This paper looks into the extension of the pymcdm library, focusing on reference point-based techniques. It introduces the implementations of methods such as the Reference Ideal Method (RIM), Preference Ranking On the Basis of Ideal-average Distance (PROBID), and Election based on Relative Value Distances (ERVD). This update is intended to meet the increasing demand for solutions tailored to decision makers’ expertise and experience. Furthermore, it introduces techniques related to sensitivity analysis and weight comparison factors of criteria. By expanding this library, the range of MCDA/MCDM tools is broadened and further progress is encouraged in the use of expert knowledge and the implementation of compromise methods.
This article analyses the performance characteristics of XGBoost models across multiple datasets for phishing URL detection, extending our previous conference paper with comprehensive cross-dataset validation and comparative analysis. Using a validation framework with three distinct datasets — a custom phishing dataset (75,738 samples), the large-scale GramBeddings dataset (639,723 samples), and the PhiUSIIL dataset (47,103 samples)—we demonstrate that XGBoost delivers robust performance across diverse data sources. Our approach addresses the issue of feature instability, showing how balanced feature engineering is crucial for reliable detection. The model achieves 90.7% accuracy and 91.2% F1 score on the custom dataset, with a solid 77.8% average accuracy across three independent test sets. In comparative benchmarks against Neural Networks, XGBoost proved superior in training efficiency and detection quality, achieving 2.1% higher accuracy and training 12.6 times faster (0.95s vs 12.01s) with 3.6 times less memory. Although Neural Networks offered faster inference (7.51ms vs 20.6ms) and smaller model sizes, XGBoost’s balance of high accuracy and rapid training makes it highly effective for practical, real-world phishing detection systems.
Developing efficient ammonia synthesis catalysts is key to reducing energy consumption and improving sustainability. This study explores barium-promoted cobalt catalysts supported on lanthanide oxides (La2O3, Nd2O3, Sm2O3, Eu2O3, Gd2O3) to understand how support choice influences catalytic performance. The catalysts were characterised using techniques such as X-ray powder diffraction (XRPD), high-resolution transmission electron microscopy (HRTEM), and temperature-programmed desorption (H2-TPD, CO2-TPD). Testing under industrially relevant conditions (400-470 degrees C, 6.3 MPa, H2/N2 = 3) revealed that lanthanide oxides strongly affect catalysts' activity, reducibility, and hydrogen adsorption. Among the tested catalysts, the La2O3-supported system exhibited the highest ammonia synthesis activity (ravg = 1.90gNH3 center dot g-cat 1 center dot h-1), likely due to its favorable hydrogen sorption properties and larger active phase surface area available for hydrogen (31 m2 & sdot;gCo-1). These findings highlight the potential of lanthanide oxides as supports and the importance of barium as a promoter in cobalt-based catalysts for ammonia synthesis.
Accurate compensation of force-induced machining errors in subtractive processes such as milling, grinding, and polishing, whether performed on machine tools or articulated robots, depends on reliable prediction of tool deflection from machine stiffness and cutting forces. However, both inputs are constrained by measurement uncertainties. This paper introduces a measurement-oriented framework that applies uncertainty analysis to evaluate the reliability of tool deflection prediction. Specifically, an acceptance criterion for measurement process capability is proposed, enabling evaluation of whether current measurement systems can meet tolerance requirements in machining. A parametric analysis is carried out across representative ranges of cutting forces (50-800 N), machine stiffness values (2-30 N/mu m), and standard tolerance grades. The results identify conditions under which existing approaches to stiffness and cutting force measurement are adequate, as well as scenarios - particularly for lowstiffness machines and tight-tolerance applications - where they fail due to excessive uncertainty. Cutting force measurement, supported by established calibration standards, is found sufficiently mature for most medium- and high-stiffness applications. In contrast, stiffness measurement lacks a universally accepted measurand, and interlaboratory validation, limiting its reliability for compensation tasks. The findings highlight that advancing stiffness metrology is critical to enabling practical and trustworthy compensation of force-induced machining errors in both machine tools and robots.
The escalating plastic pollution and the urgent demand for high-performance electrode materials for supercapacitors have become two critical global challenges. Herein, we report a green and efficient one-pot in situ activation strategy to upcycle waste agricultural plastic films into hierarchical porous carbon nanomaterials (PCNs) using potassium citrate as a bifunctional template and activator. This strategy integrates carbonization, pore formation, and activation into a single step, avoiding the use of corrosive acids, alkalis, or toxic solvents, and only requires water-based post-purification, which is in line with the principles of green chemistry. The optimized PCN-700 exhibits a well-defined hierarchical porous structure with a high specific surface area of 1131.9 m2 g-1 and a moderate defect density. When employed as an electrode material for supercapacitors, PCN-700 achieves an outstanding specific capacitance of 276.3 F g-1 at 0.5 A g-1. The symmetric supercapacitor (SC) based on PCN-700 electrodes delivers an energy density of 47.9 Wh kg-1 at 510.1 W kg-1, and retains an energy density of 36.9 Wh kg-1 even when the power density is boosted to an ultrahigh level of 5209.4 W kg-1. Moreover, the device demonstrates excellent long-term cycling stability, maintaining 88.9% of its initial capacitance with a coulombic efficiency of 99.8% after 10 000 consecutive charge-discharge cycles at a relatively high current density of 5 A g-1. Practical application validation demonstrates that two assembled SC coin cells can successfully lit a 2 V LED and sustain its stable illumination for 80 s. This work not only offers a sustainable route to valorize non-recyclable plastics but also develops low-cost, high-performance porous carbons for advanced energy storage, achieving synergistic gains for environmental protection and energy storage advancement.
