Mitigating the carbon footprint in Jordan by designing an artificial lake system to improve domestic tourism

This work investigates mitigating the carbon footprint in Jordan by altering the travel patterns of the population. This goal can be accomplished by creating a new tourist destination that has a marine environment, similar to overseas destinations. Based on recent data, it is estimated that half a million Jordanians would prefer a domestic destination instead of traveling abroad, provided that it has a marine environment. This shift in travel preferences would result in an estimated reduction of 320,000 tons of CO2 emissions every year. The proposed new destination is an artificial lake created in the Jafer basin, located in southeastern Jordan. The area has a natural depression that is suitable for this purpose. The proposal involves excavating a 140 km-long tunnel connecting the Gulf of Aqaba to the Jafer basin, where seawater is pumped into the basin to create the lake, with a water volume of 2000 MCM. The design is based on keeping the water's level steady in the lake. The proposed design is shown to be feasible, with an optimal tunnel diameter of 4 m, excavated at constant depth. In order to maintain a uniform water pressure, 15 pumping stations should be installed along the tunnel. The total cost of the project is estimated at 5.5 B$. This cost is not only used for mitigating carbon footprint but also for increasing the real estate value of the area around the lake, and a more healthy distribution of population in the country

Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models

The paper is devoted to new mathematical tools for ionospheric parameter analysis and anomaly discovery during ionospheric perturbations. The complex structure of processes under study, their a-priori uncertainty and therefore the complex structure of registered data require a set of techniques and technologies to perform mathematical modelling, data analysis, and to make final interpretations. We suggest a technique of ionospheric parameter modelling and analysis based on combining the wavelet transform with autoregressive integrated moving average models (ARIMA models). This technique makes it possible to study ionospheric parameter changes in the time domain, make predictions about variations, and discover anomalies caused by high solar activity and lithospheric processes prior to and during strong earthquakes. The technique was tested on critical frequency foF2 and total electron content (TEC) datasets from Kamchatka (a region in the Russian Far East) and Magadan (a town in the Russian Far East). The mathematical models introduced in the paper facilitated ionospheric dynamic mode analysis and proved to be efficient for making predictions with time advance equal to 5 hours. Ionospheric anomalies were found using model error estimates, those anomalies arising during increased solar activity and strong earthquakes in Kamchatka