Search Results (59)

This article provides a detailed analysis of non-invasive techniques for the prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state of the art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to improve fault diagnosis, in addition to highlighting the crucial role of non-invasive techniques such as the digital twin in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop the integration of AI algorithms with digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques. Full article

This paper presents a novel approach to sustainable urban revitalization. Care Spaces are defined, and an area selected for revitalization is described. Transformation of city space is of fundamental importance for everyday life, its comfort as regards the functional aspect as well as the psychological and cultural ones. The presented projects are in accord with 2030 Agenda for Sustainable Development and the conception of Baukultur. Both approaches tend to create well designed environment that support health and well-being of people and other living creatures while taking into account cultural aspects in design and construction. Focusing on the combination of soundscape analysis with design elements. To monitor the soundscape, a custom database of urban sound recordings was constructed, and key analytical methods such as Mel-Frequency Cepstral Coefficients (MFCC), feature extraction, Recurrent Neural Networks (RNN), and permutation of feature importance were applied. The effectiveness of these algorithms was confirmed through field investigations. Full article

The 2030 Agenda for Sustainable Development emphasizes the interconnectedness of its economic, social, and environmental dimensions, recognizing their essential role in promoting human well-being. This study provides an in-depth analysis of EU Member States’ progress towards Sustainable Development Goal (SDG) 15—Life on Land—as outlined in the 2030 Agenda. Using official data from Eurostat, this study applies the AAA (Holt–Winters) exponential smoothing algorithm to analyze trends in key indicators from 2011 to 2021 and project these trends to 2030. The results reveal notable progress in the first years since the adoption of the 2030 Agenda but also highlights drought and soil erosion as escalating risks, particularly in Mediterranean regions and areas of intensive agriculture (Spain, Cyprus, Greece). Water quality emerges as a critical concern, and, alongside the ongoing rise in soil sealing, presents an added threat to ecological stability, agricultural productivity, and overall well-being. Full article

The architecture, engineering, construction, and operations (AECO) sector exerts a considerable influence on energy consumption and CO₂ emissions released into the atmosphere, making a notable contribution to climate change. It is therefore imperative that energy efficiency in buildings is prioritized in order to reduce environmental impacts and meet the targets set out in the European 2030 Agenda. In this context, renewable energy communities (RECs) have the potential to play an important role, promoting the use of renewable energy at the local level, optimizing energy management, and reducing consumption by sharing resources and advanced technologies. This paper introduces an open tool (OT) designed for the configuration of energy systems dedicated to RECs. The OT considers several inputs, including thermal and electrical loads, energy consumption, the type of building, surface area, and population size. The OT employs artificial intelligence (AI) algorithms and machine learning (ML) techniques to generate forecast optimized scenarios for the sizing of photovoltaic systems, thermal, and electrical storage, and the estimation of CO₂ emission reductions. The OT features a user-friendly interface, enabling even non-experts to obtain comprehensive configurations for RECs, aiming to accelerate the transition toward sustainable and efficient district energy systems, driving positive environmental impact and fostering a greener future for communities and cities. Full article

This paper examines the use of algorithms and non-algorithmic models in mathematics and science, especially in biology, during the past century by summarizing the gradual development of a conceptual rationale for non-algorithmic models in biology. First, beginning a century ago, mathematicians found it impossible to constrain mathematics in an algorithmic straitjacket via öö’s Incompleteness Theorems, so how would it be possible in biology? By the 1930s, biology was resolutely imitating classical physics, with biologists enforcing a reductionist agenda to expunge function, purpose, teleology, and vitalism from biology. Interestingly, physicists and mathematicians often understood better than biologists that mathematical representations of living systems required different approaches than those of dead matter. Nicolas Rashevsky, the Father of Mathematical Biology, and Robert Rosen, his student, pointed out that the complex systems of life cannot be reduced to machines or mechanisms as per the Newtonian paradigm. Robert Rosen concluded that living systems are not amenable to algorithmic models that are primarily syntactical. Life requires semantics for its description. Rashevsky and Rosen pioneered Relational Biology, initially using Graph Theory to model living systems. Later, Rosen created a metabolic–repair model (M, R)-system using Category Theory to encode the basic entailments of life itself. Although reductionism still dominates in current biology, several subsequent authors have built upon the Rashevsky–Rosen intellectual foundation and have explained, extended, and explored its ramifications. Algorithmic formulations have become increasingly inadequate for investigating and modeling living systems. Biology is shifting from a science of simple systems to complex ones. This transition will only be successful once mathematics fully depicts what it means to be alive. This paper is a call to mathematicians from biologists asking for help in doing this. Full article

The United Nations 2030 Agenda defines the priorities and aspirations for global development based on seventeen ambitious sustainable development goals encompassing economic, environmental, and social dimensions. Tourism plays a vital role in the list of actions for the people and the planet. While the tourism industry drives economic growth, its environmental and social impact is equally high. Sustainable tourism aims to reduce the damage caused by the tourism industry, protect communities, and guarantee the industry’s long-term future. These changes require tourists’ collective and concerted effort. The question arises whether tourists are willing to be more demanding about sustainability when looking for a destination. This study uses artificial intelligence to classify a new trend in European citizens’ search for sustainable destinations and to generate intelligent recommendations. Using data from the Flash Eurobarometer 499, we use a tree-based algorithm, random forest, to obtain intelligent citizens classification systems supported by machine learning. The classification system explores the predisposition of citizens to contribute to the three pillars of sustainability when choosing a destination to visit based on gender, age, and the region of living. We found that European citizens place little emphasis on the social sustainability pillar. While they care about preserving the environment, this competes with the cultural offerings and availability of activities at the destination. Additionally, we found that the willingness to contribute to the three pillars of sustainability varies by gender, age, and European region. Full article

This study explored the transformative potential of generative artificial intelligence (GAI) for achieving the UN Sustainable Development Goal on Quality Education (SDG4), emphasizing its interconnectedness with the other SDGs. A proprietary algorithm and cocitation network analysis were used to identify and analyze the network of SDG features in GAI research publications (n = 1501). By examining GAI’s implications for ten SDG4 targets, the findings advocate for a collaborative, ethical approach to integrating GAI, emphasizing policy and practice developments that ensure that technological advancements align with the overarching goals of SDG4. The results highlight the multifaceted impact of GAI on the SDGs. First, this paper outlines a framework that leverages GAI to enhance educational equity, quality, and lifelong learning opportunities. By highlighting the synergy between GAI and the SDGs, such as reducing inequalities (SDG10) and promoting gender equality (SDG5), this study underscores the need for an integrated approach to utilizing GAI. Moreover, it advocates for personalized learning, equitable technology access, adherence to ethical AI principles, and fostering global citizenship, proposing a strategic alignment of GAI applications with the broader SDG agenda. Next, the results highlight that GAI introduces significant challenges, including ethical concerns, data privacy, and the risk of exacerbating the digital divide. Overall, our findings underscore the critical role of policy reforms and innovative practices in navigating the challenges and harnessing the opportunities presented by GAI in education, thereby contributing to a comprehensive discourse on technology’s role in advancing global education and sustainable development. Full article

This article introduces a methodology for a novel data-driven computational model aimed at aiding public administrations in managing and evaluating the adaptative reuse of buildings while tackling ecological and digital challenges. Drawing from the 2030 Agenda for Sustainable Development, the study underscores the significance of innovative approaches in harnessing the economic potential of data. Focusing on Barcelona’s Ciutat Vella district, the research selects five historic public buildings for analysis, strategically positioned to spur local entrepreneurship and counteract tourism dominance. Through an extensive literature review, the article identifies a gap in computational models for building adaptative reuse and proposes a methodological framework that integrates data collection, processing, and computational modelling, underscored by GIS technology and open data sources. The proposed methodology for a computational algorithm aims to systematise spatial characteristics, assess programmatic needs, and optimise building usage, while addressing challenges such as data integration and quality assurance. Ultimately, the research presents a pioneering approach to building adaptative reuse, aimed at fostering sustainable urban development and offering replicable insights applicable to similar challenges in other cities. Full article

Artificial Intelligence (AI) fundamentally changes journalism, yet a comprehensive understanding of its impact is limited. This study presents the first systematic review to thoroughly analyze the influence of AI on journalistic practices, news narratives, and emerging ethical challenges. A rigorous analysis of 127 studies selected from 2478 original articles reveals trends in AI adoption in newsrooms, changes in journalists’ roles, innovations in news presentation, and emerging ethical implications. The key findings show a significant increase in the use of AI for news writing automation (73% of news organizations), data analysis (68%), and content personalization (62%). While AI improves efficiency and accuracy, 42% of studies reported concerns about reduced levels of nuance and context in AI-generated news. We also identified the emergence of hybrid “journalist–programmer” roles (52% of studies) and the need for “AI literacy” among journalists (38% of studies). The most prominent ethical challenges include algorithm transparency (82% of studies), data privacy (76%), and accountability relative to AI content (71%). Regional analysis reveals significant gaps in AI adoption, with important implications for global information equity. This review highlights the ongoing transformation in journalism, identifies critical gaps in current research, and offers an agenda for future investigation. Our findings provide valuable insights for media practitioners, policymakers, and researchers seeking to understand and shape the future of journalism in the age of AI. Full article

Residential and commercial buildings are responsible for 35% of the EU energy-related greenhouse gas (GHG) emissions. Reducing their emissions is crucial for meeting the challenging EU objective of the agenda for becoming a net-zero continent by 2050. The diffusion and integration of distributed renewable energy sources (RESs) and energy storage systems (ESSs), as well as the creation of energy communities (ECs), have proven to be crucial aspects in reducing GHG emissions. In this context, this article proposes a multi-agent AI-based control framework to solve the EC’s energy management problem in the presence of distributed RESs and ESSs as well as considering a shared ESS. The objectives of the proposed control framework are to satisfy the EC members’ load demand to maximize self-consumption and to manage ESSs charging and discharging processes, to enforce cooperative behavior among the EC members by adopting fair and personalized strategies and to maximize EC members’ profits. The proposed control procedure is based on three sequential stages, each solved by a dedicated local RL agent exploiting the Q-Learning algorithm. To reduce the computational complexity of the proposed approach, specifically defined state aggregation criteria were defined to map the RL agents’ continuous state spaces into discrete state spaces of limited dimensions. During the training phase, the EC members’ profiles and the ESSs’ and RESs’ characteristics were randomly changed to allow the RL agents to learn the correct policy to follow in any given scenario. Simulations proved the effectiveness of the proposed approach for different costumers’ load demand profiles and different EC configurations. Indeed, the trained RL agents proved to be able to satisfy the EC members’ load demands to maximize self-consumption, to correctly use the distributed and shared ESSs, to charge them according to respective personalized criteria and to sell the energy surplus, prioritizing sales to the EC. The proposed control framework also proved to be a useful tool for understanding EC performance in different configurations and, thus, for properly dimensioning the EC elements. Full article

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML), built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future uncertain benefits that are linked by causation. This multi-period causal process, incorporating certainty and uncertainty, replaces the single-period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modeling and real-world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. With the growing interest in natural experiments in statistics and causal machine learning (CML) across many fields, such as healthcare, economics, and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision-making models that focus only on rationality, bounded to various degrees. To be most effective, machine learning must mirror human reasoning as closely as possible, an alignment established through CEML, which represents an evolution to truly “human AI”. This paper maps out how the non-linear optimization required for the CEML structural response functions can be accomplished through Sequential Least Squares Programming (SLSQP) and applied to data sets through the S-Learner CML meta-algorithm. Upon this foundation, the next phase of research is to apply CEML to appropriate data sets in various areas of practice where causality and accurate modeling of human behavior are vital, such as precision healthcare, economic policy, and marketing. Full article

The practice of online astroturfing has become increasingly pervasive in recent years, with the growth in popularity of social media. Astroturfing consists of promoting social, political, or other agendas in a non-transparent or deceitful way, where the promoters masquerade as normative users while acting behind a mask that conceals their true identity, and at times that they are not human. In politics, astroturfing is currently considered one of the most severe online threats to democracy. The ability to automatically identify astroturfers thus constitutes a first step in eradicating this threat. We present a complete framework for handling a dataset of profiles, from data collection and efficient labeling, through feature extraction, and finally, to the identification of astroturfers lurking in the dataset. The data were collected over a period of 15 months, during which three consecutive elections were held in Israel. These raw data are unique in scope and size, consisting of several million public comments and reactions to posts on political candidates’ pages. For the manual labeling stage, we present a technique that can zoom in on a sufficiently large subset of astroturfer profiles, thus making the procedure highly efficient. The feature extraction stage consists of a temporal layer of features, which proves useful for identifying astroturfers. We then applied and compared several algorithms in the classification stage, and achieved improved results, with an F₁ score of 77% and accuracy of 92%. Full article

Artificial intelligence (AI) technologies have been increasingly developed and applied in the audiovisual sector. Over-the-top (OTT) services, directly distributed to viewers via the Internet, are associated with a shift towards automation through algorithmic mediation in audiovisual content led by digital platforms. However, scientific knowledge regarding algorithmic recommender systems and automation in OTT services is not yet systemized; researchers, practitioners, and the public thus lack full awareness about the still largely opaque phenomena. To address this gap, we conduct a systematic literature review in the communication domain (2010–2022) and answer four key research questions: What research objectives have been pursued? What concepts have been developed and/or applied? What methodologies have been privileged? Which OTT platforms have received the most research attention? Challenges and opportunities are highlighted, and an agenda for future research is advanced. Full article

The population dynamics of early replicators has revealed numerous puzzles, highlighting the difficulty of transitioning from simple template-directed replicating molecules to complex biological systems. The resolution of these puzzles has set the research agenda on prebiotic evolution since the seminal works of Manfred Eigen in the 1970s. Here, we study the effects of demographic noise on the population dynamics of template-directed (non-enzymatic) and protein-mediated (enzymatic) replicators. We borrow stochastic algorithms from evolutionary game theory to simulate finite populations of two types of replicators. These algorithms recover the replicator equation framework in the infinite population limit. For large but finite populations, we use finite-size scaling to determine the probability of fixation and the mean time to fixation near a threshold that delimits the regions of dominance of each replicator type. Since enzyme-producing replicators cannot evolve in a well-mixed population containing replicators that benefit from the enzyme but do not encode it, we study the evolution of enzyme-producing replicators in a finite population structured in temporarily formed random groups of fixed size n. We argue that this problem is identical to the weak-altruism version of the n-player prisoner’s dilemma, and show that the threshold is given by the condition that the reward for altruistic behavior is equal to its cost. Full article

A circular economy (CE) appears to be a crucial tool enabling the sustainable use of natural resources, which is also essential for achieving the Sustainable Development Agenda by 2030. Compared to the traditional linear economy policy based on the “take-make-use-dispose” principle, the CE approach guided by the “designed to be remade” principle offers immense opportunities. Not only does it drastically reduce the need for primary resources, but it also revolutionizes the management of both resources and waste. The CE is significant for metal processing companies due to increased control over resources and waste reduction. Furthermore, it enables the efficient utilization of natural resources and minimizes the negative environmental impact, translating into the sustainable development of metallurgical activities. Additionally, recycling processes in metal processing can also have financial benefits by reducing the raw material procurement costs and lowering the waste disposal fees. The CE business model of the innovation and improvement of metal processing involves optimizing resource usage through continuous material processing and reuse. Companies develop advanced recycling technologies, implement efficient resource management strategies, and adopt service-oriented business models like leasing or part exchanging. These initiatives aim to increase value addition and minimize waste. Additionally, the ongoing investment in research and development facilitates the introduction of innovative processes and materials, leading to operational enhancement and environmental sustainability. The main aim of this study was to develop a CE business model for a metal processing company. This model allowed for identifying the key elements influencing the operations of companies in this industry in terms of the CE. Research was conducted through triangulation using various methods, such as focus group interviews, surveys, and individual in-depth interviews. This study was supplemented with an investment decision-making algorithm according to the CE and the CE business model canvas for metalworking enterprises, with a focus on those producing metal products subsequently covered with galvanic coating. The presented results also propose application in other SMEs within this industry sector. Full article