Chemical Synthesis and Characterizations of SrSi2O2N2: Eu2+ Phosphors for White LEDs Applications

白光发光二极管(whitelight-emittingdiodes，WLEDs)具有寿命长、能耗低以及结构紧凑等优点，因此被广泛的关注。目前，基于蓝光LED的荧光粉转换WLEDs(phosphor-converted-WLEDs，PC-WLEDs)是市场的主流，该方案需要搭配荧光粉以产生白光。因此，荧光粉是PC-WLEDs的关键材料之一。 为了获得较高显色指数的PC-WLED，绿色荧光粉是必不可少的。SrSi2O2N2:Eu2+(SSON)是一种性能优异的绿色荧光粉，具有较高的量子效率，较宽的吸收带(紫外到蓝光)以及良好的稳定性。通常使用高温固相法(high-temperaturesolid...White light-emitting diodes (WLEDs) have attracted great attentions due to their advantages of long lifetime, compact structure, and low power consumption. The phosphor-converted WLEDs (PC-WLEDs) that are based on blue LEDs are the mainstream of the market. This strategy requires phosphor to generate white light. Thus, the phosphor is a key material of PC-WLEDs. The green phosphor is very importa...学位：工学博士院系专业：材料学院_材料物理与化学学号：3032011015413

Floristic diversity and vegetation analysis of Brassica nigra (L.) Koch communities

The floristic composition and species diversity of Brassica nigra communities were investigated in Beni Suef Governorate, Egypt. In 46 stands, a total of 49 species belonging to 42 genera and 18 families were recorded. Vegetation classification and ordination distinguished seven groups: two in reclaimed land only, three groups in old cultivated land only, and two that occurred in both types of land. The species that dominated these groups were Brassica nigra, Sonchus oleraceus, Beta vulgaris, Cichorium endivia, Euphorbia helioscopia and Anagallis arvensis. The highest species diversity was mainly in groups from reclaimed land, and in wheat compared to other crops. Edaphic factors, especially soil texture, CaCO3 and organic carbon, contributed significantly to explaining the distribution of some weed species, but not with that of B. nigra. The allelopathic potential of Brassica nigra reported in previous studies did not seem to play a role in community composition.Keywords: allelopathy, Brassica nigra, vegetation, TWINSPAN, weed diversit

Decentralized intelligent PID based controller tuned by evolutionary algorithm for double link flexible robotic manipulator with experimental validation

In this paper, a development of decentralized intelligent proportional–integral–derivative (PID) controller for multi input multi output (MIMO) controller of double link flexible robotics manipulator is presented. Simultaneous optimization method is implemented in optimizing the parameters The controllers are incorporated with optimization algorithm that is PSO to find out the parameters of the PID controllers. Numerical simulation was carried out in MATLAB/Simulink to evaluate the system in term of tracking capability and vibration suppression for both links. The optimal values of PID controller parameters that were achieved via off-line tuning using PSO were tested experimentally on the DLFRM experimental test rig. Experimental results show that the proposed control algorithm managed to control the system to reach desired angle for both hub at lower overshoot. Meanwhile, the vibration reduction shows improvement for both link 1 and 2. This signifies that, the PSO algorithm is very effective in optimizing the PID parameters for double link flexible robotics manipulator

Intercropping of peanut–tea enhances soil enzymatic activity and soil nutrient status at different soil profiles in Subtropical Southern China

Intercropping is one of the most widely used agroforestry techniques, reducing the harmfulimpacts of external inputs such as fertilizers. It also controls soil erosion, increases soil nutrientsavailability, and reduces weed growth. In this study, the intercropping of peanut (ArachishypogaeaL.)was done with tea plants (Camellia oleifera), and it was compared with the mono-cropping of tea andpeanut. Soil health and fertility were examined by analyzing the variability in soil enzymatic activityand soil nutrients availability at different soil depths (0–10 cm, 10–20 cm, 20–30 cm, and 30–40 cm).Results showed that the peanut–tea intercropping considerably impacted the soil organic carbon(SOC), soil nutrient availability, and soil enzymatic responses at different soil depths. The activityof protease, sucrase, and acid phosphatase was higher in intercropping, while the activity of ureaseand catalase was higher in peanut monoculture. In intercropping, total phosphorus (TP) was 14.2%,34.2%, 77.7%, 61.9%; total potassium (TK) was 13.4%, 20%, 27.4%, 20%; available phosphorus (AP)was 52.9%, 26.56%, 61.1%; 146.15% and available potassium (AK) was 11.1%, 43.06%, 46.79% higherthan the mono-cropping of tea in respective soil layers. Additionally, available nitrogen (AN) was51.78%, 5.92%, and 15.32% lower in the 10–20 cm, 20–30 cm, and 30–40 cm layers of the intercroppingsystem than in the mono-cropping system of peanut. Moreover, the soil enzymatic activity wassignificantly correlated with SOC and total nitrogen (TN) content across all soil depths and croppingsystems. The depth and path analysis effect revealed that SOC directly affected sucrase, protease,urease, and catalase enzymes in an intercropping system. It was concluded that an increase in the soilenzymatic activity in the intercropping pattern improved the reaction rate at which organic matterdecomposed and released nutrients into the soil environment. Enzyme activity in the decompositionprocess plays a vital role in forest soil morphology and function. For efficient land use in the croppingsystem, it is necessary to develop coherent agroforestry practices. The results in this study revealedthat intercropping certainly enhance soil nutrients status and positively impacts soil conservation.The funding sources include the National Science and Technology Support Grant ofChina (2015BAD07B0503), Forestry Science and Technology Promotion Project of China (No. 122017) and Postdoctoral research funding of Central South University of Forestry and Technology(70702-45200003)

Acute kidney injury risk assessment at the hospital front door: what is the best measure of risk?

Background We examined the prevalence of acute kidney injury (AKI) risk factors in the emergency medical unit, generated a modified risk assessment tool and tested its ability to predict AKI. Methods A total of 1196 patients admitted to medical admission units were assessed for patient-associated AKI risk factors. Subsequently, 898 patients were assessed for a limited number of fixed risk factors with the addition of hypotension and sepsis. This was correlated to AKI episodes. Results In the first cohort, the prevalence of AKI risk factors was 2.1 ± 2.0 per patient, with a positive relationship between age and the number of risk factors and a higher number of risk factors in patients ≥65 years. In the second cohort, 12.3% presented with or developed AKI. Patients with AKI were older and had a higher number of AKI risk factors. In the AKI cohort, 72% of the patients had two or more AKI risk factors compared with 43% of the cohort with no AKI. When age ≥65 years was added as an independent risk factor, 84% of those with AKI had two or more AKI risk factors compared with 55% of those with no AKI. Receiver operating characteristic analysis suggests that the use of common patient-associated known AKI risk factors performs no better than age alone as a predictor of AKI. Conclusions Detailed assessment of well-established patient-associated AKI risk factors may not facilitate clinicians to apportion risk. This suggests that additional work is required to develop a more sensitive validated AKI-predictive tool that would be useful in this clinical setting

Nosocomial or not? A combined epidemiological and genomic investigation to understand hospital-acquired COVID-19 infection on an elderly care ward.

BACKGROUND: COVID-19 has the potential to cause outbreaks in hospitals. Given the comorbid and elderly cohort of patients hospitalized, hospital-acquired COVID-19 infection is often fatal. Pathogen genome sequencing is becoming increasingly important in infection prevention and control (IPC). AIM: To inform the understanding of in-hospital SARS-CoV-2 transmission in order to improve IPC practices and to inform the future development of virological testing for IPC. METHODS: Patients detected COVID-19 positive by polymerase chain reaction on Ward A in April and May 2020 were included with contact tracing to identify other potential cases. Genome sequencing was undertaken for a subgroup of cases. Epidemiological, genomic, and cluster analyses were performed to describe the epidemiology and to identify factors contributing to the outbreak. FINDINGS: Fourteen cases were identified on Ward A. Contact tracing identified 16 further patient cases; in addition, eight healthcare workers (HCWs) were identified as being COVID-19 positive through a round of asymptomatic testing. Genome sequencing of 16 of these cases identified viral genomes differing by two single nucleotide polymorphisms or fewer, with further cluster analysis identifying two groups of infection (a five-person group and a six-person group). CONCLUSION: Despite the temporal relationship of cases, genome sequencing identified that not all cases shared transmission events. However, 11 samples were found to be closely related and these likely represented in-hospital transmission. This included three HCWs, thereby confirming transmission between patients and HCWs.S.R. and A.B. are part-funded from Research England’s Expanding Excellence in England (E3) Fund. The sequencing costs were funded by the COVID-19 Genomics UK (COG-UK) Consortium which is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute for Health Research (NIHR) and Genome Research Limited, operating as the Wellcome Sanger Institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Changes in mediators of inflammation and pro-thrombosis after 12 months of dietary modification in adults with metabolic syndrome.

Objective: This study evaluated the effects of a 12-month dietary modification on indices of inflammation and pro-thrombosis in adults with metabolic syndrome (MS). Materials and methods: This longitudinal study involved 252 adults with MS recruited from the Bodija market, Ibadan and its environs. Participants were placed on 20%, 30% and 50% calories obtained from protein, total fat and carbohydrate respectively and were followed up monthly for 12 months. Anthropometry and blood pressure were measured using standard methods. Fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high density lipoprotein-cholesterol (HDL-C), fibrinogen, plasminogen activator inhibitor-1 (PAI-1)], interleukin-6 (IL-6) and interleukin-10 (IL-10) were measured using spectrophotometric methods and ELISA as appropriate. Data was analysed using ANCOVA, Student\u2019s t-test, Mann-Whitney U and Wilcoxon signed-rank tests. P-values less than 0.05 were considered significant. Results: After 6 months of dietary modification, there was a significant reduction in waist circumference (WC), while the levels of HDL-C, fibrinogen and PAI-1 were significantly increased when compared with the corresponding baseline values. However, WC and fibrinogen reduced significantly, while HDL-C and IL-10 significantly increased after 12 months of dietary modification as compared with the respective baseline values. Conclusion: Long-term regular dietary modification may be beneficial in ameliorating inflammation and pro-thrombosis in metabolic syndrome

Prediction of Preterm Deliveries from EHG Signals Using Machine Learning

There has been some improvement in the treatment of preterm infants, which has helped to increase their chance of survival. However, the rate of premature births is still globally increasing. As a result, this group of infants are most at risk of developing severe medical conditions that can affect the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. In extreme cases, this can also lead to long-term conditions, such as cerebral palsy, mental retardation, learning difficulties, including poor health and growth. In the US alone, the societal and economic cost of preterm births, in 2005, was estimated to be $26.2 billion, per annum. In the UK, this value was close to £2.95 billion, in 2009. Many believe that a better understanding of why preterm births occur, and a strategic focus on prevention, will help to improve the health of children and reduce healthcare costs. At present, most methods of preterm birth prediction are subjective. However, a strong body of evidence suggests the analysis of uterine electrical signals (Electrohysterography), could provide a viable way of diagnosing true labour and predict preterm deliveries. Most Electrohysterography studies focus on true labour detection during the final seven days, before labour. The challenge is to utilise Electrohysterography techniques to predict preterm delivery earlier in the pregnancy. This paper explores this idea further and presents a supervised machine learning approach that classifies term and preterm records, using an open source dataset containing 300 records (38 preterm and 262 term). The synthetic minority oversampling technique is used to oversample the minority preterm class, and cross validation techniques, are used to evaluate the dataset against other similar studies. Our approach shows an improvement on existing studies with 96% sensitivity, 90% specificity, and a 95% area under the curve value with 8% global error using the polynomial classifier

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover

Sustained proliferation in cancer: mechanisms and novel therapeutic targets

Proliferation is an important part of cancer development and progression. This is manifest by altered expression and/or activity of cell cycle related proteins. Constitutive activation of many signal transduction pathways also stimulates cell growth. Early steps in tumor development are associated with a fibrogenic response and the development of a hypoxic environment which favors the survival and proliferation of cancer stem cells. Part of the survival strategy of cancer stem cells may manifested by alterations in cell metabolism. Once tumors appear, growth and metastasis may be supported by overproduction of appropriate hormones (in hormonally dependent cancers), by promoting angiogenesis, by undergoing epithelial to mesenchymal transition, by triggering autophagy, and by taking cues from surrounding stromal cells. A number of natural compounds (e.g., curcumin, resveratrol, indole-3-carbinol, brassinin, sulforaphane, epigallocatechin-3-gallate, genistein, ellagitannins, lycopene and quercetin) have been found to inhibit one or more pathways that contribute to proliferation (e.g., hypoxia inducible factor 1, nuclear factor kappa B, phosphoinositide 3 kinase/Akt, insulin-like growth factor receptor 1, Wnt, cell cycle associated proteins, as well as androgen and estrogen receptor signaling). These data, in combination with bioinformatics analyses, will be very important for identifying signaling pathways and molecular targets that may provide early diagnostic markers and/or critical targets for the development of new drugs or drug combinations that block tumor formation and progression