How does information infrastructure shape long-term cultural evolution? Using over four centuries of professional game records from the game of Go, this study explores how strategic dynamics in opening moves reflect historical shifts in the ‘infostructure’ of skilled Go players. Drawing from recent work on how population size, AI, and information technology affect cultural evolution and innovation dynamics, I analyze over 118,000 games using measures of cultural diversity, divergence, and player network composition. The results show distinct eras of collective innovation and homogenization, including an early 20th-century explosion of novel opening strategies, a Cold-War-era die-off, and a recent increase in evolutionary tempo with the arrival of the internet and superhuman AI programmes like AlphaGo. Player population size shows an inverse-U relationship with opening move diversity, and a recent decline in strategic diversity has accompanied a shift in the player network, from many small subgroups to a few large ones. Surprisingly, the influence of AI has produced only a modest, short-lived disruption in the distribution of opening moves, suggesting convergence between humans and AI and incremental rather than revolutionary cultural change.

In recent years, dangerous gas leakage events occur frequently. Rapid and accurate location of gas leakage sources by mobile robots is the key to avoid the expansion of disasters. In order to solve the problem of discontinuous gas concentration gradient and sparse gas environment in three-dimensional space, particle filter, and whale swarm optimization algorithm are integrated to locate gas source. Firstly, the Z-shape search and comb search are used to locate the plume, and then, the particle filter algorithm is combined with the whale optimization method to guide the particle movement, and the random inertial disturbance term is designed to improve the convergence speed and search accuracy of the algorithm. Experimental results in three-dimensional environment show that the proposed information-driven particle filter whale optimization hybrid algorithm effectively guides the robot in localizing gas source within a certain range, significantly enhancing both the efficiency and accuracy of localization compared to other algorithms.

Modelling of surface and shallow subsurface data is getting more and more advanced and is demonstrated mostly for onshore (hydro)geological applications. Three-dimensional (3D) modelling techniques are used increasingly, and now include voxel modelling that often employs stochastic or probabilistic methods to assess model uncertainty. This paper presents an adapted methodological workflow for the 3D modelling of offshore sand deposits and aims at demonstrating the improvement of the estimations of lithological properties after incorporation of more geological layers in the modelling process. Importantly, this process is driven by new geological insight from the combined interpretation of seismic and borehole data. Applying 3D modelling techniques is challenging given that offshore environments may be heavily reworked through time, often leading to thin and discontinuous deposits. Since voxel and stochastic modelling allow in-depth analyses of a multitude of properties (and their associated uncertainties) that define a lithological layer, they are ideal for use in an aggregate resource exploitation context. The voxel model is now the backbone of a decision support system for long-term sand extraction on the Belgian Continental Shelf.

Data based on end-member formulae of 4872 minerals (4975 entries) were subjected to numerical treatment and plotting. Most conspicuous among the findings is the hydration trend that spans the whole mineralogical system– something that may be related to the geological history of mineral formation on Earth. Interesting negative relationships were confirmed for pairs O and F, O and Cl, and Al and Si, while the O vs. S graph documents the dual nature of the behaviour of sulfur. Determinations of Al and Si have the potential of alarming the analyst that his phase might contain hydrogen. The dataset available also permitted the plotting of histograms that illustrate the preferred concentration distribution of individual elements throughout the mineral kingdom.

This work proposes a query-by-singing (QBS) content-based music retrieval (CBMR) system that uses Approximate Karbunen–Loeve transform for noise reduction. The proposed QBS-CBMR system uses a music clip as a search key. First, a 51-dimensional matrix containing 39-Mel-frequency cepstral coefficients (MFCCs) features and 12-Chroma features are extracted from an input music clip. Next, adapted symbolic aggregate approximation (adapted SAX) is used to transform each dimension of features into a symbolic sequence. Each symbolic sequence corresponding to each dimension of MFCCs is then converted into a structure called advanced fast pattern index (AFPI) tree. The similarity between the query music clip and the songs in the database is evaluated by calculating a partial score for each AFPI tree. The final score is obtained by calculating the weighted sum of all partial scores, where the weighting of each partial score is determined by its entropy. Experimental results show that the proposed music retrieval system performs robustly and accurately with the entropy weighting mechanism.