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The optimization of composite timber beam by different reinforcement bars under binding test
No full textThe aim of this paper is to investigate behavior and optimization of the composite beam by different reinforcement bars based on the Cohesive Zone Model (CZM), which is calculated by the Finite Element Method as well as simulated by ABAQUS software. Three types of reinforcement bars, namely, Post-tensioned Tendons, Fiber Reinforced Polymer, and Fiber Glasses Polymers are utilized for enhancement and optimization of the beam. As a result, the strength and sustainability of composite beams are increased based on the number of reinforcement bars and the specimens with three reinforcement bars show better performance than wood specimens. In addition, the performance of the composite timber beam by Three Glass Fiber Reinforced-Polymer bars considerably showed higher performance and greater sustainability than other samples
Qualitative assessment of DNA isolation from fresh, frozen, and ancient human bone using a homogenizer-assisted workflow
No full textEffective DNA recovery from bone material is essential for applications in biomedical research, clinical diagnostics, and forensic and archaeogenetic investigations. In this study, DNA isolation performance was evaluated in an exploratory manner in human bone samples representing three preservation states: fresh, cryopreserved, and ancient. All samples were processed using a unified bead mill homogenization and magnetic bead–based extraction workflow in order to maintain procedural consistency. DNA quantity and purity were evaluated by spectrophotometry, and amplifiability was assessed using nuclear and mitochondrial PCR assays as well as representative STR profiling. Fresh and cryopreserved samples yielded higher DNA concentrations and more consistent amplification than ancient specimens, in which recovery was primarily constrained by postmortem degradation. PCR success demonstrated a clear dependence on amplicon length, with shorter mitochondrial and nuclear targets amplifying more reliably across all sample types. Due to the limited sample size and the use of a single individual per preservation group, the results are presented as qualitative observations rather than as statistically generalizable conclusions. Within these constraints, the study demonstrates the feasibility of using a standardized mechanical disruption and extraction workflow across bone samples of differing preservation status and provides a methodological reference for future larger-scale studies involving both modern and degraded skeletal material
HQML-NLP: A hybrid quantum machine learning framework for scholarly AI-text detection
No full textThe swift growth of the gargantuan language models has made it even harder to tell apart writings done by humans and AI especially in academia which led to the need for the setup of trustworthy detection frameworks that will satisfactorily balance accuracy, interpretability, and efficiency. In this paper, we walk you through the HQML-NLP, a detection system using a hybrid quantum-classical machine learning framework for the detection of AI-generated academic content. The system onboard the merging of Sentence-BERT semantic embeddings with quantum feature encoding that is supported by a 6-qubit, two-layer parameterized quantum circuit, thus resulting in a 390-dimensional hybrid representation which is classified through a lightweight multilayer perceptron. The framework has been tested on three benchmark datasets AI-GA, HWAI, and HAGT-1M to check its scalability and generalization to different academic writing situations. The results of the experiments show the framework has consistent and good discriminative capability achieving AUROC scores of more than 0.96 on all datasets and excellent performance (AUROC = 1.000, ACC = 99.98 %) on the large-scale data. Moreover, probability calibration by means of temperature scaling raises the trustworthiness of predicted confidence scores, leading to a 60 % reduction in the Expected Calibration Error (ECE) and without affecting the performance of the discrimination. When compared against the transformer-based and ensemble-learning detectors, HQML-NLP comes out with an equivalent and competitive detection accuracy and calibration quality yet demands more than 2000× less trainable parameters. These findings imply that hybrid quantum-classical representations act as an effective and compact alternative for the detection of AI-text in scholarly journals
Past, present and future states of research on autonomous electric vehicles
No full textAutonomous electric vehicles (AEVs) offer various opportunities for energy efficiency improvement. However, to reap the benefits, both transport and energy sectors need a system-level upgrade by considering the recent developments in AEV technologies. This chapter aims to provide a review and deep analysis of the AEV articles published by the Institute of Electrical and Electronics Engineers (IEEE), one of the leading academic societies that conduct research on AEVs. Delivering a socio-technical analysis of the articles based on the stakeholder theory, and an inspirational vision for future research, the review provided makes a unique contribution to the extant literature. The findings indicate that AEV research has shown an exponential increase in the last 15 years. Consumers and the natural environment are the major stakeholders whose relationships with the other stakeholders are studied the most. Taken as a whole, the efforts present a growing convergence to the development of an integrated transport-energy system that will operate autonomously and efficiently. Although the rise and proliferation of AEV studies has led to the implementation of a diverse set of quantitative methods, only a few studies benefit from qualitative approaches, hinting that socio-technical dynamics in the industry have been missed or ignored. The level of collaboration is the highest between China and the USA, which are the two countries that host the majority of AEV research. While the collaboration generally increases the number of stakeholder relationships studied, international collaboration enables exploration of the topic from a broader perspective. The subjects covered revolve around two main research themes, involving the design of AEVs, and the potential energy efficiency gains expected from the widespread adoption of these vehicles. Nonetheless, articles proposing innovative transport solutions that integrate these two research themes contribute to the development of a sustainable automated transport-energy system in a more significant way
Targeting Integrin α2 to Overcome Imatinib Resistance in Chronic Myeloid Leukemia Cells
Full text linkChronic myeloid leukemia (CML) is a blood disorder caused by a genetic alteration that creates the BCR-ABL fusion gene, leading to continuous activation of cell growth signals and uncontrolled proliferation of the blood cells. Imatinib (IMA) resistance remains a major obstacle in CML treatment. Integrins, particularly integrin α2 (ITGA2), have been associated with cancer progression and drug resistance. In the current study, we investigated the role of ITGA2 in IMA resistance using IMA-sensitive K562 (K562S) and IMA-resistant K562 (K562R) cells. Our findings showed that ITGA2 is overexpressed in K562R cells and ITGA2 inhibitor E7820 (2.5 µM) treatment significantly decreased cell viability and induced apoptosis in both sensitive and resistant cells. Combination treatment with E7820 and imatinib enhanced pro-apoptotic gene expression (BAX, BIM) and decreased anti-apoptotic BCL2 levels in imatinib-resistant K562R cells. Flow cytometry confirmed ITGA2 inhibition at the protein level, and rhodamine assays revealed reduced MDR1 activity in treated cells. These results demonstrate that targeting ITGA2 may overcome imatinib resistance and offer a novel therapeutic strategy for CML
EHFOA-ID: An Enhanced HawkFish Optimization-Driven Hybrid Ensemble for IoT Intrusion Detection
Full text linkIntrusion detection in Internet of Things (IoT) environments is challenged by high-dimensional traffic, heterogeneous attack behaviors, and severe class imbalance. To address these issues, this paper proposes EHFOA-ID, an intrusion detection framework driven by an Enhanced HawkFish Optimization Algorithm integrated with a hybrid deep ensemble. The proposed optimizer jointly performs feature selection and hyperparameter tuning using adaptive exploration–exploitation balancing, Lévy flight-based global searching, and diversity-preserving reinitialization, enabling efficient navigation of complex IoT feature spaces. The optimized features are processed through a multi-view ensemble that captures spatial correlations, temporal dependencies, and global contextual relationships, whose outputs are fused via a meta-learner to improve decision reliability. This unified optimization–learning pipeline reduces feature redundancy, enhances generalization, and improves robustness against diverse intrusion patterns. Experimental evaluation on benchmark IoT datasets shows that EHFOA-ID achieves detection accuracies exceeding 99% on UNSW-NB15 and 98% on SECOM, with macro-F1 scores above 0.97 and false-alarm rates reduced to below 2%, consistently outperforming state-of-the-art intrusion detection approaches
A Functionally Guided U-Net for Chronic Kidney Disease Assessment: Joint Structural Segmentation and eGFR Prediction with a Structure-Function Consistency Loss
Full text linkAn accurate assessment of chronic kidney disease (CKD) requires understanding both renal morphology and functional decline, yet most deep learning approaches treat segmentation and eGFR prediction as separate tasks. This paper proposes the Functionally Guided CKD U-Net (FG-CKD-UNet), a dual-headed multitask architecture that integrates multi-class kidney segmentation with end-to-end eGFR prediction using a structure–function consistency loss. The model incorporates a morphological biomarker extractor to derive cortical thickness, kidney volume, and cortex–medulla ratios, enabling explicit coupling between anatomy and physiology. Experiments on T2-weighted MRI and colorized CT datasets demonstrate that the proposed method surpasses state-of-the-art segmentation baselines, achieving a Dice score of 0.94 and an HD95 of 9.8 mm. For functional prediction, the model achieves an MAE of 0.039, an RMSE of 0.058, and a Pearson correlation of 0.92, outperforming CNN, MLP, and ResNet baselines. The structure–function consistency mechanism reduces the consistency error from 0.071 to 0.042, confirming coherent physiological modeling. The results indicate that the FG-CKD-UNet provides a reliable, interpretable, and physiologically grounded framework for comprehensive CKD assessment
Soluble receptor for advanced glycation end products (sRAGE) levels and RAGE rs1800624 polymorphism may affect the inflammatory cytokine profile in coronary artery ectasia
No full textMeeting abstract : P-41-053...
Adaptive Decision Tree With Random Forest Integration And Dimensionality Reduction For Efficient Botnet Forensics
No full textAdaptive Decision Tree with Random Forest Integration and Dimensionality Reduction for Efficient Botnet Forensics proposes a new method for botnet detection by combining an adaptive decision tree with random forest integration. As response variables are highly dimensioned in botnet detection, multistep and time-consuming detection processes are major challenges. With an integrated method, we could first model the response variables as multiclass and regression formats to simplify the construction of decision trees. Then, based on the adaptive decision tree feature selection, we filter out non-obvious features to efficiently establish the random forest regression model under the appropriately sized feature space. Furthermore, to handle multilabel classification, a random forest is employed as the global model in Tree-2-Rule procedures to detect botnet-affected communication behaviors. Finally, real data experiments have been conducted based on the top datasets. The results show that the adaptive decision tree has excellent improvements in efficiency and accuracy. In future research, the GPU ninthordinal censored multistate diagnosis data is useful observed materials that do not destroy the random effect influence of the data. Also, whether the application of the random forest model could save more time in analyzing existing commercial status is an issue to be clarified in future development. Additionally, the development of the method proposed in this research requires further investigation. We could improve and then propose the preferable solution based on the research results. Our proposed solution can be utilized as a tool for efficient real-time bot incident investigations, in accordance with both academic and business objectives
A Thematic Content Analysis on the Use of Artificial Intelligence in the Production Process of Cinema, Advertising, and Public Relations Films
No full textThis study examines the impact of artificial intelligence technologies on the production processes of creative industries such as cinema, public relations, and advertising through a thematic content analysis approach. The introduction outlines the increasing role of AI in creative sectors and presents the research problem and objective. The development section classifies and analyzes AI- based tools used in screenwriting, pre- production, production, post- production, and distribution- marketing stages. Additionally, AI- supported practices in content creation and advertising strategies within public relations are addressed. The conclusion emphasizes that AI not only enhances operational efficiency but also transforms creative decision- making processes, giving rise to new ethical, aesthetic, and professional debates. The study underlines the growing need for sustainable production models based on human-AI collaboration in order to ensure balanced integration of technology and creativity