. This method adjusts kernel parameters or switches kernel functions in real time according to the statistical characteristics of input data, thereby improving the model’s generalization capability and responsiveness in complex attack scenarios. Performance evaluations are conducted on both datasets using cross-validation. The results show that, compared to traditional models, the improved SVM achieves an 11.2% increase in prediction accuracy. Furthermore, the model demonstrates a 22.2% improvement in computational efficiency, measured as the ratio of prediction count to processing time. It also exhibits lower false positive rates and greater stability in detecting common cyberattacks such as distributed denial of service, phishing, and malware. In addition, this study analyzes user behavioral variations under different levels of attack pressure based on network access activity. Findings indicate that during periods of high platform load, attack frequency is positively correlated with users’ defensive behavior, confirming a potential causal sequence of “capital influx—user expansion—increased attack exposure.” This study offers a practical modeling framework and empirical foundation for improving predictive performance and enhancing users’ sense of cybersecurity.
Parallel and distributed chimp-optimized LSTM for oil well-log reconstruction in China
Super-resolution stimulated X-ray Raman spectroscopy
ATP synthase inhibition, an overlooked confounding factor in the mitochondrial stress test
The mitochondrial stress test, a widely used procedure to study energy metabolism using extracellular flux analysis, involves the inhibition of ATP synthase (a.k.a. complex V [CV]). This inhibition was recently shown to cause a glycolysis-dependent underestimation of two key mitochondrial respiration parameters, maximal respiration (MR) and spare respiratory capacity (SRC), in tumor cells. However, it is unknown if test substances (toxins, drugs, signaling molecules, etc.), especially those affecting glycolysis, can impact the underestimation of MR and SRC caused by CV inhibition and thereby produce potentially erroneous results. The objective of the present study was to determine if the inhibition of CV in the mitochondrial stress test can act as a confounding factor when measuring MR and SRC in intact non-tumor cells exposed to exemplificatory test substances that affect energy metabolism: Ni2+ and lipopolysaccharides (LPS). Murine bone marrow-derived macrophages were exposed to Ni2+ (0–72 ppm) or LPS (0 or 1 µg/mL), and oxygen consumption rates were measured by extracellular flux analysis using the mitochondrial stress test, with and without CV inhibition. Results showed that CV inhibition masked the decrease in MR induced by Ni2+ or LPS. It also caused the lack of a statistically significant effect of Ni2+ on SRC to present as an increase of SRC, and the LPS-induced decrease of SRC to be masked. Results further showed that these erroneous results arose because exposure to Ni2+ or LPS reduced the underestimation of MR and SRC caused by CV inhibition. This phenomenon was associated with increased glycolytic flux. Finally, results confirmed that underestimation of MR and SRC induced by CV inhibition can occur in non-tumor cells. In conclusion, the present study demonstrates that CV inhibition can act as a confounding factor leading to erroneous conclusions when the mitochondrial stress test is used with intact cells exposed to test substances.
Exploration of Mehrabian’s communication model with an android
The origin of the oldest solid objects in the Solar System
Skin transcriptomics of invasive Coqui frogs: stress responses, parasite signatures, and antimicrobial peptides
Resilience in amphibians lies in their ecological adaptability, driven by their genetic makeup. Eleutherodactylus coqui, native to Puerto Rico (PR) and a beloved symbol there, is among the most successful invasive amphibians. This species is extensively studied in terms of its biology and genetics, including being the first Eleutherodactylus with a draft genome. Its potential to spread to new habitats and rapid breeding are notable. Transcriptome analyses of E. coqui are limited but provide insights into their invasiveness and differential gene expression. We compared the skin transcriptomes of E. coqui from PR (native) to those from an area under citric acid treatment in Los Angeles, California (invasive) population. Our results show differences in stress response gene signatures between both populations. In the native population, we hypothesize these responses are due to immunity against diverse parasites, potentially helping control their native populations in PR. Additionally, these coquis expressed several antimicrobial peptides, which were previously reported to be absent in coquis. These peptides may play a role in the invasiveness of the common coqui and its tolerance to urban and degraded habitats. We also provide novel draft transcriptomes of close relatives of E. coqui: Eleutherodactylus planirostris, Eleutherodactylus johnstonei, Eleutherodactylus cochranae, and Pristimantis unistrigatus.
The effects of cues on task interruption recovery in a concurrent multitasking environment
A cation-exchange approach to tunable magnetic intercalation superlattices
Is there a “sweet spot” of model complexity for qualitative models used in Ecosystem-Based Management?
Ecosystem models have been developed to help support Ecosystem-Based Management and to help provide better management advice that can account for ecosystem impacts (e.g., climate, species interactions, fishing behaviour). Quantitative end-to-end models have proven to be very useful strategically for exploring future scenarios, but are data intensive, time consuming, and require considerable expertise and training. Conversely, qualitative models have different benefits: they are less dependent on data, relatively faster to develop, can incorporate different types of information that are difficult to measure or combine, and can be co-developed with a variety of audiences. There has been an increase in the use of qualitative models for marine management, however questions have arisen about how well qualitative models perform in comparison to quantitative models, and how they can be used to inform management. Here we compare results from quantitative and qualitative ecosystem models for the same region at differing levels of model complexity to explore their relative utility for EBM. We conclude that the number of linkages between model elements and trophic position of the perturbed model were influential factors in the qualitative model behaviour. When perturbing lower trophic level groups, higher complexity models performed closer to the quantitative model. Lower complexity models were recommended when estimating scenarios with perturbations to mid-trophic groups. Careful consideration among these issues is required to develop the “sweet spot” of model complexity for qualitative ecosystem models to reflect similar results to quantitative models. In addition, utilizing multiple models to determine the strongest impacts from perturbations is recommended to avoid spurious conclusions.