Abstract
Multivariate time series analysis is a vital but challenging task, with multidisciplinary applicability, tackling the characterization of multiple interconnected variables over time and their dependencies. Traditional methodologies often adapt univariate approaches or rely on assumptions specific to certain domains or problems, presenting limitations. A recent promising alternative is to map multivariate time series into high-level network structures such as multiplex networks, with past work relying on connecting successive time series components with interconnections between contemporary timestamps. In this work, we first define a novel cross-horizontal visibility mapping between lagged timestamps of different time series and then introduce the concept of multilayer horizontal visibility graphs. This allows describing cross-dimension dependencies via inter-layer edges, leveraging the entire structure of multilayer networks. To this end, a novel parameter-free topological measure is proposed and common measures are extended for the multilayer setting. Our approach is general and applicable to any kind of multivariate time series data. We provide an extensive experimental evaluation with both synthetic and real-world datasets. We first explore the proposed methodology and the data properties highlighted by each measure, showing that inter-layer edges based on cross-horizontal visibility preserve more information than previous mappings, while also complementing the information captured by commonly used intra-layer edges. We then illustrate the applicability and validity of our approach in multivariate time series mining tasks, showcasing its potential for enhanced data analysis and insights.

Abstract
Accurate power forecasting from renewable energy sources (RES) is crucial for integrating additional RES capacity into the power system and realizing sustainability goals. This work emphasizes the importance of integrating decentralized spatio-temporal data into forecasting models. However, decentralized data ownership presents a critical obstacle to the success of such spatio-temporal models, and incentive mechanisms to foster data-sharing need to be considered. The main contributions are a) a comparative analysis of the forecasting models, advocating for efficient and interpretable spline LASSO regression models, and b) a bidding mechanism within the data/analytics market to ensure fair compensation for data providers and enable both buyers and sellers to express their data price requirements. Furthermore, an incentive mechanism for time series forecasting is proposed, effectively incorporating price constraints and preventing redundant feature allocation. Results show significant accuracy improvements and potential monetary gains for data sellers. For wind power data, an average root mean squared error improvement of over 10% was achieved by comparing forecasts generated by the proposal with locally generated ones. © 2010-2012 IEEE.

Abstract
Reduced-impact logging (RIL) has been recognized as a promising strategy for biodiversity conservation and carbon sequestration within sustainable forest management (SFM) areas. However, monitoring the forest understory-a critical area for assessing logging impacts-remains challenging due to limitations in conventional methods such as field inventories and global navigation satellite system (GNSS) surveys, which are time-consuming, costly, and often lack accuracy in complex environments. Additionally, aerial and satellite imagery frequently underestimate the full extent of disturbances as the forest canopy obscures understory impacts. This study examines the effectiveness of the relative density model (RDM), derived from airborne LiDAR data, for mapping and monitoring understory disturbances. A field-based validation of LiDAR-derived RDM was conducted across 25 sites, totaling 5504.5 hectares within the Jamari National Forest, Rond & ocirc;nia, Brazil. The results indicate that the RDM accurately delineates disturbances caused by logging infrastructure, with over 90% agreement with GNSS field data. However, the model showed the greatest discrepancy for skid trails, which, despite their lower accuracy in modeling, accounted for the largest proportion of the total impacted area among infrastructure. The findings include the mapping of 35.1 km of primary roads, 117.4 km of secondary roads, 595.6 km of skid trails, and 323 log landings, with skid trails comprising the largest proportion of area occupied by logging infrastructure. It is recommended that airborne LiDAR assessments be conducted up to two years post-logging, as impacts become less detectable over time. This study highlights LiDAR data as a reliable alternative to traditional monitoring approaches, with the ability to detect understory impacts more comprehensively for monitoring selective logging in SFM areas of the Amazon, providing a valuable tool for both conservation and climate mitigation efforts.

Abstract
We present Caos: a programming framework for computer-aided design of structural operational semantics for formal models. This framework includes a set of Scala libraries and a workflow to produce visual and interactive diagrams that animate and provide insights over the structure and the semantics of a given abstract model with operational rules. Caos follows an approach where theoretical foundations and a practical tool are built together, as an alternative to foundations-first design (tool justifies theory) or tool-first design (foundations justify practice). The advantage of Caos is that the tool-under-development can immediately be used to automatically run numerous and sizeable examples in order to identify subtle mistakes, unexpected outcomes, and unforeseen limitations in the foundations-under-development, as early as possible. More concretely, Caos supports the quick creation of interactive websites that help the end-users better understand a new language, structure, or analysis. End-users can be research colleagues trying to understand a companion paper or students learning about a new simple language or operational semantics. We include a list of open-source projects with a web frontend supported by Caos that are used both in research and teaching contexts.

Abstract
Code comprehension is often supported by source code analysis tools that provide more abstract views over software systems, such as those detecting design patterns. These tools encompass analysis of source code and ensuing extraction of relevant information. However, the analysis of the source code is often specific to the target programming language. We propose DP-LARA, a multilanguage pattern detection tool that uses the multilanguage capability of the LARA framework to support finding pattern instances in a code base. LARA provides a virtual AST, which is common to multiple OOP programming languages, and DP-LARA then performs code analysis of detecting pattern instances on this abstract representation. We evaluate the detection performance and consistency of DP-LARA with a few software projects. Results show that a multilanguage approach does not compromise detection performance, and DP-LARA is consistent across the languages we tested it for (i.e., Java and C/C++). Moreover, by providing a virtual AST as the abstract representation, we believe to have decreased the effort of extending the tool to new programming languages and maintaining existing ones.

Abstract
This chapter aims to analyze and map the behaviors and strategies employed by organizations recognized for their innovation, with the goal of developing a comprehensive innovation management framework. This framework is designed to merge innovation practices with elements of traditional management, creating a hybrid model to support companies, universities, and research institutes in fostering innovation. Rooted in an understanding of human evolution, the framework will reflect changes in needs, skills, and behaviors over time, enabling institutions to adapt their innovation strategies to align with societal and individual development. Adopting an interdisciplinary approach, it will integrate concepts from innovation, organizational management, and the human sciences to establish a structure that supports sustainable innovation while addressing contemporary challenges. © 2025, IGI Global Scientific Publishing.