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Abstract Background This study examined the characteristics and outcomes of surgical aortic valve replacement (SAVR) both isolated and in combination with other cardiac surgery in Malaysia from 2015 to 2021. Methods This was a retrospective study of 1346 patients analyzed on the basis of medical records, echocardiograms and surgical reports. The overall sample was both considered as a whole and divided into aortic stenosis (AS)/aortic regurgitation (AR)-predominant and similar-severity subgroups. Results The most common diagnosis was severe AS (34.6%), with the 3 most common etiologies being bicuspid valve degeneration (45.3%), trileaflet valve degeneration (36.3%) and rheumatic valve disease (12.2%). The second most common diagnosis was severe AR (25.5%), with the most common etiologies being root dilatation (21.0%), infective endocarditis (IE) (16.6%) and fused prolapse (12.2%). Rheumatic valve disease was the most common mixed disease. A total of 54.5% had AS-predominant pathology (3 most common etiologies: bicuspid valve degeneration valve, degenerative trileaflet valve and rheumatic valve disease), 36.9% had AR-predominant pathology (top etiologies: root dilatation, rheumatic valve disease and IE), and 8.6% had similar severity of AS and AR. Overall, 62.9% of patients had trileaflet valve morphology, 33.3% bicuspid, 0.6% unicuspid and 0.3% quadricuspid. For AS, the majority were high-gradient severe AS (49.9%), followed by normal-flow low-gradient (LG) severe AS (10.0%), paradoxical low-flow (LF)-LG severe AS (6.4%) and classical LF-LG severe AS (6.1%). The overall in-hospital and total 1-year mortality rates were 6.4% and 14.8%, respectively. Pure severe AS had the highest mortality. For AS-predominant pathology, the etiology with the highest mortality was trileaflet valve degeneration; for AR-predominant pathology, it was dissection. The overall survival probability at 5 years was 79.5% in all patients, 75.7% in the AS-predominant subgroup, 83.3% in the AR-predominant subgroup, and 87.3% in the similar-severity subgroup. Conclusions The 3 most common causes of AS- predominant patients undergoing SAVR is bicuspid valve degeneration, degenerative trileaflet valve and rheumatic and for AR-predominant is root dilatation, rheumatic and IE. Rheumatic valve disease is an important etiology in our SAVR patients especially in mixed aortic valve disease. Study registration IJNREC/562/2022.

Network traffic analysis and predictions have become vital for monitoring networks. Network prediction is the process of capturing network traffic and examining it deeply to decide what is the occurrence in the network. The accuracy of analysis and estimation of network traffic are increasingly becoming significant in achieving guaranteed Quality of Service (QoS) in the network. The main aim of the presented research is to propose a new methodology to improve network traffic prediction by using sequence mining. The significance of this important topic lies in the urge to contribute to solving the research problem in network traffic prediction intelligently. We propose an integrated model that combines clustering with existing series models to enhance prediction the network traffic. Clustering granules are obtained using fuzzy c-means to analyze the network data for improving the existing time series. The novelty of the proposed research has used the clustering approach to handle the ambiguity from the entire network data for enhancing the existing time series models. Furthermore, we have suggested using the weighted exponential smoothing model as preprocessing stages for increasing the reliability of the proposed model. In this research paper, machine intelligence proposed to predict network traffic. The machine intelligence is working as pre-processing for enhancing the existing time series models. The machine intelligence combines non-crisp Fuzzy-C-Means (FCM) clustering and the weight exponential method for improving deep learning Long Short-Time Memory(LSTM)and Adaptive Neuro-Fuzzy Inference System (ANFIS)time series models. The ANFIS and LSMT time series models are applied to predict network traffic. Two real network traffic traces were conducted to test the proposed time series models. The empirical results of proposed to enhanced LSTM 97.95% and enhanced ANFIS model is R = 96.78% for cellular traffic data, with respect to the correlation indicator. It is observed that the proposed model outperforms alternative time series models. A comparative prediction results between the proposed model and existing time series models are presented. The comparisons indicate that the presented model outperforms the opponent models; the proposed method optimises the deep learning LSTM and ANFIS time series models. The proposed methodology offers more effective approach to the prediction of network traffic.

Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time.

This study aims to analyze the effect of edutainment method on mathematics learning motivation in elementary school students in tutoring environment by comparing it with non-edutainment method and Identify the factors that influence the effectiveness of edutainment method in increasing mathematics learning motivation. 75 elementary students (37 received edutainment and 38 received a non-edutainment method) were involved in this study. Primary data was on a motivational scale, and secondary data was done by interview. This research was conducted using a mixed method approach consisting of a quantitative approach and a qualitative approach. The quantitative approach was used to determine the effectiveness of the teaching method, while the qualitative approach was used to collect additional data on what makes students happy and motivated in learning. From the results of the focus group discussions conducted, qualitative information was obtained about various things that can make students happy and excited in undergoing learning, including the availability of friendly and good teaching staff, the availability of teaching aids, interesting subject matter, having studied the material before, and comfortable learning space conditions. [ABSTRACT FROM AUTHOR]

Data classification is a crucial aspect of knowledge discovery using machinelearning algorithm for supervised learning approach where the goal is to predict the categorical labels of new instances based on past observations. This research presents an innovative classification technique that utilizes Rough Set Attribute Reduction. The proposed method introduces the Re-heat Simulated Annealing (Re-heat SA) algorithm as a meta-heuristic approach. Rough set theory, a mathematical tool dealing with uncertainty and fuzziness in data, is employed to uncover hidden patterns in big data through feature selection. This paper introduces a novel meta-heuristic classification approach that utilizes rough set attribute reduction to achieve optimal accuracy. Re-heat SA effectively optimizes the problem by controlling the dependency degree to identify the minimal reducts required for classification prediction using the Rosetta software. Experimental results demonstrate that Re-heat SA outperforms comparable classification algorithms in discovering classification rules. The results reveal that three datasets achieved 100% accuracy, four datasets achieved accuracy rates ranging from 60% to 99%, and six datasets achieved accuracy rates ranging from 30% to 59%. Additionally, this paper discusses the need for standardization concerning the machine learning pipeline processes as big data and its handling grows exponentially. [ABSTRACT FROM AUTHOR]

Wireless communication in underground environments presents formidable challenges due to the diverse and complex transmission mediums, including rocks, icebergs, soil, and water. These environments exhibit distinct properties such as varying densities, salinities, and numerous obstacles. Conventional wireless communication methods encounter difficulties in these scenarios, primarily stemming from issues like electrical conduction, line-of-sight obstacles, high path loss, dynamic channel conditions, and the requirement for large antennas, multiple scattering, and propagation delays constrained by the speed of light. However, electromagnetic induction (EMI) communication has emerged as a promising alternative solution, offering advantages such as reliable connectivity, consistent channel responses, rapid propagation, frequency offset capabilities, minimal propagation delays, and energy efficiency. Despite these merits in underwater environment ecosystems communications, EMI communication suffers from a limited communication range, typically spanning just a few meters. Recent research endeavors have sought to extend the effective range of EMI communication by exploring various strategies, including manipulations of coil parameters, structural enhancements, material advancements, and configuration improvements. This comprehensive study and evaluation delves into the diverse applications, notable advantages, techniques for extending communication range, and the pressing challenges and open issue facing EMI communication in underwater ecosystem environments, requiring further investigation and new research. [ABSTRACT FROM AUTHOR]

Abstract Background The optimal timing for administering dual antiplatelet therapy (DAPT) post-coronary artery bypass grafting (CABG) remains a subject of debate. This study aimed to evaluate the safety and efficacy of early DAPT administration within 6 h post-CABG, following the implementation of a new standard operating procedure (SOP). This study was conducted in three phases at the National Heart Institute of Malaysia. Phase 1 involved a clinical audit of 80 isolated CABG patients to evaluate current DAPT practices. Phase 2 was a pilot study involving 320 patients to establish criteria for early DAPT initiation. Phase 3 comprised a prospective cohort analysis comparing outcomes between 939 propensity-matched pairs receiving early/new SOP ( 6 h) DAPT. Results The clinical audit revealed a mean DAPT administration time of 18.3 h post-CABG, highlighting variability in practice. The pilot study demonstrated that early DAPT was associated with a significantly lower chest reopening rate (0.8% vs. 21.6%) under stringent selection criteria. In the follow-up study, early DAPT was linked to reduced rates of postoperative stroke (1.0% vs. 2.5%, p = 0.013), dialysis (5.5% vs. 7.0%, p

The study of gratitude has begun to develop in Indonesia. Along with this development, there is a need for a measurement tool that can explain the concept of gratitude. This study aims to adapt the gratitude questionnaire (QG-6) to the Indonesian language. The study was conducted on 275 high school students in Surabaya City. The results of confirmatory factor analysis obtained 5 adequate items that have a factor loading value (> 0.5). Measurement model fit (p < 0.01, RMSEA =. 0.08, GFI = 0.98, CFI = 0.99, SRMR = 0.02.) Construct reliability on QG-5 is 0.813. [ABSTRACT FROM AUTHOR]