Journal of Superintelligence doi: 10.3390/superintelligence1010005

Authors: Raffaella Borasi Karen DeAngelis Yamin Zheng Benjamin J. Guerrero Md Mamunur Rashid David E. Miller Zenon Borys Matthew Brown Yu Jung Han Blaire Koerner Rachel Roberts

To illustrate the importance and complexity of considering “task–technology fit”, as well as users’ roles and purposes, when studying AI adoption within any occupation, we present findings from a 2023-25 mixed-method study about professional musicians’ uses and perceptions of AI in music-making. The initial interviews conducted with a diverse group of 42 U.S. musicians suggested that individual levels of interest in and adoption of AI in their music-making depended on the specific task considered, whether the task was perceived as “core” versus “supportive” to one’s professional role, and the intention of using AI to “assist” versus “replace” one’s work. Responses to a subsequent 2025 survey, designed to further explore these insights, as well as to collect additional information about musicians’ AI adoption, confirmed the value of eliciting respondents’ interest, uses, and feelings about using AI for specific tasks rather than in general terms. Exploring the impact of the respondents’ professional roles and purpose proved to be more challenging. Nevertheless, role-based differences were documented, several statistically significant. The study has methodological implications for studies of AI adoptions across fields.

Journal of Superintelligence doi: 10.3390/superintelligence1010004

Authors: Vilmar Steffen Maiquiel Schmidt de Oliveira Maressa Fontana Mezoni

The integration of artificial intelligence (AI) techniques into biomass pyrolysis research has attracted increasing attention in recent years; however, the existing literature remains fragmented across diverse methodological approaches and application domains. This study presents a systematic literature review of AI applications in biomass pyrolysis, combining bibliometric and qualitative analyses to map the current state of the art, identify prevailing research trends, and highlight existing knowledge gaps. Following a structured search conducted in the Scopus database, 33 peer-reviewed journal articles published in English between 2003 and 2026 were selected according to predefined eligibility criteria. The final portfolio was prioritized using an adapted version of the Normalized Index for Ranking Papers (NIRP 2.0), while the review procedure followed, whenever applicable, the recommendations of PRISMA, PRISMA for Abstracts, and PRISMA-S guidelines. The ranking methodology incorporated four scientometric indicators: Field-Weighted Citation Impact, average citations per year, SNIP, and CiteScore. The bibliometric analysis revealed a significant intensification of research activity during the last five years, with China, India, and Pakistan emerging as the most productive countries in the field. Machine learning techniques, particularly ensemble learning methods such as Extreme Gradient Boosting, Random Forest, and Gradient Boosting Decision Trees, were identified as the dominant approaches, especially in applications related to product yield prediction (biochar, bio-oil, and gas), kinetic and thermodynamic modeling, co-pyrolysis optimization, and process parameter estimation. Recent studies have also demonstrated growing interest in explainable artificial intelligence methods aimed at improving model interpretability and supporting physical understanding of pyrolysis systems. Despite the promising predictive and optimization capabilities demonstrated by AI-based models, important challenges remain, including limited dataset sizes, data heterogeneity, inconsistent terminology, reduced model generalizability, and the absence of physically informed constraints in many machine learning frameworks. The findings of this review indicate that future advances in the field will strongly depend on the development of standardized and publicly accessible databases, harmonized reporting protocols, and the integration of physics-informed artificial intelligence approaches capable of providing reliable, interpretable, and transferable predictions for biomass pyrolysis processes.

Journal of Superintelligence doi: 10.3390/superintelligence1010003

Authors: Ai-Qing Tian Fei-Fei Liu Xiao-Yang Wang

To address the challenges of combinatorial explosion and expensive evaluations in truck–drone (truck–UAV) collaborative delivery under complex geographical constraints, this paper proposes a Surrogate-assisted Rezone-Enhanced Multi-objective Adaptive Evolutionary Algorithm (SRE-MAEA). As a knowledge-driven decomposition-based surrogate-assisted framework, the proposed algorithm aims to synergistically optimize a four-dimensional conflicting objective space consisting of economic cost, social satisfaction, environmental emissions, and battery resilience. To overcome the curse of dimensionality in high-dimensional and strongly constrained environments, SRE-MAEA constructs an adaptive Rezone Search architecture. By dynamically deconstructing the decision space, it transforms global search pressure into refined knowledge mining within high-potential local regions. The core mechanism incorporates an intelligent sampling strategy based on the Multi-Armed Bandit (MAB). By utilizing real-time evolutionary feedback to dynamically prioritize the Pareto contribution of each rezone, the MAB achieves pruning-level scheduling of expensive evaluation resources. Simulation results on 15 benchmark instances with clustered, random, and mixed spatial distributions demonstrate that SRE-MAEA exhibits superior convergence boundaries and distribution uniformity in terms of IGD and HV metrics, significantly outperforming state-of-the-art regression-based strategies. Furthermore, computational efficiency analysis confirms that by precisely identifying invalid search paths via the MAB mechanism, SRE-MAEA maintains a high-precision Pareto front while reducing the average CPU time by approximately 35.2–48.5%. This effectively resolves the computational bottleneck caused by complex battery resilience integral models. This research provides an efficient algorithmic paradigm for resilient logistics scheduling in extreme environments and holds significant academic value and engineering application prospects.

Journal of Superintelligence doi: 10.3390/superintelligence1010002

Authors: Ru Wang Junxiang Li Ziyi Yang

In response to global climate change, virtual power plants (VPPs) have emerged as critical entities for integrating distributed energy resources and enabling demand response. However, the design of multi-energy collaborative pricing mechanisms for VPPs remains a significant challenge, particularly under carbon trading regulation. This paper addresses this gap by proposing a bi-level optimization model that captures the real-time interactions between users and energy suppliers. The model is designed to simultaneously maximize user utility and minimize supplier costs, explicitly accounting for energy costs, equipment operation and maintenance (O&M) costs, carbon emission costs, and power generation structure constraints. A particle swarm optimization (PSO) algorithm is employed to solve the formulated problem. The results of a case study demonstrate that the proposed mechanism effectively guides users toward peak shaving and valley filling, achieving a real-time balance between supply and demand. Furthermore, the simulation results indicate that the model significantly enhances power system operational efficiency and economic benefits while reducing carbon emissions. This work offers a practical approach for improving renewable energy integration and overall system performance within a carbon-constrained environment.

Journal of Superintelligence doi: 10.3390/superintelligence1010001

Authors: Zhikui Chen

Humanity is approaching a pivotal moment in the evolution of intelligence [...]