The last years brought many novel challenges for the Internet of Things (IoT). Low capital and operational expenditures, massive deployments of devices, reliability and security are among the most crucial ones. The recently introduced Low-power wide area (LPWA) technologies provide one possible way of addressing these challenges. In the current paper, we focus on one of the most mature LPWA technology, namely Sigfox. We provide a brief security assessment of this technology and highlight the main security imperfections. Notably, we also consider the recent changes introduced in the last revision of the Sigfox specification released in the fourth quarter of 2017. Importantly, this paper discusses the highlighted issues and compares three selected cryptographic encryption solutions (AES, ChaCha and OTP) in respect to the main IoT triad of performance, security and cost. We investigate the encryption solutions and characterize their energy consumption in a real-life implementation. The results herein presented are useful for understanding the cost of enabling security aspects and enable selecting the most efficient encryption protocol. CCS CONCEPTS• Security and privacy → Block and stream ciphers;

African countries have faced competition and several challenges to attract foreign direct investment given the role that FDIs play in the development process. Several efforts made have been futile because of numerous factors that play against the business environment for foreign investments. Our paper analyses the influence of tax incentives on foreign direct investment in African economies based on data from 2000–2018. We utilized panel data on forty (40) African countries and an econometric model of four proxies of tax incentives, after controlling other variables, with robust Random Effect as our discussion estimator. Our results revealed that FDI responds to lower corporate income tax (CTR). Furthermore, foreign direct investment predominates in African economies with longer tax holidays and withholding tax. However, tax concession is insignificant to the inflows of FDIs in Africa. Summarizing, our results recommend that without proper restructuring of the tax incentives to deal with policy lapses by the governments of Africa, achieving the four main goals, i.e., poverty eradication, sustainable growth and development, African integration in the competitive global economy, and women empowerment, will be hindered.

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The paper presents new approaches to the visualisation of origin-destination flows, in which all three basic parameters of flows between pairs of geographic objects are cartographically expressed simply and clearly: the length of flows, their intensity, and the proportional distribution of both directions between pairs of objects (polarisation of flows). The data on population movements based on mobile phone location are used as the input information, which were collected from the whole territory of the Czech Republic. Apart from the visualisation of origin-destination flows, the paper addresses the issue of the transformation of these data through the application of two different interaction measures. The transformed flows are also cartographically visualised and the functional regions based on the respective interaction measures are used as base maps.

Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month to assess their corresponding predictive ability by taking maize production (silage and grain) in Czechia. We present a thorough assessment of county-level maize yield prediction in Czechia using a machine learning algorithm (extreme learning machine (ELM)) and an extensive set of weather data and maize yields from 2002 to 2018. Results show that sunshine in June and water deficit in July were vastly influential factors for silage maize yield. The two primary climate parameters for grain maize yield are minimum temperature in September and water deficit in May. The average absolute relative deviation (AARD), root mean square error (RMSE), and coefficient (R2) of the proposed models are 6.565–32.148%, 1.006–1.071%, 0.641–0.716, respectively. Based on the results, silage yield will decrease by 1.367 t/ha (3.826% loss), and grain yield will increase by 0.337 t/ha (5.394% increase) when the max temperature in May increases by 2 °C. In conclusion, ELM models show a great potential application for predicting maize yield.