Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte

Background: The primary stability of the dental implant is a crucial factor determining the ability to initiate temporary implant-supported prosthesis and for subsequent successful osseointegration, especially in the maxillary non-molar sites. This study assessed the reliability of the insertion torque of dental implants by relating it to the implant stability quotient values measured by the Osstell device. Material and methods: This study included healthy, non-smoker patients with no history of diabetes or other metabolic, or debilitating diseases that may affect bone healing, having non-restorable fractured teeth and retained roots in the maxillary non-molar sites. Primary dental implant stability was evaluated using a torque ratc

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

Many oil and gas processes, including oil recovery, oil transportation, and petroleum processing, are negatively impacted by the precipitation and deposition of asphaltene. Screening methods for determining the stability of asphaltenes in crude oil have been developed due to the high cost of remediating asphaltene deposition in crude oil production and processing. The colloidal instability index, the Asphaltene-resin ratio, the De Boer plot, and the modified colloidal instability index were used to predict the stability of asphaltene in crude oil in this study. The screening approaches were investigated in detail, as done for the experimental results obtained from them. The factors regulating the asphaltene precipitation are different fr

  Acinetobacter baumannii (A. baumannii ) is considered a critical healthcare problem for patients in intensive care units due to its high ability to be multidrug-resistant to most commercially available antibiotics. The aim of this study is to develop a colorimetric assay to quantitatively detect the target DNA of A. baumannii based on unmodified gold nanoparticles (AuNPs) from different clinical samples (burns, surgical wounds, sputum, blood and urine). A total of thirty-six A. baumannii clinical isolates were collected from five Iraqi hospitals in Erbil and Mosul provinces within the period from September 2020 to January 2021. Bacterial isolation and biochemical identification of isolates