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Viimeksi tallennetut

  • Toward intelligent and resilient microgrids: A survey of machine learning approaches for renewable energy integration
    Razmi, Darioush; Razmi, Peyman; Babayomi, Oluleke; Zhang, Zhenbin (Elsevier, 2026)
    Artikkeli
    The integration of renewable energy and distributed generation has transformed microgrids into complex, adaptive, and data-driven systems. Managing these systems requires advanced forecasting and control strategies beyond traditional rule-based or model-driven approaches. Machine learning provides a data-centric framework to optimize microgrid operations, enabling power flow management, demand forecasting, anomaly detection, and resilience enhancement. Existing reviews remain fragmented, often focusing on single domains, while emerging approaches such as federated learning, physics-informed learning, and generative artificial intelligence (AI) are scarcely addressed. To fill this gap, this paper presents a comprehensive review of recent machine learning applications in microgrids, covering supervised, unsupervised, reinforcement, and deep learning techniques. The review surveys peer-reviewed research from 2019 to 2025, with a focus on key domains such as load forecasting, energy management, voltage/frequency regulation, cybersecurity, and adaptive protection. The review highlights key technical challenges, including data scarcity, generalization, cybersecurity, and explainability, and explores emerging directions such as federated learning, transfer learning, and physics-informed learning, discussing their potential for advancing microgrid intelligence and resilience. By synthesizing state-of-the-art developments and outlining future opportunities, this review aims to guide researchers and practitioners in building intelligent, secure, and adaptive microgrid systems.
  • Deception By Design: Deepfakes And Malicious Insider Deviance In Cybersecurity
    Anti, Emmanuel; Dang, Duong; Bui, Quang (Association for Information Systems, 2026)
    Artikkeli
    Deepfake technology poses emerging risks for organizations by enabling the manipulation of audio, video, and images in ways that insiders can exploit to commit fraud, impersonate colleagues, or sabotage operations. This study extends Fraud Triangle Theory (FTT) to examine how deepfakes influence insider deviant behavior by reshaping perceptions of pressure, opportunity, and rationalization. This study will use quantitative methods to survey professionals across Europe, America, and Asia (n=250), testing a model of deepfake-enabled insider deviance while examining context-specific threats, motivations, and ethical rationalizations. Structural equation modeling will be used to validate and interpret findings. This study contributes to the IS literature by integrating emerging technologies into fraud theory, highlighting the misuse of deepfakes as a critical internal threat, and offering practical guidance for security governance, policy development, and AI-based detection strategies.
  • Boundary Management And Cybersecurity Behavior In Remote Work: Insights From An Empirical Study
    Anti, Emmanuel; Levaniuk, Daria; Ebojoh, Sandra; Naqvi, Bilal (Association for Information Systems, 2026)
    Artikkeli
    This study investigates how remote workers’ boundary management strategies shape cybersecurity behavior in everyday contexts. Drawing on Boundary Management Theory and qualitative data from 14 interviews with remote workers across Europe, we identified three strategies: segmentation, integration, and blurred boundaries that influence how individuals engage with cybersecurity routines. Our findings show that boundary strategies affect the stability, consistency, and cognitive demands of secure behavior. Clear boundaries support routine compliance, while blurred boundaries increase risk through distraction, fatigue, and role conflict. Organizational factors such as policy clarity, tool usability, and leadership expectations further shape how employees sustain secure practices in distributed work settings. This study contributes to IS security research by highlighting boundary management as an important context for understanding behavioral cybersecurity in remote work. We offer practical implications for designing boundary-aware policies and tools that better support secure practices in remote and hybrid environments
  • Interpretable Machine Learning Framework for Embodied Carbon Estimation in Reinforced Concrete Wall Elements.
    Shikdar, MD Tarek (2026-04-23)
    Pro gradu -tutkielma
    Buildings represent a significant proportion of total anthropogenic greenhouse gas emissions, as operational carbon is gradually reduced by energy efficiency in design. Therefore, embodied carbon produced during the extraction of materials, transportation, manufacture, assembly, disassembly, and disposal of structural components is receiving increasing attention in literature. Conventional Life-Cycle Assessment (LCA) techniques, while comprehensive, have proven to be slow and tedious and cannot be applied repeatedly during early-stage design. This thesis addresses the above gap through the development of a machine learning model capable of predicting the embodied carbon intensity of Reinforced Concrete (RC) walls based on early-stage design inputs. Synthetic data of a considerable size was created by systematically permuting ten design variables related to wall configuration (geometrical properties, type and strength of concrete used, ratio of reinforcement), and transport distances, along with the results of life cycle carbon calculation using the Inventory of Carbon and Energy (ICE) emission factors and cradle-to-grave system boundary defined by EN 15804. The dataset includes a variety of structural configurations that can be considered realistic for low-to mid-rise structures in Finland. An XGBoost regression model was trained on the dataset and assessed using regression metrics and five-fold cross-validation. The model interpretability was analyzed using various methods of explanation, namely, Global and Local SHAP, LIME-based local interpretation, Partial Dependence Plots (PDP), Individual Conditional Expectation curves (ICE), and Two-Way PDPs. The above methods were selected to achieve sufficient model interpretability and avoid black-box estimation. As a result, an XGBoost model was obtained with nearly perfect predictive performance demonstrated across all cross-validation folds. Model interpretability revealed wall thickness to be the most influential design variable, followed by the compressive strength of concrete and reinforcement ratio. This was expected since the above variables define material volumes, as well as associated carbon emission rates and their domination was observed across all methods of model explainability. Transport-related variables exhibited systematic impact to the extent lower than material-related, while wall length and wall height proved to be relatively unimportant for predicting embodied carbon in units of area. This study proves the applicability of an interpretable ML model for rapid evaluation of multiple alternative configurations at an early stage of design, without the need for complete LCA calculation for each. The current study contributes to the body of literature, as it focuses on predicting embodied carbon on a structural element level rather than material-level and whole-building prediction. For future research, validation of the developed model against empirically collected data and expansion of the framework for predicting the embodied carbon of other RC elements are recommended.
  • Accounting for biodiversity: a systematic literature review
    King, Timothy; King, Tatiana (Emerald, 2026)
    Artikkeli
    Purpose Following a recent worldwide regulatory push to improve the identification, assessment and disclosure of climate and, more narrowly, biodiversity risks, this paper provides a timely review of the state-of-the-art of literature on biodiversity. Design/methodology/approach We employ a systematic literature review. The final corpus comprises 120 academic papers published in accounting, finance, economics and management journals ranked in the Academic Journal Guide (AJG) from 2021 to 2024. From this, we identify five thematic clusters and critically analyze how biodiversity is conceptualized, measured, disclosed and financialized in the literature. Findings Our review reveals that biodiversity accounting is still at an embryonic stage. Despite new regulations, an ongoing challenge is linked to the difficulty in establishing what constitutes biodiversity from a firm perspective and what data should be collected, how it should be reported, disclosed and verified. Research limitations/implications Further research is required to support the efforts of policymakers to ensure firms can better capture biodiversity-related risks and impacts, while also examining the assurance and reporting mechanisms that can support credible disclosure. Originality/value Our paper makes several contributions. First, we provide the most up-to-date synthesis of interdisciplinary research in the fields of accounting, finance, economics and management. Second, we identify tensions that arise when accounting logic of comparability, aggregation and periodic reporting faces the complexity and context-specific biodiversity information. Third, we develop a future research agenda that links biodiversity measurement choices to recognition, accountability and assurance debates in accounting research.