Science

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Artificial intelligence: A powerful paradigm for scientific research

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming…

The Innovation

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ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

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Deep forest

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Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

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Highly accurate protein structure prediction with AlphaFold

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1-4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6,7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to addr…

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A generative artificial intelligence approach for peptide antibiotic optimization

Abstract Antibiotic resistance is rising globally, demanding faster, more reliable routes to design antimicrobial candidates. Although artificial-intelligence-based methods have accelerated antimicrobial discovery, most are designed to screen fixed libraries or generate candidates broadly, rather than optimize existing peptide scaffolds under practical design constraints. Here, to address this challenge, we present APEX generative optimization (ApexGO). ApexGO uses a transformer variational autoencoder that embe…

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Digital Technologies and Sustainable Development: Evidence from FinTech, AI, and Blockchain Adoption in G20 Economies

In the wake of rapid digital transformation, emerging technologies like FinTech, AI, and Blockchain are reimagining how countries pursue sustainable development. This study examines how FinTech adoption, Artificial Intelligence (AI) readiness, and Blockchain activity influence sustainable development performance across G20 economies over the period 2015–2023. Drawing on Innovation-Driven Growth Theory, the Technology–Organization–Environment framework, and Institutional Theory, the analysis evaluates both the di…

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Optimizing solar and wind forecasting with iHow optimization algorithm and multi-scale attention networks

Deep learning models often encounter two key challenges in developing intelligent and scalable forecasting frameworks for renewable energy systems: input feature space dimensionality and sensitivity to hyperparameter settings. These limitations increase computational cost and compromise generalization and robustness. This paper presents a hybrid deep learning-optimization framework that leverages cognitively inspired metaheuristics to address these challenges, employing the Binary iHow Optimization Algorithm (bi…

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Metaheuristic-optimized machine learning framework for remote sensing-based alteration mapping of porphyry copper systems

Remote sensing-based hydrothermal alteration mapping is a pivotal technique in critical mineral exploration, particularly for identifying porphyry copper deposits (PCDs). However, conventional multispectral approaches are often constrained by linear assumptions, spectral mixing, and suboptimal parameter selection, limiting their ability to resolve complex alteration assemblages. This study presents a metaheuristic-optimized machine learning framework that integrates Boosted Trees (BT) and Quadratic Support Vecto…

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Towards end-to-end automation of AI research

Abstract The automation of science is a long-standing ambition in artificial intelligence (AI) research 1,2 . Although the community has made substantial progress in automating individual components of the scientific process, a system that autonomously navigates the entire research life cycle—from conception to publication—has remained out of reach. Here we present a pipeline for automating the entire scientific process end to end. We present The AI Scientist, which creates research ideas, writes code, runs expe…

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They Think AI Can Do More Than It Actually Can: Practices, Challenges, & Opportunities of AI-Supported Reporting In Local Journalism

Declining newspaper revenues prompt local newsrooms to adopt automation to maintain efficiency and keep the community informed. However, current research provides a limited understanding of how local journalists work with digital data and which newsroom processes would benefit most from AI-supported (data) reporting. To bridge this gap, we conducted 21 semi-structured interviews with local journalists in Germany. Our study investigates how local journalists use data and AI (RQ1); the challenges they encounter wh…

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A meta-analysis of the effects of AI agent implementation on customer-level outcomes

Firms increasingly use AI agents, such as recommendation algorithms and chatbots, to enhance customer value, yet research documents mixed effects on customer outcomes. To address and clarify these heterogeneous findings, we conduct a meta -analysis of 468 effect sizes reported in 95 articles with 82,751 participants examining AI agent implementation across both substitution contexts (AI replacing humans) and adoption contexts (AI introduced into previously non-AI processes). Results indicate that customers, on a…

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An integrated method of advanced optimisation and adaptive ensemble learning for ship fuel consumption prediction

• Proposes an adaptive cluster-based multi-ensemble (ACME) model for robust ship fuel consumption prediction. • Develops a SHAP-weighted multi-model feature selection (SWFS) algorithm for dimensionality reduction. • Introduces a hierarchical adaptive parameter space exploration (HAPSE) method for efficient tuning. • Establishes a dual-layer SHAP framework for global and local model interpretability. • Integrates multi-source data fusion to enhance prediction accuracy and operational relevance. Accurate predictio…

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AI and human designers: How consumers see the genuine care in product design as more sustainable

Abstract Across a series of studies, we find that despite AI’s potential for greater efficiency in sustainable product designs, AI-designed products are perceived by consumers as less sustainable than those designed by humans. We demonstrate that this effect is driven by a perceived lack of genuine care, defined as the emotional component believed to go into a product’s design. Since sustainability is symbolically linked to values such as love and care, the absence of human involvement undermines perceptions of…

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AI-powered voice assistants for older adults: a literature review of insights, research practices, and future directions

As the global population of older adults grows, AI-powered Voice Assistants (VAs) are explored as tools to mitigate loneliness, social isolation, and cognitive decline. By enabling intuitive, hands-free interaction, VAs provide a means to enhance independence, emotional well-being, and daily functioning for older adults. This paper presents a systematic review on older adults’ interactions with VAs, analysing 48 studies, which is more than double the scope of prior reviews. Studies were selected from an initial…