Salinity of irrigation water selects distinct bacterial communities associated with date palm (Phoenix dactylifera L.) root.

Saline water irrigation has been used in date palm (Phoenix dactylifera L.) agriculture as an alternative to non-saline water due to water scarcity in hyper-arid environments. However, the knowledge pertaining to saline water irrigation impact on the root-associated bacterial communities of arid agroecosystems is scarce. In this study, we investigated the effect of irrigation sources (non-saline freshwater vs saline groundwater) on date palm root-associated bacterial communities using 16S rDNA metabarcoding. The bacterial richness, Shannon diversity and evenness didn't differ significantly between the irrigation sources. Soil electrical conductivity (EC) and irrigation water pH were negatively related to Shannon diversity and evenness respectively, while soil organic matter displayed a positive correlation with Shannon diversity. 40.5% of total Operational Taxonomic Units were unique to non-saline freshwater irrigation, while 26% were unique to saline groundwater irrigation. The multivariate analyses displayed strong structuring of bacterial communities according to irrigation sources, and both soil EC and irrigation water pH were the major factors affecting bacterial communities. The genera Bacillus, Micromonospora and Mycobacterium were dominated while saline water irrigation whereas contrasting pattern was observed for Rhizobium, Streptomyces and Acidibacter. Taken together, we suggest that date-palm roots select specific bacterial taxa under saline groundwater irrigation, which possibly help in alleviating salinity stress and promote growth of the host plant.

Ride or Not to Ride: Does the Customer Deviate toward Ridesharing?

Traditional taxi services have now been transformed into e-hailing applications (EHA) such as Uber, Careem, Hailo, and Grab Car globally due to the proliferation of smartphone technology. On the one hand, these applications provide transport facilities. On the other hand, users are facing multiple issues in the adoption of EHAs. Despite problems, EHAs are still widely adopted globally. However, a sparse amount of research has been conducted related to EHAs, particular in regards to exploring the significant factors of intention behind using EHAs Therefore, there is a need to identify influencing factors that have a great impact on the adoption and acceptance of these applications. Hence, this research aims to present an empirical study on the factors influencing customers' intentions towards EHAs. The Technology Acceptance Model (TAM) was extended with four external factors: perceived mobility value, effort expectancy, perceived locational accuracy, and perceived price. A questionnaire was developed for the measurement of these factors. A survey was conducted with 211 users of EHAs to collect data. Structural equation modeling (SEM) was used to analyze the collected data. The results of this study exposed that perceived usefulness, perceived price, and perceived ease of use affect behavior intention to use EHAs. Furthermore, perceived ease of use was impacted by effort expectancy, perceived locational accuracy, and perceived mobility. The findings of the study provide a foundation to develop new guidelines for such applications that will be beneficial for developers and designers of these applications.

A Lightweight Radio Propagation Model for Vehicular Communication in Road Tunnels.

Radio propagation models (RPMs) are generally employed in Vehicular Ad Hoc Networks (VANETs) to predict path loss in multiple operating environments (e.g. modern road infrastructure such as flyovers, underpasses and road tunnels). For example, different RPMs have been developed to predict propagation behaviour in road tunnels. However, most existing RPMs for road tunnels are computationally complex and are based on field measurements in frequency band not suitable for VANET deployment. Furthermore, in tunnel applications, consequences of moving radio obstacles, such as large buses and delivery trucks, are generally not considered in existing RPMs. This paper proposes a computationally inexpensive RPM with minimal set of parameters to predict path loss in an acceptable range for road tunnels. The proposed RPM utilizes geometric properties of the tunnel, such as height and width along with the distance between sender and receiver, to predict the path loss. The proposed RPM also considers the additional attenuation caused by the moving radio obstacles in road tunnels, while requiring a negligible overhead in terms of computational complexity. To demonstrate the utility of our proposed RPM, we conduct a comparative summary and evaluate its performance. Specifically, an extensive data gathering campaign is carried out in order to evaluate the proposed RPM. The field measurements use the 5 GHz frequency band, which is suitable for vehicular communication. The results demonstrate that a close match exists between the predicted values and measured values of path loss. In particular, an average accuracy of 94% is found with R2 = 0.86.

Critical product features' identification using an opinion analyzer.

The increasing use and ubiquity of the Internet facilitate dissemination of word-of-mouth through blogs, online forums, newsgroups, and consumer's reviews. Online consumer's reviews present tremendous opportunities and challenges for consumers and marketers. One of the challenges is to develop interactive marketing practices for making connections with target consumers that capitalize consumer-to-consumer communications for generating product adoption. Opinion mining is employed in marketing to help consumers and enterprises in the analysis of online consumers' reviews by highlighting the strengths and weaknesses of the products. This paper describes an opinion mining system based on novel review and feature ranking methods to empower consumers and enterprises for identifying critical product features from enormous consumers' reviews. Consumers and business analysts are the main target group for the proposed system who want to explore consumers' feedback for determining purchase decisions and enterprise strategies. We evaluate the proposed system on real dataset. Results show that integration of review and feature-ranking methods improves the decision making processes significantly.