Browningyusuf1897

We demonstrate that the luminescence lifetime can be derived from the emission signal when it contains at least two harmonics, because in this case the amplitude and phase of one of the harmonics can be used as reference. We present the theoretical formulation as well as an example of application to an oxygen measuring system. The proposed self-referenced lifetime estimation provides two practical advantages for luminescence chemical sensors. On one hand, it simplifies the instrument architecture, since only one analog-to-digital converter (for the emission signal) is necessary. On the other hand, the self-referenced estimation of the lifetime improves the robustness against degradation of the sensing phase or variations in the optical coupling, which reduces the recalibration requirements when the lifetimes are based on amplitudes.Ambient air pollution is a growing public health concern in major African cities, including Addis Ababa (Ethiopia), where little information is available on fine particulate matter (PM2.5, with aerodynamic diameter less then 2.5 µm) pollution. This paper aims to characterize annual PM2.5, including bulk composition and seasonal patterns, in Addis Ababa. We collected 24-h PM2.5 samples in the central city every 6 days from November 2015 to November 2016. The mean (±SD) daily PM2.5 concentration was 53.8 (±25.0) µg/m3, with 90% of sampled days exceeding the World Health Organization's guidelines. Principal components were organic matter (OM, 44.5%), elemental carbon (EC, 25.4%), soil dust (13.5%), and SNA (sulfate, nitrate, and ammonium ions, 8.2%). Higher PM2.5 concentrations were observed during the heavy rain season, while crustal dust concentrations ranged from 2.9 to 37.6%, with higher levels during dry months. Meteorological variables, vehicle emissions, biomass fuels, unpaved roads, and construction activity contribute to poor air quality. Compared to the Air Quality Index (AQI), 31% and 36% of observed days were unhealthy for everyone and unhealthy for sensitive groups, respectively. We recommend adopting effective prevention strategies and pursuing research on vehicle emissions, biomass burning, and dust control to curb air pollution in the city.

to evaluate the effects of abscisic acid (ABA), contained in dwarf peaches, on the regression of impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) conditions.

sixty-five patients with IFG or IGT were randomized to take ABA or placebo for 3 months. We evaluated fasting plasma glucose (FPG), postprandial plasma glucose (PPG), glycated hemoglobin (HbA

), fasting plasma insulin (FPI), homeostatic model assessment of insulin resistance (HOMA-IR), lipid profile and high sensitivity C-reactive protein (Hs-CRP). At baseline, and after 3 months, all patients underwent an oral glucose tolerance test (OGTT), an euglycemic hyperinsulinemic clamp, and a glucagon test.

a significant reduction of HbA

, FPG, PPG, FPI and HOMA-IR was observed in the ABA group. After 3 months, 26.7% of patients returned to a normal glycemic status in the ABA group versus zero patients in placebo group; 20.0% were classified as IFG and 53.3% as IGT in the nutraceutical group versus 33.3% and 63.3% in the placebo group. The M value was higher in the ABA group at the end of the treatment. Finally, Hs-CRP was reduced after 3 months of ABA consumption.

abscisic acid can be effective in ameliorating glyco-metabolic compensation and in reducing inflammatory status in patients with IFG or IGT.

abscisic acid can be effective in ameliorating glyco-metabolic compensation and in reducing inflammatory status in patients with IFG or IGT.Based on the data of the Chinese General Social Survey 2015 (CGSS2015), this article conducts an empirical analysis on the relationship between education and health status of Chinese residents by using the structural equation model (SEM), the propensity score matching (PSM) method, and generalized ordered logit (Gologit) model. Our study found that education promotes both the subjective and objective health of residents, and the finding holds true after considering the selection bias. In addition to having a direct role, education could promote health through improved mental health, economic status, and healthy behaviors. The finding is consistent with the explanations in existing research of "efficiency-improving effect", "mental health effect", and "budget relaxation effect". Further research on the mechanism of education affecting health through structural equation modeling finds that mental health plays a more important role than healthy behaviors and economic status. In terms of the differences of various groups, education has stronger effect on vulnerable groups with fewer social resources, which shows that education helps reduce health inequality. The conclusion has important policy significance.Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To this end, the current work presents the implementation of image recognition techniques alongside the original the quality assessment of a Parabolic Trough Collector (PTC) reflector surface to locate and identify surface irregularities by classifying images as either acceptable or non-acceptable. The method consists of a three-step solution that promotes an affordable implementation in a relatively small time period. More specifically, a 3D Computer Aided Design (CAD) of the PTC was used for the pre-training of neural networks, while an aluminum reflector surface was used to verify algorithm performance. The results are promising, as this method proved applicable in cases where the actual part was manufactured in small batches or under the concept of customized manufacturing. Consequently, the algorithm is capable of being trained with a limited number of data.Italy was the first European country to be affected by COVID-19, facing an unprecedented situation. The reaction required drastic solutions and highly restrictive measures, which severely tested the trust of the Italian people. Nevertheless, the effectiveness of the introduced measures was not only linked to political decisions, but also to the choice of the Italian people to trust and rely on institutions, accepting such necessary measures. In this context, the role of information sources was fundamental, since they strongly influence public opinion. The central focus of this research was to assess the information seeking behavior (ISB) of the Italian citizens, to understand how they related to information and how their specific use of information influenced public opinion. By making use of a survey addressed to 4260 Italian citizens, we identified extraordinarily virtuous behavior in the population people strongly modified their ISB in order to address the most reliable sources. In particular, we found a very high reliance on scientists, which is particularly striking, if compared to the past. Moreover, starting from the survey results, we used social simulation to estimate the evolution of public opinion. Comparing the ISB during and before COVID-19, we discovered that the shift in the ISB, during the pandemic, may have actually positively influenced public opinion, facilitating the acceptance of the costly restrictions introduced.Ranunculus glacialis grows and reproduces successfully, although the snow-free time period is short (2-3 months) and night frosts are frequent. At a nival site (3185 m a.s.l.), we disentangled the interplay between the atmospheric temperature, leaf temperatures, and leaf freezing frequency to assess the actual strain. For a comprehensive understanding, the freezing behavior from the whole plant to the leaf and cellular level and its physiological after-effects as well as cell wall chemistry were studied. The atmospheric temperatures did not mirror the leaf temperatures, which could be 9.3 °C lower. Leaf freezing occurred even when the air temperature was above 0 °C. Ice nucleation at on average -2.6 °C started usually independently in each leaf, as the shoot is deep-seated in unfrozen soil. All the mesophyll cells were subjected to freezing cytorrhysis. Huge ice masses formed in the intercellular spaces of the spongy parenchyma. After thawing, photosynthesis was unaffected regardless of whether ice had formed. The cell walls were pectin-rich and triglycerides occurred, particularly in the spongy parenchyma. At high elevations, atmospheric temperatures fail to predict plant freezing. Shoot burial prevents ice spreading, specific tissue architecture enables ice management, and the flexibility of cell walls allows recurrent freezing cytorrhysis. The peculiar patterning of triglycerides close to ice rewards further investigation.The application of 2D semiconductor nanomaterials in the field of SERS is limited due to its weak enhancement effect and the unclear enhancement mechanism. MDL-800 In this study, we changed the surface morphology and energy level structure of 2D SnSe2 nanosheets using different amounts of S dopant. This caused the vibration coupling of the substrate and the adsorbed molecules and affects the SERS activities of the SnSe2 nanosheets. SERS performance of the 2D semiconductor substrate can effectively be improved by suitable doping, which can effectively break the limitation of 2D semiconductor compounds in SERS detection and will have very important significance in the fields of chemical, biological, and materials sciences. In this work, the intensities of SERS signals for R6G molecules on SnSe0.93S0.94 are 1.3 to 1.7 times stronger than those on pure SnSe2 substrate. It not only provides a new way to effectively improve the SERS activity of a semiconductor SERS substrates but also helps to design more efficient and stable semiconductor SERS substrates for practical application.The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.