In contrast to the conventional fund management industry with a profit-oriented logic based on risk and return, ethical and faith-based funds should follow the religious principles of their investment-style philosophy. Islamic funds should obey the theological teachings of the primary sources of Islam, the Quran and Sunnah, as stakeholders expect these religious teachings to influence the investment decisions of fund managers. In practice, Islamic fund managers use Accounting and Auditing Organization for Islamic Financial Institutions ’s screening criteria, based on secondary (...) sources of Islam, which allow investments that are only partially halal to be included in their portfolios. This study finds that a more religious logic in screening practices, although impairing diversification, does not necessarily harm performance. Thus, Islamic investment funds, and the wider ethical fund management industry, should, and could, adopt stricter screening criteria that match their investment mandates and bring more ethical business practices to the industry. (shrink)

A new continuous version of the inverse flexible Weibull model is proposed and studied. Some of its properties such as quantile function, moments and generating functions, incomplete moments, mean deviation, Lorenz and Bonferroni curves, the mean residual life function, the mean inactivity time, and the strong mean inactivity time are derived. The failure rate of the new model can be “increasing-constant,” “bathtub-constant,” “bathtub,” “constant,” “J-HRF,” “upside down bathtub,” “increasing,” “upside down-increasing-constant,” and “upside down.” Different copulas are used for deriving many (...) bivariate and multivariate type extensions. Different non-Bayesian well-known estimation methods under uncensored scheme are considered and discussed such as the maximum likelihood estimation, Anderson Darling estimation, ordinary least square estimation, Cramér-von-Mises estimation, weighted least square estimation, and right tail Anderson Darling estimation methods. Simulation studies are performed for comparing these estimation methods. Finally, two real datasets are analyzed to illustrate the importance of the new model. (shrink)

Impaired neurodevelopmental outcome, in particular cognitive impairment, after neonatal hypoxic-ischemic encephalopathy is a major concern for parents, clinicians, and society. This study aims to investigate the potential benefits of using advanced quantitative electroencephalography analysis for early prediction of cognitive outcomes, assessed here at 2 years of age. EEG data were recorded within the first week after birth from a cohort of twenty infants with neonatal hypoxic-ischemic encephalopathy. A proposed regression framework was based on two different sets of features, namely graph-theoretical (...) features derived from the weighted phase-lag index and entropies metrics represented by sample entropy, permutation entropy, and spectral entropy. Both sets of features were calculated within the noise-assisted multivariate empirical mode decomposition domain. Correlation analysis showed a significant association in the delta band between the proposed features, graph attributes and entropy features from the neonatal EEG data and the cognitive development at age two years. These features were used to train and test the tree ensemble regression models. The highest prediction performance was reached to 14.27 root mean square error, 12.07 mean absolute error, and 0.45 R-squared using the entropy features with a boosted tree regression model. Thus, the results demonstrate that the proposed qEEG features show the state of brain function at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate identifying those who might benefit from early targeted intervention. (shrink)

The proposed work describes an approach for the segmentation of abnormal lung CT scans of COVID-19. Lung diseases are the leading killer in both men and women. The pulmonary experts normally make attempts, such as early detection of patients by tomography tests before lung specialists treat patients who are tortured by lung disease. Moreover, lung specialists do their best to detect the presence of lung conditions. X rays or CT scan checks are performed for tomography tests. The finest approach for (...) medical diagnosis and a wide range of uses is computed tomography. This kind of imaging offers elaborate cross-sectional pictures of skinny slices of the organic structure. However, the preprocessing and denoising methods of Lung CT scans may mask some important image features. To address this challenge, we propose a novel framework involving an optimization technique algorithm to solve a multilevel thresholding problem based on information theory to segment abnormal lung CT scans. The proposed framework will evaluate a sample of CT scan images taken from a well-known benchmark database. The evaluation results will assess subjectively and objectively to demonstrate the effectiveness of the proposed framework. (shrink)

Taking an authorial stance is essential in academic writing but remains a challenge for novice researchers, especially EFL/ESL writers. This study explores how authors of English and Arabic research article discussions employ evaluative language resources while commenting on their results. To this end, the study investigated the employment of Engagement resources within Appraisal Theory. The findings exhibited a great divergence between the two language groups as Arabic discussions relied more on Contracting strategies, which indicate the tendency to close down the (...) space for dialogic alternatives, while their English counterparts preferred Expanding resources, which open up the dialogic space for alternative voices. The study, therefore, bears some pedagogical implications for L2 learners. (shrink)

The 3D Prandtl fluid flow through a bidirectional extending surface is analytically investigated. Cattaneo–Christov fluid model is employed to govern the heat and mass flux during fluid motion. The Prandtl fluid motion is mathematically modeled using the law of conservations of mass, momentum, and energy. The set of coupled nonlinear PDEs is converted to ODEs by employing appropriate similarity relations. The system of coupled ODEs is analytically solved using the well-established mathematical technique of HAM. The impacts of various physical parameters (...) over the fluid state variables are investigated by displaying their corresponding plots. The augmenting Prandtl parameter enhances the fluid velocity and reduces the temperature and concentration of the fluid. The momentum boundary layer boosts while the thermal boundary layer mitigates with the rising elastic parameter strength. Furthermore, the enhancing thermal relaxation parameter ) reduces the temperature distribution, whereas the augmenting concentration parameter drops the strength of the concentration profile. The increasing Prandtl parameter declines the fluid temperature while the augmenting Schmidt number drops the fluid concentration. The comparison of the HAM technique with the numerical solution shows an excellent agreement and hence ascertains the accuracy of the applied analytical technique. This work finds applications in numerous fields involving the flow of non-Newtonian fluids. (shrink)

The increasing global demand for energy necessitates devoted attention to the formulation and exploration of mechanisms of thermal heat exchangers to explore and save heat energy. Thus, innovative thermal transport fluids require to boost thermal conductivity and heat flow features to upsurge convection heat rate, and nanofluids have been effectively employed as standard heat transfer fluids. With such intention, herein, we formulated and developed the constitutive flow laws by utilizing the Rossland diffusion approximation and Stephen’s law along with the MHD (...) effect. The mathematical formulation is based on boundary layer theory pioneered by Prandtl. Governing nonlinear partial differential flow equations are changed to ODEs via the implementation of the similarity variables. A well-known computational algorithm BVPh2 has been utilized for the solution of the nonlinear system of ODEs. The consequence of innumerable physical parameters on flow field, thermal distribution, and solutal field, such as magnetic field, Lewis number, velocity parameter, Prandtl number, drag force, Nusselt number, and Sherwood number, is plotted via graphs. Finally, numerical consequences are compared with the homotopic solution as a limiting case, and an exceptional agreement is found. (shrink)

We present a new viable nonlinear chaotic paradigm. This paradigm has four nonlinear terms. The essential features of the new paradigm have been investigated. Our new system is confirmed to have chaotic behaviors by calculating its Lyapunov exponents. The relations of the system states are displayed by a suggested new signal flow graph. The proposed SFG is discussed via some graph theory tools, and some of its hidden features are calculated. In addition, the system is realized via constructing its electronic (...) circuit which helps in the real applications. Also, a robust controller for the system is designed with the aid of a genetic algorithm. (shrink)

Clinical responses to dopamine replacement therapy for individuals with Parkinson’s disease are often difficult to predict. We characterized changes in MDS-UPDRS motor factor scores resulting from a short-duration L-Dopa response, and investigated how the inter-subject clinical differences could be predicted from motor cortical magnetoencephalography. MDS-UPDRS motor factor scores and resting-state MEG recordings were collected during SDR from twenty individuals with a PD diagnosis. We used a novel subject-specific strategy based on linear support vector machines to quantify motor cortical oscillatory frequency (...) profiles that best predicted medication state. Motor cortical profiles differed substantially across individuals and showed consistency across multiple data folds. There was a linear relationship between classification accuracy and SDR of lower limb bradykinesia, although this relationship did not persist after multiple comparison correction, suggesting that combinations of spectral power features alone are insufficient to predict clinical state. Factor score analysis of therapeutic response and novel subject-specific machine learning approaches based on subject-specific neuroimaging provide tools to predict outcomes of therapies for PD. (shrink)

Introduction: Cognitive behavioral therapy for children and adolescents has shown efficacy in treating different psychiatric disorders. It has been added to multiple clinical guidelines as the first-line treatment. However, despite more studies of its efficacy, CBT is underutilized in clinical settings due to a lack of rigorous training programs and qualified CBT therapists. The limited knowledge of parents in this intervention and their negative attitudes toward it have been considered as possible reasons.Methods: This is a cross-sectional survey-based study among 464 (...) Saudi parents living in Riyadh city. We aimed to evaluate the preference, knowledge, and attitudes of Saudi parents toward CBT for their children. We compared the difference in the level of knowledge and attitudes toward CBT in relation to the characteristics of parents. An online questionnaire that included 39 questions was carefully reconstructed from four validated scales, approved by an expert panel, and piloted. Participants were recruited to participate through online social media.Results: Saudi parents had average knowledge about CBT; however, they had positive attitudes toward the therapy itself and its role in treating the behavioral issues of children. Male participants showed better knowledge than female participants. Participants with higher education and those with high income had more favorable attitudes toward CBT than others.Conclusion: The knowledge of parents is considered inadequate and indicated the need for more awareness and perhaps mass education. In contrast, they maintained positive attitudes and were interested in evidence-based treatment, with more preference toward non-psychopharmacological interventions. (shrink)

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For (...) the textual attribute “statement,” Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset. (shrink)