bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Perspective on Improving Hospital Energy Administration Based on Recurrence Interval Analysis
Authors: Wang Fei , Chao Wei
Published: May 2018

Based on 15-min high-frequency power load data from a Chinese hospital, by adopting recurrence interval analysis, an attempt is made to provide a new perspective for improving hospital energy administration in electrical efficiency and safety. Initially, the definition of extreme fluctuation of the power load, as well as the recurrence interval, is given. Next, the stretched exponential distribution function is provided, which fits quite well with the probability density distribution of recurrence intervals. Then, tests on recurrence intervals, including scaling behavior and short-term and long-term memory effect are conducted. At last, a risk estimation method of VaR is proposed for hospital energy administrator to forecast risk probability. Results clearly indicate that the recurrence interval analysis (RIA) method works well on forecasting extreme power load fluctuation in hospital. However, there is no evidence to support the existence of the long-term memory effect of recurrence intervals, which means that hospital energy management plans have to be continuously fixed and updated with time. Some relevant applicant suggestions are provided for the energy administrator at the end of this paper.
Experimental Validation of Peer-to-Peer Distributed Voltage Control System
Authors: Almasalma Hamada , Claeys Sander , Mikhaylov Konstantin , Haapola Jussi , Pouttu Ari , Deconinck Geert
Published: May 2018

This paper presents experimental validation of a distributed optimization-based voltage control system. The dual-decomposition method is used in this paper to solve the voltage optimization problem in a fully distributed way. Device-to-device communication is implemented to enable peer-to-peer data exchange between agents of the proposed voltage control system. The paper presents the design, development and hardware setup of a laboratory-based testbed used to validate the performance of the proposed dual-decomposition-based peer-to-peer voltage control. The architecture of the setup consists of four layers: microgrid, control, communication, and monitoring. The key question motivating this research was whether distributed voltage control systems are a technically effective alternative to centralized ones. The results discussed in this paper show that distributed voltage control systems can indeed provide satisfactory regulation of the voltage profiles.
ACEnet: Approximate Thinning-Based Judicious Network Control for Energy-Efficient Ultra-Dense Networks
Authors: Lee Wonseok , Jung Bang Chul , Lee Howon
Published: May 2018

This study considers a ultra-dense network (UDN) in which the enormous number of base stations (BSs) are densely deployed to support the massive amount of data traffic generated by many mobile devices. In this paper, we propose an approximate thinning-based judicious network control algorithm for energy-efficient UDNs (ACEnet) to improve the area throughput while diminishing the network energy consumption. The main idea of the proposed ACEnet algorithm is to judiciously adjust the modes of the BSs according to active-user density based on the thinning operation in stochastic geometry framework. The stochastic geometry framework is exploited to analyze the performance of the proposed algorithm, which includes the signal-to-interference-plus-noise ratio (SINR), average achievable rate of users, area throughput, and energy efficiency. Through intensive simulations, it shows that the proposed algorithm outperforms conventional algorithms. We also demonstrate that the analytical results are well matched with the simulation results.
Polycyclic Aromatic Hydrocarbons (PAHs) Associated with PM2.5 in Guadalajara, Mexico: Environmental Levels, Health Risks and Possible Sources
Authors: Murillo-Tovar Mario Alfonso , Barradas-Gimate Adriana , Arias-Montoya Mónica Ivonne , Saldarriaga-Noreña Hugo Albeiro
Published: May 2018

PM2.5 samples were collected from January 2009 to January 2010 at two sampling sites located in the downtown (Centro) and toward the southwest (Miravalle) in the city of Guadalajara, Mexico. The environmental concentrations of 14 selected polycyclic aromatic hydrocarbons (PAHs) in PM2.5 were identified and quantified. The most abundant PAHs in PM2.5 samples were benzo[ghi]perylene, indeno[1,2,3-cd]pyrene, benzo[b]fluoranthene, benzo[a]pyrene and benzo[k]fluoranthene, accounting for approximately 75% of the total PAHs. The total PAH concentrations at the two sampling sites ranged from 0.65 to 19.62 ng·m−3. Spatial variations were found during the dry-warm season, which were attributed mainly to differing intensities of local traffic and less dispersion of air pollutants in Miravalle. Seasonal variations were associated with increases in rainfall (June-September) and differences in temperature (January–May and October–January). The benzo(a)pyrene-equivalent (BaPE) and BaP results suggest that exposure to PM2.5-containing carcinogenic PAHs (C-PAHs) in Miravalle during the warm-dry and cold-dry seasons can be seen as representing a serious risk to human health. The contributions from potential sources to PAHs in PM2.5 were evaluated by the diagnostic ratios between PAHs and principal component analysis (PCA). In the whole sampling period, vehicular emission activity, probably related to light and heavy traffic, was found to be the predominant contributor to PM2.5-bound PAHs.
Estimation of Forest Aboveground Biomass and Leaf Area Index Based on Digital Aerial Photograph Data in Northeast China
Authors: Li Dan , Gu Xingfa , Pang Yong , Chen Bowei , Liu Luxia
Published: May 2018

Forest aboveground biomass (AGB) and leaf area index (LAI) are two important parameters for evaluating forest growth and health. It is of great significance to estimate AGB and LAI accurately using remote sensing technology. Considering the temporal resolution and data acquisition costs, digital aerial photographs (DAPs) from a digital camera mounted on an unmanned aerial vehicle or light, small aircraft have been widely used in forest inventory. In this study, the aerial photograph data was acquired on 5 and 9 June, 2017 by a Hasselblad60 digital camera of the CAF-LiCHy system in a Y-5 aircraft in the Mengjiagang forest farm of Northeast China, and the digital orthophoto mosaic (DOM) and photogrammetric point cloud (PPC) were generated from an aerial overlap photograph. Forest red-green-blue (RGB) vegetation indices and textural factors were extracted from the DOM. Forest vertical structure features and canopy cover were extracted from normalized PPC. Regression analysis was carried out considering only DOM data, only PPC data, and a combination of both. A recursive feature elimination (RFE) method using a random forest was used for variable selection. Four different machine-learning (ML) algorithms (random forest, k-nearest neighbor, Cubist and supporting vector machine) were used to build regression models. Experimental results showed that PPC data alone could estimate AGB, and DOM data alone could estimate LAI with relatively high accuracy. The combination of features from DOM and PPC data was the most effective, in all the experiments considered, for the estimation of AGB and LAI. The results showed that the height and coverage variables of PPC, texture mean value, and the visible differential vegetation index (VDVI) of the DOM are significantly related to the estimated AGB (R2 = 0.73, RMSE = 20 t/ha). The results also showed that the canopy cover of PPC and green red ratio index (GRRI) of DOM are the most strongly related to the estimated LAI, and the height and coverage variables of PPC, the texture mean value and visible atmospherically resistant index (VARI), and the VDVI of DOM followed (R2 = 0.79, RMSE = 0.48).
Solid Wood Properties Assessed by Non-Destructive Measurements of Standing European Larch (Larix decidua Mill.): Environmental Effects on Variation within and among Trees and Forest Stands
Authors: Škorpík Petr , Konrad Heino , Geburek Thomas , Schuh Michael , Vasold David , Eberhardt Michael , Schueler Silvio
Published: May 2018

To avoid unintentional loss of wood quality when selecting for higher productivity in tree breeding programs, non-destructive methods for fast and reliable assessment of wood quality on standing trees are required. In this study, we tested and applied Pilodyn penetration (PP) and measures of stress wave velocity (SWV) in trees within a European larch (Larix decidua Mill.) breeding program. Through testing PP in 4267 trees on 21 afforestation sites across a broad climatic spectrum, we analysed the effects of climate, tree age, and site conditions on PP. Moreover, detailed measures within two selected stands allowed us to estimate measurement variation within and among trees in relation to the measurement angle and individual tree characteristics. We found significant variation of PP and SWV among forests stands, single trees, and even within trees, if measured on opposite sides in mountainous terrain. Both measurements exhibited a high degree of genetic determination, i.e., repeatability was 0.32–0.61 for PP and 0.56 for SWV, respectively. The obtained estimates for wood stiffness were comparable to measures on harvested wood samples of European or hybrid larch. Our results demonstrate that the integration of wood quality parameters into larch breeding programs is highly recommended, and reliable tools are available. Results are discussed in relation to environmental and measurement variation and methods to optimize field measurements are suggested.
Short-Term Vegetation Responses Following Windthrow Disturbance on Preserved Forest Lands
Authors: Manning Douglas R. , Kaye Margot W. , Perles Stephanie J. , Mortensen David A.
Published: May 2018

Invasive exotic plants pose a serious threat to the ecological integrity of forests in the eastern United States. Presence and expansion of these plants are closely associated with human-caused disturbances. Land preservation to exclude human-caused disturbances could protect against invasions, yet natural disturbances persist. We ask if windthrow forest disturbances in preserved National Park lands facilitate exotic species invasions. We hypothesized that exotic plant expansion is positively correlated with forest canopy disturbance from windthrow and proximity of disturbed area to forest edge. Pre and post-disturbance data from National Park Service long-term vegetation monitoring were used to analyze exotic plant richness and abundance in four National Park Service units affected by 2012 severe storms. No significant difference in exotic plant richness or cover occurred between disturbed (n = 18) and undisturbed plots (n = 262) over three years following disturbance. Exotic plant cover prior to disturbance was positively correlated with the amount of nearby linear edge habitat, but there were no significant correlations between edge and change in exotic plant cover following disturbance. Lack of increase in exotic plants after windthrow disturbance suggests that land preservation provides short-term resistance to invasion.
Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
Authors: Hoang Xuan Dau , Nguyen Quynh Chi
Published: May 2018

In recent years, botnets have become one of the major threats to information security because they have been constantly evolving in both size and sophistication. A number of botnet detection measures, such as honeynet-based and Intrusion Detection System (IDS)-based, have been proposed. However, IDS-based solutions that use signatures seem to be ineffective because recent botnets are equipped with sophisticated code update and evasion techniques. A number of studies have shown that abnormal botnet detection methods are more effective than signature-based methods because anomaly-based botnet detection methods do not require pre-built botnet signatures and hence they have the capability to detect new or unknown botnets. In this direction, this paper proposes a botnet detection model based on machine learning using Domain Name Service query data and evaluates its effectiveness using popular machine learning techniques. Experimental results show that machine learning algorithms can be used effectively in botnet detection and the random forest algorithm produces the best overall detection accuracy of over 90%.
Butyrylation of Maize and Potato Starches and Characterization of the Products by Nuclear Magnetic Resonance and In Vitro Fermentation
Authors: Nielsen Tina Skau , Canibe Nuria , Larsen Flemming Hofmann
Published: May 2018

Intake of butyrylated starches may increase colonic butyrate supply, which can be of public health and clinical benefit by maintaining colonic health. The objective was to investigate if an organocatalytic method with tartaric acid as a catalyst could be applied to produce butyrylated products from different starch sources and to characterize their chemical structure and fermentation capability by using solid-state 13C MAS NMR (magic angle spinning nuclear magnetic resonance) spectroscopy and an in vitro fermentation model, respectively. Low-amylose and high-amylose potato starch (LAPS and HAPS) and low-amylose and high-amylose maize starch (LAMS and HAMS) were subjected to organocatalytic butyrylation. This resulted in products with an increasing degree of substitution (DS) measured by heterogenous saponification and back titration with the HCl (chemical method) depending on reaction time. NMR analysis, however, showed that the major part of the acylation was induced by tartarate (75–89%) and only a minor part (11–25%) by butyrate. Generally, the chemical method overestimated the DS by 38% to 91% compared with the DS determination by NMR. Increasing the DS appeared to lower the in vitro fermentation capability of starches independent of the starch source and, therefore, do not seem to present a feasible method to deliver more butyrate to the colon than lower DS products.
Frozen State of Sephadex® Gels of Different Crosslink Density Analyzed by X-ray Computed Tomography and X-ray Diffraction
Authors: Murase Norio , Uetake Yuki , Sato Yuki , Irie Kentaro , Ueno Yohei , Hirauchi Toru , Kawahara Toshio , Hirai Mitsuhiro
Published: May 2018

Water in Sephadex® (crosslinked dextran) gels is known to indicate different freezing behavior which is dependent on the density of the crosslinks, and water in a Sephadex® G25 gel remains partially unfrozen during cooling and crystallizes during rewarming. The mechanism of anomalous ice crystallization during rewarming is still unclear. The objective of this study is to observe the ice grains that form in Sephadex® beads and to comprehend their frozen state with a focus on the ice crystallization during rewarming. Sephadex® beads containing 50 wt % water were prepared and used for the measurements. The observation of the ice grains was carried out by using synchrotron radiation-sourced X-ray CT (computed tomography). XRD (X-ray diffraction) analysis was also conducted to investigate the frozen state. As a result, ice grains that were larger than ~1 μm were hardly observed after the slow cooling of Sephadex® beads, except in the G25 beads. However, at the occurrence of ice crystallization during rewarming, ice grains that were larger than 10 μm appeared in the G25 beads. Using XRD, it was found that small incomplete ice crystals were formed in G25 beads and the presence of glassy water was indicated in the gel. In conclusion, the size and distribution of ice grains that formed in Sephadex® beads were different depending on the density of the crosslinks.
Georeferenced Agricultural Data for Statistical Reuse
Authors: Tóth Katalin
Published: May 2018

The guidelines to the Public Sector Information (PSI) Directive states: “opening up public sector information (PSI) for reuse brings major socioeconomic benefits”, which has been recognised in various domains. However, the reuse may be limited due to organisational and technical reasons. This study addresses the collaboration between the statistical and the agricultural domain using the example of the Integrated Administration and Control System (IACS) and the Integrated Farm Statistics (IFS). After the comparison of the spatial data requirements in IACS and IFS, a conceptual collaboration model was developed that makes clear how the challenges of interoperability can be resolved by technical arrangements and work organisation.
The Riddle: Form and Performance
Authors: Kaivola-Bregenhøj Annikki
Published: May 2018

The article concentrates on the true or the ordinary riddle, which is the best-known of the old riddles. True riddles consist of two parts, one functioning as a question, the other as an answer. In riddling the answerer or riddlee tries to find an acceptable answer to the question. Sometimes riddlees are deliberately misled because the “right” answer is completely unexpected. Riddles are “texts” only in archives and publications; in the field, they are always oral lore closely tied to their performing context. Study of social and cultural contexts is a new part of riddle research. Field researchers’ studies and findings are important. The article includes riddle definitions and analysis of subjects, metaphors and formulae of riddles as well as the functions of riddling. New challenges are the driving force behind research. I attempt to find something new in my material. New for me has been discovering the humour in riddles. Reading dozens and even hundreds of riddle variants begins to give me some idea of the fun and humour inherent in riddles. There are still questions in riddle materials waiting to be asked; it is always possible to discover something new.
Development of Monsoonal Rainfall Intensity-Duration-Frequency (IDF) Relationship and Empirical Model for Data-Scarce Situations: The Case of the Central-Western Hills (Panchase Region) of Nepal
Authors: Devkota Sanjaya , Shakya Narendra Man , Sudmeier-Rieux Karen , Jaboyedoff Michel , Van Westen Cees J. , Mcadoo Brian G. , Adhikari Anu
Published: May 2018

Intense monsoonal rain is one of the major triggering factors of floods and mass movements in Nepal that needs to be better understood in order to reduce human and economic losses and improve infrastructure planning and design. This phenomena is better understood through intensity-duration-frequency (IDF) relationships, which is a statistical method derived from historical rainfall data. In Nepal, the use of IDF for disaster management and project design is very limited. This study explored the rainfall variability and possibility to establish IDF relationships in data-scarce situations, such as in the Central-Western hills of Nepal, one of the highest rainfall zones of the country (~4500 mm annually), which was chosen for this study. Homogeneous daily rainfall series of 8 stations, available from the government’s meteorological department, were analyzed by grouping them into hydrological years. The monsoonal daily rainfall was disaggregated to hourly synthetic series in a stochastic environment. Utilizing the historical statistical characteristics of rainfall, a disaggregation model was parameterized and implemented in HyetosMinute, software that disaggregates daily rainfall to finer time resolution. With the help of recorded daily and disaggregated hourly rainfall, reference IDF scenarios were developed adopting the Gumbel frequency factor. A mathematical model [i = a(T)/b(d)] was parameterized to model the station-specific IDF utilizing the best-fitted probability distribution function (PDF) and evaluated utilizing the reference IDF. The test statistics revealed optimal adjustment of empirical IDF parameters, required for a better statistical fit of the data. The model was calibrated, adjusting the parameters by minimizing standard error of prediction; accordingly a station-specific empirical IDF model was developed. To regionalize the IDF for ungauged locations, regional frequency analysis (RFA) based on L-moments was implemented. The heterogeneous region was divided into two homogeneous sub-regions; accordingly, regional L-moment ratios and growth curves were evaluated. Utilizing the reasonably acceptable distribution function, the regional growth curve was developed. Together with the hourly mean (extreme) precipitation and other dynamic parameters, regional empirical IDF models were developed. The adopted approach to derive station-specific and regional empirical IDF models was statistically significant and useful for obtaining extreme rainfall intensities at the given station and ungauged locations. The analysis revealed that the region contains two distinct meteorological sub-regions highly variable in rain volume and intensity.
Adult Premenopausal Bone Health Related to Reproductive Characteristics—Population-Based Data from the Canadian Multicentre Osteoporosis Study (CaMos)
Authors: Goshtasebi Azita , Berger Claudie , Barr Susan I. , Kovacs Christopher S. , Towheed Tanveer , Davison K. Shawn , Prior Jerilynn C.
Published: May 2018

Amenorrhea is important for women’s bone health. However, few have reported reproductive, anthropometric (body mass index [BMI], height) and bone health (areal bone mineral density [BMD], prevalent fractures) in a population-based study. The purposes of this cross-sectional study of women in the randomly-selected Canadian Multicentre Osteoporosis Study (CaMos) population were: (1) to describe reproductive, demographic, anthropometric and lifestyle variables; and (2) in menstruating women, to relate reproductive and other variables to BMD at the lumbar spine (L1-4, LS), femoral neck (FN) and total hip (TH) sites and to prevalent fragility fractures. This study describes the reproductive characteristics of 1532 women aged 30–60 years. BMD relationships with reproductive and other variables were described in the 499 menstruating women. Mean menarche age was 12.8 years, 96% of women were parous and 95% had used combined hormonal contraceptives (CHC). Infertility was reported by 9%, androgen excess by 13%, amenorrhea by 8% and nulliparity by 4%. LS BMD was negatively associated with amenorrhea and androgen excess and positively related to current BMI and height. A later age at menarche negatively related to FN BMD. BMI and height were strongly related to BMD at all sites. Prevalent fragility fractures were significantly associated with quartiles of both LS and TH BMD.
Smoking, Blood Pressure, and Cardiovascular Disease Mortality in a Large Cohort of Chinese Men with 15 Years Follow-up
Authors: Tan Jibin , Zhang Xiumin , Wang Weihua , Yin Peng , Guo Xiaomin , Zhou Maigeng
Published: May 2018

Background: To examine the joint effects of smoking and blood pressure on the risk of mortality from cardiovascular disease (CVD) in a cohort of Chinese men. Methods: This study followed a cohort of 213,221 men over 40 years of age who were recruited from 45 district/counties across China between 1990–1991, and whose cause-specific mortality was examined for 15 years, up to 31 December 2005. We calculated hazard ratios for all-cause mortality and CVD, ischemic heart disease (IHD), and stroke mortality for the combined sets of smoking status and blood pressure levels using the Cox proportional hazard model, adjusting for potential individual-level and contextual-level risk factors. Results: During the 15 years of follow-up, 52,795 deaths occurred, including 18,833 deaths from CVD, 3744 deaths from IHD, and 11,288 deaths from stroke. The risk of mortality from CVD, IHD, and stroke increased significantly, with increased systolic blood pressure (SBP), diastolic blood pressure (DBP), and with more pack years of smoking. Compared with never-smokers with normal blood pressure, the hazard ratios and 95% CI of CVD, IHD, and stroke mortality for those who smoked over 20 pack years with hypertension were remarkably increased to 2.30 (95% CI: 2.12–2.50), 1.78 (95% CI: 1.48–2.14), and 2.74 (95% CI: 2.45–3.07), respectively. Conclusion: There was a combined effect on the risk of CVD, IHD, and stroke mortality between smoking and hypertension. The joint efforts on smoking cessation and lowered blood pressure should be made to prevent cardiovascular disease mortality in Chinese men.
Sensitivity Analysis of Weather Variables on Offsite Consequence Analysis Tools in South Korea and the United States
Authors: Kim Min-Uk , Moon Kyong Whan , Sohn Jong-Ryeul , Byeon Sang-Hoon
Published: May 2018

We studied sensitive weather variables for consequence analysis, in the case of chemical leaks on the user side of offsite consequence analysis (OCA) tools. We used OCA tools Korea Offsite Risk Assessment (KORA) and Areal Location of Hazardous Atmospheres (ALOHA) in South Korea and the United States, respectively. The chemicals used for this analysis were 28% ammonia (NH3), 35% hydrogen chloride (HCl), 50% hydrofluoric acid (HF), and 69% nitric acid (HNO3). The accident scenarios were based on leakage accidents in storage tanks. The weather variables were air temperature, wind speed, humidity, and atmospheric stability. Sensitivity analysis was performed using the Statistical Package for the Social Sciences (SPSS) program for dummy regression analysis. Sensitivity analysis showed that impact distance was not sensitive to humidity. Impact distance was most sensitive to atmospheric stability, and was also more sensitive to air temperature than wind speed, according to both the KORA and ALOHA tools. Moreover, the weather variables were more sensitive in rural conditions than in urban conditions, with the ALOHA tool being more influenced by weather variables than the KORA tool. Therefore, if using the ALOHA tool instead of the KORA tool in rural conditions, users should be careful not to cause any differences in impact distance due to input errors of weather variables, with the most sensitive one being atmospheric stability.
An Effectiveness Assessment of China’s WEEE Treatment Fund
Authors: Zhao Wenyan , Yang Jianxin
Published: May 2018

Policy is essential to the management of waste electrical and electronic equipment (WEEE). In order to present valuable findings for policy improvement, we performed a quantitative effectiveness assessment of China’s WEEE treatment fund from 2012 to 2015. The achievement of the general goal of the WEEE treatment fund was evaluated with various indicators. We calculated the values of all indicators and analyzed the changes in them. The results show that the WEEE treatment fund was important in promoting WEEE collection and recycling activities and has provided great benefits in the form of resources and the environment. Moreover, the authorized enterprises also experienced progress in their development. In a word, the WEEE treatment fund was effective to some extent. However, because of the limited subsidies and other factors, the WEEE treatment fund had different effects on five categories of WEEE. We found that its approach worked best for the TV set. Although the promotional effects on the other four categories of WEEE have been increasingly significant since 2014, there is room for improvement. Fortunately, the subsidy rates have been adjusted by administrations and new subsidies were provided in 2016. As it is crucial for the effectiveness of the WEEE treatment fund, new subsidy rates should be evaluated regularly.
Effects of Pb Smelting on the Soil Bacterial Community near a Secondary Lead Plant
Authors: Luo Zhanbin , Ma Jing , Chen Fu , Li Xiaoxiao , Zhang Shaoliang
Published: May 2018

Secondary lead smelting is a widespread industrial activity which has exacerbated Pb or Cd contamination of soil and water across the world. Soil physicochemical properties, soil enzyme activities, heavy metal concentrations, and bacterial diversity near a secondary lead plant in Xuzhou, China were examined in this study. The results showed that secondary lead smelting activities influenced nearby soils. Soil acidification decreased one order of magnitude, with a mean value of 7.3. Soil organic matter also showed a downward trend, while potassium and nitrogen appeared to accumulate. Soil urease and protease activity increased in samples with greater heavy metal pollution, but overall the soil microbial biodiversity decreased. Soil heavy metal concentration—especially Pb and Cd—greatly exceeded the concentrations of Chinese Environmental Quality Standard for Soils (GB 15618-1995). Some environmental factors—such as pH, organic matter, enzyme activity, and the concentration of heavy metals—significantly affected bacterial diversity: compared with the control site, the Chao1 estimator decreased about 50%, while the Shannon diversity index dropped approximately 20%. Moreover, some genera have significant relationships with heavy metal concentration—such as Ramlibacter with Zn and Steroidobacter with Cd—which might act as bio-indicators for soil remediation. These results will provide a new insight in the future for reclaiming soil contaminants caused by secondary lead smelting.
Removing Organic Matter and Nutrients from Pig Farm Wastewater with a Constructed Wetland System
Authors: De La Mora-Orozco Celia , González-Acuña Irma Julieta , Saucedo-Terán Ruben Alfonso , Flores-López Hugo Ernesto , Rubio-Arias Hector Osbaldo , Ochoa-Rivero Jesús Manuel
Published: May 2018

Pollutants from pig farms in Mexico have caused problems in many surface water reservoirs. Growing concern has driven the search for low-cost wastewater treatment solutions. The objective of this research was to evaluate the potential of an in-series constructed wetland to remove nutrients from wastewater from a pig farm. The wetland system had a horizontal flow that consisted of three cells, the first a surface water wetland, the second a sedimentation cell, and the third a subsurface flow wetland. The vegetation used was Thypa sp. and Scirpus sp. A mix of soil with red volcanic rock (10–30 mm diameter) and yellow sand (2–8 mm diameter) was used as a substrate for the vegetation. The experiments were carried out in duplicate. Water samples were collected at the inflow and outflow of the cells. Two hydraulic retention times (HRT) (5 and 10 days) and three treatments were evaluated: 400, 800, and 1200 mg·L−1 of chemical oxygen demand (COD) concentration. Data was collected in situ for temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), and total dissolved solids (TDS). COD, total Kjeldahl nitrogen (TKN), ammonia nitrogen (NH3–N), and total phosphorous (TP) were analyzed in the laboratory. The results showed that the in-series constructed wetland is a feasible system for nutrient pollutant removal, with COD removal efficiency of 76% and 80% mg·L−1 for a 5- and 10-day HRT, respectively. The removal efficiency for TKN, NH3–N, and TP reached about 70% with a 5-day HRT, while a removal of 85% was obtained with a 10-day HRT. The wetland reached the maximum removal efficiency with a 10-day HRT and an inflow load of 400 mg·L−1 of organic matter. The results indicate that HRT positively affects removal efficiency of COD and TDS. On the other hand, the HRT was not the determining factor for TP removal. Treatment one, with an initial COD concentration of 400 mg·L−1, had the highest removal of the assessed pollutants, allowing for the use of water for irrigation according to Mexican regulatory standards (NOM-001). The water quality resulting from treatments two and three (T2 = 800 mg·L−1 of COD and T3 = 1200 mg·L−1 of COD) did not comply with minimal requirements for irrigation water.
A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition
Authors: Zhang Xike , Zhang Qiuwen , Zhang Gui , Nie Zhiping , Gui Zifan , Que Huafei
Published: May 2018

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijaing stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.
An Endmember Initialization Scheme for Nonnegative Matrix Factorization and Its Application in Hyperspectral Unmixing
Authors: Cao Jingjing , Zhuo Li , Tao Haiyan
Published: May 2018

Nonnegative matrix factorization (NMF) is a blind source separation (BSS) method often used in hyperspectral unmixing. However, it tends to converge to a local optimum. To overcome this limitation, we present a simple, but effective endmember initialization scheme for NMF, which is realized by improving initial values through the application of the automatic target generation process (ATGP) algorithm. The initial spectra and abundances of target endmembers are first obtained using the ATGP algorithm and nonnegative least squares (NNLS) method, respectively. The preliminary results are then optimized through iterative application of NMF. To validate the applicability and effectiveness of the proposed method, we analyzed the improvement of NMF by the ATGP algorithm, using the synthetic hyperspectral data and real hyperspectral images. The results from the proposed method are compared with those of the vertex component analysis (VCA)-NMF algorithm, which uses the VCA algorithm to perform initialization for NMF, the minimum volume constrained NMF (MVC-NMF) algorithm, the traditional two-step VCA-fully-constrained least squares (FCLS) algorithm, which uses the VCA to extract the endmember matrix, and the FCLS algorithm to estimate the abundance matrix. The comparison results prove that proper endmember initialization can help the NMF algorithm yield better estimation results. Through the optimization of target endmembers’ initial values, the proposed ATGP-NMF algorithm can consistently produce good results at a lower computational cost, especially in the case of a real hyperspectral image for which pure pixels do not exist and there is little prior knowledge. With its high applicability and effectiveness, the ATGP-NMF algorithm has a great potential to solve hyperspectral unmixing problems.
Reproduction of Characteristics of Extracellular Matrices in Specific Longitudinal Depth Zone Cartilage within Spherical Organoids in Response to Changes in Osmotic Pressure
Authors: Takada Eiichiro , Mizuno Shuichi
Published: May 2018

Articular cartilage is compressed with joint-loading and weight-bearing stresses, followed by a bulging of the tissue during times of off-loading. This loading and off-loading causes changes in water content, and thus alterations in osmotic pressure. Another unique characteristic of articular cartilage is that it has longitudinal depth: surface, middle, and deep zones. Since each zone is composed of unique components of highly negative extracellular matrices, each zone has a different level of osmotic pressure. It was unclear how changes in osmotic pressure affected chondrocyte matrix turnover in specific longitudinal zones. Therefore, we hypothesized that a change in extrinsic osmotic pressure would alter the production of extracellular matrices by zone-specific chondrocytes. We incubated spheroidal cartilage organoids, formed by specific longitudinal depth zone-derived chondrocytes, under different levels of osmotic pressure. We compared the gene expression and the immunohistology of the matrix proteins produced by the zone-specific chondrocytes. We found that high osmotic pressure significantly upregulated the transient expression of aggrecan and collagen type-II by all zone-derived chondrocytes (p < 0.05). At a high osmotic pressure, surface-zone chondrocytes significantly upregulated the expression of collagen type-I (p < 0.05), and middle- and deep-zone chondrocytes significantly upregulated matrix metalloproteinase-13 (p < 0.05). The spheroids, once exposed to high osmotic pressure, accumulated extracellular matrices with empty spaces. Our findings show that chondrocytes have zone-specific turnover of extracellular matrices in response to changes in osmotic pressure.
Copaifera of the Neotropics: A Review of the Phytochemistry and Pharmacology
Authors: Da Trindade Rafaela , Da Silva Joyce Kelly , Setzer William N.
Published: May 2018

The oleoresin of Copaifera trees has been widely used as a traditional medicine in Neotropical regions for thousands of years and remains a popular treatment for a variety of ailments. The copaiba resins are generally composed of a volatile oil made up largely of sesquiterpene hydrocarbons, such as β-caryophyllene, α-copaene, β-elemene, α-humulene, and germacrene D. In addition, the oleoresin is also made up of several biologically active diterpene acids, including copalic acid, kaurenoic acid, alepterolic acid, and polyalthic acid. This review presents a summary of the ecology and distribution of Copaifera species, the traditional uses, the biological activities, and the phytochemistry of copaiba oleoresins. In addition, several biomolecular targets relevant to the bioactivities have been implicated by molecular docking methods.
Jatrorrhizine Hydrochloride Suppresses Proliferation, Migration, and Secretion of Synoviocytes In Vitro and Ameliorates Rat Models of Rheumatoid Arthritis In Vivo
Authors: Qiu Haiwen , Sun Shengnan , Ma Xuemei , Cui Congcong , Chen Gang , Liu Zhenzhou , Li Hui , Liu Mei
Published: May 2018

Jatrorrhizine hydrochloride (JH), an active component isolated from the traditional Chinese herb Coptis chinensis, has been reported to have antimicrobial, antitumor, antihypercholesterolemic, and neuroprotective activities. However, its antirheumatoid arthritis (RA) property remains unknown. In this study, a collagen-induced arthritis (CIA) rat model was used to evaluate the therapeutic effects of JH on RA by using arthritis score, radiological evaluation, and histopathological assessment. The in vitro effects of JH on proliferation, migration, and production of inflammatory mediators in RA-derived fibroblast-like synoviocyte MH7A cells were determined by the EdU incorporation assay, wound healing assay, real-time PCR, and ELISA, respectively. The in vivo studies showed that JH treatment significantly prevented the progression and development of RA in CIA rats through anti-inflammation and suppressing bone destruction. The in vitro studies revealed that JH could effectively attenuate the destructive phenotypes of MH7A cells, including inhibiting proliferation, migration, and production of inflammatory mediators. Further mechanistic analysis demonstrated that JH suppressed tumor necrosis factor alpha (TNFα)-stimulated activations of nuclear factor of kappaB (NF-κB) and mitogen-activated protein kinases (MAPKs) (ERK and p38) leading to the downregulation of proinflammatory cytokines, which might be beneficial to the antiproliferative and antimigratory activities of FLS cells. Collectively, our results demonstrated that JH has a great potential to be developed into a novel therapeutic agent for treating RA.
Deleterious Effect of Advanced CKD on Glyoxalase System Activity not Limited to Diabetes Aetiology
Authors: Pácal Lukáš , Chalásová Katarína , Pleskačová Anna , Řehořová Jitka , Tomandl Josef , Kaňková Kateřina
Published: May 2018

Methylglyoxal production is increased in diabetes. Methylglyoxal is efficiently detoxified by enzyme glyoxalase 1 (GLO1). The aim was to study the effect of diabetic and CKD milieu on (a) GLO1 gene expression in peripheral blood mononuclear cells; (b) GLO1 protein levels in whole blood; and (c) GLO1 activity in RBCs in vivo in diabetic vs. non-diabetic subjects with normal or slightly reduced vs. considerably reduced renal function (CKD1-2 vs. CKD3-4). A total of 83 subjects were included in the study. Gene expression was measured using real-time PCR, and protein levels were quantified using Western blotting. Erythrocyte GLO1 activity was measured spectrophotometrically. GLO1 gene expression was significantly higher in subjects with CKD1-2 compared to CKD3-4. GLO1 protein level was lower in diabetics than in non-diabetics. GLO1 activity in RBCs differed between the four groups being significantly higher in diabetics with CKD1-2 vs. healthy subjects and vs. nondiabeticsfig with CKD3-4. GLO1 activity was significantly higher in diabetics compared to nondiabetics. In conclusion, both diabetes and CKD affects the glyoxalase system. It appears that CKD in advanced stages has prevailing and suppressive effects compared to hyperglycaemia. CKD decreases GLO1 gene expression and protein levels (together with diabetes) without concomitant changes of GLO1 activity.
Chlorogenic Acid Improves the Regorafenib Effects in Human Hepatocellular Carcinoma Cells
Authors: Refolo Maria Grazia , Lippolis Catia , Carella Nicola , Cavallini Aldo , Messa Caterina , D’Alessandro Rosalba
Published: May 2018

Chlorogenic acid (CGA) is a polyphenol present in many human dietary foods. Several studies indicated a beneficial role of CGA in the prevention of cancer and an enhancement of chemotherapy when combined with CGA in the treatment of human hepatocarcinoma (HCC). Drug toxicity, resistance and subsequent disease progression represent a problem in HCC management, although treatment with the multikinase inhibitor Regorafenib improved overall survival. This study focused on the evaluation of the effects of combined treatment using both low Regorafenib concentrations and CGA as natural compound in HCC cells. The analysis of cell proliferation by Ki67 staining and cell cycle progression showed that CGA enhanced Regorafenib-mediated cell growth inhibition. Moreover, CGA potentiated the apoptotic effect of Regorafenib by the activation of the pro-apoptotic Annexin V, Bax and Caspase 3/7 and the inhibition of anti-apoptotic Bcl2 and Bcl-xL. Combined treatments were also effective in inhibiting cell motility. The mechanisms underlying the positive effects of combining CGA and Regorafenib were also addressed and an increased inhibition of MAPK (mitogen-activated protein kinase)and PI3K/Akt/mTORC (phosphatidylinositol-3-kinase (PI3K)/Akt and the mammalian target of rapamycin (mTOR) signaling was observed. Overall, these data demonstrated that co-treatment with Regorafenib and CGA enhanced Regorafenib action, reducing its cytotoxicity in HCC cells. In conclusion, this drug combination could be considered as a safe and more effective approach in HCC therapy.
Ferromagnetic Cluster Spin Wave Theory: Concepts and Applications to Magnetic Molecules
Authors: Prša Krunoslav , Waldmann Oliver
Published: May 2018

Ferromagnetic cluster spin wave theory (FCSWT) provides an exact and concise description of the low-energy excitations from the ferromagnetic ground state in finite magnetic systems, such as bounded magnetic molecules. In particular, this theory is applicable to the description of experimental inelastic neutron scattering (INS) spectra at low temperatures. We provide a detailed conceptual overview of the FCSWT. Additionally, we introduce a pictorial representation of calculated wavefunctions, similar to the usual depiction of vibrational normal modes in molecules. We argue that this representation leads to a better intuitive understanding of the excitations, their symmetry properties, and has links to the energy and wavevector dependence of intensity in the neutron scattering experiments. We apply FCSWT and illustrate the results on a series of examples with available low-temperature INS data, ranging from the Mn10 supertetrahedron, the Mn7 disk to the Mn6 single molecule magnet.
Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies
Authors: Reis Mariane S. , Escada Maria Isabel S. , Dutra Luciano V. , Sant’Anna Sidnei J. S. , Vogt Nathan D.
Published: May 2018

Land Use and Land Cover (LULC) classes defined by subjective criteria can diminish the significance of a study, hindering the reproducibility and the comparison of results with other studies. Having a standard legend for a given study area and objective could benefit a group of researchers focused on long-term or multidisciplinary studies in a given area, in the sense that they would be able to maintain class definition among different works, done by different teams. To allow for reproducibility, it is important that the classes in this legend are described using quantifiable elements of land cover, which can be measured on the ground, as is recommended by Land Cover Meta Language (LCML). The present study aims to propose LCML formalized hierarchical legends for LULC classes, focusing on the southwest of Pará state, within the Brazilian Amazon. In order to illustrate the potential of these legends, a secondary objective of the current study is to analyze classification results using legends derived from a particular Remote Sensing dataset and compare these results with the classification obtained using the LCML hierarchical legend proposed. To perform this analysis, firstly, we proposed a conceptual class model based on existing classification systems for the upland Brazilian Amazon Biome. From this model, 16 LULC classes were described in LCML, using quantifiable and easily recognizable physiognomic characteristics of land cover classes measured on the lower Tapajós river, in Pará state. These classes were grouped into legends with different levels of detail (number of classes), based on our model or on the image and clustering algorithms. All legends were used in supervised classification of a Landsat5/TM image. Results indicate that it is necessary to incorporate multi-temporal knowledge for class definition as well as the proposed thresholds (height and cover proportion of soil, litter, herbaceous vegetation, shrubs, and trees) in order to properly describe classes. However, the thresholds are useful to delimit classes that happen in a successive way. Classification results revealed that classes formed by the same elements of land cover with similar thresholds present high confusion. Additionally, classifications obtained using legends based on the class separability in a given Remote Sensing image tend to be more accurate but not always useful because they can hide or mix important classes. It was observed that the more generalized the legend (those with few details and number of classes), the more accurate the classifications results are for all types of legends.