Response to Dr. Nicholas Foray’s commentary on the paper by Rousseau et al. entitled “Efficacy of intracerebral delivery of cisplatin in combination with photon irradiation for treatment of brain tumors”

Journal of Neuro-Oncology -     

A review of Kallmann syndrome: genetics, pathophysiology, and clinical management

Kallmann syndrome is a genetic disorder with the hallmarks of anosmia and hypogonadotrophic hypogonadism. It has a male preponderance. With the elucidation of the genetic pathways involved, affected females and inheritance patterns are becoming more clearly identified. It is an eminently treatable disorder, but it must first be recognized by the physician. With treatment, favorable reproductive outcomes can be attained in addition to maturation of secondary sex characteristics.     

Untersuchungen über die Brauchbarkeit der neuen Desinfektionsmittel „Delusan” und „Percid” im Vergleich zu Sagrotan und Zephirol unter Anwendung mehrerer Untersuchungsverfahren

Ohne Zusammenfassung     

Studies of Selective Recovery of Zinc and Manganese from Alkaline Batteries Scrap by Leaching and Precipitation

Recovery of zinc and manganese from scrapped alkaline batteries were carried out in the following way: leaching in H₂SO₄ and selective precipitation of zinc and manganese by alkalization/neutralization. As a result of non-selective leaching, 95.6–99.7% Zn was leached and 83.7–99.3% Mn was leached. A critical technological parameter is the liquid/solid treatment (l/s) ratio, which should be at least 20 mL∙g⁻¹. Selective leaching, which allows the leaching of zinc only, takes place with a leaching yield of 84.8–98.5% Zn, with minimal manganese co-leaching, 0.7–12.3%. The optimal H₂SO₄ concentration is 0.25 mol∙L⁻¹. Precipitation of zinc and manganese from the solution after non-selective leaching, with the use of NaOH at pH = 13, and then with H₂SO₄ to pH = 9, turned out to be ineffective: the manganese concentrate contained 19.9 wt.% Zn and zinc concentrate, and 21.46 wt.% Mn. Better selectivity results were obtained if zinc was precipitated from the solution after selective leaching: at pH = 6.5, 90% of Zn precipitated, and only 2% manganese. Moreover, the obtained concentrate contained over 90% of ZnO. The precipitation of zinc with sodium phosphate and sodium carbonate is non-selective, despite its relatively high efficiency: up to 93.70% of Zn and 4.48–93.18% of Mn and up to 95.22% of Zn and 19.55–99.71% Mn, respectively for Na₃PO₄ and Na₂CO₃. Recovered zinc and manganese compounds could have commercial values with suitable refining processes.     

Algorithm for Determining Time Series of Phase Transformations in the Solid State Using Long-Short-Term Memory Neural Network

In the numerical analysis of manufacturing processes of metal parts, many material properties depending on, for example, the temperature or stress state, must be taken into account. Often these data are dependent on the temperature changes over time. Strongly non-linear material property relationships are usually represented using diagrams. In numerical calculations, these diagrams are analyzed in order to take into account the coupling between the properties. An example of these types of material properties is the dependence of the kinetics of phase transformations in the solid state on the rate and history of temperature change. In literature, these data are visualized Continuous Heating Transformation (CHT) and Continuous Cooling Transformation (CCT) diagrams. Therefore, it can be concluded that time series analysis is important in numerical modeling. This analysis can also be performed using neural networks. This work presents a new approach to storing and analyzing the data contained in the discussed CCT diagrams. The application of Long-Short-Term Memory (LSTM) neural networks and their architecture to determine the correct values of phase fractions depending on the history of temperature change was analyzed. Moreover, an area of research was elements that determine what type of information should be stored by LSTM network coefficients, e.g., whether the network should store information about changes of single phase transformations, or whether it would be better to extract data from differences between several networks with similar architecture. The purpose of the studied network is strongly different from typical applications of artificial neural networks. The main goal of the network was to store information (even by overfitting the network) rather than some form of generalization that allows computation for unknown cases. Therefore, the authors primarily investigated in the ability of the layer-based LSTM network to store nonlinear time series data. The analyses presented in this paper are an extension of the issues presented in the paper entitled “Model of the Austenite Decomposition during Cooling of the Medium Carbon Steel Using LSTM Recurrent Neural Network”.