Geosmin contamination in water is a worldwide concern, owing to its strong odor at trace levels and limited removal by typical water treatment methods. In this study, bentonite-alginate-magnetic (Bent-alg-mag) beads were prepared using the ionic gelation method for the removal of geosmin from aqueous solutions. The adsorbent's physicochemical properties were characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) analysis. The influence of factors such as contact time, solution pH, initial geosmin concentration, and adsorbent dosage on adsorption performance was systematically investigated. Under optimal conditions, over 96% of geosmin was removed within 480 min. The adsorption kinetics were best described by the pseudo-first-order model (R2 = 0.9918), indicating that the process is primarily controlled by surface adsorption. Adsorption equilibrium data were well fitted by the Langmuir isotherm model (R2 = 0.9705) and a maximum monolayer capacity of 16.064 ng/g. The adsorbent exhibited 70% removal efficiency after three adsorption-desorption cycles, showing good regeneration potential, though long-term stability may be limited. Overall, the Bent-alg-mag beads proved to be an effective and promising material for the removal of geosmin from water.
Biological invasions, driven by the spread of non-native species, have become a critical global issue because of their far-reaching ecological and socioeconomic impacts. Effective communication of the risks of biological invasions is essential for implementing robust policy and legislation and gaining public support for conservation efforts. However, current policies often suffer from fragmentation and ineffectiveness, largely due to inadequate risk communication and complex multi-level governance. To address this challenge, we develop a global framework designed to enhance clearer communication about biological invasion risks. The framework contextualizes key terms across three domains in invasion science: species invasiveness, risk analysis, and decision support tools. Using both diffusion-of-English and ecology-of-language paradigms, and following a three-step process involving preliminary consensus, AI querying, and ground-truthing with final consensus, we validate the framework in 70 non-English languages which, together with English, have official status in at least one country and collectively cover all 195 countries worldwide. Our findings reveal that while terminology for risk analysis is well established, terminology for species invasiveness and, especially, for decision support tools remains underdeveloped in many languages, hindering effective communication and policy implementation. Our framework underscores the importance of cultural and political neutrality. By promoting clearer risk communication among scientists, policymakers, and the public globally, we aim to reduce policy fragmentation and foster enhanced collaboration in risk mitigation. We recommend expanding multilingual decision support tools to include the full risk analysis process: risk identification, risk assessment, and risk management. This will support intergovernmental mitigation efforts and promote a unified global response to biological invasions.
We report a sulfur-induced Janusization of GaSe (Se-Ga-Ga-Se) to form two-dimensional Janus S-Ga-Ga-Se atomic layers. GaSe single crystals were synthesized by vertical Bridgman method, and their exfoliated flakes were annealed in a sulfur atmosphere. Distinct Raman spectra were obtained above 390 degrees C with new peaks observed around 250 and 300 cm-1. This indicates the onset of Janus formation in consistent with first-principles calculations. Photoluminescence measurements yielded an emission peak at 2.15 eV, which lies between those of GaS and GaSe. Scanning electron microscope observations and energy-dispersive X-ray spectroscopy mappings had further confirmed the selective sulfur incorporation into the GaSe flakes. This study provides the first experimental evidence for the Janusization of GaSe. It establishes a basis for further structural analyses and a possible way for the Janusization of other layered group-13 monochalcogenides.
Quantum reinforcement learning has emerged as a framework combining quantum computation with sequential decision-making, and applications to the multi-armed bandit (MAB) problem have been reported. The graph bandit problem extends the MAB setting by introducing spatial constraints, where the accessibility of arms is restricted by graph connectivity, yet quantum approaches to this setting remain limited. In this paper, we formulate best-arm identification in graph bandits and propose a quantum algorithmic framework, termed quantum spatial best-arm identification, which is applicable to general graph structures. This framework uses quantum walks to encode superpositions over graph-constrained actions, thereby extending amplitude amplification and generalizing the quantum BAI algorithm via Szegedy’s walk framework. We focus our theoretical analysis on complete and bipartite graphs, deriving the maximal success probability of identifying the best arm and the time step at which it is achieved. Our results clarify how quantum walk-based search can be adapted to structurally constrained decision problems and provide a foundation for quantum best-arm identification in graph-structured environments.
This paper comprehensively surveys research trends in imitation learning (IL) for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge in robotics due to their nonlinear dynamics and sensitivity to small positional deviations. The paper examines demonstration collection methodologies, including teaching methods and sensory modalities crucial for capturing subtle interaction dynamics. We then analyze IL approaches, highlighting their applications to contact-rich manipulation. Recent advances in multimodal learning and foundation models have significantly enhanced performance in complex contact tasks across industrial, household, and healthcare domains. Through systematic organization of current research and identification of challenges, this survey provides a foundation for future advancements in contact-rich robotic manipulation.
