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The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

Refactoring improves maintainability without altering externally observable behavior, yet it remains costly and error-prone when applied manually at scale. While large language models (LLMs) can generate plausible refactorings, practical adoption is limited by uncontrolled edit scope, inconsistent outputs under stochastic decoding, and weak traceability of why a change was produced. This paper proposes a smell-targeted, scope-bound refactoring framework for JavaScript that couples deterministic AST-based smell detection with constrained LLM transformation. The key design principle is to bind generation to explicitly detected smell instances, enforce a structured output contract (refactored code plus per-smell rationale), and log full refactoring artifacts for repeatable evaluation. We implement the framework as a microservice-based prototype (detector, prompt construction and routing, orchestrator, analytics, and UI) and evaluate it on LeetCode-style solutions and multiple GitHub repositories. Across the evaluated projects, the approach achieves an average smell reduction of 83.96% and an average maintainability index improvement of +5.366, while maintaining a mean developer acceptance rate of 91.66%. A targeted temperature study identifies an operating point around 0.4 that maximizes acceptance (approximately 95% in controlled trials), balancing determinism with sufficient flexibility for structure-improving edits. These results suggest that explicit scope control and structured traceability are central to making LLM-based refactoring reliable and reviewable, and motivate future integration with automated validation (tests, linting) and repository-conditioned policies.

Higher education institutions aim to forecast student success which is an important research subject. Forecasting student success can enable teachers to prevent students from dropping out before final examinations, identify those who need additional help and boost institution ranking and prestige. Machine learning techniques in educational data mining aim to develop a model for discovering meaningful hidden patterns and exploring useful information from educational settings. The key traditional characteristics of students (demographic, academic background and behavioural features) are the main essential factors that can represent the training dataset for supervised machine learning algorithms. In this study, we compared the performances of several supervised machine learning algorithms, such as Decision Tree, Naïve Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbour, Sequential Minimal Optimisation and Neural Network. We trained a model by using datasets provided by courses in the bachelor study programmes of the College of Computer Science and Information Technology, University of Basra, for academic years 2017-2018 and 2018-2019 to predict student performance on final examinations. Results indicated that logistic regression classifier is the most accurate in predicting the exact final grades of students (68.7% for passed and 88.8% for failed).

An essential aspect of positive-sense RNA virus replication is anchoring the replication complex (RC) to cellular membranes. Positive-sense RNA viruses employ diverse strategies, including co-translational membrane targeting through signal peptides and co-opting cellular membrane trafficking components. Often, N-terminal nonstructural proteins play a crucial role in linking the RC to membranes, facilitating the early association of the replication machinery. Astroviruses utilize a polyprotein strategy to synthesize nonstructural proteins, relying on subsequent processing to form replication-competent complexes. This study provides evidence for the perinuclear ER membrane association of RCs in five distinct human astrovirus strains. Using tagged recombinant classical human astrovirus 1 and neurotropic MLB2 strains, we establish that the N-terminal domain guides the ER membrane association. We identified di-arginine motifs responsible for the perinuclear ER retention and formation of functional RCs through mutational analysis of the N-terminal domain in replicon and reverse genetics systems. In addition, we demonstrate the association of key components of the astrovirus replication complex: double-stranded RNA, RNA-dependent RNA polymerase, protease, and N-terminal protein. Our findings highlight the intricate virus-ER interaction mechanism employed by astroviruses, potentially leading to the development of novel antiviral intervention strategies.

Nowadays, technology is developed rapidly in communication technology. Several new technologies have been introduced due to the evolution of wireless communication and this provided the way to communicate among vehicles, using a Vehicular Ad-Hoc Network (VANETs). Routing in VANETs becomes most challenging because of the huge mobility and dynamical topology changes, which lead to reduced efficiency in the network. The core idea of this network is to increase the efficiency during the process of the communication. The most suited routing protocol for VANETs is Geographic routing, for the reason that it provides higher scalability and low overheads. The major challenges in VANETs are the selection of best neighbor in dynamically changing VANET topology. Furthermore, to provide better QoS needful actions are essential. In this paper, we introduced a new MAC/NET with Updated Genetic Algorithm—A Cross Layer Approach, (MNUG-CLA) based on a MAC layer and network layer to overcome the drawbacks of the network. In the network layer, a new neighbor discovery protocol is developed to select the best next hop for the dynamically varying network. In the MAC layer, in order to improve the quality, multi-channel MAC model is introduced for instantaneous transmission from various service channels. For overall optimal path selection, we used an updated GA algorithm. The performance was demonstrated through the use of an extensive simulation environment, NS-2. The simulation results prove that this protocol provides better results, in terms of energy efficiency, energy consumption and successive packet transmission, when compared with the earlier approaches.

This precise study performed a focalized investigation to examine the association of environmental effects, new product development performance, superior customers’ value, and corporate social responsibility (CSR) on sustainable performance. This research study aimed to investigate how social media marketing application moderates the association between corporate social responsibility and sustainable performance of the firms located in Multan Division, Pakistan. This study applied a simple random sampling approach to execute this research, and the authors sent a questionnaire with an invitation letter and informed consent form to 752 respondents. Based on 548 valid responses from the targeted population, the first step was to screen and analyze data through Statistical Package for the Social Sciences (SPSS-V25) and the Smart PLS V-3.2.8. The results indicated that corporate social responsibility presented a positive impact on firms’ sustainable performance. The findings also revealed that social media marketing tools moderated the relationship between CSR and sustainable production of business firms. As a final point, the study only included respondents from Multan Division, therefore, limiting the generalizability of the findings to other Pakistani business firms. The implications of this study may provide further directions for researchers and academicians to consider the larger sample size and the addition of new variables in other regions worldwide. The findings are useful for filling the gap between the relationship of environmental effects, CSR, and social media marketing application to calculate the sustainable performance of business firms.

INTRODUCTION: Skull bones typically possess complex structures and features. When scanned with ordinary sensors, they are easily affected by noise due to the small difference between abnormal areas and normal tissue. OBJECTIVES: In order to solve the problem of small differences between abnormal areas and normal tissues, which make them susceptible to noise interference, this paper proposes a multi-sensor fusion based skull scan image anomaly detection method. METHODS: Firstly, the frequency correction factor is utilized to modify the frequency domain characteristics of the sensor signal during the skull scanning image acquisition process, aiming to enhance signal quality and reduce noise impact during acquisition. Secondly, bilateral filters and discrete wavelet transform are employed to subject the skull scanning image to dual domain decomposition in spatial and transformation domains, aiding in distinguishing between normal and abnormal regions. Subsequently, the low-frequency fusion algorithm guided by filtering and the high-frequency fusion algorithm based on multi-scale morphological gradients are fused, and the fused dual frequency components are merged back into the original spatial domain to retain important details. The fused reconstructed image aids in improving the accuracy of anomaly detection. Finally, a backbone network with an auto encoder structure is established to learn the feature representation of fused images, and an unsupervised deep neural network is employed to establish a detection model for anomaly detection in skull scanning images. RESULTS: Through experiments, it has been demonstrated that the F1 score approaches 1, the ROC curve closely approaches the upper left corner, and the AUC value approaches 1 after applying the proposed method for anomaly detection in skull scanning images. CONCLUSION: This algorithm has high sensitivity and low specificity, achieving high detection accuracy and demonstrating good performance.

This study addresses the optimization of fully convolutional networks (FCNs) for deployment on resource-limited devices in real-time scenarios. While prior research has extensively applied quantization techniques to architectures like VGG-16, there is limited exploration of comprehensive layer-wise quantization specifically within the FCN-8 architecture. To fill this gap, we propose an innovative approach utilizing full-layer quantization with an error minimization algorithm, accompanied by sensitivity analysis to optimize fixed-point representation of network weights. Our results demonstrate that this method significantly enhances sparsity, achieving up to 40%, while preserving performance, yielding an impressive 89.3% pixel accuracy under extreme quantization conditions. The findings highlight the efficacy of full-layer quantization and retraining in simultaneously reducing network complexity and maintaining accuracy in both image classification and semantic segmentation tasks.

During the last decade, the detection of neurotropic astroviruses has increased dramatically. The MLB genogroup of astroviruses represents a genetically distinct group of zoonotic astroviruses associated with gastroenteritis and severe neurological complications in young children, the immunocompromised, and the elderly. Using different virus evolution approaches, we identified dispensable regions in the 3′ end of the capsid-coding region responsible for attenuation of MLB astroviruses in susceptible cell lines. To create recombinant viruses with identified deletions, MLB reverse genetics (RG) and replicon systems were developed. Recombinant truncated MLB viruses resulted in imbalanced RNA synthesis and strong attenuation in iPSC-derived neuronal cultures confirming the location of neurotropism determinants. This approach can be used for the development of vaccine candidates using attenuated astroviruses that infect humans, livestock animals, and poultry.

Prompt coronary catheterization and revascularization have markedly improved the outcomes of myocardial infarction, but have also resulted in a growing number of surviving patients with permanent structural damage of the heart, which frequently leads to heart failure. There is an unmet clinical need for treatments for this condition 1 , particularly given the inability of cardiomyocytes to replicate and thereby regenerate the lost contractile tissue 2 . Here we show that expression of human microRNA-199a in infarcted pig hearts can stimulate cardiac repair. One month after myocardial infarction and delivery of this microRNA through an adeno-associated viral vector, treated animals showed marked improvements in both global and regional contractility, increased muscle mass and reduced scar size. These functional and morphological findings correlated with cardiomyocyte de-differentiation and proliferation. However, subsequent persistent and uncontrolled expression of the microRNA resulted in sudden arrhythmic death of most of the treated pigs. Such events were concurrent with myocardial infiltration of proliferating cells displaying a poorly differentiated myoblastic phenotype. These results show that achieving cardiac repair through the stimulation of endogenous cardiomyocyte proliferation is attainable in large mammals, however dosage of this therapy needs to be tightly controlled. MicroRNAs delivered by adeno-associated viral vectors improve global and regional contractility, increase muscle mass and reduce scar size in a porcine model of myocardial infarction.