This review focuses on the use of metal–organic frameworks (MOFs) for environmental remediation through adsorption processes in both liquid and gas phases. Due to their high surface areas and chemical tunability, MOFs offer promising performance in adsorbing environmental pollutants compared to traditional materials. In this work, we discuss advanced synthesis techniques, including solvothermal, room temperature, and mechanochemical techniques, and how each technique influences the resulting MOF’s structural properties. Furthermore, we analyze the use of MOFs as adsorbents for CO2 and volatile organic compounds (VOCs) in the gas phase, as well as their role in removing heavy metals, fluorides, dyes, and emerging pharmaceutical contaminants. Although MOFs possess intrinsic limitations, such as instability in the presence of water and challenges in cyclic regeneration, their combination with other materials has aimed to overcome these drawbacks by leveraging the best properties of each component in new hybrid materials. Finally, we evaluate various challenges, such as large-scale implementation, toxicity, and long-term stability, proposing sustainable solutions for environmental remediation.
We give the construction of the universal, natural up to homotopy Chern-Weil differential graded algebra homomorphism: cw : I(G) -> Omega*(BG,R) for infinite-dimensional Milnor regular Lie groups G, where Omega*(BG,R) is a certain de Rham algebra of BG (Milnor BG up to a natural weak homotopy equivalence) and where I(G) is the algebra of continuous Ad(G) invariant multilinear functionals on the Lie algebra. In particular, this applies to the group of compactly generated Hamiltonian symplectomorphisms, using which we verify a conjecture of Reznikov. For the construction of cw, we introduce a basic geometric-categorical notion of a smooth simplicial set. Loosely, this is to Chen spaces as simplicial sets are to spaces. We then give a new construction of the classifying space of G as a smooth Kan complex, with the geometric realization weakly equivalent to the Milnor BG.
Eating behaviours influence food intake and long-term eating habits. This study aimed to assess the association between specific eating behaviour traits and the consumption of food and beverage in children aged 3 to 6 years, based on data from the CORAL study. Data were obtained from the Spanish CORAL study, a 10-year longitudinal cohort including 1407 participants (699 boys, 708 girls; 4.8 ± 1.0 years). Eating behaviours were assessed using the Child Eating Behaviour Questionnaire (CEBQ) and dietary intake was measured using a validated COME-Kids Food and Beverage Frequency Questionnaire. Principal Component Analysis (PCA) was applied to identify dietary patterns. Associations were analysed with multivariable linear regression adjusted for covariates. In both sexes, higher Enjoyment of Food scores were associated with greater consumption of fish, fruits, vegetables, pulses, whole grains, and lower consumption of sweets. In contrast, higher Food Fussiness scores were associated with lower consumption of fruits, vegetables, fish, and pulses, and higher intake of sweets. PCA revealed five dietary patterns per sex, explaining 36.62
A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions-purposively selected from 24 runs monitored during a six-month deployment-span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency Gamma s >= 0.98), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring.
We have performed a detailed exploration of the energy landscape for configurations of points on the sphere, interacting via the logarithmic potential, and corresponding to local minima of the total energy, up to N = 160 . The growth of N_conf (number of distinct configurations) is exponential, as for the Thomson problem, although weaker. Using the techniques described in our previous paper [1] we have also explored the solution landscape of this problem for N ≤ 24 , and found that the number of stationary states is growing exponentially.
