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Is God an Abstract Entity?
No full textAccording to the abstract notion of God, God is an abstract object, and we use theological intuition to know about God. According to the concrete notion of God, by contrast, God is a concrete object, and we use the same cognitive faculties to know about God and other concrete objects. I raise the following objections to the abstract notion. It multiplies cognitive faculties beyond necessity. It conflicts with the Quine-Putnam indispensability argument, with the theological view that God created the world, with the traditional arguments for the existence of God, and with the notion of God in the Bible. The concrete notion works better than the abstract notion for religious practitioners
Photodynamic treatment of acute vascular occlusion by using an iron-nitrosyl complex
No full textRetinal vascular occlusion (RVO) is a common cause of visual impair-ment. Although several approaches, including vasodilators, have been explored to treat retinal vascular occlusion, there is no proper method to treat this obstruction today. We report a strategy that aims to pierce clogged blood vessels with a spatiotemporally controllable nitric oxide transporter, [Fe(TBDAP)(NO)(H2O)]2+ (1), which was synthesized and precisely characterized by various phys-icochemical methods, including X-ray crystallography. In the animal model, normal retinal blood vessels were confirmed to be dilated by the photoresponsive iron-nitrosyl complex. Furthermore, occluded retinal blood vessels were effectively reperfused after the immedi-ate delivery of nitric oxide using light in animal disease models. These studies suggest an unprecedentedly selective and control-lable treatment option for acute vascular occlusive diseases, including cardiovascular and cerebrovascular diseases
Alkyl chain length of quaternized SBA-15 and solution conditions determine hydrophobic and electrostatic interactions for carbamazepine adsorption
No full textSanta Barbara Amorphous-15 (SBA) is a stable and mesoporous silica material. Quaternized SBA-15 with alkyl chains (QSBA) exhibits electrostatic attraction for anionic molecules via the N+ moiety of the ammonium group, whereas its alkyl chain length determines its hydrophobic interactions. In this study, QSBA with different alkyl chain lengths were synthesized using the trimethyl, dimethyloctyl, and dimethyoctadecyl groups (C1QSBA, C8QSBA, and C18QSBA, respectively). Carbamazepine (CBZ) is a widely prescribed pharmaceutical compound, but is difficult to remove using conventional water treatments. The CBZ adsorption characteristics of QSBA were examined to determine its adsorption mechanism by changing the alkyl chain length and solution conditions (pH and ionic strength). A longer alkyl chain resulted in slower adsorption (up to 120 min), while the amount of CBZ adsorbed was higher for longer alkyl chains per unit mass of QSBA at equilibrium. The maximum adsorption capacities of C1QSBA, C8QSBA, and C18QSBA, were 3.14, 6.56, and 24.5 mg/g, respectively, as obtained using the Langmuir model. For the tested initial CBZ concentrations (2???100 mg/L), the adsorption capacity increased with increasing alkyl chain length. Because CBZ does not dissociate readily (pKa???=???13.9), stable hydrophobic adsorption was observed despite the changes in pH (0.41???0.92, 1.70???2.24, and 7.56???9.10 mg/g for C1QSBA, C8QSBA, and C18QSBA, respectively); the exception was pH 2. Increasing the ionic strength from 0.1 to 100 mM enhanced the adsorption capacity of C18QSBA from 9.27????????0.42 to 14.94????????0.17 mg/g because the hydrophobic interactions were increased while the electrostatic attraction of the N+ was reduced. Thus, the ionic strength was a stronger control factor determining hydrophobic adsorption of CBZ than the solution pH. Based on the changes in hydrophobicity, which depends on the alkyl chain length, it was possible to enhance CBZ adsorption and investigate the adsorption mechanism in detail. Thus, this study aids the development of adsorbents suitable for pharmaceuticals with controlling molecular structure of QSBA and solution conditions
All solid battery for enhancing energy density, and method of manufacturing the same
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Development of an artificial neural network model for generating macroscopic cross-sections for RAST-AI
No full textHomogenized macroscopic cross-sections (XS) are necessary for running core-wise nodal diffusion calculations. XS sets are usually generated using time-consuming lattice physics codes. In this study, a pre-trained artificial neural network was developed and used for XS generation. The model was trained to produce macroscopic XS, pin powers, and assembly discontinuity factors for 16 x 16 and 17 x 17 fuel assembly types with independent variable enrichments of each fuel pin loaded with fresh UO2 fuel without burnable poisons. The training dataset optimization method was described and used for defining the required number of variations in input parameters, such as pin arrangements and thermal hydraulics parameters. The optimized dataset's generation took only 248 core-hours, which is below 3 days on a modern 4-core CPU. For the worst-case out-of-range testing data, the maximum observed difference with the reference was found below 3% for pin powers, and below 4.5% for XS values
Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning
No full textUrban rainfall events can lead to the runoff of pollutants, including industrial, pesticide, and pharmaceutical chemicals. Transporting micropollutants (MPs) into water systems can harm both human health and aquatic species. Therefore, it is necessary to investigate the dynamics of MPs during rainfall events. However, few studies have examined MPs during rainfall events due to the high analytical expenses and extensive spatiotemporal variability. Few studies have investigated the occurrence patterns of MPs and factors that influence their transport, such as rainfall duration, antecedent dry periods, and variations in streamflow. Moreover, while there have been many analyses of nutrients, suspended solids, and heavy metals during the first flush effect (FFE), studies on the transport of MPs during FFE are insufficient. This study aimed to identify the dynamics of MPs and FFE in an urban catchment, using high-resolution monitoring and machine learning methods. Hierarchical clustering analysis and partial least squares regression (PLSR) were implemented to estimate the similarity be-tween each MP and identify the factors influencing their transport during rainfall events. Eleven dominant MPs comprised 75% of the total MP concentration and had a 100% detection frequency. During rainfall events, pesticides and pharmaceutical MPs showed a higher FFE than industrial MPs. Moreover, the initial 30% of the runoff volume contained 78.0% of pesticide and 50.1% of pharmaceutical substances for events W1 (July 5 to July 6, 2021) and W6 (August 31 to September 1, 2021), respectively. The PLSR model suggested that stormflow (m3/s) and the duration of antecedent dry hours (h) significantly influenced MP dynamics, yielding the variable importance on projection scores greater than 1.0. Hence, our findings indicate that MPs in urban waters should be managed by considering FFE
Real-time in-depth damage identification and health index system for carbon fiber-reinforced composites using electromechanical behavior and data processing tools
No full textStructural health monitoring using electromechanical behavior can help detect various damage types and failure modes in composites. However, only the presence of damage and structural failure can be monitored. For a thorough identification of damage in composites, this paper proposes an electromechanical data analysis and processing methodology using principal component analysis and k -means clustering. The health state of unidi-rectional carbon fiber-reinforced plastic (CFRP) composites was monitored using self-sensing data. Various types of damage and failure modes in carbon fibers with different directionality were investigated based on in-depth damage analysis using a machine-learning-based data processing technique. A novel health index system for damage propagation investigation was proposed based on an electromechanical behavior analysis. The results produced by the damage index system were compared with those obtained by ABAQUS simulation and me-chanical behavior analysis to determine the rationality of the system. An advanced condition-based monitoring methodology can help investigate the current health state of composites and the propagation of different types of damage. The proposed system has potential applications, and our results provide guidelines for self-sensing research