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The procurement process is one of the most important phases in any project life cycle, particularly when it comes to selecting the right contractor for the job. Awarding the contract to the best bid proposal is a critical step to ensure the greatest value. BIM has been recognized as not only a geometric modelling of buildings, but also, it facilitates the different stages in management of construction projects. The purpose of this paper is to study the impact of using Building Information Modeling (BIM) in the tendering process from the contractor’s perspective, based on a probability model able to predict winning probability, regardless of relative weight. The main objective of this research is to measure the likelihood of winning a tender in the case of implementing BIM strategy, compared with contractors who do not use BIM. The research uses a literature review, surveys, and interviews with experts to develop a model that predicts the probability of winning a contract; this is determined by measuring the BIM impact on each selection criterion in a multicriteria selection process using the Analytical Hierarchy Process (AHP) to develop a probability-based model. The results of the survey and the interview show that BIM strategy has a variant influence on the score the contractor could have on each of them raising the probability of winning the tender. The main result of this paper is the property-based model, which is able to predict BIM winning probability regardless of relative weight, which can be applied in any country. Nonetheless, the Saudi case study shows that utilizing BIM when proposing could increase the winning probability by up to 9.42% in the case of Quality-Based Selection (QBS), and to 5.5% in the case of Cost-Based Selection (CBS).

Waterborne Escherichia coli are a major reservoir of antimicrobial resistance (AMR). Carbapenem-resistance, especially when mediated by transferable carbapenemase-encoding genes, is spreading worldwide and causing dramatically limiting treatment options. In our country, studies for the detection of carbapenem resistance in drinking water do not exist; therefore, this work was carried out to determine the prevalence of carbapenem-resistant genes “blaKPC, blaIMP, blaNDM, blaSPM, blaVIM, and blaOXA-48” among Escherichia coli isolated from drinking water in Khartoum, Sudan. A total of forty-five E. coli bacteria were isolated from different sources of drinking water. Antimicrobial susceptibility testing was performed using imipenem (10 mg/disc), gentamicin (10 mg/disc), ceftriaxone (30 mg/disc), ciprofloxacin (5 mg/disc), chloramphenicol (30 mg/disc), and tetracycline (30 mg/disc). “Sensitive” or “resistant” patterns of E. coli were judged using antibiotic minimum inhibitory concentration (MIC). Bacterial genomic DNA was extracted by the boiling method, and then multiplex polymerase chain reaction was performed to detect the carbapenemase genes (blaKPC, blaIMP, blaNDM, blaSPM, blaVIM, and blaOXA-48). Multiplex PCR assays confirmed the presence of carbapenemase genes in 28% of all water isolates. OXA-48 gene was the most predominant gene, detected in 15.5% of the isolates. The blaKPC and blaSPM genes were also detected in 4.4% and 8.8% of the isolates, respectively. However, the isolates were negative for blaNDM, blaVIM, and blaIMP genes. The isolates showed a high rate of tetracycline resistance (97.7%), followed by gentamicin (57.7%), ciprofloxacin (46.6%), ceftriaxone (35.5%), and chloramphenicol (31.1%). In conclusion, this study confirmed for the first time the presence of E. coli carried carbapenem-resistant genes in the drinking water of Khartoum state, Sudan. These isolates commonly carried OXA-48 (7/45), followed by SPM (4/45) and KPC (2/45).

Coronavirus disease 2019 (COVID-19), a serious infectious disease caused by the recently discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a major global health crisis. Although no specific antiviral drugs have been proven to be fully effective against COVID-19, remdesivir (GS-5734), a nucleoside analogue prodrug, has shown beneficial effects when used to treat severe hospitalized COVID-19 cases. The molecular mechanism underlying this beneficial therapeutic effect is still vaguely understood. In this study, we assessed the effect of remdesivir treatment on the pattern of circulating miRNAs in the plasma of COVID-19 patients, which was analyzed using MiRCURY LNA miRNA miRNome qPCR Panels and confirmed by quantitative real-time RT-PCR (qRT-PCR). The results revealed that remdesivir treatment can restore the levels of miRNAs that are upregulated in COVID-19 patients to the range observed in healthy subjects. Bioinformatics analysis revealed that these miRNAs are involved in diverse biological processes, including the transforming growth factor beta (TGF-β), hippo, P53, mucin-type O-glycan biosynthesis, and glycosaminoglycan biosynthesis signaling pathways. On the other hand, three miRNAs (hsa-miR-7-5p, hsa-miR-10b-5p, and hsa-miR-130b-3p) were found to be upregulated in patients receiving remdesivir treatment and in patients who experienced natural remission. These upregulated miRNAs could serve as biomarkers of COVID-19 remission. This study highlights that the therapeutic potential of remdesivir involves alteration of certain miRNA-regulated biological processes. Targeting of these miRNAs should therefore be considered for future COVID-19 treatment strategies.

In this work, we investigate epilepsy seizure detection using machine learning. In the literature, a machine learning model is usually trained to help automate the epileptic detection process, eliminating the need for human intervention. Typically, the dataset is split into training and test sets in a way to maximize the detection accuracy. This requires the training set to include enough EEG samples for every possible patient in order to improve the accuracy numbers during the prediction. However, this might not be easy or practical in real life. A new patient might not have a previous record in the training set, and hence, the prediction for this particular patient might not meet the expected accuracy. The main contribution in this work is to study the impact of the training and test datasets selection from practical point of view on the accuracy and efficacy of the CNN prediction. In this work, a CNN model, namely AlexNet, is trained to detect epileptic states, namely preictal, interictal and ictal, in subjects using electroencephalogram (EEG) signals. The dataset includes the three epileptic zones of subjects taken from three medical centers, collected by the Fragility Multi-Center Retrospective Study. Furthermore, we propose a framework to utilize a feature extraction technique that exploits the available multiple channels of EEG signals to minimize information loss. As part of the main contribution, three different approaches are proposed to split the EEG sample dataset into the training and test sets. Thus, the prediction performance is evaluated based on the prior knowledge extracted from the particular samples picked for the training set. The results show that the proposed framework achieves an overall accuracy of 94.44% when the training contained samples from each patient. The accuracy is reduced to 92.98% when the training set contained a subset of the patient pool. A binary classification is also performed with up to 98% accuracy for both scenarios.

This paper presents a novel design concept for near-field wireless power transfer (WPT) systems that leverages the seamless integration of metamaterials (MTMs) into slotted resonators. This approach enhances magnetic field ( B ) distribution, strengthens evanescent wave coupling, and hence improves overall WPT performance. An MTM structure exhibiting negative permeability at the operating frequency of 433 MHz was designed and incorporated into a square-slot-based resonator. Electromagnetic (EM) analysis revealed increased current density and subsequently enlarged B around the resonator. Additionally, impedance matching, key parameter extraction, and equivalent circuit (EC) modeling were performed using the effective J-inverter method. The developed WPT system, with compact dimensions of 30 × 30 2 , achieved a power transfer efficiency of 82% at 33 mm. The design was experimentally validated, showing excellent agreement among EM simulations, EC modeling, and measurement results. Finally, performance comparison with other reported systems confirmed the superiority and practical effectiveness of the proposed design in enabling compact and highly efficient WPT systems.