Harnessing Automation in Data Mining: A Review on the Impact of PyESAPI in Radiation Oncology Data Extraction and Management

Data extraction and management are crucial components of research and clinical workflows in Radiation Oncology (RO), where accurate and comprehensive data are imperative to inform treatment planning and delivery. The advent of automated data mining scripts, particularly using the Python Environment for Scripting APIs (PyESAPI), has been a promising stride towards enhancing efficiency, accuracy, and reliability in extracting data from RO Information Systems (ROIS) and Treatment Planning Systems (TPS). This review dissects the role, efficiency, and challenges of implementing PyESAPI in RO data extraction and management, juxtaposing manual data extraction techniques and explicating future avenue

Cooperative-hierarchical based edge-computing approach for resources allocation of distributed mobile and IoT applications

Using mobile and Internet of Things (IoT) applications is becoming very popular and obtained researchers’ interest and commercial investment, in order to fulfill future vision and the requirements for smart cities. These applications have common demands such as fast response, distributed nature, and awareness of service location. However, these requirements’ nature cannot be satisfied by central systems services that reside in the clouds. Therefore, edge computing paradigm has emerged to satisfy such demands, by providing an extension for cloud resources at the network edge, and consequently, they become closer to end-user devices. In this paper, exploiting edge resources is studied; therefore, a cooperative-hierarchical approach for executing the pre-partitioned applications’ modules between edges resources is proposed, in order to reduce traffic between the network core and the cloud, where this proposed approach has a polynomial-time complexity. Furthermore, edge computing increases the efficiency of providing services, and improves end-user experience. To validate our proposed cooperative-hierarchical approach for modules placement between edge nodes’ resources, iFogSim toolkit is used. The obtained simulation results show that the proposed approach reduces network’s load and the total delay compared to a baseline approach for modules’ placement, moreover, it increases the network’s overall throughput

التضمین النحويّ؛ وجوھھ وأغراضھ وأحكامھ

التضمین في العربیة مصطلح لھ وجوده في علوم عدیدة، كالبلاغة، والعروض، وعلم الكلام، والنحو،وھو في معناه العامّ یدلّ على إدخال شيء في شيء واتّحاده معھ، ونركّز في بحثنا ھذا على التضمین النحويّبخاصّة. یظھر لنا من تأمّل التضمین النحويّ وأبعاده أنھ ظاھرة فكریة وفنّیة بآنٍ معًا؛ إذ إنھ ثمرة لتفكیر القائل فياستخدام مكثّف للألفاظ للتعبیر عن المعاني الكثیرة؛ بمعنى إبداع كلام بدالّ واحد وبمدلولین، وھذا الإبداع تجلّى فيكتاب لله في غیر موضع، كما تجلّى في أشعار العرب، وھا نحن نحاول أن نتلمّس مواطن أسرار الإبداع فيالتضمین النحويّ ببیان وجوھھ وأغراضھ وأحكامھ

ELK1 Gene Transfection Effect in Prostate Cancer Cell Line Proliferation Activity

Three type of prostate cancer cell line was selected for this study (PC3, DU145 & LNCaP) as a model, transfected by liposome with the ELK1 gene ( control & knock down) , then detecting the proliferation ability of the cultured cell lines by the Mtt or (proliferation) assay that shows clear effect of ELK1 gene in this cells comparing with the control cell transfected with the knock down gene, using for each type of cells 6 repeats ,for each type there was two groups 1st for control ELK1 and the 2nd was knock down or (sh) ELK1, all works was done in Johns Hopkins University / School of Medicine / Pathology Department ( MD, USA

The Elk1 gene effect on prostate cancer cell line wound healing ability

In this study prostate cancer cell line model was used selecting three type of them (Pc3, DU145 & LNCaP) , gene transfecting was done by using the liposome into the cell culture cells then detecting the healing ability by the Wound Healing or (scratching) test that shows clear effect of ELK1 gene in this cells comparing with the control cell transfected with the knock down gene, using for each type of cells 6 repeats ,for each type there was two groups 1st for control ELK1 and the 2nd was knock down or (sh) ELK1, all works was done in Johns Hopkins University / School of Medicine / Pathology Department ( MD, USA

A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network

Power system planning and expansion start with forecasting the anticipated future load requirement. Load forecasting is essential for the engineering perspective and a financial perspective. It effectively plays a vital role in the conventional monopolistic operation and electrical utility planning to enhance power system operation, security, stability, minimization of operation cost, and zero emissions. TwoWell-developed cases are discussed here to quantify the benefits of additional models, observation, resolution, data type, and how data are necessary for the perception and evolution of the electrical load forecasting in Jordan. Actual load data for more than a year is obtained from the leading electricity company in Jordan. These cases are based on total daily demand and hourly daily demand. This work’s main aim is for easy and accurate computation of week ahead electrical system load forecasting based on Jordan’s current load measurements. The uncertainties in forecasting have the potential to waste money and resources. This research proposes an optimized multi-layered feed-forward neural network using the recent Grey Wolf Optimizer (GWO). The problem of power forecasting is formulated as a minimization problem. The experimental results are compared with popular optimization methods and show that the proposed method provides very competitive forecasting results

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused people. To be able to detect such COVID-19 misinformation, an effective detection method should be applied to obtain more accurate information. This will help people and researchers easily differentiate between true and fake news. The objective of this research was to introduce an enhanced evolutionary detection approach to obtain better results compared with the previous approaches. The proposed approach aimed to reduce the number of symmetrical features and obtain a high accuracy after implementing three wrapper feature selections for evolutionary classifications using particle swarm optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained prediction results, the proposed model revealed an optimistic and superior predictability performance with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison with other state-of-the-art classifiers, our results showed that the proposed detection method with the genetic algorithm model outperformed other classifiers in the accurac