Automorphism groups of metacyclic groups of class two

An automorphism of a group G is an isomorphism from G to G, which is one to one, onto and preserving operation. The automorphism of G forms a group under composition, and is denoted as Aut ?G?. A group is metacyclic if there is a normal cyclic subgroup whose quotient group is also cyclic. In 1973, King classified metacyclic p ? group while in 1987, Newman developed a new approach to metacyclic p ? groups suggested by the p ? group generation algorithm. They found new presentation for these groups. The automorphism groups can be separated to inner and outer automorphisms. An inner automorphism is an automorphism corresponding to conjugation by some element a. The set of all automorphisms form a normal subgroup of Aut ?G?. The automorphism group which is not inner is called outer automorphism and denoted as Out ?G?. In this research, automorphism groups of split and non-split metacyclic groups of class two will be investigated including the inner and outer automorphisms

Power Quality and System Stability Impact of Large-Scale Distributed Generation on the Distribution Network: Case Study of 60 MW Derna Wind Farm

Wind energy (WE) has become one of the most promising and developed forms of renewable energy source due to its efficiency and the availability of different capacities according to the loading requirements. The integration of wind turbines in the Libyan network has become an indispensable choice due to Libya’s distinguished location and for the Libyan National Initiative. Despite the numerous benefits of WE, the penetration of WE sources in the distribution network has some negative impacts related to the quality and reliability of the electric power supplied to the network. Owing to, the intermittent nature of these sources and electronic circuits needed to regulate the extracted power to comply with the grid requirements. In this chapter, implementation of the eastern Libyan network in NEPLAN and MATLAB/SIMULINK packages are carried out to investigate and analyze the significance of wind farm penetration in the medium voltage level of Libyan Distribution Network. A 60 MVA wind farm system has been connected to the Libyan distribution network according to the Libyan National Initiative. Different penetration scenarios are simulated to testify the technical aspects of integrating WE on the distribution level

The intercontinental schizophrenia outpatient health outcomes (IC-SOHO) study: baseline clinical and functional characteristics and antipsychotic use patterns in the North Africa and Middle Eastern (AMEA) region: original article

Objective: To describe the baseline findings of the Intercontinental Schizophrenia Outpatient Health Outcomes (IC-SOHO) study in the North Africa and Middle Eastern sub-region (AMEA-SOHO) Method: The IC-SOHO study is an ongoing prospective, three-year, non-interventional observational study of schizophrenia treatment, clinical characteristics and mental health services utilization in two North African and two Middle Eastern countries. The study population consists of non-hospitalised patients who had initiated treatment with or changed to a new antipsychotic. Results: The baseline findings of the IC-SOHO study (AMEA Subset) appear to reflect clinical practice in Turkey, Saudi Arabia, Egypt and Algeria (N=1, 398). Overall, the patients were moderately to markedly ill and either overweight (46%) or obese (8%) when they entered the study. Functionally, the majority of patients were not involved in social activities, could not care for themselves and were unemployed. Substance and alcohol dependency/abuse was not a problem in this study population. At baseline the majority of patients were treated with typical antipsychotics (oral and depot); and anticholinergics were the most commonly prescribed concomitant medication. Sexual side effects were most frequently reported among the surveyed adverse events. Overall compliance/adherence to medication was good. Conclusion: The baseline IC-SOHO data highlighted various clinical and functional characteristics and antipsychotic use patterns in a group of outpatients with schizophrenia in a naturalistic setting. Once completed, the IC-SOHO study will add further to this knowledge base. SA Psychiatry Rev. Vol.7(3) 2004: 27-3

Normal Tissue Complication Probability (NTCP) Prediction Model for Osteoradionecrosis of the Mandible in Patients With Head and Neck Cancer After Radiation Therapy:Large-Scale Observational Cohort

Purpose: Osteoradionecrosis (ORN) of the mandible represents a severe, debilitating complication of radiation therapy (RT) for head and neck cancer (HNC). At present, no normal tissue complication probability (NTCP) models for risk of ORN exist. The aim of this study was to develop a multivariable clinical/dose-based NTCP model for the prediction of ORN any grade (ORNI-IV) and grade IV (ORNIV) after RT (+/- chemotherapy) in patients with HNC.Methods and Materials: Included patients with HNC were treated with (chemo-)RT between 2005 and 2015. Mandible bone radiation dose-volume parameters and clinical variables (ie, age, sex, tumor site, pre-RT dental extractions, chemotherapy history, postoperative RT, and smoking status) were considered as potential predictors. The patient cohort was randomly divided into a training (70%) and independent test (30%) cohort. Bootstrapped forward variable selection was performed in the training cohort to select the predictors for the NTCP models. Final NTCP model(s) were validated on the holdback test subset.Results: Of 1259 included patients with HNC, 13.7% (n = 173 patients) developed any grade ORN (ORNI-IV primary endpoint) and 5% (n = 65) ORNIV (secondary endpoint). All dose and volume parameters of the mandible bone were significantly associated with the development of ORN in univariable models. Multivariable analyses identified D30% and pre-RT dental extraction as independent predictors for both ORNI-IV and ORNIV best-performing NTCP models with an area under the curve (AUC) of 0.78 (AUCvalidation = 0.75 [0.69-0.82]) and 0.81 (AUCvalidation = 0.82 [0.74-0.89]), respectively.Conclusions: This study presented NTCP models based on mandible bone D30% and pre-RT dental extraction that predict ORNI-IV and ORNIV (ie, needing invasive surgical intervention) after HNC RT. Our results suggest that less than 30% of the mandible should receive a dose of 35 Gy or more for an ORNI-IV risk lower than 5%. These NTCP models can improve ORN prevention and management by identifying patients at risk of ORN. (C) 2021 The Author(s). Published by Elsevier Inc.</p

Strategic Creativity in Islamic Banks in Palestine between Reality and Implementation

It aimed to identify the strategic creativity in Islamic banks in Palestine between reality and implementation. The study adopted the descriptive analytical approach. A questionnaire was designed as a tool for the study. The study community consisted of all employees in Islamic banks from the top and middle management and the study has been applied to the Palestinian Islamic bank and the Arab Islamic Bank. The comprehensive inventory method was used, given the small size of the study sample, as questionnaires were distributed to (175) employees, and a number of (5) categories were chosen from each branch of the bank(general manager, deputy general manager, director Branch, department head, department manager). (164) questionnaires have been used Recovered with a recovery rate of (93.71%). The study showed a number of results, the most important of which is the availability of dimensions of strategic innovation at a high level in Islamic banks in Palestine with a relative weight of (82.22%). In addition, that there are no differences between the averages estimates about the reality of the study variables in Islamic banks due to (gender, age group, educational qualification, number of years of service, job title). The study also presented a set of recommendations, including that the bank should provide the environment and the appropriate climate for employees to invest their intellectual energy, urge them to strategic creativity, and deal fairly with all creative ideas regardless of their source

DASS Good: Explainable Data Mining of Spatial Cohort Data

Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe the co-design of a modeling system, DASS, to support the hybrid human-machine development and validation of predictive models for estimating long-term toxicities related to radiotherapy doses in head and neck cancer patients. Developed in collaboration with domain experts in oncology and data mining, DASS incorporates human-in-the-loop visual steering, spatial data, and explainable AI to augment domain knowledge with automatic data mining. We demonstrate DASS with the development of two practical clinical stratification models and report feedback from domain experts. Finally, we describe the design lessons learned from this collaborative experience.Comment: 10 pages, 9 figure

Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images

Auto-segmentation of primary tumors in oropharyngeal cancer using PET/CT images is an unmet need that has the potential to improve radiation oncology workflows. In this study, we develop a series of deep learning models based on a 3D Residual Unet (ResUnet) architecture that can segment oropharyngeal tumors with high performance as demonstrated through internal and external validation of large-scale datasets (training size = 224 patients, testing size = 101 patients) as part of the 2021 HECKTOR Challenge. Specifically, we leverage ResUNet models with either 256 or 512 bottleneck layer channels that demonstrate internal validation (10-fold cross-validation) mean Dice similarity coefficient (DSC) up to 0.771 and median 95% Hausdorff distance (95% HD) as low as 2.919 mm. We employ label fusion ensemble approaches, including Simultaneous Truth and Performance Level Estimation (STAPLE) and a voxel-level threshold approach based on majority voting (AVERAGE), to generate consensus segmentations on the test data by combining the segmentations produced through different trained cross-validation models. We demonstrate that our best performing ensembling approach (256 channels AVERAGE) achieves a mean DSC of 0.770 and median 95% HD of 3.143 mm through independent external validation on the test set. Our DSC and 95% HD test results are within 0.01 and 0.06 mm of the top ranked model in the competition, respectively. Concordance of internal and external validation results suggests our models are robust and can generalize well to unseen PET/CT data. We advocate that ResUNet models coupled to label fusion ensembling approaches are promising candidates for PET/CT oropharyngeal primary tumors auto-segmentation. Future investigations should target the ideal combination of channel combinations and label fusion strategies to maximize segmentation performance.</p