Comparison the efficiency of Fenton and Photo–fenton processes for the removal of Reactive red 198 Dye from the aqueous solution

The textile industry produces a large amount of polluted effluents discharged into the environment. Therefore, this research was carried out to compare the efficiency of removal of Reactive red 198 (RR–198) dye by fenton and photo–fenton processes and determine the optimal conditions for maximum removal. This study was conducted on a laboratory scale. The effect of influential parameters, including pH (3–9), Fe(II) concentration (10–200mg/L), H2O2 concentration (25–150mg/L), dye concentration (50–200mg/L) and reaction time (15–90min.) on dye removal was investigated and the optimal conditions were determined according to the maximum dye removal efficiency. The results indicated that the dye removal rate increased as the pH and Fe(II) concentration decreased. The optimal conditions for RR–198 removals from the aqueous solution are pH of 3, Fe (II) concentration of 10mg/L, H2O2 concentration of 50mg/L, the initial dye concentration of 50mg/L, and the reaction time of 15min.. The maximum dye removal efficiency under optimal conditions was 98.82%. The results of this study revealed that the photo–fenton process was superior to the removal of dye compared to fenton process

Explainable Predictive Maintenance

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.Comment: 51 pages, 9 figure

Explain your clusters with words : The role of metadata in interactive clustering

In this preliminary work, we present an approach for the augmentation of clustering with natural language explanations. In clustering there are 2 main challenges: a) choice of a proper, reasonable number of clusters, and b) cluster analysis and profiling. There is a plethora of technics for a) but not so much for b), which is in general a laborious task of explaining obtained clusters. We propose a method that aids experts in cluster analysis by providing an iterative, human-in-the-loop methodology of generating cluster explanations. In an illustrative example, we show how the process of clustering on a set of objective variables could be facilitated with textual metadata. In our case, images of products from online fashion store are used for clustering. Then, product descriptions are used for profiling clusters. © 2022 Copyright for this paper by its authors. This paper is funded from the XPM (ExplainablePredictive Maintenance) project funded by the National Science Center, Poland under CHIST-ERAprogramme Grant Agreement No. 857925 (NCNUMO-2020/02/Y/ST6/00070).The work of Szymon Bobek has been additionallysupported by a HuLCKA grant from the PriorityResearch Area (Digiworld) under the Strategic Programme Excellence Initiative at the JagiellonianUniversity (U1U/P06/NO/02.16).The work of Samaneh Jamshidi was supportedby CHIST-ERA grant CHIST-ERA-19-XAI-012funded by Swedish Research Council.</p

Impact and Fracture Strength of Simulated Immature Teeth Treated with Mineral Trioxide Aggregate Apical Plug and Fiber Post Versus Revascularization

Introduction: Immature necrotic teeth are at a high risk of fracture, especially at the cervical region, after treatment. This study aimed to compare the impact and fracture strength of immature permanent teeth treated with revascularization versus a mineral trioxide aggregate (MTA) plug and fiber post. Methods: This in vitro, experimental study was conducted on 160 maxillary central incisors, which were randomly divided into 10 groups. The groups included a fracture (F) and impact (I) negative control group, F and I positive control groups, F and I MTA groups, F and I revascularizing group, and F and I revascularized groups. Fracture strength was measured using a universal testing machine with a crosshead speed of 1 mm/min. Other tooth samples were then subjected to the Charpy impact test for impact strength measurements, and the amount of energy absorbed by the teeth was determined. Data were analyzed using the Kolmogorov-Smirnov test, analysis of variance, and the Tukey test. Results: The mean load to fracture of the negative, positive, MTA, revacularizing, and revascularized groups was 1931.8, 1350.1, 1003.8, 1262.5, and 1100.2 N, respectively, and the mean impact strength was 5.04, 3.6, 3.68, 3.16, and 3.65 J, respectively. The fracture and impact strength of the negative control group was significantly higher than that of the other groups (P \u3c .05), but the other groups were not significantly different in this respect (P \u3e .05). Conclusions: Despite the limitations of this study, the results showed that none of the tested modalities could significantly increase the impact and fracture strength of simulated immature teeth