Reducing the Highway Networks Energy Bills using Renewable Energy System

Jordan has significant renewable energy potential due to its remarkable geographical location and climate conditions. This potential elevates engaging several innovative renewable alternatives in energy development, which may efficiently minimize the excessive import of traditional energy sources. The objective of this research is to study the potential of utilizing clean and affordable solar energy along roadways such as Jordan’s Desert Highway-15 to be in line with the United Nations Sustainable Development Goals (UN-SDG’s) by installing selected solar panels that possess adequate friction and the ability to allow solar radiation to reach the solar cells, in addition to allowing the load to be bypassed around the cells. The shoulder of the highway, with a length of 315 km and a width of 3.0 meters, has been exploited in order to supply the neighboring areas with energy for those roads, particularly those paved roads, which are poorly lit at night. Furthermore, this study provides direction and guidance concerning the structural performance of non-traditional pavement materials, which are a form of subgrade or pavement reinforcement. The performance of a prototype board on a variety of structural bases has also been evaluated. Overall, this paper found that it is possible to design a solar road panel to withstand traffic loading and that the concrete structural base allows for a significant improvement of the analyzed prototype design, especially in countries with limited energy sources and dependent on imports such as Jordan. Doi: 10.28991/CEJ-2023-09-11-019 Full Text: PD

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Detection of Online Phishing Email using Dynamic Evolving Neural Network Based on Reinforcement Learning

Despite state-of-the-art solutions to detect phishing attacks, there is still a lack of accuracy for the detection systems in the online mode which leading to loopholes in web-based transactions. In this research, a novel framework is proposed which combines a neural network with reinforcement learning to detect phishing attacks in the online mode for the first time. The proposed model has the ability to adapt itself to produce a new phishing email detection system that reflects changes in newly explored behaviours, which is accomplished by adopting the idea of reinforcement learning to enhance the system dynamically over time. The proposed model solve the problem of limited dataset by automatically add more emails to the online dataset in the online mode. A novel algorithm is proposed to explore any new phishing behaviours in the new dataset. Through rigorous testing using the well-known data sets, we demonstrate that the proposed technique can handle zero-day phishing attacks with high performance levels achieving high accuracy, TPR, and TNR at 98.63%, 99.07%, and 98.19% respectively. In addition, it shows low FPR and FNR, at 1.81% and 0.93% respectively. Comparison with other similar techniques on the same dataset shows that the proposed model outperforms the existing methods

Detection of online phishing email using dynamic evolving neural network based on reinforcement learning

Phishing has been the most frequent cybercrime activity over the last 15 years and has caused billions of dollars to be stolen. This happens due to the fact that phishing attackers always use new (zero-day) and sophisticated techniques to deceive online customers. The most common way to initiate a phishing attack is by using email. In this thesis, a novel framework is proposed that combines a neural network with reinforcement learning for detecting online phishing attacks. This thesis addresses the effectiveness of phishing email detection, and it makes the following contributions. Firstly, a novel pre-processing system has been designed to gather and extract the features and patterns of phishing email. To cover all behaviour that phishers use to deceive online customers, fifty features were selected. Pre-processing is divided into three layers, based on the main types of email content. Secondly, a novel algorithm has been proposed for the exploration of new phishing behaviour. The proposed algorithm has the ability to select the effective list of features from the list of features extracted in the pre-processing phase. Thirdly, this thesis proposed a novel Dynamic Evolving Neural Network using Reinforcement Learning (DENNuRL) algorithm, which can be used to generate the best neural network for classification problem based on reinforcement learning idea. Finally, a novel framework for Phishing Email Detection System (PEDS) has been proposed. The PEDS has the ability to adapt itself to generate a new PEDS that reflects changes in behaviour. Therefore, reinforcement learning is adopted in the proposed framework combined with neural network to enhance the system dynamically over time in the online mode. The proposed technique can effectively handle zero-day phishing attacks. The proposed phishing email detection model was trained, tested and validated in the online mode using an approved dataset. The promising results showed that the DENNuRL can provide an effective means of phishing detection. The proposed model successfully classified and identified approximately 98.6% of phishing emails selected from the test dataset, with low false positive rates of 1.8%. A comparison with other similar techniques using the same dataset shows that the proposed technique outperforms the existing methods

The marketing of urban bus services in a developing country: the case of Greater Amman

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Detection of phishing emails using data mining algorithms

This paper proposes an intelligent model for detection of phishing emails which depends on a preprocessing phase that extracts a set of features concerning different email parts. The extracted features are classified using the J48 classification algorithm. We experimented with a total of 23 features that have been used in the literature. Ten-fold cross-validation was applied for training, testing and validation. The primary focus of this paper is to enhance the overall metrics values of email classification by focusing on the preprocessing phase and determine the best algorithm that can be used in this field. The results show the benefits of using our preprocessing phase to extract features from the dataset. The model achieved 98.87% accuracy for the random forest algorithm, which is the highest registered so far for an approved dataset. A comparison of ten different classification algorithms demonstrates their merits and capabilities through a set of experiments