Search results for: root uptake models

Authors: Rakesh Namdeti

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The liquid waste-wastewater- is essentially the water supply of the community after it has been used in a variety of applications. In recent years, heavy metal concentrations, besides other pollutants, have increased to reach dangerous levels for the living environment in many regions. Among the heavy metals, Lead has the most damaging effects on human health. It can enter the human body through the uptake of food (65%), water (20%), and air (15%). In this background, certain low-cost and easily available biosorbent was used and reported in this study. The scope of the present study is to remove Lead from its aqueous solution using Olea EuropaeaResin as biosorbent. The results showed that the biosorption capacity of Olea EuropaeaResin biosorbent was more for Lead removal. The Langmuir, Freundlich, Tempkin, and Dubinin-Radushkevich (D-R) models were used to describe the biosorption equilibrium of Lead Olea EuropaeaResin biosorbent, and the biosorption followed the Langmuir isotherm. The kinetic models showed that the pseudo-second-order rate expression was found to represent well the biosorption data for the biosorbent.

Keywords: novel biosorbent, central composite design, Lead, isotherms, kinetics

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Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

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Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

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Authors: Seok Woo Chang

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Aim: Capseal I and Capseal II are calcium silicate and calcium phosphate based experimental root canal sealer. The aim of this study was to evaluate the biocompatibility and mineralization potential of Capseal I and Capseal II. Materials and Methods: The biocompatibility and mineralization-related gene expression (alkaline phosphatase (ALP), bone sialoprotein (BSP), and osteocalcin (OCN)) of Capseal I and Capseal II were compared using methylthiazol tetrazolium assay and reverse transcription-polymerization chain reaction analysis, respectively. The results were analyzed by Kruskal-Wallis test. P-value of < 0.05 was considered significant. Result: Both Capseal I and Capseal II were favorable in biocompatibility and influenced the messenger RNA expression of ALP and BSP. Conclusion: Within the limitation of this study, Capseal is biocompatible and have mineralization promoting potential, and thus could be a promising root canal sealer.

Keywords: biocompatibility, mineralization-related gene expression, Capseal I, Capseal II

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Authors: Kudzanayi Chiteka, Wellington Makondo

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The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

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Authors: Alexandros Pournaras, Anastasia Papadopoulou, Serafim Kontos, Anastasios Karakostas

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The air quality of modern cities is an important concern, as poor air quality contributes to human health and environmental issues. Reliable air quality forecasting has, thus, gained scientific and governmental attention as an essential tool that enables authorities to take proactive measures for public safety. In this study, the potential of Machine Learning (ML) models to forecast PM2.5 at local scale is investigated in the city of Thessaloniki, the second largest city in Greece, which has been struggling with the persistent issue of air pollution. ML models, with proven ability to address timeseries forecasting, are employed to predict the PM2.5 concentrations and the respective Air Quality Index 5-days ahead by learning from daily historical air quality and meteorological data from 2014 to 2016 and gathered from two stations with different land use characteristics in the urban fabric of Thessaloniki. The performance of the ML models on PM2.5 concentrations is evaluated with common statistical methods, such as R squared (r²) and Root Mean Squared Error (RMSE), utilizing a portion of the stations’ measurements as test set. A multi-categorical evaluation is utilized for the assessment of their performance on respective AQIs. Several conclusions were made from the experiments conducted. Experimenting on MLs’ configuration revealed a moderate effect of various parameters and training schemas on the model’s predictions. Their performance of all these models were found to produce satisfactory results on PM2.5 concentrations. In addition, their application on untrained stations showed that these models can perform well, indicating a generalized behavior. Moreover, their performance on AQI was even better, showing that the MLs can be used as predictors for AQI, which is the direct information provided to the general public.

Keywords: Air Quality, AQ Forecasting, AQI, Machine Learning, PM2.5

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Authors: Nor Zila Abd Hamid, Mohd Salmi M. Noorani

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This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series.

Keywords: chaotic approach, phase space, Cao method, local linear approximation method

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Authors: Opeyemi Atiba-Oyewo, Taile Y. Leswfi, Maurice S. Onyango, Christian Wolkersdorfer

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This paper describes the preparation of surface modified montmorillonite using hexadecyltrimethylammonium bromide (HDTMA-Br) for the removal of vanadium from mine water. The adsorbent before and after adsorption was characterised by Fourier transform infra-red (FT-IR), X-ray diffraction (XRD) and scanning electron microscopy (SEM), while the amount of vanadium adsorbed was determined by ICP-OES. The batch adsorption method was employed using vanadium concentrations in solution ranging from 50 to 320 mg/L and vanadium tailings seepage water from a South African mine. Also, solution pH, temperature and sorbent mass were varied. Results show that the adsorption capacity was affected by solution pH, temperature, sorbent mass and the initial concentration. Electrical conductivity of the mine water before and after adsorption was measured to estimate the total dissolved solids in the mine water. Equilibrium isotherm results revealed that vanadium sorption follows the Freundlich isotherm, indicating that the surface of the sorbent was heterogeneous. The pseudo-second order kinetic model gave the best fit to the kinetic experimental data compared to the first order and Elovich models. The results of this study may be used to predict the uptake efficiency of South Africa montmorillonite in view of its application for the removal of vanadium from mine water. However, the choice of this adsorbent for the uptake of vanadium or other contaminants will depend on the composition of the effluent to be treated.

Keywords: adsorption, vanadium, modified montmorillonite, equilibrium, kinetics, mine water

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Authors: Clarissa L. de Araujo, Kátia N. Suzuki, Wilson T. V. Machado, Luis F. Bellido, Alfredo V.B. Bellido

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This study aims at contributing to the characterization of the process of biological incorporation of chromium by two benthonic species, the macroalgae Ulva sp. and the aquatic macrophyte Ruppia maritima, to subsidize future activities of monitoring the contamination of aquatic biota. This study is based on laboratory experiments to characterize the incorporation kinetics of the radiotracer ⁵¹Cr in two oxidation states (III and VI), under different salinities (7, 15, and 21 ‰). Samples of two benthonic species were collected on the margins of Rodrigo de Freitas Lagoon (Rio de Janeiro, Brazil), acclimated in the laboratory and subsequently subjected to experiments. In tests with 51Cr (III and IV), it was observed that accumulation of the metal in Ulva sp. has inverse relationship with salinity, while for R. maritima, the maximum accumulation occurs in salinity 21‰. In experiments with Cr(III), increases in the uptake of ion by both species were verified. The activity of Cr(III) was up to 19 times greater than the Cr(VI). As regards the potential for accumulation of metals, a better sensitivity of Ulva sp. for any chromium tri or hexavalent forms was verified, while for the Cr(VI) it will require low salinities and longer exposure (>24h). For R. maritima, the results showed the uptake of Cr(VI) increase along with time (>20h), because this species is more resistant for the hexavalent form and useful for any salinity as well.

Keywords: chromium, Cr-51, macroalgae, macrophyte, uptake

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Authors: Shahbaz G. Hassan

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Water quality models are very important to predict the changes in surface water quality for environmental management. The aim of this paper is to give an overview of the water qualities, and to provide directions for selecting models in specific situation. Water quality models include one kind of model based on a mechanistic approach, while other models simulate water quality without considering a mechanism. Mechanistic models can be widely applied and have capabilities for long-time simulation, with highly complexity. Therefore, more spaces are provided to explain the principle and application experience of mechanistic models. Mechanism models have certain assumptions on rivers, lakes and estuaries, which limits the application range of the model, this paper introduces the principles and applications of water quality model based on the above three scenarios. On the other hand, mechanistic models are more easily to compute, and with no limit to the geographical conditions, but they cannot be used with confidence to simulate long term changes. This paper divides the empirical models into two broad categories according to the difference of mathematical algorithm, models based on artificial intelligence and models based on statistical methods.

Keywords: empirical models, mathematical, statistical, water quality

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Authors: A. M. Daneshian Moghaddam, A. H. Hosseinzadeh, A. Bandehagh

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To investigate the effect of cadmium heavy metal stress on five ecotype of dill, this experiment was conducted in the greenhouse of Tabriz University and Shabestar Islamic Azad University’s laboratories with tree replications. After growing the plants, cadmium treatments (concentration 0,300, 600 µmol) were applied. The essential oil of the samples was measured by hydro distillation and using a Clevenger apparatus. Variables used in this study include: wet and dry roots and aerial part of plant, plant height, stem diameter, and root length. The results showed that different concentrations of heavy metal has statistical difference (p < 0.01) on the fresh weight, dry weight, plant height and root length but hadn’t significant difference on essential oil percentage and root length. Dill ecotypes have statistical significant difference on essential oil percent, fresh plant weight, plant height, root length, except plant dry weight. The interactions between Cd concentration and dill ecotypes have not significant effect on all traits, except root length. Maximum fresh weight (4.98 gr) and minimum amount (3.13 gr) were obtained in control trait and 600 ppm of cd concentration, respectively. Highest amount of fresh weight (4.78 gr) was obtained in Birjand ecotype. Maximum plant dry weight (1.2 gr) was obtained at control. The highest plant height (32.54 cm) was obtained in control and with applies cadmium concentrations from zero to 300 and 600 ppm was found significantly reduced in plant height.

Keywords: pollution, essential oil, ecotype, dill, heavy metals, cadmium

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Authors: Sarthak Deshpande, Akshay Patil, Pradip Pandhare, Nikhil Wankhede, Rushali Deshmukh

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Among the highly significant duties for any language most effective is the sentiment analysis, which is also a key area of NLP, that recently made impressive strides. There are several models and datasets available for those tasks in popular and commonly used languages like English, Russian, and Spanish. While sentiment analysis research is performed extensively, however it is lagging behind for the regional languages having few resources such as Hindi, Marathi. Marathi is one of the languages that included in the Indian Constitution’s 8th schedule and is the third most widely spoken language in the country and primarily spoken in the Deccan region, which encompasses Maharashtra and Goa. There isn’t sufficient study on sentiment analysis methods based on Marathi text due to lack of available resources, information. Therefore, this project proposes the use of different ML/NLP models for the analysis of Marathi data from the comments below YouTube content, tweets or Instagram posts. We aim to achieve a short and precise analysis and summary of the related data using our dataset (Dates, names, root words) and lexicons to locate exact information.

Keywords: multilingual sentiment analysis, Marathi, natural language processing, text summarization, lexicon-based approaches

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Authors: Moses A. Guto Maobe, Leonard Gitu, Erastus Gatebe

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The plants Carissa spinarum and Toddalia asiatica have historically been used as herbal medicines in Kisii and Nyamira Counties region, Kenya. But, there is limited study about heavy metal contents in their different plant parts. Such information is necessary for proper use of the two plant species as herbal medicines. So, precise determination of heavy metal contents in different part of these herbs is required for quality, efficacy and safety use in the treatment of ailments. The main aim of this study was to standardize the two herbs of interest. The objective of this study was to evaluate the levels of heavy metal contents in the root of Carissa spinarum and Toddalia asiatica. A wet digestion method with concentrated nitric-hydrochloric acid was used for the dissolution of each herb part prior to elemental analysis. Standard solutions of various concentrations of each pure metal of analytical grade arsenic (As), cadmium (Cd) and mercury (Hg) were prepared and used. The analysis of As, Cd and Hg in each of two herbs was conducted by atomic absorption spectroscopy (AAS) Shimadzu model No. 6200. Data obtained from root of Carissa spinarum indicated concentration (mgkg⁻¹) of Arsenic (As), Cadmium (Cd) and Mercury (Hg) were 0.87 x 10⁻³, 7.02 x 10⁻⁶ and 0.66 x 10⁻³ respectively. Results obtained from root of Toddalia asiatica showed concentration (mgkg⁻¹) of Arsenic (As), Cadmium (Cd) and Mercury (Hg) were 1.33 x 10⁻³, 7.32 x 10⁻⁶ and 1.13 x 10⁻³, respectively. The permissible limits set by WHO for As, Cd and Hg in herbs are (mgkg⁻¹) < 1 - 5, < 0.3 – 1 and < 0.1- 0.5 respectively. The concentrations of As, Cd, and Hg determined were relatively higher in the root of Toddalia asiatica than the root of Carissa spinarum. It was concluded that levels of heavy metal contents of As, Cd, and Hg in the root of Carissa spinarum and Toddalia asiatica were within permissible limits set by WHO/FAO.

Keywords: heavy metals, Carissa spinarum, Toddalia asiatica, wet digestion, pollutants, AAS

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Authors: Clive L. Morley

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Gravity models and panel data modelling of tourism flows are receiving renewed attention, after decades of general neglect. Such models have quite different underpinnings from conventional demand models derived from micro-economic theory. They operate at a different level of data and with different theoretical bases. These differences have important consequences for the interpretation of the results and their policy and managerial implications. This review compares and contrasts the two model forms, clarifying the distinguishing features and the estimation requirements of each. In general, gravity models are not recommended for use to address specific management and marketing purposes.

Keywords: gravity models, micro-economics, demand models, marketing

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Authors: Faisal Alazzaz, Andrew Whyte

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Off-site construction methods have played an important role in the construction sector in the past few decades. It is increasingly becoming a major alternative technique and strategic direction compared to traditional in-situ method. It produces a significant amount of value for the construction industry and the economy more generally. To date, an impressive number of studies have been lunched on the perceived perception of off-site construction. However, it seems that a quantifying benefit on the offsite construction area is lacking. Therefore, this paper examines the recent research literature on the benefits of off- site construction and provides future direction. In the beginning, this paper provides a brief history and current value of the off-site construction followed by a detailed discussion on the benefit of off-site construction. These benefits include but not limited to time saving, quality improvement, relieving skills shortages, cost reduction and productivity improvement. Toward this end, off-site construction should learn from other productive industry similar to services or manufacturing industry by applying operational management tools and techniques with extensive focus on employee empowerment will shed the light on future uptake of Off-site construction. This study is of value in providing scholars have a clear picture of perceived benefit of off-site construction research and give an opportunities for future uptake of off-site method.

Keywords: building projects, employer empowerment, off-site construction benefits, productivity

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Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000 cm-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R2 = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R2 = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, pasta, moisture determination, food engineering

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Authors: Eyal Rosen

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This study is aimed to report a possible effect of the presence of dental implants on the development of crown or root fractures in adjacent natural teeth. A series of 26 cases of teeth diagnosed with crown or root fractures following the placement of adjacent dental implants is presented. In addition, a comprehensive systematic review of the literature was performed to detect other studies that evaluated this possible complication. The case series analysis revealed that all crown-fractured teeth were non-endodontically treated teeth (n=18), and all root fractured teeth were endodontically treated teeth (n=8). The time from implant loading to the diagnosis of a fracture in an adjacent tooth was longer than 1 year in 78% of cases. The majority of crown or root fractures occurred in female patients, over 50 years of age, with an average age of 59 in the crown fractures group, and 54 in the root fractures group. Most of the patients received 2 or more implants. Nine (50%) of the teeth with crown fracture were molars, 7 (39%) were mandibular premolars, and 2 (11%) were incisor teeth. The majority of teeth with root fracture were premolar or mandibular molar teeth (6 (75%)). The systematic review of the literature did not reveal additional studies that reported on this possible complication. To the best of the author’s knowledge this case series, although limited in its extent, is the first clinical report of a possible serious complication of implants, associated fractures in adjacent endodontically and non-endodontically treated natural teeth. The most common patient profile found in this series was a woman over 50 years of age, having a fractured premolar tooth, which was diagnosed more than 1 year after reconstruction that was based on multiple adjacent implants. Additional clinical studies are required in order to shed light on this potential serious complication.

Keywords: complications, dental implants, endodontics, fractured teeth

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Authors: Vikas Sharma, V. K. Dixit

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The roots of Anacyclus pyrethrum DC (AP) (Asteraceae) are frequently used in traditional medicine as Vajikarana Rasayana. An ethanolic extract of root of Anacyclus pyrethrum demonstrated its potential to enhance the sexual behaviour of male rats, with a dose dependent effect on sperm count and androgens concentration. Phytochemical analysis of ethanolic extract of Anacyclus pyrethrum revealed that it is rich in N-alkylamide. This study therefore sought to assess characterization of ethanolic extract of Anacyclus pyrethrum root. Root extract was performed using a gradient reversed phase high performance liquid chromatography/UV/electrospray ionization ion trap mass spectrometry (HPLC/ESI-MS) method on an embedded polar column. MS1 and MS2 fragmentation data were used for identification purposes, while UV was used for quantification. Thirteen N-alkylamides (five N-isobutylamides, three N-methyl isobutylamides, four tyramides, and one 2-phenylethylamide) were detected. Five of them identified as undeca-2E,4E-diene-8,10-diynoic acid N-methyl isobutylamide, tetradeca-2E,4E-diene-8,10-diynoic acid tyramide, deca-2E,4E-dienoic acid N-methyl isobutylamide, tetradeca-2E,4E,XE/Z-trienoic acid tyramide and tetradeca-2E,4E,8Z,10Z-tetraenoic isobutylamide are novel compounds, which have never been identified in Anacyclus pyrethrum.

Keywords: Anacyclus pyrethrum (Asteraceae), LC-MS plant profiling, N-alkylamides, pellitorine, anacycline

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Authors: Kachout Mnaouer, Bel Hadj Tahar Jamel, Choubani Fethi

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In this paper, three electric equivalent models to evaluate crosstalk between three-conductor transmission lines are proposed. First, electric equivalent models for three-conductor transmission lines are presented. Secondly, rigorous equations to calculate the per-unit length inductive and capacitive parameters are developed. These models allow us to calculate crosstalk between conductors. Finally, to validate the presented models, we compare the theoretical results with simulation data. Obtained results show that proposed models can be used to predict crosstalk performance.

Keywords: near-end crosstalk, inductive parameter, L, Π, T models

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Authors: Hamid Karimi, Naegar Mahdavi, Hossein Tayefi Nasrabadi

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Spermatozoids production rate and its quality are more important factors in the diagnosis of infertility. Also, spematozids activity have a more important role in fertilization. Some medicinal plants as Asparagus(Asparagus officinalis) has many antioxidant component. Therefore, They can affect testes tissue to production more and high-quality spermatozoids. In this survey, Asparagus root extract is studied on spermatogenesis index in the laboratory mouse testes. Hydro-alcoholic extract of asparagus root is prepared and examined on four group of the mature male mouse. Blank group without extract, group 1,100ml/kg dose, group 2, 200 ml/kg dose and group 3, 300ml/kg dose. Then, mice are euthanized, and testes are removed. Testes are weighted, and paraffinized blocks are prepared. TDI(Tubular Differentiation Index) and SPI(Spermiation Index) are studied on histological sections by light microscope. This study results were showed that TDI and SPI in treatments groups with 200 and 300 ml/kg dose had significant enhancement (P<0.05). Consequently, Extract of Asparagus root can enhance spermatozoid production and, therefore, cause improve fertility in male laboratory mice.

Keywords: histology, spermatozoid, ASP [aragus, testes

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

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Authors: Emily Fortune Super, Ameerah Khadaroo, Aurore Bardey

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Fashion models are often assumed to have a glamorous job with limited consideration for their well-being. This study aims to assess the well-being of models through semi-structured interviews with six professional fashion models and six industry professionals. Thematic analysis revealed that although models experienced improved self-confidence, they also reported heightened anxiety levels, body image issues, and the negative influence of modelling on their self-esteem. By contrast, industry professionals reported no or minimum concerns about anxious behaviours or the general well-being of fashion models. Being resilient as a model was perceived as an essential attribute to have by both models and industry professionals as they face recurrent rejection in this industry. These results demonstrate a significant gap in the current understanding of the well-being of fashion models between industry professionals and the models themselves. Findings imply that there is an inherent need for change in the modelling industry to promote and enhance their well-being.

Keywords: body image, fashion industry, modelling, well-being

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Authors: Elfatih M. Abdel-Rahman, Tobias Landmann, Richard Kyalo, George Ong’amo, Bruno Le Ru

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Maize (Zea mays L.) is a major staple food crop in Africa, particularly in the eastern region of the continent. The maize growing area in Africa spans over 25 million ha and 84% of rural households in Africa cultivate maize mainly as a means to generate food and income. Average maize yields in Sub Saharan Africa are 1.4 t/ha as compared to global average of 2.5–3.9 t/ha due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In East Africa, yield losses due to stem borers are currently estimated between 12% to 40% of the total production. The objective of the present study was therefore to predict stem borer larvae density in maize fields using RapidEye reflectance data and generalized linear models (GLMs). RapidEye images were captured for a test site in Kenya (Machakos) in January and in February 2015. Stem borer larva numbers were modeled using GLMs assuming Poisson (Po) and negative binomial (NB) distributions with error with log arithmetic link. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were employed to assess the models performance using a leave one-out cross-validation approach. Results showed that NB models outperformed Po ones in all study sites. RMSE and RPD ranged between 0.95 and 2.70, and between 2.39 and 6.81, respectively. Overall, all models performed similar when used the January and the February image data. We conclude that reflectance data from RapidEye data can be used to estimate stem borer larvae density. The developed models could to improve decision making regarding controlling maize stem borers using various integrated pest management (IPM) protocols.

Keywords: maize, stem borers, density, RapidEye, GLM

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Authors: Ashraf M. S. Soliman, Mohsen Elhasadi

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The present study was undertaken to evaluate the impact of ambient particles emitted from an industrial complex located at El-Fatyah on growth, phytomass partitioning and accumulation, pigment content and nutrient uptake of two economically important crop species; barley (Hordeum vulgare L.Family: Poaceae) and broad bean (Vicia faba L. Family: Fabaceae) growing in the region. It was obvious from the present investigation that chlorophyll and carotenoid content showed significant responses to the industrial dust. Generally, the total pigment content of the two investigated crops in the two locations continually increased till the plant age reached 70 days after sowing then begins to decrease till the end of the growing season..The total uptake of N, P and K in the two studied species decreased in response to industrial dust in the study area compared to control location. In conclusion, barley and broad bean are very sensitive to air pollutants, and may consider as bioindicators for atmospheric pollution. Pollutants caused damage of their leaves, impair plant growth, hindered nutrient uptake and consequently limit primary productivity.

Keywords: Effect of Industrial Complex on barley and broad bean

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Authors: Zheng Zhang, Xin Chen, Guoqing Ding

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Electronic apex locators (EAL) has been widely used clinically for measuring root canal working length with high accuracy, which is crucial for successful endodontic treatment. In order to maintain high accuracy in different measurement environments, this study presented a system for root canal length measurement based on multifrequency impedance method. This measuring system can generate a sweep current with frequencies from 100 Hz to 1 MHz through a direct digital synthesizer. Multiple impedance ratios with different combinations of frequencies were obtained and transmitted by an analog-to-digital converter and several of them with representatives will be selected after data process. The system analyzed the functional relationship between these impedance ratios and the distance between the file and the apex with statistics by measuring plenty of teeth. The position of the apical foramen can be determined by the statistical model using these impedance ratios. The experimental results revealed that the accuracy of the system based on multifrequency impedance ratios method to determine the position of the apical foramen was higher than the dual-frequency impedance ratio method. Besides that, for more complex measurement environments, the performance of the system was more stable.

Keywords: root canal length, apex locator, multifrequency impedance, sweep frequency

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Authors: Piyarat Parklug, Busaba Supawattanaobodee, Penpat Pinyowattanasilp

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The radioactive iodine thyroid uptake (RAIU) has been widely used to differentiate the cause of thyrotoxicosis and treatment. Twenty-four hours RAIU is routinely used to calculate the dose of radioactive iodine (RAI) therapy; however, 2 days protocol is required. This study aims to evaluate the modification of Penpat Pinyowattanasilp equation application by the exclusion of outlier data, 3 hours RAIU less than 20% and more than 80%, to improve prediction of 24-hour uptake. The equation is predicted 24 hours RAIU (P24RAIU) = 32.5+0.702 (3 hours RAIU). Then calculating separation first and second therapeutic doses in Graves’ disease patients. Methods; This study was a retrospective study at Faculty of Medicine Vajira Hospital in Bangkok, Thailand. Inclusion were Graves’ disease patients who visited RAI clinic between January 2014-March 2019. We divided subjects into 2 groups according to first and second therapeutic doses. Results; Our study had a total of 151 patients. The study was done in 115 patients with first RAI dose and 36 patients with second RAI dose. The P24RAIU are highly correlated with actual 24-hour RAIU in first and second therapeutic doses (r = 0.913, 95% CI = 0.876 to 0.939 and r = 0.806, 95% CI = 0.649 to 0.897). Bland-Altman plot shows that mean differences between predictive and actual 24 hours RAI in the first dose and second dose were 2.14% (95%CI 0.83-3.46) and 1.37% (95%CI -1.41-4.14). The mean first actual and predictive therapeutic doses are 8.33 ± 4.93 and 7.38 ± 3.43 milliCuries (mCi) respectively. The mean second actual and predictive therapeutic doses are 6.51 ± 3.96 and 6.01 ± 3.11 mCi respectively. The predictive therapeutic doses are highly correlated with the actual dose in first and second therapeutic doses (r = 0.907, 95% CI = 0.868 to 0.935 and r = 0.953, 95% CI = 0.909 to 0.976). Bland-Altman plot shows that mean difference between predictive and actual P24RAIU in the first dose and second dose were less than 1 mCi (-0.94 and -0.5 mCi). This modification equation application is simply used in clinical practice especially patient with 3 hours RAIU in range of 20-80% in a Thai population. Before use, this equation for other population should be tested for the correlation.

Keywords: equation, Graves’disease, prediction, 24-hour uptake

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Authors: Aniqa Jabeen

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The aim of this study was to evaluate the effectiveness of Tc-99m MIBI (Technetium 99-methoxy-iso-butyl-isonitrile) scintigraphy to differentiate malignancies from benign lesions, which were detected on planar bone scans. Materials and Methods: 59 patients with bone lesions were enrolled in the study. The scintigraphic findings were compared with the clinical, radiological and the histological findings. Each patient initially underwent a three-phase bone scan with Tc-99m MDP (Methylene Diphosphonate) and if evidence of lesion found, the patient then underwent a dynamic and static MIBI scintigraphy after three to four days. The MDP and MIBI scans were evaluated visually and quantitatively. For quantitative analysis count ratios of lesions and contralateral normal side (L/C) were taken by region of interests drawn on scans. The Student T test was applied to assess the significant difference between benign and malignant lesions p-value < 0.05 was considered significant. Result: The MDP scans showed the increase tracer uptake, but there was no significant difference between benign and malignant uptake of the radiotracer. However significant difference (p-value 0.015), in uptake was seen in malignant (L/C = 3.51 ± 1.02) and benign lesion (L/C = 2.50±0.42) on MIBI scan. Three of thirty benign lesions did not show significant MIBI uptake. Seven malignant appeared as false negatives. Specificity of the scan was 86.66%, and its Negative Predictive Value (NPV) was 81.25% whereas the sensitivity of scan was 79.31%. In excluding the axial metastasis from the lesions, the sensitivity of MIBI scan increased to 91.66% and the NPV also increased to 92.85%. Conclusion: MIBI scintigraphy provides its usefulness by distinguishing malignant from benign lesions. MIBI also correctly identifies metastatic lesions. The negative predictive value of the scan points towards its ability to accurately diagnose the normal (benign) cases. However, biopsy remains the gold standard and a definitive diagnostic modality in musculoskeletal tumors. MIBI scan provides useful information in preoperative assessment and in distinguishing between malignant and benign lesions.

Keywords: benign, malignancies, MDP bone scan, MIBI scintigraphy

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Authors: Ammani K., Glory M.

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Demands of traditional herbal medicines are increasing day by day over the world. Considering the growing demand of medicinal plants in curative treatments and the role of VAM fungi in augmentation of the production of active secondary metabolites by the medicinal plants, the present work has been undertaken to survey the mycorrhizal status in 30 different medicinal plants belonging to various families from Krishna district, Andhra Pradesh. The roots were collected carefully and stained by the Phillips & Hayman technique. Basing on the occurrence of vesicles and arbuscules, categorized into four grades; Excellent: mycelia, vesicles or arbuscules present more than 75% of root bits, Good: mycelia, vesicles or arbuscules present 50-75% in surface of root bits, moderate: mycelia, vesicles or arbuscules present 25-50% in surface of root bits, and poor: mycelia, vesicles or arbuscules present 1-25% in surface of root bits. The study reveals that the roots of all plants were colonized by AM fungi. Percentage of root colonization by AM fungi was more in Aloe vera, Phylanthus emblica, Azadiracta indica and least in plants such as Aerva lanata, Vinca rosea, Crotalaria verrucosa among the 30 medicinal plants in present study. The enhancement of growth and vigour and increased production of bioactive compounds of the medicinal plants is desirable which may be achieved by inoculation of the roots with Arbuscular mycorrhizal fungi. There is a steady increase in the cultivation of medicinal plants to maintain a steady supply to support the increasing demand but corresponding researches of VAM fungi and their association in medicinal plants have received very little attention as compared to the studies on forest species and field crops. So a vast research on this field is necessary for a better tomorrow.

Keywords: Arbuscular mycorrhizae, colonization, categories, medicinal plants

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Authors: M. Soleimani, D. Cree, C. Chafe, L. Bates

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The accidental release of petroleum products into the environment could have harmful consequences to our ecosystem. Different techniques such as mechanical separation, membrane filtration, incineration, treatment processes using enzymes and dispersants, bioremediation, and sorption process using sorbents have been applied for oil spill remediation. Most of the techniques investigated are too costly or do not have high enough efficiency. This study was conducted to determine the sorption performance of hemp byproducts (cellulosic materials) in terms of sorption capacity and kinetics for hydrophobic and hydrophilic fluids. In this study, heavy oil, light oil, diesel fuel, and water/water vapor were used as sorbate fluids. Hemp stalk in different forms, including loose material (hammer milled (HM) and shredded (Sh) with low bulk densities) and densified forms (pellet form (P) and crumbled pellets (CP)) with high bulk densities, were used as sorbents. The sorption/retention tests were conducted according to ASTM 726 standard. For a quick-purpose application of the sorbents, the sorption tests were conducted for 15 min, and for an ideal sorption capacity of the materials, the tests were carried out for 24 h. During the test, the sorbent material was exposed to the fluid by immersion, followed by filtration through a stainless-steel wire screen. Water vapor adsorption was carried out in a controlled environment chamber with the capability of controlling relative humidity (RH) and temperature. To determine the kinetics of sorption for each fluid and sorbent, the retention capacity also was determined intervalley for up to 24 h. To analyze the kinetics of sorption, pseudo-first-order, pseudo-second order and intraparticle diffusion models were employed with the objective of minimal deviation of the experimental results from the models. The results indicated that HM and Sh materials had the highest sorption capacity for the hydrophobic fluids with approximately 6 times compared to P and CP materials. For example, average retention values of heavy oil on HM and Sh was 560% and 470% of the mass of the sorbents, respectively. Whereas, the retention of heavy oil on P and CP was up to 85% of the mass of the sorbents. This lower sorption capacity for P and CP can be due to the less exposed surface area of these materials and compacted voids or capillary tubes in the structures. For water uptake application, HM and Sh resulted in at least 40% higher sorption capacity compared to those obtained for P and CP. On average, the performance of sorbate uptake from high to low was as follows: water, heavy oil, light oil, diesel fuel. The kinetic analysis indicated that the second-pseudo order model can describe the sorption process of the oil and diesel better than other models. However, the kinetics of water absorption was better described by the pseudo-first-order model. Acetylation of HM materials could improve its oil and diesel sorption to some extent. Water vapor adsorption of hemp fiber was a function of temperature and RH, and among the models studied, the modified Oswin model was the best model in describing this phenomenon.

Keywords: environment, fiber, petroleum, sorption

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Authors: E. Chakwizira, D. J. Moot, M. Andrews, E. Teixeira

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Partitioning of macro-nutrients in wheat (Triticum aestivum L.) plant organs have not been extensively studied, particularly for modern genotypes grown under contrasting N supply. Nutrient accumulation and partitioning of phosphorus, potassium, calcium, magnesium and sulphur (P, K, Ca, Mg and S) were determined for six wheat genotypes [12S2-2021, 12S3-3019, 13S3-2026, Discovery, Duchess and Reliance] grown with (200 kg/ha) or without (0 kg/ha) nitrogen (N), in a fully irrigated field experiment in 2017-18 season at Lincoln, New Zealand. Data were collected at three growth stages (GS): tillering (GS21), anthesis (GS60) and grain maturity (GS92). Grain yield varied with both N and genotype; from 6-7.5 t/ha for the 0 kg N/ha crops and 8.1-9.3 t/ha for the 200 kg N/ha treatments. Plant nutrient uptake at maturity responded to both N supply and genotype for all nutrients, except S which did not differ among the genotypes. For example, total P uptake averaged 13.5 (12.4-14.3) kg/ha for the 0 kg N/ha treatments and 17.8 (15.1-19.7) kg/ha when 200 kg N/ha was applied. Similarly, K uptake increased from an average of 23 (21.6-25.3) for the 0 kg N/ha treatments to 34.3 (32.4-40.8) kg/ha when 200 kg N/ha was applied. Similar trends were observed for Ca and Mg. The S content only responded to N supply but not to genotype, increasing from 7.9 kg/ha for the 0 kg N treatments to 12.8 kg/ha when 200 kg N was applied. Relative nutrient content at anthesis compared with those at maturity were 30% for P, 100% for both K and Ca and 34% of Mg. Sulphur content at anthesis decreased 29% with N supply and was highest for genotypes 12S2-2021 compared with the other five genotype. At grain maturity, the ratio of nutrients in grain to total plant nutrient, defined as the nutrient harvest index (NHI) varied with both N supply and genotype. Averaged across treatments, the NHI was 0.96 for P, 0.53 for K, 0.58 for Ca, 0.90 for Mg and 0.85 for S. These results suggest that Ca and K should be provided earlier in the season as there is limited or no uptake after anthesis. These results also show that Ca and K are important for structural functions, while P, Mg and S are remobilised to the grains and become important for quality.

Keywords: anthesis, genotype, nutrient harvests index, NHI, Triticum aestivum L.

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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