Following photodissociation of gaseous acryloyl chloride, CH2CHC(O)Cl, at 193 nm, temporally resolved vibration-rotational emission spectra of HCl (v ≤ 7, J ≤ 35) in region 2350-3250 cm(-1) and of CO (v ≤ 4, J ≤ 67) in region 1865-2300 cm(-1) were recorded with a step-scan Fourier-transform spectrometer. The HCl emission shows a minor low-J component for v ≤ 4 with average rotational energy Erot = 9 ± 3 kJ mol(-1) and vibrational energy Evib = 28 ± 7 kJ mol(-1) and a major high-J component for v ≤ 7 with average rotational energy Erot = 36 ± 6 kJ mol(-1) and vibrational energy Evib = 49 ± 9 kJ mol(-1); the branching ratio of these two channels is ∼0.2:0.8. Using electronic structure calculations to characterize the transition states and each intrinsic reaction coordinate, we find that the minor pathway corresponds to the four-center HCl-elimination of CH2ClCHCO following a 1,3-Cl-shift of CH2CHC(O)Cl, whereas the major pathway corresponds to the direct four-center HCl-elimination of CH2CHC(O)Cl. Although several channels are expected for CO produced from the secondary dissociation of C2H3CO and H2C═C═C═O, each produced from two possible dissociation channels of CH2CHC(O)Cl, the CO emission shows a near-Boltzmann rotational distribution with average rotational energy Erot = 21 ± 4 kJ mol(-1) and average vibrational energy Evib = 10 ± 4 kJ mol(-1). Consideration of the branching fractions suggests that the CO observed with greater vibrational excitation might result from secondary decomposition of H2C═C═C═O that was produced via the minor low-J HCl-elimination channel, while the internal state distributions of CO produced from the other three channels are indistinguishable. We also introduce a method for choosing the correct point along the intrinsic reaction coordinate for a roaming HCl elimination channel to generate a Franck-Condon prediction for the HCl vibrational energy.

Taiwan has a lower incidence rate of PCKD than Western countries. In Taiwan, there is little difference in the long-term survival between dialysis patients with and without PCKD.

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The effects of pH, contact time, initial dye concentration, numbers of dip coating, light sources and repetitive usage of dip-coated TiO2 were studied in batch experiments in order to investigate the photodegradation efficiency of malachite green in aqueous solution by using titanium dioxide, TiO2 immobilized on glass plates. The photodegradation of malachite green was found to be more effective at lower initial dye concentration. Kinetic studies showed good correlation coefficient for a pseudo-first order kinetic model. The removal of malachite green was dependent on the TiO2 loading where the percentage removal of malachite green was 92.15, 94.28 and 98.43 % for 5, 10 and 15 number of TiO2 dip-coating, respectively. Among the three light sources used, sunlight possessed the highest removal efficiency with 100 % of removal of dye in 6 h of irradiation. The degradation of malachite green was enhanced in basic solution compared with acidic solution due to the amphoteric property of TiO2. The decolourization efficiency was found to be decreased after each repetitive usage of dip-coated TiO2 glass plates. However, the immobilized TiO2 still displayed a good performance in the removal of malachite green.

Background: Large financial companies are perpetually creating and updating customer scoring techniques. From a risk management view, this research for the predictive accuracy of probability is of vital importance than the traditional binary result of classification, i.e., non-credible and credible customers. The customer's default payment in Taiwan is explored for the case study. Objective: The aim is to audit the comparison between the predictive accuracy of the probability of default with various techniques of statistics and machine learning. Method: In this paper, nine predictive models are compared from which the results of the six models are taken into consideration. Deep learning-based H2O, XGBoost, logistic regression, gradient boosting, naïve Bayes, logit model, and probit regression comparative analysis is performed. The software tools such as R and SAS (university edition) is employed for machine learning and statistical model evaluation. Results: Through the experimental study, we demonstrate that XGBoost performs better than other AI and ML algorithms. Conclusion: Machine learning approach such as XGBoost effectively used for credit scoring, among other data mining and statistical approaches.