We consider a hypergraphmodel for the protein complex network obtained from a large-scale experimental study to characterize the proteome of the yeast. Our model views the yeast proteome as a hypergraph, with the proteins cor- responding to vertices and the complexes corresponding to hyperedges. Previous work has modeled the protein com- plex data as a protein-protein interaction graph or as a com- plex intersection graph; both models lose information and require more space. Our results show that the yeast protein complex hyper-graph is a small-world and power-law hy- pergraph. We design an algorithm for computing the -core of a hypergraph, and use it to identify the core proteome, the maximum core of the protein complex hypergraph. We show that the core proteome of the yeast is enriched in es- sential and homologousproteins. We implement greedy ap- proximation algorithms for variant minimum weight vertex covers of a hypergraph; these algorithms can be used to improve the reliability and efficiency of the experimental method that identifies the protein complex network.

Improper hole cleaning or drilled-cutting transportation impacts drilling operations. To illustrate, inefficient cleaning of the wellbore can lead to many drilling problems such as low drilling rate (i.e. low ROP), early bit wear and, in the severe cases, a complete loss of the well due to stuck pipe. To understand efficiency in cutting transport in drilling and to provide solutions for the problem, many studies have been conducted. In all cases, they provide empirical models based on experimental data. In this study, different artificial intelligence (AI) techniques are employed to estimate the concentration of cuttings present in the wellbore. The purpose of this study is to indirectly measure the hole-cleaning efficiency in order to predict the cutting concentration from drilling parameters using artificial intelligence techniques. The study is based on 116 experimental data records from the studies. Two AI techniques were selected, namely artificial neural network (ANN) and support vector machine (SVM), to estimate the cutting concentration in the wellbore. The input parameters comprise mud density and mud rheological properties (yield point and plastic viscosity) in addition to drilling parameters including the hole inclination angle, pipe eccentricity (i.e. location of the drill pipe from the axis of the well), the rate of penetration (ROP), flow rate (GPM), drill pipe rotary speed (RPM) and temperature. The results obtained show the ability of the two employed techniques to accurately predict the cutting concentration in the wellbore with average absolute errors (AAE) less than 5% and correlation coefficients (R) higher than 0.9. Comparison of these results with a literature model showed that the AI techniques provide better predictions of cutting concentration and higher accuracy than that model. Applying the developed AI technique will help the drilling engineers to assess the hole cleaning in a real time.

Massive biological datasets are generated in different locations all over the world. Analysis of these datasets is required in order to extract knowledge that might be helpful for biologists, physicians and pharmacists. Recently, analysis of biological networks has received a lot of attention, as an understanding of the network can reveal information about life at the cellular level. Biological networks can be generated that examine the interaction between proteins or the relationship amongst different genes at the expression level. Identifying information from biological networks is recognized as a significant challenge, due to the inherent complexity of the structures. Computational techniques are used to analyze such complex networks with varying success. In this paper, we construct a new method for predicting phenotype-gene association in breast cancer using biological network analysis. Several network topological measures have been computed and fed as features into two classification models to investigate phenotype-gene association in breast cancer. More importantly, to overcome the problem of the skewed datasets, a synthetic minority oversampling technique (SMOTE) is adapted in order to transform an imbalanced dataset to a balanced one. We have applied our method on the gene co-expression network (GCN), protein–protein interaction network (PPI), and the integrated functional interaction network (FI), which combined the PPIs and gene co-expression, amongst others. We assess the quality of our proposed method using a slightly modified cross-validation. Our method can identify phenotype-gene association in breast cancer. Moreover, use of the integrated functional interaction network (FI) has the potential to reveal more information and hidden patterns than the other networks. The software and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/NetTop.zip .

Calcium chloride brine-based drill-in fluid is commonly used within the reservoir section, as it is specially formulated to maximize drilling experience, and to protect the reservoir from being damaged. Monitoring the drilling fluid rheology including plastic viscosity, $PV,$ apparent viscosity, $AV,$ yield point, $Y_p,$ flow behavior index, $n,$ and flow consistency index, $k,$ has great importance in evaluating hole cleaning and optimizing drilling hydraulics. Therefore, it is very crucial for the mud rheology to be checked periodically during drilling, in order to control its persistent change. Such properties are often measured in the field twice a day, and in practice, this takes a long time (2–3 h for taking measurements and cleaning the instruments). However, mud weight, $MW$ , and Marsh funnel viscosity, $MF$ , are periodically measured every 15–20 min. The objective of this study is to develop new models using artificial neural network, ANN, to predict the rheological properties of calcium chloride brine-based mud using $MW$ and $MF$ measurements then extract empirical correlations in a white-box mode to predict these properties based on $MW$ and $MF$ . Field measurements, 515 points, representing actual mud samples, were collected to build the proposed ANN models. The optimized parameters of these models resulted in highly accurate results indicated by a high correlation coefficient, R, between the predicted and measured values, which exceeded 0.97, with an average absolute percentage error, AAPE, that did not exceed 6.1%. Accordingly, the developed models are very useful for monitoring the mud rheology to optimize the drilling operation and avoid many problems such as hole cleaning issues, pipe sticking and loss of circulation.

Protein–protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein–protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein–protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

The major objective of the present work is to investigate into the appropriate tilt angles of south-oriented solar panels in Saudi Arabia for maximum performance. This is done with the estimation of the annual energy sums received on surfaces with tilt angles in the range 15°–55° inclined to south at 82 locations covering all Saudi Arabia. The analysis shows that tilt angles of 20°, 25° and 30° towards south are the optimum ones depending on site. These optimum tilt angles define three distinct solar energy zones in Saudi Arabia. The variation of the energy sums in each energy zone on annual, seasonal and monthly basis is given; the analysis provides regression equations for the energy sums as function of time in each case. Furthermore, the spatial distribution of the annual global inclined solar energy in Saudi Arabia is shown in a solar map specially derived. The annual energy sums are found to vary between 1612 kWhm⁻²year⁻¹ and 2977 kWhm⁻²year⁻¹ across the country. Finally, the notion of a correction factor is introduced, defined, and employed. This factor can be used to correct energy values estimated by a reference ground albedo to those based on near-real ground albedo.