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Osuva on Vaasan yliopiston avoin julkaisuarkisto. Osuva sisältää Vaasan yliopiston omat julkaisut, opinnäytteet ja tieteellisten artikkeleiden rinnakkaistallenteet. Osuvaan sisältyy julkaisujen viitetietoja, tiivistelmiä ja kokotekstejä. Sähköisten arkistokokoelmien sisältö ei ole luettavissa verkossa.

Kokoelmat Osuvassa

Viimeksi tallennetut

  • Corporate political activity and MNEs’ sustainability performance: Evidence from sub-Saharan Africa
    Adomako, Samuel; Donbesuur, Francis; Nyuur, Richard B.; Apprey, Pius Lord; Abu-Arja, Ahmad (Elsevier, 2026)
    Artikkeli
    This article examines the effect of corporate political activities (CPA) on sustainability performance through the mediating mechanism of political influence. It further investigates the contingent factors influencing the relationship between political influence and corporate sustainability performance. Our hypotheses are largely supported by time-lagged data from 348 multinational enterprises (MNEs) operating in four sub-Saharan African countries. Our findings extend nonmarket strategies (NMS) and sustainability literature by testing a mediating and contingent model that assesses the influence of CPA on corporate sustainability performance. This nuanced analysis broadens the conceptual scope and applicability of NMS and sustainability activities, particularly in non-Western contexts.
  • Banana peel biomass-derived carbon and nitrogen-doped quantum dot/UiO-66 hybrid nanocomposites for high-performance asymmetric supercapacitors
    Abu Hassanisazin, Nur Irdina Atasya; Siva Kumar, Harivalagan; Shamsudin, Siti Aisyah; Omar, Fatin Saiha; Manfo, Theodore Azemtsop (Elsevier, 2026)
    Artikkeli
    Supercapacitors are promising energy storage devices, yet their wide application is limited by low energy density. Similarly, UiO-66, a zirconium-based metal–organic framework (MOF), possesses a high surface area but suffers from poor electrical conductivity. To overcome these limitations, nitrogen-doped carbon quantum dots (NCQDs) derived from banana peel biomass were synthesised via a green hydrothermal approach and embedded into the UiO-66 framework. This integration preserved the MOF's structural integrity while the introduced nitrogen functionalities enhanced interfacial interactions, electrical conductivity and electron-transfer pathways. Consequently, the UiO-66/NCQDs hybrid exhibited lower charge-transfer resistance and superior redox activity compared with pristine UiO-66 and UiO-66/CQDs. In an asymmetric supercapacitor assembled with activated carbon, the hybrid electrode delivered a specific energy of 30.32 Wh kg−1 at a power density of 2449.38 W kg−1, retaining 93.45% of its capacitance after 4000 cycles. These findings demonstrate a sustainable strategy for engineering high-performance, biomass-derived MOF/QD energy storage devices.
  • Condition-based monitoring of HCCI engines utilizing vibration signals
    Hasanzadeh Shadiani, Abolfazl; Nasajian Moghaddam, Amir-Hossein; Shamekhi, Amir-Mohammad; Basher, Abol; Hunicz, Jacek; Boutellier, Jani; Mikulski, Maciej (Sage publications, 2026)
    Artikkeli
    Marine transportation moves approximately 80% of the world’s cargo volume, playing a vital role in global logistics. Ever-increasing environmental challenges and fuel economy concerns demand efficient, low-emission engine technologies. Homogeneous charge compression ignition (HCCI) offers improved thermal efficiency at minimum engine-out emissions, but maintaining combustion stability across varying operating conditions limits its widespread adoption. Established in-cylinder pressure-based control systems perform reliably when combustion phasing variations remain small but lack responsiveness under larger deviations, limiting robust real-time combustion state detection and correction. This study presents a vibration-based sensing and classification framework for detecting combustion states in an HCCI engine. Vibration signals recorded from a single-cylinder HCCI research engine were processed to extract a compact set of time- and frequency-domain features, which were used to train supervised classification models based on combustion phasing (CA50). Combustion states were categorized as Normal, Late, or Very Late Combustion. Six machine learning models were evaluated: K-nearest neighbors (KNN), support vector machines (SVM), Artificial neural networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a convolutional neural network (CNN) as a deep learning benchmark. All models demonstrated high accuracy and robustness, achieving F1-scores exceeding 98% for the Normal and Very Late Combustion classes. The ANN achieved the highest test accuracy of 98.36%, outperforming the CNN benchmark, particularly for the challenging Late Combustion class, demonstrating the effectiveness of the physically informed feature-based approach. The performance for the Late Combustion class was slightly lower, but the method remains promising for real-time applications due to its minimal computational overhead.
  • Tieteellisten kirjastojen asiantuntijoiden yhteistyötä kohti kestävämpää tulevaisuutta: katsaus vuoteen 2025
    Lehto, Anne; Ahola, Marita (Suomen tieteellinen kirjastoseura, 2026)
    Artikkeli
    Signum-lehti toimi tieteellisen kirjastoalan ajankohtaisen keskustelun ja osaamisen jakamisen alustana. STKS:n vaikuttamistyö konkretisoitui mm. Varastokirjaston maksuttomuuden puolesta laaditussa kannanotossa. Osallistuminen kansainvälisiin kokouksiin edisti verkostoitumista. Toimintavuonna vahvistui käsitys yhteistyön ja jatkuvan oppimisen keskeisestä merkityksestä tieteellisten kirjastojen kehittämisessä.
  • Public evaluations of AI-mediated healthcare when considering people with diverse access and support needs: a mixed-methods survey
    Piispanen, Joni-Roy; Kauttonen, Janne; Alamäki, Ari; Rousi, Rebekah (Elsevier, 2026)
    Artikkeli
    Background Artificial intelligence (AI) is increasingly being introduced into healthcare, including patient communication, monitoring, triage, decision support, and care-related services. Accessibility is central to these developments, particularly when AI-mediated healthcare is considered in relation to people with diverse access and support needs. Objective This study examined how accessibility-related evaluations of AI in healthcare are patterned when respondents are prompted to consider people with diverse access and support needs, and how respondents describe risks and conditions of acceptable AI-mediated care in this context. Methods We conducted a mixed-methods questionnaire study with a panel sample (N = 1,151). Quantitative analyses combined Bayesian regression, confirmatory factor analysis, and correlation analysis to examine accessibility-related evaluations of healthcare AI. Qualitative analysis examined open-ended concern elaborations from a subgroup of respondents (n = 117) using codebook thematic analysis with collaborative coding. Results Support-oriented evaluations clustered together more strongly than they aligned with concern. In the selected regression models, technological acceptance and age were the clearest correlates: higher technological acceptance was associated with more supportive evaluations, while older age was associated with greater concern and lower endorsement of AI’s supportive potential in some models. Four qualitative themes were identified: Human Recourse and Relational Care; Communicative Accessibility and Misunderstanding; Safety, Reliability, and Override Capacity; and Privacy, Misuse, and Unfair Classification. Respondents articulated concern through questions of relational care, communicative fit, accountability, fairness, safety, and access to human support when AI systems fail or are misunderstood. Conclusions The findings suggest that healthcare AI implementation should address intelligibility, communicative accessibility, bounded system roles, accountability, and human recourse as central conditions for acceptable AI-mediated care.