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C32H48CoMo2N16O7, triclinic, P1‾ $P\overline{1}$ (no. 2), a = 10.425(2) Å, b = 10.517(2) Å, c = 11.187(2) Å, α = 94.671(7)°, β = 112.270(6)°, γ = 104.108(7)°, V = 1080.0(3) Å3, Z = 1, Rgt (F) = 0.0407, wRref (F 2) = 0.1015, T = 273 K.

Objective To survey the development status and actual needs of virtual autopsy technology in China and to clarify the applicability of forensic virtual autopsy laboratory accreditation. Methods The questionnaire was set up included three aspects： （1） the current status of virtual autopsy technology development; （2） the accreditation elements such as personnel, equipment, entrustment and acceptance, methods, environmental facilities; （3） the needs and suggestions of practicing institutions. A total of 130 forensic pathology institutions were surveyed by online participation through the Questionnaire Star platform. Results Among the 130 institutions, 43.08% were familiar with the characteristics of virtual autopsy technology, 35.38% conducted or received training in virtual autopsy, and 70.77% have establishment needs （including maintenance）. Relevant elements were suitable for laboratory accreditation. Conclusion Virtual autopsy identification has gained social recognition. There is a demand for accreditation of forensic virtual autopsy laboratory. After the preliminary assessment, considering the characteristics and current situation of this technology, China National Accreditation Service for Conformity Assessment （CNAS） can first carry out the accreditation pilot of virtual autopsy project at large comprehensive forensic institutions with higher identification capability, and then CNAS can popularize the accreditation in a wide range when the conditions are suitable.

In this paper, we propose a novel energy-stable least-squares finite element method for solving the Allen–Cahn equation. The given equation can be written as a first-order system by introducing a flux variable. We develop an efficient and linear solver for first-order system by the definition of weighted least squares formulation. One novelty of the proposed scheme is by adding a stabilized term into the least-squares functional to ensure the energy stability at a discrete level. We strictly prove the unique solvability, energy stability and optimal error estimates for the scheme. Finally, a few numerical examples are given to illustrate the accuracy, energy stability and applicability. [ABSTRACT FROM AUTHOR]

Lysosomal acidity relies on H+ inflow, which requires counter-ion flows (Cl− and K+) to balance charge. A lysosome targeting ternary recognition fluorescent probe for Cl−, K+, and pH was developed for lysosome acidification counter-ion research. The probe was used to study counter-ion changes when the Cl− channel was blocked and under oxidative pressure. [ABSTRACT FROM AUTHOR]

Fatigue damage can lead to failures of structural systems. To reduce the failure risk and enhance the reliability of structural systems, inspection and maintenance interventions are required, and it is important to develop an efficient inspection strategy. This study, for the first time, develops a system-level reliability growth model to establish efficient inspection planning. System-level reliability growth is defined as an increase in the percentage of the system reliability index with and without inspection. The probabilistic S-N approach is used to obtain the reliability index without inspection. Moreover, advanced risk analysis and Bayesian inference techniques are used to obtain the reliability index with inspection. The optimal inspection planning is obtained by maximizing system-level reliability growth. This model is applied to an offshore wind farm. The results show that inspection efficiency can be improved by increasing the number of repair objects in response to a 'detection' inspection outcome, changing the inspection object for each inspection, and increasing the inspection quality. The maximum system-level reliability growth gained from one additional inspection decreases as the number of inspections increases. This study quantifies the inspection efficiency of offshore wind farms by explicit system-level reliability growth computation, offering valuable insights for promoting sustainable energy solutions. [ABSTRACT FROM AUTHOR]

The accurate prediction of short-term platform motions in a real environment is crucial for the safe design, operation, and maintenance of floating offshore wind turbines (FOWTs). Numerical simulations of motions are typically associated with high uncertainties due to abstracted theoretical models, empirical parameters, initial environment parameters, etc. Therefore, it is necessary to integrate other sources of information associated with less uncertainty, e.g., monitoring data, for accurate predictions. In this paper, we propose a probabilistic prediction based on the Bayesian approach that logically integrates motion monitoring data with simulated motion predictions of FOWTs, considering uncertainties in the environment model, structural properties, motion prediction method, monitoring data, etc. The approach consists of constructing a prior probability density function (PDF) of a random variable (which characterizes the largest value of the initial motion response) via numerical simulations and a likelihood function based on platform motion monitoring data and deriving a posterior PDF of the random variable by Bayesian updating. Then, posterior distributions of short-term extreme motion responses are derived using the posterior PDF of the random variable, representing lower uncertainty and improved accuracy. A Metropolis–Hastings algorithm is adopted to obtain PDFs of complex probability distributions. The effectiveness of the approach is demonstrated on a real FOWT platform in Scotland. The proposed probabilistic prediction approach results in posterior distributions of short-term extreme platform motions associated with less uncertainty and higher accuracy, which is attributed to integrating prior knowledge with monitoring data. [ABSTRACT FROM AUTHOR]

C32H48CoMo2N16O7, triclinic, P 1 ‾ (no. 2), a = 10.425(2) Å, b = 10.517(2) Å, c = 11.187(2) Å, α = 94.671(7)°, β = 112.270(6)°, γ = 104.108(7)°, V = 1080.0(3) Å3, Z = 1, Rgt(F) = 0.0407, wRref(F2) = 0.1015, T = 273 K. [ABSTRACT FROM AUTHOR]