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This paper summarizes the 18th Academic Publishing in Europe (APE) Conference: Berlin Re-Visited: Building Technological Support for Scholarship and Scientific Publishing, held as a hybrid event 10 and 11 January 2023, and organized by the Berlin Institute of Scholarly Publishing (BISP), a not-for-profit organization dedicated to bringing publishers, researchers, funders, and policymakers together. This year's conference theme "Out with the old, in with the new!" was discussed and presented in keynote speeches, the APE lecture, and several panel discussions. Current challenges within scholarly publishing, e.g., with research integrity, trust, and research assessment, have much to do with the old ways of doing things. To move science forward, new technologies and innovations, like the decentralized web, FAIR digital objects, and blockchain technology are needed to shape new paradigms. In many sessions it was stressed that not just new technologies are needed to move science forward, but human collaboration and partnerships as well. The changing role of the journal and the importance to recognize more diverse research outputs, beyond the journal article, was a topic of importance. Not only research assessment reforms and improved collaboration amongst different stakeholder groups are needed to address this, new publishing systems, better metadata, and open infrastructures as well. A session presenting different start-ups showcased how Artificial Intelligence, Natural Language Processing and software technology can be used to tackle problems in e.g., finding relevant funders and peer reviewers, and detecting image plagiarism. It was also discussed what publishers can do to help achieve the Sustainable Development Goals. Collaboration, data transparency and sharing best practices amongst researchers, funders, and policy makers are key. Another important topic during this year's APE was how publishers can support Early Career Researchers: establishing new workflows and infrastructures that enable publication of a wider range of research outputs, and broader recognition of these outputs, will incentivize ECRs. This year's APE was concluded with the APE Award Ceremony. The winner - Vsevolod Solovyov from Prophy - has used AI to enhance the current peer review system and made a very important contribution to improving the scholarly communication system. [ABSTRACT FROM AUTHOR]

A rich academic understanding of the relation between e-governance and sustainable development is crucial for studying the current 'twin transition', i.e. the connection between the digital and sustainability transition. An analysis of current literature highlights that there is a strong emphasis on the question how e-governance can be an instrument for realizing sustainable development. This research is important but does not cover all relevant problem areas. Our conceptual analysis extends the dominant focus on the digital and sustainable transitions to include destructive connections – the environmental costs of e-government – and an institutional perspective on e-governance. Developing multiple perspectives on the twin transition is crucial since the current, seemingly unconditional, faith in the power of technology, may hamper a fundamental debate on the contribution of e-governance to sustainable development. This paper concludes with a research agenda that stresses the need to study three key issues: (1) green e-governance as mitigating the environmental costs of e-governance, (2) e-governance as a barrier for the sustainability transition, and (3) the ecology logic as a basis for shaping the twin transition. [ABSTRACT FROM AUTHOR]

This document introduces the special section on digital government and sustainable development goals (SDGs) in the journal "Information Polity." It highlights the potential benefits of digital government, such as efficiency gains, improved service provision, and increased citizen engagement. The document emphasizes the urgent need to address sustainability challenges and the role of digital government in achieving the 17 SDGs outlined by the United Nations. It also calls for more research in this area, including exploring the links between digital government and each SDG, expanding theoretical depth, and developing benchmarking indicators for sustainability. The document includes two selected papers that provide conceptual analyses and highlight the importance of considering environmental impacts and digital literacy in digital government initiatives for sustainable development. Overall, the document emphasizes the need for innovative and well-informed research on the relationship between digital government and the SDGs. [Extracted from the article]

The datification processes have driven National Statistical Institutes (NSIs) to explore new data sources with the goal of enhancing the quality of statistical information. The creation of innovative statistical products, known as Trusted Smart Statistics (TSS), necessitates new production processes that integrate with traditional methods, maintaining levels of relevance, quality, and trust.To facilitate this transition in statistical production, NSIs must embark on change management initiatives involving all organizational dimensions engaged in statistical processes. In the scientific debate, sustainability has assumed a broad connotation. This paper delves into the organizational aspect, viewing sustainability as a paradigm that encompasses all organizational areas and their interactions crucial to supporting the shift to new statistical production models, without compromising traditional practices.The paper explores the path taken by Istat in this direction, the organizational solutions implemented, the benefits realized, and investments in research and innovation initiated years ago on new data sources, aligning with developments in the European statistical community. Istat has been at the forefront of big data experimentation in Europe for over a decade, implementing a modernization program and establishing research infrastructures. Istat took a significant step by founding the TSS Center to guide organizational adaptation at technological, methodological, legal, communication, and human resources levels toward the new production system. [ABSTRACT FROM AUTHOR]

BACKGROUND: Waste production and management from residents and collection for sorting are systems heavily dependent on territorial dimensions. Ergonomic research needs to better integrate such territorial determinants to improve work conditions and design sustainable work systems. OBJECTIVE: Through studies in France and Brazil, this paper analyzes the territorial relations that raise work systems' sustainability challenges for materials recovery facilities (MRFs) and waste management in both countries and examines the links between work activity and territory in MRFs. METHODS: Both studies were based on the principles of activity-oriented ergonomics and used analyses of work activity and sociotechnical systems. The French study focused on interventions conducted over a 42-month period in five MRFs. The Brazilian study was based on a 20-month longitudinal qualitative and quantitative study. RESULTS: In this paper, we argue that territory is a key determining factor in waste production and work. Notably, the consumption patterns of residents and the economic flows within a geographic space determine the waste composition; and the territorially specified public policies, which define technical and social dimensions of waste collection and sorting. However, the territorial dimensions of waste are poorly considered in facility design. Workers' health and sorting system performance are thus affected and negatively compromising plant performance. CONCLUSION: The territory appears as a blind spot in the design of work systems. One of the challenges is to create interfaces and devices that could help to integrate better human activity and waste territorialized anchorage, in a multilevel organization, from local communities to the global recycling chains. [ABSTRACT FROM AUTHOR]

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. [ABSTRACT FROM AUTHOR]

Selecting a sustainable facility location is a crucial strategy for manufacturing companies to achieve long-term success in today's competitive environment. Various quantitative and qualitative criteria with different importance in a multiple level structure must be considered and aggregated to assist the company in decision-making. How to determine these criteria weights and select the sustainable manufacturing facility locations have become research questions. To resolve this problem, this paper proposes a total distance ranking approach to fuzzy analytic hierarchical process (AHP)-based multiple criteria decision-making (MCDM) method. Currently, the membership functions of fuzzy weighted ratings in the MCDM with a multilevel structure cannot be developed. A ranking method is needed to defuzzify those fuzzy numbers used for fuzzy AHP and qualitative criteria in the MCDM for better executing decision-making procedure. The total distance ranking method related to the centroid on x-axis, centroid on y-axis and the distances of centroids point to the two tangent lines of a fuzzy number are considered in the proposed ranking approach. Formulas of the proposed approach will be presented, and some properties will be investigated to derive formulas for trapezoidal and triangular fuzzy numbers. A comparison with relevant ranking methods will be made to show the advantages of the proposed ranking approach. The proposed ranking approach is then applied to defuzzify the fuzzy numbers used in fuzzy AHP and linguistic values under qualitative criteria to obtain the criteria weights under multi-level structure and crisp values under qualitative criteria, respectively. The final scores of alternatives can be obtained by aggregating crisp criteria values and their corresponding weights by simple additive weighting method to obtain the ranking result. A numerical example will be conducted to show the effectiveness of the proposed model. Finally, a comparison with Best-Worst method (BWM) will be presented to show the persuasiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Energy sustainability indicators are essential for evaluating and measuring energy systems' environmental, social, and economic impact. These indicators can be used to assess the sustainability of different energy sources, such as renewable or fossil fuels, as well as the performance of energy systems in various regions or countries. The goal of this paper is to propose a new energy sustainability index based on fuzzy logic for the Amazon region. The fuzzy inference system enabled the operationalization of subjective sustainability concepts, resulting in a final index that can evaluate the performance of the states in the Legal Amazon and compare them to each other. The results indicated that Mato Grosso had the highest ranking, followed by Tocantins, Amapá, Roraima, Rondônia, Pará, Acre, Maranhão, and Amazonas in the last position. These findings demonstrate that the selected indicators and the final index are effective tools for evaluating the energy sustainability of the Amazon region and can aid public managers in making decisions and proposing sustainable regional development policies for the region. [ABSTRACT FROM AUTHOR]

The worldwide generation of waste electrical and electronic equipment is continuously growing, with electric vehicle batteries reaching their end-of-life having become a key concern for both the environment and human health in recent years. In this context, the proliferation of Internet of Things standards and data ecosystems is advancing the feasibility of data-driven condition monitoring and remanufacturing. This is particularly desirable for the end-of-life recovery of high-value equipment towards sustainable closed-loop production systems. Low-Power Wide-Area Networks, despite being relatively recent, are starting to be conceived as key-enabling technologies built upon the principles of long-range communication and negligible energy consumption. While LoRaWAN is considered the open standard with the highest level of acceptance from both industry and academia, it is its random access protocol (Aloha) that limits its capacity in large-scale deployments to some extent. Although time-slotted scheduling has proved to alleviate certain scalability limitations, the constrained nature of end nodes and their application-oriented requirements significantly increase the complexity of time-slotted network management tasks. To shed light on this matter, a multi-agent network management system for the on-demand allocation of resources in end-of-life monitoring applications for remanufacturing is introduced in this work. It leverages LoRa's spreading factor orthogonality and network-wide knowledge to increase the number of nodes served in time-slotted monitoring setups. The proposed system is validated and evaluated for end-of-life monitoring where two representative end-node distributions were emulated, with the achieved network capacity improvements ranging from 75.27% to 249.46% with respect to LoRaWAN's legacy operation. As a result, the suitability of different agent-based strategies has been evaluated and a number of lessons have been drawnaccording to different application and hardware constraints. While the presented findings can be used to further improve the explainability of the proposed models (in line with the concept of eXplainable AI), the overall framework represents a step forward in lightweight end-of-life condition monitoring for remanufacturing. [ABSTRACT FROM AUTHOR]