Search Results (440)

This study proposes a hybrid approach to analyze and forecast non-stationary financial time series by combining statistical models with deep neural networks. A model is introduced that integrates three key components: the Generalized Autoregressive Score (GAS) model, which captures volatility dynamics; an attention mechanism (ATT), which identifies the most relevant features within the sequence; and a Long Short-Term Memory (LSTM) neural network, which receives the outputs of the previous modules to generate price forecasts. This architecture is referred to as GAS-ATT-LSTM. Both unidirectional and bidirectional variants were evaluated using real financial data from the Nasdaq Composite Index, Invesco QQQ Trust, ProShares UltraPro QQQ, Bitcoin, and gold and silver futures. The proposed model’s performance was compared against five benchmark architectures: LSTM Bidirectional, GARCH-LSTM Bidirectional, ATT-LSTM, GAS-LSTM, and GAS-LSTM Bidirectional, under sliding windows of 3, 5, and 7 days. The results show that GAS-ATT-LSTM, particularly in its bidirectional form, consistently outperforms the benchmark models across most assets and forecasting horizons. It stands out for its adaptability to varying volatility levels and temporal structures, achieving significant improvements in both accuracy and stability. These findings confirm the effectiveness of the proposed hybrid model as a robust tool for forecasting complex financial time series. Full article

Data sharing within and across large, complex systems is one of the most topical challenges in the current IT landscape, and the energy domain is no exception. As the sector becomes more and more digitized, decentralized, and complex, new Big Data and AI tools are constantly emerging to empower stakeholders to exploit opportunities and tackle challenges. They enable advancements such as the efficient operation and maintenance of assets, forecasting of demand and production, and improved decision-making. However, in turn, innovative systems are necessary for using and operating such tools, as they often require large amounts of disparate data and intelligent preprocessing. The integration of and communication between numerous up-and-coming technologies is necessary to ensure the maximum exploitation of renewable energy. Building on existing developments and initiatives, this paper introduces a multi-layer Reference Architecture for the reliable, secure, and trusted exchange of data and facilitation of services within the energy domain. Full article

This paper explores the experiences of parents of children with autistic spectrum disorder (ASD) who have participated in parenting programmes in the UK. The parents attended generic programmes before they were able to access more specific programmes geared towards the needs of children with ASD. The parents found that the generic programmes were detrimental to the needs of their children and disruptive of family life, resulting in parental relationship breakdown in some instances. Whilst the ASD-specific programmes did provide some level of support, the most decisive factor was the sharing of experiences amongst those parents as well as the ongoing support that this fostered. The conclusion of this study is that the neoliberal responsibilisation of parents via parenting programmes is undermining the support networks of parents of children with ASD, and an asset-based community development approach would be more beneficial. Full article

The digitisation of cultural heritage has transformed how GLAM (Galleries, Libraries, Archives and Museums) institutions manage and share collections. Digital catalogues are indispensable for documenting and granting public access to cultural assets. However, integrating spatial data remains challenging due to the ambiguity, uncertainty, granularity, and heterogeneity of historical data. This study addresses these issues through a case study on the Museo de América’s “Place of Provenance” data, proposing a methodology for data cleaning and evaluating geocoding accuracy using Nominatim, ArcGIS, and GeoNames APIs. We assess these APIs by quantifying geocoding errors through a “balance sheet” method, identifying instances of over-representation, under-representation, or neutral results for geographical regions. The effectiveness of each API is analysed using confusion matrices and interactive cartograms, offering insights into misallocations. Our findings reveal varying accuracy among the APIs in processing heterogeneous historical spatial data. Nominatim achieved a 40.91% neutral result in correctly geocoding countries, underscoring challenges in spatial data representation. This research provides valuable methodological experiences and insights for researchers and GLAM institutions working with cultural heritage datasets. By enhancing spatial dispersion visualisation, this work contributes to understanding cultural circulations and historical patterns. This interdisciplinary work was developed as part of the ClioViz project, integrating Data Science, data Visualisation, and art history. Full article

The increasing penetration of renewable energy sources (RESs) has significantly altered the operational characteristics of modern power systems, resulting in reduced system inertia and fault current capacity. These developments introduce new challenges for maintaining frequency and voltage stability, particularly in low-inertia grids that are dominated by inverter-based resources (IBRs). This paper presents a hierarchical control framework that integrates synchronous condensers (SCs) and grid-forming (GFM) inverters through a leader–follower consensus control architecture to address these issues. In this approach, selected GFMs act as leaders to restore nominal voltage and frequency, while follower GFMs and SCs collaboratively share active and reactive power. The primary control employs droop-based regulation, and a distributed secondary layer enables proportional power sharing via peer-to-peer communication. A modified IEEE 14-bus test system is implemented in PSCAD to validate the proposed strategy under scenarios including load disturbances, reactive demand variations, and plug-and-play operations. Compared to conventional droop-based control, the proposed framework reduces frequency nadir by up to 0.3 Hz and voltage deviation by 1.1%, achieving optimised sharing indices. Results demonstrate that consensus-based coordination enhances dynamic stability and power-sharing fairness and supports the flexible integration of heterogeneous assets without requiring centralised control. Full article

As sustainability becomes increasingly central to organizational strategy, social economy organizations (SEOs) are rethinking their business models. This study employs stakeholder analysis using the value mapping (VM) tool developed by Short, Rana, Bocken, and Evans for the development of the VOLTO JÁ project. The objective of the VOLTO JÁ project is to operationalize a senior exchange programme between SEOs. The VM approach extends beyond conventional customer value propositions to prioritize sustainability for all stakeholders and identify key drivers of sustainable business model (SBM) innovation. The multi-stakeholder methodology comprises the following elements: (1) sequential focus groups aimed at enhancing sustainable business thinking; (2) semi-structured interviews; and (3) workshop to facilitate qualitative analysis and co-create the VM. The findings are then categorized into four value dimensions: (1) value captured—improved participant well-being, enhanced reputational capital, mitigation of social asymmetries, and affordable service experiences; (2) value lost—underused community assets; (3) value destroyed—institutional and systemic barriers to innovation; and (4) new value opportunities—knowledge sharing, service diversification, and open innovation to foster collaborative networks. The study demonstrates that the application of VM in SEOs supports SBM development by generating strategic insights, enhancing resource efficiency, and fostering the delivery of socially impactful services. Full article

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin’s Q ratio, market capitalization, return on assets, leverage, and volatility. A range of overconfidence proxies is employed, including changes in trading volume, turnover rate, changes in outstanding shares, and alternative measures of excessive trading. We observe a significant positive relationship between overconfidence (as measured by changes in trading volume) and firm valuation, suggesting that investor biases contribute to notable pricing distortions. Leverage has a significant negative relationship with firm valuation. In contrast, market capitalization has a significant positive relationship with firm valuation, implying that meme stock investors respond to both speculative sentiment and traditional firm fundamentals. Robustness checks using alternative proxies reveal that turnover rate and changes in the number of shares are negatively related to valuation. This shows the complex dynamics of meme stocks, where psychological factors intersect with firm-specific indicators. However, results from a dynamic panel model estimated using the Dynamic System Generalized Method of Moments (GMM) show that the turnover rate has a significantly positive relationship with firm valuation. These results offer valuable insights into the pricing behavior of meme stocks, revealing how investor sentiment impacts periodic valuation adjustments in speculative markets. Full article

In recent years, the concept of the commons has gained relevance across academic, legal and civic spheres as an alternative framework for managing shared resources. Rooted in Elinor Ostrom’s seminal work on collective governance, contemporary debates on the commons increasingly challenge the traditional binary of private versus public ownership by foregrounding the social function of these resources and the role of their communities in their administration. Urban commons, in particular, have emerged as dynamic spaces of experimentation, where local actors reclaim underutilised or abandoned assets and activate them through bottom-up processes of care, cultural production, and civic innovation. While international literature provides a robust conceptual foundation for understanding commoning practices, their practical implementation mostly depends on local specificities, such as legal and social contexts. Despite the important milestone of the Rodotà Commission’s proposal (2007) and Law 168/2017 on collective domains, the Italian regulatory framework remains highly fragmented, with regional and municipal regulations pioneering policies and practices frequently driven by community-led efforts to redefine the use and governance of public assets. This paper contributes to the growing debate on urban commons by investigating how, since 2011, experiences of collaborative care of the commons in Southern Italy have rekindled interest in the issue raised in 2007, highlighting a strong connection between the regulatory and social dimensions of these processes. The study develops a methodological framework—the COMmons Places ASSessment (COMPASS)—to evaluate the enabling conditions and governance dynamics of these processes. The research specifically focuses on five case studies in the Campania region, where diverse actors have mobilised to reclaim and transform public heritage through cultural, social, and creative activities. From the results, insights on collective management practices emerge potentials and criticalities of the analysed governance, as well as of the designed decision-making process and their effectiveness for the open, participatory, and sustainable management of urban commons. Full article

Machine Learning (ML) is increasingly applied across various domains, addressing tasks such as predictive analytics, anomaly detection, and decision-making. Many of these applications share similar underlying tasks, offering potential for systematic reuse. However, existing reuse in ML is often fragmented, small-scale, and ad hoc, focusing on isolated components such as pretrained models or datasets without a cohesive framework. Product Line Engineering (PLE) is a well-established approach for achieving large-scale systematic reuse in traditional engineering. It enables efficient management of core assets like requirements, models, and code across product families. However, traditional PLE is not designed to accommodate ML-specific assets—such as datasets, feature pipelines, and hyperparameters—and is not aligned with the iterative, data-driven workflows of ML systems. To address this gap, we propose Machine Learning Product Line Engineering (ML PLE), a framework that adapts PLE principles for ML systems. In contrast to conventional ML reuse methods such as transfer learning or fine-tuning, our framework introduces a systematic, variability-aware reuse approach that spans the entire lifecycle of ML development, including datasets, pipelines, models, and configuration assets. The proposed framework introduces the key requirements for ML PLE and the lifecycle process tailored to machine-learning-intensive systems. We illustrate the approach using an industrial case study in the context of space systems, where ML PLE is applied for data analytics of satellite missions. Full article

With the development of global digital economy and the digital transformation of enterprises, the demand for cross-border cross-domain sharing and circulation of highly sensitive and high-value data assets is becoming more and more obvious. In the process of shared circulation, data assets are faced with some problems, such as unreliable communication network, uncontrollable cloud storage service, untrusted participants and so on, which leads to data tampering, stealing, blocking and tracing back to the source. However, the existing security protection means are difficult to systematically ensure the safe circulation and utilization of data assets in an uncontrolled, high threat and strong confrontation environment. Therefore, this paper establishes a security protection model of data assets in the whole life cycle with cryptography technology as the core, and designs a security technical framework that runs through each link of data asset sharing and circulation. In addition, an architecture design scheme of data asset security sharing and circulation system based on cryptography service technology is proposed, which can systematically solve the security problem of data asset sharing and circulation in uncontrolled environments, and can improve the ability of on-demand deployment, flexible access and dynamic adjustment while maximizing the security of data assets. Full article

Recent developments in dynamic portfolio optimization have focused on the role played by portfolio frictions. Portfolio frictions make the portfolio’s response to financial shocks weaker and more gradual than in a model without frictions. At the same time, institutional investors are prevented from short-selling, a situation in which investors are restricted from taking negative positions in an asset, while other types of investors can short-sell. However, the literature has not yet discussed the implication of a short-selling constraint in a model of optimal portfolio choice with frictions. We solve a general equilibrium model of portfolio choice with frictions and a short-selling constraint. The model features investors who own firms and allocate capital across firms, households who work in the firms and earn revenues, and firms that produce the final good using capital and labor and redistribute profits to investors. We show the conditions under which negative financial conditions reduce the optimal share invested in a firm to zero. Finally, we simulate the model to show that the short-selling constraint prevents investors from amplifying financial shocks, which leads to a more stable business cycle. Our results are important for financial regulators as they suggest forbidding short-selling. Full article

While digital transformation in e-commerce receives the most publicity, applications in energy and water utilities have been ongoing for decades. Using a methodology based on a systematic review, the paper offers a model of how it occurs in water utilities, reviews experiences from the field, and derives lessons learned to create a road map for future research and implementation. Innovation in water utilities occurs more in the field than through organized research, and utilities share their experiences globally through networks such as water associations, focus groups, and media outlets. Their digital transformation journeys are evident in business practices, operations, and asset management, including methods like decision support systems, SCADA systems, digital twins, and process optimization. Meanwhile, they operate traditional regulated services while being challenged by issues like aging infrastructure and workforce capacity. They operate complex and expensive distribution systems that require grafting of new controls onto older systems with vulnerable components. Digital transformation in utilities is driven by return on investment and regulatory and workforce constraints and leads to cautious adoption of innovative methods unless required by external pressures. Utility adoption occurs gradually as digital tools help utilities to leverage system data for maintenance management, system renewal, and water loss control. Digital twins offer the advantages of enterprise data, decision support, and simulation models and can support distribution system optimization by integrating advanced metering infrastructure devices and water loss control through more granular pressure control. Models to anticipate water main breaks can also be included. With such advances, concerns about cyber security will grow. The lessons learned from the review indicate that research and development for new digital tools will continue, but utility adoption will continue to evolve slowly, even as many utilities globally are too stressed with difficult issues to adopt them. Rather than rely on government and academics for research support, utilities will need help from their support community of regulators, consultants, vendors, and all researchers to navigate the pathways that lie ahead. Full article

The European Union (EU) is advancing a digital twin of its electricity grid as a flagship initiative to accelerate the dual transitions of decarbonization and digitalization. By creating a real-time virtual replica of the EU-27 power network, policymakers and industry stakeholders aim to enhance grid efficiency, resilience, and renewable energy integration. This review provides a comprehensive analysis of the three critical dimensions shaping the digital twin’s development: (1) regulatory barriers, including fragmented policies, inconsistent data governance frameworks, and the need for harmonized standards and incentives across member states; (2) technological challenges, such as achieving interoperability, integrating real-time data, developing robust cybersecurity measures, and ensuring scalable infrastructure; and (3) economic opportunities, centered on potential cost savings, optimized asset management, new flexibility services, and pathways for innovation and investment. Drawing on European Commission policy documents, regulatory reports, academic studies, and industry projects like the Horizon Europe TwinEU initiative, this review highlights that significant groundwork has been laid to prototype and federate local grid twins into a cohesive continental system. However, achieving the full potential of a pan-European digital twin will require additional regulatory harmonization, more mature data-sharing protocols, and sustained financial commitment. This review concludes with an outlook on the strategic convergence of policy reforms, collaborative R&D, and targeted funding, emphasizing how institutional momentum, federated architectures, and cross-sector integration are advancing a secure, resilient, and economically viable digital twin that is envisioned as a foundational layer in the operational and planning infrastructure of Europe’s future electricity system. Full article

Cultural heritage reflects accumulated memories, generational practices, and esthetic and cultural ideologies that shape identities. The inherent diversity and uniqueness of these identities define heritage elements, which, however, remain fragile, non-renewable, irreplaceable, and vulnerable. In regions affected by the significant devastation caused by contemporary socio-economic activities, policies often neglect the intrinsic historical and heritage value(s). The historical landscapes with tourism potential that experience decline, degradation, and destruction need even more protection and policies to aid their regeneration and preservation. This study seeks to develop a comprehensive framework for preserving a highly endangered historical road, along with its invaluable monuments and cultural landscapes, as a means to stimulate regional revitalization. The methodological approach relies on observation, field work, and semi-structured interviews to provide a comprehensive historical overview of the Estrada do Paraibuna (Paraibuna Road). This region has suffered significant degradation of its historical and natural assets as a result of intensive resource exploitation. This study underscores the heritage elements with strong tourism potential along the route between Ouro Preto and Barbacena, while underlining the critical need to combat landscape degradation. This study pursues to create a shared vision rooted in promoting sustainable practices that leverage natural and cultural resources, safeguard local culture, and encourage community collaboration in regenerative efforts. Full article

This study examines the performance and resilience of Islamic banks during the COVID-19 pandemic, a period marked by unprecedented global economic disruption. Drawing on empirical data and a comparative analysis with conventional banking institutions, the research evaluates key financial indicators—liquidity, profitability, asset quality, and capital adequacy—to assess how Islamic banks responded to the crisis. The unique principles of Islamic finance, including risk-sharing, asset-backed financing, and the prohibition of interest and speculative activities, provide a distinct framework for crisis response. By analyzing how these features influenced bank performance during the pandemic, the study offers valuable insights into the relative robustness of Islamic versus conventional banking models. The findings contribute to the academic discourse on financial stability and risk management, offering practical implications for policymakers, regulators, and stakeholders to strengthen financial systems against future global shocks. Full article