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This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in single phase grid-connected photovoltaic (PV) systems. To overcome the non-detection zone, excess and deficit power imbalance conditions are considered for different loading conditions. These imbalances are characterized by the voltage dip scenario and were subjected to feature extraction for training with the machine learning technique. This is experimentally realized by training the machine learning classifier with different events on a 5   kW grid-connected system. Using the concept of detection and false alarm rates, the performance of the trained classifier is tested for multiple faults and power imbalance conditions. The results showed the effective operation of the classifier with a detection rate of 99.2% and a false alarm rate of 0.2%.

Background: Screening approved medications to identify therapeutics for drug repurposing is an effective tactic, and a deep research into off label drug use (OLDU) is required. Unfortunately, OLDU has not been extensively studied in Middle East. Our study aimed to evaluate the extent of OLDU in Saudi Arabia.Methods: Retrospective study carried out during 12 month period at six tertiary hospitals in Saudi Arabia. Each prescription was evaluated as unlicensed or OLDU based on the product information or based on Food and drug Administration (FDA) approval.Results: A total of 288 prescriptions were analyzed, where the reasons for off-label prescribing were OLDU by indication (94.42%), OLDU by different age group (2.09%), and other reasons represented (3.48%). Adults/geriatrics (≥18 years) received (89.05%) of the orders, and children (1-11 years) received (7.78%) of the orders. Both adolescents (12-18 years) and neonates (1-29 days) received (1.42%) of the orders per each category, while infants (1 month-1 year) received (0.36%) of the orders. The therapeutic classes most often prescribed were antidepressants (21.88%), antidiabetics (17.71%), and atypical antipsychotics (10.06%).Conclusions: Off-label prescribing was found in most adults/geriatrics suffering from depression, and diabetes mellitus. The most common reason for off-label prescription was off-label by indication. The results call for the need to conduct more studies with larger sample size, do more investigations on the OLDU in the whole kingdom, and develop policy for OLDU across hospitals.

In this paper, we construct the solutions expressions for a system of three‐dimensional nonlinear difference equations Rn+1=a1Tn−1Sn−1Rn−1+Sn−1+Tn−1,Sn+1=a2Tn−1Rn−1Rn−1+Sn−1+Tn−1,Tn+1=a3Rn−1Sn−1Rn−1+Sn−1+Tn−1. In addition, we prove some properties of the proposed system such as boundedness and periodicity of the positive solutions and the global asymptotic stability of the equilibrium points. Finally, we present some numerical examples in order to illustrate the theoretical results.