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Transverse stretching effects in buckling of laminated composites: a mixed finite element study
No full textThis study investigates the buckling behavior of laminated composite structures by employing a mixed finite element formulation based on a higher-order shear deformation theory that incorporates transverse stretching effects. The displacement field is defined through higher-order polynomial variations across the thickness, enabling accurate representation of both transverse shear and normal deformations without requiring shear correction factors. The mixed approach is derived using the Hellinger–Reissner variational principle, allowing both displacement and stress resultants to be treated as independent variables for improved numerical stability and convergence. Governing equations are developed and implemented in a C0 finite element framework suitable for rectangular laminates under mechanical loading. Benchmark comparisons demonstrate strong agreement with available analytical and three-dimensional elasticity solutions, validating the precision of the proposed model. Parametric analyses further reveal the significant influence of transverse stretching and stacking sequence on the critical buckling loads of laminated plates. The main novelty lies in extending the mixed finite element framework to include transverse stretching effects within the HSDT for buckling analysis, offering higher accuracy and efficiency compared with existing plate theories
Family of Fuzzy Mandelblog Sets
Full text linkIn this paper, we consider the family of parameterized Mandelbrot-like sets generated as any point (Formula presented.) of the complex plane belongs to any member of this family for a real parameter (Formula presented.), provided that its corresponding orbit of 0 does not escape to infinity under iteration (Formula presented.) ; otherwise, it is not a member of this set. This classically means there is only a binary membership possibility for all points. Here, we call this type of fractal set a Mandelblog set, and then we introduce a membership function that assigns a degree to each c to be an element of a fuzzy Mandelblog set under the iterations, even if the orbits of the points are not limited. Moreover, we provide numerical examples and gray-scale graphics that illustrate the membership degrees of the points of the fuzzy Mandelblog sets under the effects of iteration parameters. This approach enables the formation of graphs for these fuzzy fractal sets by representing points that belong to the set as white pixels, points that do not belong as black pixels, and other points, based on their membership degrees, as gray-toned pixels. Furthermore, the membership function facilitates the direct proofs of the symmetry criteria for these fractal sets
Projecting the FIA’s GHG Emissions: A Forecast for the 2030 Sustainability Target
Full text linkThis study provides a data-driven evaluation of the Fédération Internationale de l’Automobile’s (FIA) progress toward its 2030 net-zero emissions goal, based on publicly reported data from 2019 to 2023. Using SPSS-based regression analysis, we first identify business travel emissions and the number of championships, trophies, challenges, and cups as the most significant drivers of the FIA’s total carbon footprint, jointly explaining 99.3% of its variance. These drivers then inform a three-stage forecasting model developed in MATLAB to project future emissions. The results indicate a projected 18% increase in total emissions from 2024 to 2030. This upward trajectory stands in sharp contrast to the FIA’s target of a 50% reduction by 2030, revealing a significant implementation gap. Our analysis concludes that the FIA’s current path is insufficient to meet its ambitious climate targets, underscoring the urgent need for more decisive interventions, such as emissions-based event planning and AI-powered logistics optimization. The methodology offers a replicable framework for forecasting emissions in other data-constrained, high-emission sectors
Machine Learning Approaches for Predicting Breast Cancer Recurrence: A Comparative Analysis
Full text linkThis paper reports a comparative analysis of four supervised machine learning algorithms: RF, SVM (using radial and linear kernels), Logistic Regression, and Multi-Layer Perceptron, for breast cancer recurrence prediction on a carefully curated clinical dataset. The data set, first collected by Royston and Altman and subsequently released on Kaggle, has patient age, menopausal status, tumor size, histological grade, lymph node status, estrogen and progesterone receptor levels, hormone therapy for treatment, recurrence-free survival time, and a binary recurrence outcome. The data set was then divided after the elimination of identifiers and z-score normalization in an 80:20 ratio using stratified sampling. Models were compared based on accuracy, precision, recall, F1-score, and area under the ROC curve, with RF and Logistic Regression having the highest test-set accuracy of 0.703. Feature significance analysis Gini impurity in R F, linear model absolute coefficients, and permutation importance in neural networks all showed lymph node count, survival time, and hormone receptor levels to be significant predictors. Visualized confusion matrices, ROC curves, and correlation heatmaps enhanced interpretability. The results illustrate the potential of explainable machine learning to enhance individualized surveillance and treatment planning in breast cancer care
Effect of Different Drying Protocols on the Bond Strength of a Bioceramic Root Canal Sealer
No full textThis study aimed to investigate how varying levels of dentine moisture affect the push-out bond strength of a bioceramic root canal sealer. Forty-eight root canals were randomly divided into four groups according to drying protocol. Moist group: canals were dried until the last paper point appeared dry, Dry group: 95% ethanol was applied for 10 s, Half-dry group: canals were dried with a single paper point for 5 s, Wet group: canals were left completely flooded. All root canals were obturated with iRoot SP. Root slices were prepared from each sample for push-out testing. The data were statistically analysed using the Kruskal-Wallis and Mann-Whitney U test for pairwise comparisons at a significance level of p 0.05)
Circular economy and water footprint: Tools for effective water cycle management
No full textThe rising demand for water due to global warming and human population growth necessitates innovative approaches to water management. This chapter explores the possibility of combining water footprint (WF) evaluation with circular economy (CE) principles to improve water cycle management. Sustainable water uses and production, as well as waste minimization at every stage of the water cycle, are all helped by the CE strategy's emphasis on reusing and recycling water resources. It stresses the need to shift focus from linear to circular water consumption. In addition to highlighting inefficiencies and proposing solutions for more sustainable water management, the WF concept provides quantitative data on water consumption and pollution. Reducing water shortages, improving water security, and mitigating environmental impacts are all achieved through the effective combination of CE and WF techniques. That is proved in this chapter by compiling the most recent findings from research, policy evaluations, and case studies. In doing so, it encourages the widespread use of resilient water management technologies in communities and businesses. This narrative shows how innovative technology, cross-sector cooperation, and supportive policy are necessary for a circular water economy to come to fruition
Designing a Robust Machine Learning-Based Framework for Secure Data Transmission in Internet of Things (IoT) Environments: A Multifaceted Approach to Security Challenges
Full text linkThis research develops a machine learning framework for protecting data as it is transmitted in Internet of Things (IoT) configurations. The main objective of the proposed framework to address the major security issues using two intelligent machine learning methods are Random Forest and Support Vector Machine (SVM). They are applied to detect strange behaviour and potential threats within IoT data. The system was evaluated based on accuracy, precision, recall, and F1-score to determine how successful it was. Performance indicated Random Forest performed very well with 93.5% accuracy, slightly higher than SVM 91.2%. The system was also quite good at detecting cyber-attacks such as DDoS and malware, and did not raise many false alerts. This indicates that the system can actually contribute to making IoT much safer, building on what we have in this field. This study implies that incorporating machine learning into IoT security can assist in developing improved defenses against emerging cyber-attacks. In the long term, this research can assist in subsequent studies in order to improve security systems for various uses of IoT, address existing problems, and utilize more data
The effect of positive psychology interventions on job satisfaction work engagement and withdrawal intentions among remote working cancer survivors
Full text linkAdvances in cancer treatment have significantly increased the survival rate of cancer patients, but these survivors often face challenges in the workplace. Existing literature highlights the significant influence of cancer on job performance, job satisfaction, and the increased risk of withdrawal intention. However, the effects of positive psychology interventions on cancer survivors, particularly in less urbanized settings and remote worker communities, remain underexplored. This study investigates the effects of positive psychology interventions on job satisfaction, work engagement, and withdrawal intentions among cancer survivors in rural and remote workforce communities. A Randomized Control Trial (RCT) was employed, involving 68 cancer survivors. The study used the Minnesota Job Satisfaction Questionnaire, the Utrecht Work Engagement Scale, and the Withdrawal Intention Scale to measure outcomes following a 14-session positive psychology intervention. The results revealed statistically significant improvements in the experimental group compared to the control group. Job satisfaction mean scores increased from 50.23 to 58.94, work engagement mean scores rose from 26.79 to 31.05, and withdrawal intentions mean scores decreased from 48.35 to 39.05. These findings highlight the potential of positive psychology interventions to address the unique challenges faced by cancer survivors in remote workforce communities, particularly in less urbanized areas. By enhancing job satisfaction and work engagement while reducing withdrawal intentions, these interventions can significantly contribute to the occupational well-being of cancer survivors, advocating for their integration into cancer care and organizational practices
Faster, better?: Testing artificial intelligence accuracy for neurosurgical literature analysis
Full text linkWith the rapid rise of artificial intelligence tools, applications like ChatPDF are seen as promising for supporting academic tasks in neurosurgery, such as literature review, summarization, and question generation. However, its accuracy and relevance remain to be critically assessed. This study assesses ChatPDF's accuracy in interpreting neurosurgical research articles, aiming to identify its strengths and limitations. Articles from the 10 highest-ranked neurosurgical journals were reviewed by selecting the first original research article from each journal's 2023 volume. Ten detailed questions were independently generated by 2 researchers based on each article's content. Each article was then uploaded to ChatPDF, which generated its own questions and provided responses to both its questions and those posed by the researchers. Responses were categorized as completely correct, partially correct, or incorrect. Source reliability was also evaluated to determine ChatPDF's performance. An overall accuracy rate of 89% was achieved by ChatPDF across 100 questions, with 89% of responses classified as completely correct, 5% as partially correct, and 6% as incorrect. Source reliability averaged 83%, although variability was noted, particularly in journals such as the Journal of Neurosurgery: Spine and Neurosurgery Clinics, which showed lower reliability rates. Substantial accuracy and potential were demonstrated by ChatPDF as a supplementary tool for neurosurgical literature review. However, limitations such as inconsistent source reliability and lack of visual content analysis highlight the need for ongoing refinement. While promising, ChatPDF should be used alongside manual verification to ensure comprehensive and accurate literature interpretation in neurosurgical research
Application of Graph Neural Networks to Model Stem Cell Donor–Recipient Compatibility in the Detection and Classification of Leukemia
Full text linkStem cell transplants are a common treatment for leukemia, and close donor–recipient matching improves their success. Machine learning models like support vector machine (SVM), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) can have difficulty handling the complexity of genomic and immune data, which then lowers the accuracy of clinical predictions. This study looks at using graph neural networks (GNNs) in a different way. This method combines data such as single-nucleotide polymorphisms (SNPs), human leukocyte antigen (HLA) typing, and clinical details to create a graph that shows the relationship between donor and recipient pairs. The framework uses graph attention networks (GATs) to focus on key compatibility traits and Dynamic GNNs (DGNNs) to monitor changes in the immune system and the disease’s progression. With data from the 1000 Genomes Project, the model correctly identified matches with 97.68% to 99.74% accuracy and classified them with 98.76% to 99.4% accuracy, outperforming standard machine learning models. The model uses SNP similarity and HLA mismatches to assess compatibility, which enhances its match prediction and compatibility explanation capabilities. The results suggest that GNNs offer a helpful and understandable way to model donor–recipient matching, potentially assisting in early leukemia detection and personalized stem cell transplant plans