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This book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms. The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it. Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained. Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing. Chapter 5 focuses on advanced Giraph programming, discussing common Giraph algorithmic optimizations, tunable Giraph configurations that determine the system?s utilization of the underlying resources, and how to write a custom graph input and output format. Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph. This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository. Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems.

The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm −1 is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits.

In recent years, light detection and ranging (LiDAR) technology has gained huge popularity in various applications such as navigation, robotics, remote sensing, and advanced driving assistance systems (ADAS). This popularity is mainly due to the improvements in LiDAR performance in terms of range detection, accuracy, power consumption, as well as physical features such as dimension and weight. Although a number of literatures on LiDAR technology have been published earlier, not many has been reported on the state-of-the-art LiDAR scanning mechanisms. The aim of this article is to review the scanning mechanisms employed in LiDAR technology from past research works to the current commercial products. The review highlights four commonly used mechanisms in LiDAR systems: Opto-mechanical, electromechanical, micro-electromechanical systems (MEMS), and solid-state scanning. The study reveals that electro-mechanical scanning is the most prominent technology in use today. The commercially available 1D time of flight (TOF) LiDAR instrument is currently the most attractive option for conversion from 1D to 3D LiDAR system, provided that low scanning rate is not an issue. As for applications with low size, weight, and power (SWaP) requirements, MEMS scanning is found to be the better alternative. MEMS scanning is by far the more matured technology compared to solid-state scanning and is currently given great emphasis to increase its robustness for fulfilling the requirements of ADAS applications. Finally, solid-state LiDAR systems are expected to fill in the gap in ADAS applications despite the low technology readiness in comparison to MEMS scanners. However, since solid-state scanning is believed to have superior robustness, field of view (FOV), and scanning rate potential, great efforts are given by both academics and industries to further develop this technology.

Background Various systems of nickel-titanium (NiTi) instrument have long been commercially available. However, the preparation of narrow and curved root canals has always been challenging. The purpose of this study was to compare the shaping ability of two NiTi systems (2Shape and NeoNiTi) in severely curved root canals with different morphological patterns using micro-computed tomography (Micro-CT). Methods A total of 22 human extracted permanent teeth of mandibular first molars, with the exact mesial angle of curvature of 25 and 35 degrees, according to Schneider’s technique, were distributed randomly into two groups (group I: 2Shape, group II: NeoNiTi) based on the rotary system used (n = 22). The groups were subdivided into two subgroups corresponding to the angle of canal curvature (25° and 35°) (n = 11). Canals were scanned using Micro-CT pre- and post-preparation to assess the volume of dentin removed; canal transportation; and canal centering ratio at 3, 6, and 9 mm from the apex. The Mann–Whitney U test was utilized to determine any significant differences between the two systems. The level of statistical significance was set at p  < 0.05. Results There was no significant difference between the two groups in volume of dentin removed; canal transportation; and centering ability for 25° and 35° canal curvatures at 3, 6, and 9 mm from the apex (coronal, middle, and apical) thirds ( p  > 0.05). At the middle third, the NeoNiTi group demonstrated a statistically significant increase in volume of dentin removed for 35° canal curvatures compared to the 2Shape group. Conclusion Within the limitation of our in vitro study, 2Shape and NeoNiTi systems with severely curved canals were confirmed to be relatively safe in preparation and to respect original canal anatomy. Nevertheless, NeoNiTi instruments produced more centered preparation and minimal canal deviation compared to the 2Shape system.

This paper comprehensively reviews advanced signal processing methods for partial discharge (PD) analysis, covering traditional time-frequency techniques, wavelet transform, Hilbert-Huang transform, and artificial intelligence-based methods. This paper critically examines the principles, advantages, limitations, and applicable scenarios of each method. A key contribution of this review is the systematic comparison of these methods, highlighting their evolution and complementary roles in processing non-stationary and noisy PD signals. However, a significant gap in current research remains the lack of standardized, explainable, and embeddable AI solutions for real-time, fine-grained PD classification. Future trends point to hybrid approaches and edge AI systems that combine physical insights with lightweight deep learning models to improve diagnostic accuracy and deployability.

Adherence to treatment remains the cornerstone of long term viral suppression and successful treatment outcomes among patients receiving Antiretroviral Therapy (ART). Evaluate the effectiveness of mobile phone reminders and peer counseling in improving adherence and treatment outcomes among HIV positive patients on ART in Malaysia. A single-blind, parallel group RCT conducted in Hospital Sungai Buloh, Malaysia in which 242 adult Malaysian patients were randomized to intervention or control groups. Intervention consisted of a reminder module delivered through SMS and telephone call reminders by trained research assistants for 24 consecutive weeks (starting from date of ART initiation), in addition to adherence counseling at every clinic visit. The length of intended follow up for each patient was 6 months. Data on adherence behavior of patients was collected using specialized, pre-validated Adult AIDS Clinical Trial Group (AACTG) adherence questionnaires. Data on weight, clinical symptoms, CD4 count and viral load tests were also collected. Data was analyzed using SPSS version 22 and R software. Repeated measures ANOVA, Friedman's ANOVA and Multivariate regression models were used to evaluate efficacy of the intervention. The response rate after 6 months follow up was 93%. There were no significant differences at baseline in gender, employment status, income distribution and residential location of respondents between the intervention and control group. After 6 months follow up, the mean adherence was significantly higher in the intervention group (95.7; 95% CI: 94.39-96.97) as compared to the control group (87.5; 95% CI: 86.14-88.81). The proportion of respondents who had Good (>95%) adherence was significantly higher in the intervention group (92.2%) compared to the control group (54.6%). A significantly lower frequency in missed appointments (14.0% vs 35.5%) (p = 0.001), lower viral load (p = 0.001), higher rise in CD4 count (p = 0.017), lower incidence of tuberculosis (p = 0.001) and OIs (p = 0.001) at 6 months follow up, was observed among patients in the intervention group. Mobile phone reminders (SMS and telephone call reminders) and peer counseling are effective in improving adherence and treatment outcomes among HIV positive patients on ART in Malaysia. These findings may be of potential benefit for collaborative adherence planning between patients and health care providers at ART commencement.

Background The need for blood during a surgical procedure is greater than what blood banks are able to provide. There is an excessive amount of blood being ordered for elective surgeries, surpassing the actual requirements. Only 30% of the cross matched blood is actually used in these surgeries. The accuracy of estimating the transfusion needs before a surgical procedure can be determined by looking at the cross match to transfusion ratio and the transfusion index. “These indicators play a crucial role in developing the maximum surgical blood ordering schedule; in this study, these indicators were tested.” Aim of study Is to determine the efficiency of blood ordering and transfusion practices for patients undergoing elective surgeries. Methods This study is a prospective cross-sectional hospital-based study done at Omdurman Teaching Hospital-Sudan. Conducted for the duration of 6 months period from July to December 2019.The study participants were patients who underwent elective surgical procedures in general surgery and Urology departments as total coverage sample over a period of study duration. Ethical clearance obtained from ethical committee of Sudan Medical Specialization Board. Results Two hundreds seven patients included in this study, the amount of blood units requested were 443-unit, cross matching for 98.6% (n 437) of units were done. Only 100 unit were Transfused (22,8%). The calculated CT ratio was 4.4, transfusion index was 1.6 and transfusion probability was 29.9%. Conclusion Transfusion probability and transfusion index of the present study were optimal but comparatively higher than the standard guidelines as most of the cross matched blood was not utilized.

Therapeutic radiopharmaceuticals have been researched extensively in the last decade as a result of the growing research interest in personalized medicine to improve diagnostic accuracy and intensify intensive therapy while limiting side effects. Radiometal-based drugs are of substantial interest because of their greater versatility for clinical translation compared to non-metal radionuclides. This paper comprehensively discusses various components commonly used as chemical scaffolds to build radiopharmaceutical agents, i.e., radionuclides, pharmacokinetic-modifying linkers, and chelators, whose characteristics are explained and can be used as a guide for the researcher.

More than 111 million people worldwide have been affected by the COVID-19 outbreak caused by SARS-CoV-2. The main therapeutic target of COVID-19 is main protease (Mpro). It plays a key role as an enzyme in the SARS-CoV-2 replication and transcription. In this case, the alpha-mangostin potentially has antiviral activity against Mpro by inhibiting this enzyme. Nevertheless, the alpha-mangostin has low solubility and a lack of information about alpha-mangostin activity against the SARS-CoV-2. The aim of this study is to describe the molecular interactions and identify the pharmacokinetics profile between alpha-mangostin and its derivatives. in silico study was conducted by pharmacokinetics and toxicity prediction, molecular docking simulation, and Lipinski's rule of five. FKS9 has a Gibbs free energy value of-10.5 kcal/mol with an inhibition constant of 36.45 μM and an interaction with amino acid His41 residue. Its human intestinal absorption and Caco-2 values were 95.13% and 47.71% while the plasma protein binding and blood-brain barrier values were 96.66% and 6.99%. FKS9 also has no mutagenic and carcinogenic potential. FKS9 as an alpha-mangostin derivative had the best interaction with the Mpro enzyme and its pharmacokinetic profiles was identified.

Non-small cell lung carcinoma (NSCLC) is a type of lung cancer with the highest prevalence and mortality rate worldwide. Many cases of this type of cancer are overexpression on epidermal growth factor receptor (EGFR). The use of currently available EGFR inhibitors as one of the treatment options for NSCLC still shows various shortcomings, especially the high failure rate of therapy due to resistance. It is important to find NSCLC drug candidates with EGFR inhibitory activity. There are various published articles and it is prominent to draw evidence-based scientific conclusions as a basis of decision-making to select potential compounds for further research. Polymer matrix composites and ScienceDirect are used as a database for article screening. Research using molecular docking method targeted to EGFR with parameters of Gibbs energy and amino acid interactions between ligands and drug targets are included in inclusion criteria. Compounds that achieve docking parameters and have comparable activity to NSCLC guideline drugs are conscientiously ranked. There are only 11 compounds that achieved the docking parameters and had comparable EGFR inhibitory potential. Top-rated compounds include 1,3,5-trisubstituted pyrazoline (3c), 1,3,5-trisubstituted pyrazoline (6c), 1,3,5-trisubstituted pyrazoline (8d), N-(3,4-Dimethylphenyl)-2-[(4-oxo-3-(4-sulfamoylphenyl)-3,4-dihydrobenzo[g] quinazolin-2-yl) thio] acetamide. The top-rated compounds can be used and considered for further research processes.