55-Week Treatment of Mice with the Unani and Ayurvedic Medicine Pomegranate Flower Ameliorates Ageing-Associated Insulin Resistance and Skin Abnormalities

PPARs play a pivotal role in regulating lipid and glucose homeostasis and are involved in diverse biological activities in skin. Pomegranate flower (PGF, an antidiabetic therapy in Unani and Ayurvedic medicines) has been previously demonstrated to activate both PPARalpha/gamma. Here, we found that treatment of mice with the diet containing PGF powder over 55 weeks attenuated ageing-induced abnormal increases in the homeostasis model assessment of insulin resistance, glucose concentrations during oral glucose tolerance test, and adipose insulin resistance index. The diet tended to decrease the excessive peri-ovary fat mass. It, however, increased the thinned subcutaneous fat thickness. In addition, the diet restored decreases in skin water content, epidermis thickness, and collagen density in corium. Thus, our results demonstrate that long-term treatment with the Unani and Ayurvedic therapy ameliorates ageing-induced insulin resistance, which is associated with reversal of ageing-induced fat redistribution. Further, PGF attenuates ageing-mediated undesirable skin abnormalities

Learning graph-Fourier spectra of textured surface images for defect localization

In the realm of industrial manufacturing, product inspection remains a significant bottleneck, with only a small fraction of manufactured items undergoing inspection for surface defects. Advances in imaging systems and AI can allow automated full inspection of manufactured surfaces. However, even the most contemporary imaging and machine learning methods perform poorly for detecting defects in images with highly textured backgrounds, that stem from diverse manufacturing processes. This paper introduces an approach based on graph Fourier analysis to automatically identify defective images, as well as crucial graph Fourier coefficients that inform the defects in images amidst highly textured backgrounds. The approach capitalizes on the ability of graph representations to capture the complex dynamics inherent in high-dimensional data, preserving crucial locality properties in a lower dimensional space. A convolutional neural network model (1D-CNN) was trained with the coefficients of the graph Fourier transform of the images as the input to identify, with classification accuracy of 99.4%, if the image contains a defect. An explainable AI method using SHAP (SHapley Additive exPlanations) was used to further analyze the trained 1D-CNN model to discern important spectral coefficients for each image. This approach sheds light on the crucial contribution of low-frequency graph eigen waveforms to precisely localize surface defects in images, thereby advancing the realization of zero-defect manufacturing

Data analysis : operating crew characteristics and interactions during steam generator tube rupture simulation

Statement of responsibility on title page reads: Y. Huang, N. Siu, D. Lanning ,and J. Carroll"June 1990."Includes bibliographical references (leaf 11)This report provides an analysis of the data collected during a one-month visit to a 2-unit, non-U.S. PWR. The data consist of results from interviews (largely with plant operators and former shift engineers) and from reviews of videotapes covering crew responses to steam generator tube rupture training exercises. The interviews were aimed at indicating perceptions of individual and group skills. The analysis shows that the interview results are fairly consistent, that the time required to perform key actions does not generally correlate very well with the team quality ratings obtained from the interviews, and that the team quality ratings obtained from interviews correlate reasonably with ratings of the team performances (during the exercises) developed using the 7-dimension scale described in PNL-7250.Supported by the Office of Nuclear Regulatory Research, United States Nuclear Regulatory Commission NRC-04-89-35

Systems model for dynamic human error during accident sequences

Statement of responsibility on title page reads: Y. Huang, N. Siu, D. Lanning, J. Carroll, and V. Dang"December 1991."Includes bibliographical references (pages 153-155)Final report: "A systems model for dynamic human error during accident sequences"This report describes a systems-based operating crew model designed to simulate the behavior of an nuclear power plant control room crew during an accident scenario. This model can lead to an improved treatment of potential operator-induced multiple failures, since it deals directly with the causal factors underlying individual and group behavior. It is intended that the model, or more advanced developments of the model, will be used in the human reliability analysis portion of a probabilistic risk assessment study, where careful treatment of multiple, dependent failures is required. The model treats the members of the control room crew as separate, reasoning entities. These entities receive information from the plant and each other, process that information, perform actions that affect the plant, and provide information to the other crew members.The information retrieval, processing, and output activities are affected by the characteristics of the individual operator (e.g., his technical ability) and his relationship (measured in terms of "confidence level") with his fellow operators. Group behavior is modeled as the implicit result of individual operator behavior and the interactions between operators. The model is applied towards the analysis of steam generator tube rupture (SGTR) accidents at a non-U.S. pressurized water reactor, using the SIMSCRIPT 11.5 programming language. Benchmark runs, comparing the model predictions with videotaped observations of the performances of three different crews during SGTR training exercises, are performed to tune a small number of model parameters. The tuned model is then applied in a blind test analysis of a fourth crew. In both the benchmarking and blind test runs, the model performs quite well in predicting the occurrence, ordering, and timing of key events.The model is also employed in a number of sensitivity analyses that demonstrate the robustness of the model (it generates plausible results even when the model parameters are assigned values not representative of observed crews) and the model's usefulness in investigating key issues (e.g., the effect of stress buildup on crew performance). iSupported by the Office of Nuclear Regulatory Research, United States Nuclear Regulatory Commission: NRC-04-89-35

Nematic Fluctuations in Iron-Oxychalcogenide Mott Insulators

Nematic fluctuations occur in a wide range of physical systems from liquid crystals to biological molecules to solids such as exotic magnets, cuprates and iron-based high-$T_c$ superconductors. Nematic fluctuations are thought to be closely linked to the formation of Cooper-pairs in iron-based superconductors. It is unclear whether the anisotropy inherent in this nematicity arises from electronic spin or orbital degrees of freedom. We have studied the iron-based Mott insulators La$_{2}$O$_{2}$Fe$_{2}$O$M$$_{2}$ $M$ = (S, Se) which are structurally similar to the iron pnictide superconductors. They are also in close electronic phase diagram proximity to the iron pnictides. Nuclear magnetic resonance (NMR) revealed a critical slowing down of nematic fluctuations as observed by the spin-lattice relaxation rate ($1/T_1$). This is complemented by the observation of a change of electrical field gradient over a similar temperature range using M\"ossbauer spectroscopy. The neutron pair distribution function technique applied to the nuclear structure reveals the presence of local nematic $C_2$ fluctuations over a wide temperature range while neutron diffraction indicates that global $C_{4}$ symmetry is preserved. Theoretical modeling of a geometrically frustrated spin-$1$ Heisenberg model with biquadratic and single-ion anisotropic terms provides the interpretation of magnetic fluctuations in terms of hidden quadrupolar spin fluctuations. Nematicity is closely linked to geometrically frustrated magnetism, which emerges from orbital selectivity. The results highlight orbital order and spin fluctuations in the emergence of nematicity in Fe-based oxychalcogenides. The detection of nematic fluctuation within these Mott insulator expands the group of iron-based materials that show short-range symmetry-breaking

Evolution of a technology standard alliance based on an echo model developed through complex adaptive system theory

The evolution of the technology standard alliance (TSA) is examined using complex adaptive system (CAS) theory. Taking TSA as a dynamic CAS, an echo model is constructed to depict the mechanism of its evolution, and a model is simulated on the NetLogo platform. The echo model includes a basic model, an extended model, and a three-layer echo model. The adhesive aggregation of agents is explained, and the three evolutionary stages of agents’ entry, migration, and exit are analyzed. Moreover, the adaptability of agents in TSA is quantified. The results of simulation show the evolution of the TSA in relation to the two aspects of agent adhesion aggregation and agent resource interaction, and they demonstrate the dynamic and complex hierarchical structure of the TSA system. It is proposed that greater matching ability, moderate behavior income, and lower behavior cost are more conducive to the evolution and development of TSA. Additionally, the echo model is reconstructed to expand the range of application of CAS theory