Abstract
Robust wireless communication networks are a cornerstone of the modern world, allowing data to be transferred quickly and reliably. Establishing such a network at sea, a Maritime Internet of Things (MIoT), would enhance services related to safety and security at sea, environmental protection, and research. However, given the remote and harsh nature of the sea, installing robust wireless communication networks with adequate data rates and low cost is a difficult endeavor. This paper reviews recent MIoT systems developed and deployed by researchers and engineers over the past few years. It contains an analysis of short-range and long-range over-the-air radio-frequency wireless communication protocols and the synergy between these two in the pursuit of an MIoT. The goal of this paper is to serve as a go-to guide for engineers and researchers that need to implement a wireless sensor network at sea. The selection criterion for the papers included in this review was that the implemented wireless communication networks were tested in a real-world scenario.

Abstract
This research aims to represent a novel approach to detect malicious nodes in Ad-hoc On-demand Distance Vector (AODV) within the next-generation smart cities. Smart city applications have a critical role in improving public services quality, and security is their main weakness. Hence, a systematic multidimensional approach is required for data storage and security. Routing attacks, especially sinkholes, can direct the network data to an attacker and can also disrupt the network equipment. Communications need to be with integrity, confidentiality, and authentication. So, the smart city and urban Internet of Things (IoT) network, must be secure, and the data exchanged across the network must be encrypted. To solve these challenges, a new protocol using CLustering Multi-Layer Security Protocol (CL-MLSP) with AODV has been proposed. The Advanced Encryption Standard (AES) algorithm is aligned with the proposed protocol for encryption and decryption. The shortest path is obtained by the clustering method based on energy, mobility, and distribution for each node. Ns2 is used to evaluate the CL-MLSP performance, and the parameters are network lifetime, latency, packet loss, and security. We have compared CL-MLPS with ECP-AODV, Probe, and Multi-Path. The proposed method superiority rates in energy consumption, drop rate, delay, throughput, and security performance are 6.54%, 12.87%, 8.12%, 9.46%, respectively.

Abstract
There is an increasing number of academic publications on studying the impact of the gig economy and digital platforms. Some of them involve entrepreneurship and business models. However, there is a lack of a global picture depicting the scientific structure of knowledge regarding the gig economy and entrepreneurship. This paper presents a conceptual, intellectual, and social bibliometric overview, using Bibliometrix and Biblioshiny (R-packages). To this end, total of 345 published articles were analyzed, covering 245 sources, 44 countries and 751 authors. There are several important findings: five main clusters emerged from the study (Self-employment and social economy; Sharing economy and sustainable development; Entrepreneurship and innovation; Gig economy and platform economy; and Digitalization); the main themes that emerge deal with sharing, gig, and platform economy, digitalization, teleworking, career participation and platforms; finally, gig workers are key for developing strategies, policies, and actions to achieve a social welfare through entrepreneurship in the platform ecosystem. It is also important to highlight the role of communities and social capital in the development of sustainable collaborative initiatives through digital entrepreneurship.

Abstract
With the evolution of technology and all the associated paradigms, the business reality had the need to adapt and incorporate all this evolution. Gamification strategies in social networks are used to optimize the use and involvement of users. Thus, in a business context, these strategies are used to increase user engagement in the company/brand's social networks with the aim of increasing and strengthening trust. This chapter's main objectives are to understand if internet users and social media users know the term gamification and if the associated strategies are perceptible in the social media context. Therefore, to better analyze the users' behavior regarding these strategies and their knowledge about gamification, an online survey was developed and applied. After the survey, it was possible to conclude that 16.3% of the respondents already knew the term gamification. Through the survey it was also possible to conclude that most respondents had already been in contact with gamification strategies, even if 90.6% did not adhere to them frequently.

Abstract
Deep Learning (DL) based classification algorithms have been shown to achieve top results in clinical diagnosis, namely with lung cancer datasets. However, the complexity and opaqueness of the models together with the still scant training datasets call for the development of explainable modeling methods enabling the interpretation of the results. To this end, in this paper we propose a novel interpretability approach and demonstrate how it can be used on a malignancy lung cancer DL classifier to assess its stability and congruence even when fed a low amount of image samples. Additionally, by disclosing the regions of the medical images most relevant to the resulting classification the approach provides important insights to the correspondent clinical meaning apprehended by the algorithm. Explanations of the results provided by ten different models against the same test sample are compared. These attest the stability of the approach and the algorithm focus on the same image regions.

Abstract
The objective of this article is to analyze and empirically validate the differential effects in the daily schedules of the induced electricity prices by selling bids for three different technologies, namely hydraulic, thermal and renewable energy sources (RES), in hourly values, by daily observations for the year 2018. To achieve this objective, we employ an autoregressive distributed lag (ARDL) model-bound testing approach The results of the ADRL-ECM method, which also reports the long-run analysis, show that (a) the renewable and thermal technologies positively and significantly affect the electricity price for Endesa and Hidroeletrica del Cantabrico generators and (b) the hydraulic technology impacts negatively the electricity price, both at a 1% level of significance. In addition, following a long-term perspective it must be highlighted that RES negatively impact the price of electricity with a 1% level of significance for the Iberdrola, E.ON Energy, Union Fenosa and EDP Energy of Portugal generators. However based on a short-term perspective, the results report a positive effect between the quantities traded by hydraulic and thermal technologies on the electricity price for Endesa, Iberdrola, Hidroeletrica del Cantabrico and EDP Energy of Portugal, at a 1% level of significance. (C) 2022 The Author(s). Published by Elsevier Ltd.