The rising digitisation of the construction sector has increased the demand for automated, data-driven decision-making in BIM-enabled operations. However, material routing decisions in modular construction are still primarily manual or based on opaque optimisation models, which limits their integration with construction automation systems. To solve this, the Explainable AI-Integrated Multi-Objective Optimisation (XAIOM) framework is presented, which allows for automated material routing straight from BIM-derived data. The system combines machine learning (ML), SHAP and LIME explainability, non-dominated sorting genetic algorithm II (NSGA-II) optimisation, and Monte Carlo uncertainty analysis into a single pipeline. Explainability is built into the optimisation process to aid the development of Pareto-optimal solutions based on feature contributions. Validation of a modular residential construction in Finland shows clear trade-offs between CO₂ reduction and economic value under uncertainty. The findings confirm that the framework enables transparent, reliable, and scalable automated decision-making, advancing AI-driven construction automation by linking BIM-based digital models with intelligent optimisation workflows.
Per- and Polyfluoroalkyl Substances (PFAS) are characterized by strong carbon–fluorine bond formation. Those strong C–F bond formations justify the applicability of PFAS as a strong performer across industrial applications, due to their comprehensive properties, including resistance to water, heat, and chemicals, which lead to gradual accumulation and cause adverse health impacts. The complex behavior of PFAS in soil with different physicochemical characteristics has resulted in a lack of studies that utilize the molecular-level behavior in soil for further modelling purposes, which is important for a strong foundation for remediation measures. Therefore, this study focuses on elucidating theoretical, novel descriptions to fulfill the above-identified requirements. The developed paradigm, “Hydrophobically Driven Paradigm”, explains the strong attachment of PFAS to soil and the building of the PFAS-soil interface, employing the soil–water partitioning coefficient for clay soil from the literature. After confirming the strong attachment of PFAS to soil particles, the molecular-level interactions between PFAS and soil particles were explained by the framework, “System Retention Framework,” based on PFAS Zeta Potential (ZP) calculations derived under electrokinetic principles to describe the molecular-level retention behavior of PFAS. ZP values for two long-chain and short-chain PFAS were calculated using Henry’s and Nernst-Einstein equations. Results highlight that PFAS with higher ZP values have higher retention, while lower ZP values exhibit lower retention, in soil, confirming that the paradigm and framework can be used to define charge-driven interactions, followed by hydrophobically energized strong attractions, which are readily available for describing PFAS molecular-level behavior for future modelling with system contaminants.
Career selection is a multi-criteria decision-making problem that can be challenging, especially when uncertainties are involved. To tackle such a problem, several decision-making techniques have been used to compare and rank the alternatives. Pythagorean Fuzzy Sets (PyFS), which are used to describe uncertainty, have also been used by some researchers in attempts to solve decision making applications. This paper introduces PyFS in type-2 Fuzzy Environments as type-2 PyFS (T2PyFS) to handle the uncertainty of a decision making problem more appropriately. Unlike conventional PyFS, T2PyFS incorporates secondary membership functions that capture higher-order uncertainty and hesitation in expert judgments, thereby providing a more realistic and flexible modeling framework. We define several arithmetic operations and algebraic properties related to it, and propose its level sets, we investigate related properties. We also develop the Hamming and Euclidean distance of our T2PyFS, and design three decision making algorithms based on the level sets, max-min-max composition and distance measure. Along the lines of academic performance, we apply the proposed decision making algorithms to quantify and compare the different criteria and alternatives systematically. Finally, we conclude this study with a comparative analysis of the proposed algorithms.
Recently, the world has been living in a situation of constant uncertainty and insecurity. Russia’s aggression against Ukraine has entered its fifth year; the conflict between Israel and Hamas has been going on for more than two years; there is also long-standing tension between China and Taiwan, etc. So, it would not be wrong to say that we live in the presence of the constant threat of global war. This is especially true of the countries of Eastern Europe, and even more so of the Baltic states living in the neighbourhood of aggressive Russia. Under such conditions, these states should be ready for a defensive war. And here a whole series of theoretical–philosophical questions arise. What kind of war is moral and just? What are the prerequisites and conditions for such a war? What are the rules, norms, and principles for waging it? In the pursuit of answers, it is imperative to keep in view a historical perspective, thereby facilitating the acquisition of insights from past missteps. A pertinent exemplar of this perspective is the theory of the just war as expounded by the scholars of modern scholasticism during the fifteenth to eighteenth centuries. The most prominent proponents of this theory were Francisco de Vitoria (1480–1546), a member of the Dominican Order, and Francisco Suárez (1548–1617), a member of the Society of Jesus. These scholars built upon the philosophical contributions of St. Augustine and St. Thomas Aquinas, adapting them to the emerging geopolitical landscape of the New World, which was being explored and colonised during this period. The fundamental rights of nations and states, as well as the conditions, rules and principles of their defence in a just war (bellum justum), were established by their works, which were published prior to Hugo Grotius’s treatises. In fact, most of these principles and rules remain relevant in the contemporary world, where war is an inseparable element of everyday reality. This article focuses specifically on these issues, as well as their applicability in the contemporary context of troubled times.
Research on alternative fuels has received increased attention in recent years because they not only address the sustainability issues associated with fossil fuels but also mitigate the harmful gases released during combustion. This study focuses on the use of recycled transformer oil (WTO) in diesel engines, aiming to harness its energy content, which is typically discarded as waste. The WTO was recycled via catalytic cracking, and CCWTO50 (a 50% CCWTO and 50% neat diesel blend) was used as the test fuel. Furthermore, a re-entrant combustion chamber (RCC) and a hemispherical open-type combustion chamber (HCC) were employed to investigate the combustion and emission characteristics of WTO. The combustion characteristics, engine performance, and exhaust gas emission levels were recorded when the CCWTO50 powered the engine for both HCC and RCC geometries. The results indicate that CCWTO50 improved engine performance and reduced emissions compared to diesel fuel. Further improvements were observed when RCC was employed. A higher heat release rate of 169.3 kJ/m3/°C and a peak pressure of 61.54 bar were observed for CCWTO50-RCC. CCWTO50-RCC improved brake thermal efficiency by 3% compared to diesel-HCC. In addition, CCWTO50-RCC reduced emissions of hydrocarbons (HC), carbon monoxide (CO), and smoke by 8%, 6%, and 5%, respectively, compared to diesel-HCC. Thus, the authors suggest that CCWTO50 with RCC can be effectively used in diesel engines to enhance engine performance and reduce exhaust gas emissions.
