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In the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) are designed to provide low energy consumption while maintaining a long communications’ range for End Devices (EDs). LoRa is a communication protocol that can cover a wide range with low energy consumption. To evaluate the efficiency of the LoRa Wide-Area Network (LoRaWAN), three criteria can be considered, namely, the Packet Delivery Rate (PDR), Energy Consumption (EC), and coverage area. A set of transmission parameters have to be configured to establish a communication link. These parameters can affect the data rate, noise resistance, receiver sensitivity, and EC. The Adaptive Data Rate (ADR) algorithm is a mechanism to configure the transmission parameters of EDs aiming to improve the PDR. Therefore, we introduce a new algorithm using the Multi-Armed Bandit (MAB) technique, to configure the EDs’ transmission parameters in a centralized manner on the Network Server (NS) side, while improving the EC, too. The performance of the proposed algorithm, the Low-Power Multi-Armed Bandit (LP-MAB), is evaluated through simulation results and is compared with other approaches in different scenarios. The simulation results indicate that the LP-MAB’s EC outperforms other algorithms while maintaining a relatively high PDR in various circumstances.
Germany is considered a role model for dealing with past mass atrocities. In particular, the social reappraisal of the Holocaust is emblematic of this. However, when considering the genocide on the Herero and Nama in present-day Namibia, it is puzzling that an official recognition was only pronounced after almost 120 years, in May 2021. For a long time, silence surrounded this colonial cruelty in German political discourse. Although the discourse on German responsibility toward Namibia emerged after the end of World War II, it initially appeared detached from the genocide. That silence on colonial atrocities is to be considered a cruelty itself. Studies on silence have been expanding and becoming richer. Building on these works, the paper sets two goals: First, it advances the theorization of silence by producing a new typology, which is then integrated into discourse-bound identity theory. Second, it applies this theory to the analysis of the silencing and later acknowledging of the genocide on the Herero and Nama by German political elites. To this end, Bundestag debates, official documents, and statements by relevant political actors are analyzed in the period from 1980 to 2021. The results reveal the dynamics between hegemonic and counter-hegemonic discursive formations, how those are shifting in a period of 40 years, and what role silence plays in it. Beyond our emphasis on the genocide on the Herero and Nama, our findings might benefit future studies as the approach proposed in this paper can make silence a tangible research object for global studies.
The power demand (kW) and energy consumption (kWh) of data centers were augmenteddrastically due to the increased communication and computation needs of IT services. Leveragingdemand and energy management within data centers is a necessity. Thanks to the automated ICTinfrastructure empowered by the IoT technology, such types of management are becoming more feasiblethan ever. In this paper, we look at management from two different perspectives: (1) minimization of theoverall energy consumption and (2) reduction of peak power demand during demand-response periods.Both perspectives have a positive impact on total cost of ownership for data centers. We exhaustivelyreviewed the potential mechanisms in data centers that provided flexibilities together with flexiblecontracts such as green service level and supply-demand agreements. We extended state-of-the-artby introducing the methodological building blocks and foundations of management systems for theabove mentioned two perspectives. We validated our results by conducting experiments on a lab-gradescale cloud computing data center at the premises of HPE in Milano. The obtained results support thetheoretical model, by highlighting the excellent potential of flexible service level agreements in Green IT:33% of overall energy savings and 50% of power demand reduction during demand-response periods inthe case of data center federation.
After the enactment of the GDPR in 2018, many companies were forced to rethink their privacy management in order to comply with the new legal framework. These changes mostly affect the Controller to achieve GDPR-compliant privacy policies and management.However, measures to give users a better understanding of privacy, which is essential to generate legitimate interest in the Controller, are often skipped. We recommend addressing this issue by the usage of privacy preference languages, whereas users define rules regarding their preferences for privacy handling. In the literature, preference languages only work with their corresponding privacy language, which limits their applicability. In this paper, we propose the ConTra preference language, which we envision to support users during privacy policy negotiation while meeting current technical and legal requirements. Therefore, ConTra preferences are defined showing its expressiveness, extensibility, and applicability in resource-limited IoT scenarios. In addition, we introduce a generic approach which provides privacy language compatibility for unified preference matching.
Der aus Malta stammende Jesuit Tony Mifsud, Professor für Moraltheologie an der Universität Alberto Hurtado (Santiago de Chile), legte 1984 erstmals ein vierbändiges Werk unter dem Titel „Moral de Discernimiento“ vor. Fast zwei Jahrzehnte lang überarbeitet er dieses umfangreiche Konzept und publizierte es in aktualisierten Neuauflagen. Die „Moral de Discernimiento“ gilt als der erste Versuch, die Ideen der Theologie der Befreiung in eine Gesamtsystematik der Moraltheologie zu integrieren.
Der jüdische Philosoph Hans Jonas legte 1979 mit „Das Prinzip Verantwortung. Versuch einer Ethik für die technologische Zivilisation“ eines der am meisten gelesenen moralphilosophischen Bücher der Nachkriegszeit vor. Er reagiert damit auf die Tatsache, dass „die Verheißung der modernen Technik in Drohung umgeschlagen ist oder diese sich mit jener unlösbar verbunden hat“. Der Beitrag beleuchtet mit dem Abstand von drei Jahrzehnten die zentralen Thesen und die bleibende Bedeutung dieses Entwurfs.
Bei dem Forschungsprojekt, auf das sich diese Datendokumentation bezieht, geht es um die Erfassung des Rezeptionsverhaltens im Museum. Dazu wurden Probanden gebeten, während des Besuchs der ersten sieben Räume im Museum Veste Oberhaus in Passau die Gedanken zu verbalisieren, die ihnen beim Betrachten der Objekte durch den Kopf gingen. Die Verbaltranskripte der Tonaufnahmen zum Lauten Denken sind hier abgedruckt und stehen der Forschung zur Verfügung zusammen mit den Fotografien der Museumsobjekte aus diesen Räumen.
Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that educa-tion lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper con-cludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education.
Prior to the emergence of Big Data and technologies such as Learning Analytics (LA), classroom research focused mainly on measuring learning outcomes of a small sample through tests. Research on online environments shows that learners’ engagement is a critical precondition for successful learning and lack of engagement is associated with failure and dropout. LA helps instructors to track, measure and visualize students’ online behavior and use such digital traces to improve instruction and provide individualized support, i.e., feedback. This paper examines 1) metrics or indicators of learners’ engagement as extracted and displayed by LA, 2) their relationship with academic achievement and performance, and 3) some freely available LA tools for instructors and their usability. The paper concludes with making recommendations for practice and further research by considering challenges associated with using LA in classrooms.
The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs.
This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70%
for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%.