The mechanical and microstructural responses of ordinary Portland cement (OPC) and one-part geopolymer concrete (OPGC) to fibre reinforcement have not been scientifically explored, hence generating a research gap. As more construction industries seek high-performance and ecologically friendly building materials, geopolymer concrete is becoming more popular as an alternative to OPC. Because it does not need liquid activators, a one-part geopolymer system is advantageous, and adding fibres increases its tensile and flexural strength. Despite these benefits, a comprehensive evaluation of its strength performance in comparison to conventional OPC concretes requires more research. Four concrete mixes, which are ordinary Portland cement (OPC), fibre-reinforced ordinary Portland cement (FROPC), one-part geopolymer concrete (OPGC), and fibre-reinforced one-part geopolymer concrete (FROPGC), are examined in this study for their fresh, mechanical, and micro-structural characteristics. Workability was assessed using slump tests, and at 7, 14, and 28 days, compressive, flexural, and split tensile strengths were measured. Stiffness, permeability, and internal quality were evaluated using the modulus of elasticity, water absorption, and ultrasonic pulse velocity (UPV), and microstructural examination was conducted using scanning electron microscopy (SEM). According to the findings, geopolymer concretes had better fresh qualities than OPC, with slump that were 20-31% higher. While fibre insertion greatly increased tensile and flexural strengths, it decreased workability. The highest compressive strength (63.78 MPa) was obtained by OPGC, whereas the highest flexural (15.1 MPa) and tensile (9.15 MPa) strengths were attained by FROPGC. Additionally, FROPGC showed the highest modulus of elasticity (44,040 N/mm2), and a refined microstructure with fewer vacancies and well-bonded fibres was shown in FROPC and FROPGC.
We introduce a formally verifiable framework for multi-agent coordination, grounded in the mathematical language of topos and sheaf theory. The proposal is to achieve robust and predictable collective behavior in complex, decentralized systems. The proposed framework models a multi-agent system as a cellular sheaf, where agents' local states and beliefs are represented as data on stalks, and inter-agent constraints are encoded in restriction maps. Information from the environment is assimilated by individual agents through the categorical operation of a pullback, creating a contextualized basis for reasoning. Deliberation is performed via constructive proof within the topos's internal intuitionistic logic, ensuring that all deductions are verifiable. These locally-derived “proof objects” are then synthesized into a globally consistent state of agreement-a global section of the sheaf-via distributed consensus dynamics. This approach offers significant advantages in formal verification, inherent support for agent heterogeneity, and explainability, paving the way for a new class of provably reliable autonomous systems.
Electrocoagulation (EC) has emerged as a viable alternative for oily wastewater treatment, offering distinct advantages over conventional treatment methods. In EC, an electric current dissolves sacrificial anode, releasing metal ions that help clump and remove a wide range of contaminants. This technology is applicable across diverse industries and effectively addresses complex water challenges, including oil-contaminated wastewater. The efficiency of EC is influenced by operational parameters such as current density, electrode material, pH, electrode spacing, and treatment time, all of which require careful optimization. Notwithstanding its benefits, such as ease of operation, minimal chemical usage, and small sludge production, EC faces challenges related to electrode passivation and energy consumption, necessitating enhancement strategies. Given the growing interest in EC and the diversity of treatment conditions, a comprehensive synthesis of current knowledge is essential to guide both research and industrial applications. This review evaluates the principles and mechanisms of EC, the role of operational parameters, and strategies for process optimization. Recent innovations are highlighted, particularly hybrid systems that integrate EC with membrane filtration, advanced oxidation processes, and renewable energy sources. Applications across various wastewater types are discussed, alongside economic feasibility and scalability considerations. By identifying key research gaps, particularly in system scale-up, cost reduction, and long-term performance, this review provides a comprehensive resource to inform the future development of sustainable oily wastewater treatment technologies.
We detail the construction of an “Explanation Topos,” a mathematical framework designed to model and reason about knowledge in multi-agent systems. The foundation of this framework is a novel base site, defined as a spatiotemporal poset that captures the causal structure of agent interactions, including memory and communication. On this site, we construct a sheaf of annotated knowledge, which assigns to each context a set of locally consistent knowledge records, each tagged with provenance and temporal metadata. The Explanation Topos is the category of all sheaves on this site. Its primary power lies in its internal logic, which provides a language to express and prove properties about the knowledge it contains from within the model itself. We demonstrate how to define predicates as subobjects within the topos to formally explain provenance, temporality and consistency. The result integrates network theory, database theory, and formal logic, offering a foundational model for distributed intelligence where logical reasoning emerges from the dynamic structure of the system.
Este estudio presenta el proceso de diseño, validación y análisis psicométrico de un cuestionario para medir el uso académico de la inteligencia artificial generativa (IAGen) en estudiantes de educación superior. La validez de contenido se estableció mediante el coeficiente V de Aiken con jueces expertos, lo que permitió ajustar y depurar ítems. Posteriormente, se aplicó el cuestionario a una muestra de 905 estudiantes universitarios. El Análisis Factorial Exploratorio (AFE) identificó una estructura de siete factores, en lugar de las nueve dimensiones iniciales, explicando el 64% de la variabilidad total. Las cargas factoriales oscilaron entre .44 y .96, con la proporción de varianza de cada ítem explicada por los factores entre .41 y .79. El Análisis Factorial Confirmatorio (AFC) mostró que el modelo de siete dimensiones se ajustó mejor a los datos (CFI = .90, TLI = .90, RMSEA = .06, SRMR = .04) que el modelo teórico de nueve. En conclusión, el instrumento constituye una herramienta válida, confiable y aplicable para evaluar percepciones estudiantiles sobre el uso académico de la IAGen en educación superior.
