Last mile logistics represent one of the most important challenging issues in online grocery shopping. Online customers are expecting high logistical services, demanding convenience, high reliable and on-time delivery services. As such, online retailers have to respond to these expectations by providing convenient logistical services while keeping this process cost efficient as much as possible. This research aims to design an e-commerce logistical decision support system for online grocery shopping in Jordan as a case study from the developing countries. Online grocery retailers are supposed to use this model in order to select the most suitable delivery operating system in the future. To implement and evaluate this model, one of the available routing and scheduling online solutions (i.e. “My Route Online”) is used to identify, analyse, and compare the cost efficiencies of the available alternative delivery solutions. The system is tested using real data over three different delivery alternatives (i.e. home delivery, delivery point and pickup point) in order to evaluate and compare their cost efficiencies. The findings from the experiments show that there are significant differences amongst the three delivery alternatives on the basis of three KPIs: cost, distance and time. The findings also indicate that the time indicator has more powerful change effect on cost than distance for all delivery alternatives. According to the level of investments online grocery retailers are willing to offer, customer preferences, and the experimental results, it is concluded that pickup point solution is the best logistical strategy for online grocery retailers to start with.

The main aim of this study is to assess the effectiveness of using a developed asthma mobile application to enhance medication adherence in Jordan. Asthma patients visiting outpatient respiratory clinics and using inhalers were recruited. Patients were assigned into two groups: intervention and control. The intervention group was instructed to download and use the application. Asthma control was assessed using Asthma Control Test (ACT) at baseline and at follow-up of 3 months for both groups. A total of 171 patients (control, n = 83, and intervention, n = 88) participated in the study. After 3 months of usage, patients in the intervention group achieved a significant improvement in ACT score compared to control ( p-value <0.05), and reported a significant satisfaction of the application use. Therefore, the asthma mobile application is found as an effective tool to enhance medication adherence in asthma patients.

Electricity theft-induced power loss is a pressing issue in both traditional and smart grid environments. In smart grids, smart meters can be used to track power consumption behaviour and detect any suspicious activity. However, smart meter readings can be compromised by deploying intrusion tactics or launching cyber attacks. In this regard, machine learning models can be used to assess the daily consumption patterns of customers and detect potential electricity theft incidents. Whilst existing research efforts have extensively focused on batch learning algorithms, this paper investigates the use of online machine learning algorithms for electricity theft detection in smart grid environments, based on a recently proposed dataset. Several algorithms including Naive Bayes, K-nearest Neighbours, K-nearest Neighbours with self-adjusting memory, Hoeffding Tree, Extremely Fast Decision Tree, Adaptive Random Forest and Leveraging Bagging are considered. These algorithms are evaluated using an online machine learning platform considering both binary and multi-class theft detection scenarios. Evaluation metrics include prediction accuracy, precision, recall, F-1 score and kappa statistic. Evaluation results demonstrate the ability of the Leveraging Bagging algorithm with an Adaptive Random Forest base classifier to surpass all other algorithms in terms of all the considered metrics, for both binary and multi-class theft detection. Hence, it can be considered as a viable option for electricity theft detection in smart grid environments.

No abstract

No abstract