Abstract
Purpose This paper aims to examine how foreign direct investment (FDI) and firm-specific advantages (FSAs) of US multinational enterprises (MNEs) majority-owned subsidiaries affect environmental pollution in host countries. The research results contribute to helping managers and policymakers understand the environmental impact of MNEs activities, and encourage these firms to develop environmentally responsible management (ERM) as an element of their corporate social responsibility practice. Design/methodology/approach Panel data consisting of developing and developed countries spanning the years 2004 through 2014 are used. The dynamic panel generalised method of moments technique is implemented. This method avoids common estimation bias, such as endogeneity, heteroscedasticity and autocorrelation. Findings This paper finds that the direct environmental impacts of FDI vary significantly between the two groups of countries. The environmental benefits of FDI to the recipient country are achieved through capital and technology transfer. The study also reveals that R&D intensity moderates the relationship between FDI and environmental pollution in both developing and developed countries in such a way that environmental pollution decreases. Research limitations/implications - Future research could explore the environmental impact of MNEs on host countries by considering both equity and non-equity entry modes. The findings offer some support to host government policies offering generous incentive packages to attract R&D investment to improve environmental pollution. This research raises questions as to the reasons corporations operating in developing and developed countries should pursue their ERM practices. Originality/value This research examines both the direct effect of FDI and the moderating effects of FSAs on the relationship between FDI and the environment. Although previous studies have already looked at the relationship between FDI and the environment, the moderating effect of FSAs is very under-developed in this relationship.

Abstract
There is a great deal of information about caregivers’ suffering. However, there is a lack of scales in Portugal that evaluate this dimension of human behavior. The purpose of this study was to validate and adapt the Caregiver Grief Scale (CGS), developed by Franziska Meichsner, Denise Schinköthe, and Gabriele Wilz (2016) for the German population, to assess the degree of suffering of caregivers. To analyze the instrument’s psychometric properties, we used confirmatory factorial analysis with a sample of 150 caregivers (formal and informal). The results did not confirm the proposal of a 4-factor model (emotional pain, relational loss, complete loss, and loss acceptance), as proposed by the German study. Subsequently, we used the exploratory factorial analysis that suggested a unifactorial model, which confirmatory factorial analysis showed was more adequate for the study sample, with values of: ?² / df = 2.023; CFI = .970; GIF = .923; RMSEA = .083; AIC = 110.724. The internal consistency analysis indicated a Cronbach alpha of .936, and the Composite Reliability was .934. In conclusion, the German Caregiver Grief Scale multifactorial structure was not replicated in the Portuguese sample. For the Portuguese population, the caregivers suffering scale presented a unifactorial construct. In Portugal, it is recommended that future studies be carried out with a differentiated sample of caregivers.

Abstract
Smart Governence as its roots in e-government, in the principles of good governance, and in the assumptions of citizens' participation and involvement in public decision-making and is considered one of the six main characteristics of smart cities. The present investigation was intended to answer the question: 'What smart governance practices are being implemented in European smart cities' through an extensive literature review and content analysis of the websites of six European smart cities: Amsterdam; Barcelona; Copenhagen; Lisbon; Manchester and Stockholm. The objective was to identify the presence of factors related with eparticipation; e-services; and the functioning of local public administration on the city's websites. It was concluded that all the smart cities analyzed presented some factors related with smart governance, but with different levels of development and application. © 2019 AISTI.

Abstract
Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova's (SoTa), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova's, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

Abstract
This paper addresses an optimized management of a storage energy battery which is part of a microgrid with a connection to the main grid and is supplied by a photovoltaic (PV) power plant. The main contribution of this paper is to consider uncertainty in electricity price while managing the battery storage. The forecasted value for demand and PV unit are predicted by a seasonal autoregressive integrated moving average model (SARIMA)-capable of accurately characterizing both seasonality effects and tail fatness. The optimal operation of the battery is determined by resolving a linear optimization program in which the objective function comprises the conditional value at risk (CVaR). Using CVaR ensures that the demand is fully supplied while minimizing the risk and operational cost. The cost function is the difference between power sold and bought subject to the charging and discharging rates for the battery and defining upper and lower bounds for the level of battery charge. The simulation results confirm that the risk consideration has a significant effect on the optimized management of a storage energy battery in a photovoltaic grid-connected microgrid.

Abstract
We construct functional encryption (FE) schemes for the orthogonality (OFE) relation where each ciphertext encrypts some vector (Formula Presented) and each decryption key, associated to some vector (Formula Presented), allows to determine if (Formula Presented) is orthogonal to (Formula Presented) or not. Motivated by compelling applications, we aim at schemes which are function hidding, i.e. (Formula Presented) is not leaked. Our main contribution are two such schemes, both rooted in existing constructions of FE for inner products (IPFE), i.e., where decryption keys reveal the inner product of (Formula Presented) and (Formula Presented). The first construction builds upon the very efficient IPFE by Kim et al. (SCN 2018) but just like the original scheme its security holds in the generic group model (GGM). The second scheme builds on recent developments in the construction of efficient IPFE schemes in the standard model and extends the work of Wee (TCC 2017) in leveraging these results for the construction of FE for Boolean functions. Conceptually, both our constructions can be seen as further evidence that shutting down leakage from inner product values to only a single bit for the orthogonality relation can be done with little overhead, not only in the GGM, but also in the standard model. We discuss potential applications of our constructions to secure databases and provide efficiency benchmarks. Our implementation shows that the first scheme is extremely fast and ready to be deployed in practical applications.